27ICPR Book of Abstracts

2023 ICPR27 1 Generated with 27th International Conference on Production Research organized by the International Foundation for Production Research and hosted by the Technical University of Cluj-Napoca under the theme “Building resilience into production” Book of Abstracts Keynote presentations’ abstracts can be accessed here Submission index [109], [157], [217], [288], [292], [352], [419], [420], [500], [627], [655], [698], [820], [874], [920], [939], [946], [954], [986], [1051], [1071], [1096], [1247], [1418], [1488], [1506], [1774], [1778], [1780], [1815], [1819], [1864], [1893], [1926], [1953], [1958], [1977], [2148], [2164], [2241], [2308], [2320], [2361], [2453], [2511], [2532], [2578], [2645], [2681], [2736], [2748], [2777], [2810], [2813], [2841], [2859], [2888], [2925], [2928], [3096], [3172], [3211], [3222], [3374], [3434], [3507], [3549], [3565], [3575], [3602], [3621], [3629], [3688], [3732], [3792], [3797], [3802], [3803], [4051], [4191], [4282], [4322], [4478], [4485], [4505], [4517], [4747], [4839], [4931], [4932], [5124], [5142], [5252], [5424], [5443], [5472], [5489], [5573], [5642], [5645], [5675], [5683], [5777], [5847], [5856], [5857], [5904], [5935], [6013], [6148], [6233], [6370], [6418], [6455], [6505], [6521], [6531], [6536], [6719], [6746], [6826], [6832], [7053], [7071], [7214], [7232], [7263], [7273], [7279], [7406], [7439], [7456], [7516], [7533], [7555], [7589], [7628], [7710], [8002], [8061], [8088], [8091], [8230], [8273], [8298], [8301], [8493], [8523], [8568], [8666], [8688], [8811], [8854], [8951], [9139], [9199], [9200], [9212], [9323], [9329], [9353], [9382], [9407], [9418], [9478], [9485], [9493], [9607], [9618], [9630], [9683], [9685], [9695], [9747], [9887], [9927], [9995] Disclaimer: The Organizing Committee of the 27th International Conference on Production Research and the Technical University of ClujNapoca accept no responsibility for errors and omissions in the abstracts, including the issues referring to the style and formulation in English language, and shall not be liable for any damage caused by the contents of the published papers. Copyright © 2023 International Foundation for Production Research and the Technical University of Cluj-Napoca

2023 ICPR27 2 [109] Management models for electric scooter recharging under gig economy Minjeong Kim (Department of Industrial Engineering, Seoul National University), Ilkyeong Moon (Department of Industrial Engineering, Seoul National University) and Bogyeom Lee (Department of Industrial Engineering, Seoul National University). Abstract Electric scooters are experiencing a recent boom as a means of shared personal transportation in a smart city. To survive in competition and maintain the service, one company uses a gig economy operation in which contracted individuals, called gig workers, are responsible for collecting, charging, and repositioning the low-battery scooters, getting certain rewards per tasks. Unlike a traditional company courier, gig worker is an independent third-party worker who prioritizes its own profit and competes each other. Therefore, it is a challenge for the company to balance the battery distribution of the scooters and manage low-battery scooters, which unable smooth service turnover for the next period. From the idea that bundles of scooters may have higher attractiveness from the perspective of gig workers, this study presents bundling models that offers bundle configurations of scooters to improve gig workers' activeness. It is expected to increase scooter collection rate by allowing collectors to make reservation as bundles. The performance of each model is compared by a juicer collection simulation and multi-depot vehicle routing model that calculates the upper bound of the collection rate when no bundles were provided. The results show that bundles are effective in that lowering the uncollected low-battery scooters and increasing juicer’s activity as well, thereby expanding the effectiveness of the gig economy. [157] SCOPING REVIEW OF MULTICRITERIA DECISION-MAKING/ANALYSIS METHODS TO ASSESS URBAN QUALITY OF LIFE Lianne Pimenta (Graduate Program in Environmental Sciences of University of Para State), Norma Beltrao (University of Pará State) and Renata Oliveira (University of Pará State). Abstract This paper employs the Scoping Review Method to explore recent literature on Urban Quality of Life (UQoL) and Sustainable Cities, identifying gaps in the field. The study successfully generated a significant number of articles using selected keywords, highlighting leading countries in relevant research, including China, Iran, Turkey, and India. The research identified the Analytic Hierarchy Process (AHP) and Techniques for Order Preference by Similarity to Ideal Solutions (TOPSIS) as the most common Multiple Criteria Decision-Making/Analysis (MCDM/A) methods used. The study extracted 70 Key Performance Indicators (KPIs) related to urban environmental quality, categorized into Environmental, Infrastructure/Physical, and Social dimensions. These indicators can be effectively integrated with MCDM/A methods within a Geographic Information System (GIS) environment, providing valuable insights into urban sustainability. The findings present significant opportunities for urban planners and policymakers, guiding the development of more sustainable and resilient urban environments. The study underscores the importance of using a comprehensive set of indicators and sophisticated decision-making methods to address the complex challenges of urban sustainability. It contributes to a deeper understanding of the current research landscape and provides a robust foundation for future studies. [217] High accuracy dynamically balanced spherical arm for industrial and medical applications Gabriela Rus (CESTER - Technical University of Cluj-Napoca), Iulia Andras (“Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania), Calin Vaida (CESTER - Technical University of Cluj-Napoca), Corina Radu (“Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania), Bogdan Gherman (Technical University of Cluj-Napoca), Paul Tucan (Technical University of Cluj-Napoca), Alexandru Pusca (CESTER - Technical University of ClujNapoca), Nadim Al Hajjar (“Iuliu Hatieganu” University of Medicine and Pharmacy, Cluj-Napoca, Romania) and Doina Pisla (CESTER - Technical University of Cluj-Napoca). Abstract The paper presents the detailed design of a spherical robotic arm with dynamic balancing of the end-effector for highly accurate tasks covering industrial and medical applications. The robotic arm is specially designed to perform different tasks based on the concept of the Remote Center of Motion (RCM), suitable for the manipulation of objects located in a

2023 ICPR27 3 sealed environment with a specific access port. The specific advantages of an architecturally constraint RCM will be demonstrated in terms of safety in operation, ease of control and motion decomposition. The mechanism is balanced to allow a large working envelope with minimum torque variations for the actuators improving both the accuracy and stiffness. A case study will be presented where the end-effector is represented by a dexterous surgical tool, which adds four additional degrees of freedom to the gripper along demonstrating through a mathematical model the specific gains in accuracy and stiffness. Through computer-based simulations the specific advantages of the balancing mechanism will be illustrated along with a simple solution which allows the robot to adapt to different end-effectors and working loads. [288] A systematic literature review on the interplay between Industry 4.0 technologies and human resources Ottó Csiki (Faculty of Economics and Business Administration, Babeş–Bolyai University, Cluj-Napoca, Romania) and Levente Szász (Faculty of Economics and Business Administration, Babeş–Bolyai University, Cluj-Napoca, Romania). Abstract Purpose: Currently emerging digital manufacturing technologies (identified commonly under the umbrella term of Industry 4.0 – I4.0) are predicted to fundamentally change how tasks are carried out by employees in manufacturing companies. The implementation of new technologies coupled with a proper human resource management can further enhance the performance benefits expected. While initially the human factor received far less attention in the digital manufacturing literature than technological aspects, in the last couple of years a stream of literature started to emerge focusing on various human resource related implications of I4.0 technologies, giving even birth to a separate Industry 5.0 stream which focuses on the human and sustainability domains related to these technologies. Therefore, the purpose of this paper is to identify and review this body of literature to summarize current knowledge and to identify uncharted domains that could provide promising future research areas. Methodology: To conduct the review, we follow the recommendations of Tranfield et al. (2003) and Durach et al. (2017), starting with the formulation of a research question that guides the literature review: what is the role of human resources in the adoption of novel digital manufacturing technologies? To ensure the practical relevance of the analysis of a relatively recent body of literature, we conducted interviews with practitioners from two manufacturing companies from the automotive industry to make sure that all relevant human-related topics are included in the analysis. Results: The topical analysis of the literature focuses on both managerial, development experts and employee-level aspects, and categorizes findings related to all relevant stages of the new technology implementation (before, during and after implementing new technologies). Current research mainly focuses on the before stage when investigating managerial questions. This is due to the fact that management plays the most defining role in the pre-implementation phase, being the ones who make the decisions regarding the implementation of the new technologies. Developments experts is a rarely investigated category, many researchers focusing only on shop-floor employee and/or managerial level, but the lack of expertise as a major challenge for implementation can be tackled by involving experts, such as design engineers or technical engineers. Turning to the employee perspective, many articles highlight different skills and competencies that are required for the modified or newly emerging jobs as a result of implementing the new technologies. Contextual analysis of human resource implications and advanced research on the new actions and the new skills, capabilities required for these activities to a successful I4.0 implementation are among the most relevant future research directions identified in this study. Keywords: Industry 4.0; Industry 5.0; digitalization; manufacturing technology; human resources Key references: Durach, C., Kembro, J., Wieland, A. (2017). A new paradigm for systematic literature reviews in supply chain management. Journal of Supply Chain Management. 53(4), 67-85. Tranfield, D., Denyer, D., Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management. 14(3), 207-222.

2023 ICPR27 4 [292] Research on Functional Values and Emotional Values of Customers by Implementing Multiple Services in the Hairdressing Industry Mei-Yao Chang (Cheng Shiu University), Jan-Pan Hwang (National Chin-Yi University) and Rong-Song Wu (National Chin-Yi University). Abstract Thirty years ago, Taiwan's hairdressing industry focused on technology inheritance and basic skills training. Now, under the trend of deepening professional division of labor, scalp care has gradually received more attention and is branched by hairdressing. Therefore, this study aims to explore the impact of multiple service occupations such as scalp managers and nurses on customer values, so as to provide reference development trends and effective strategies for the future hairdressing industry to integrate multiple services. This study adopts cross-analysis of literature review and data collection, and semi-structured interviews with consumers and practitioners to explore the key to the success of multiple services in the hairdressing industry. The semi-structured interview is based on the two aspects of functional value and emotional value, as well as consumers and practitioners, and compiles interviews with four axes. The analysis results after data collection by the aforementioned research methods show that customers have a significant correlation between the functional value and emotional value of multi-services, indicating that multi-services meet the increasingly diverse needs of consumers; on the other hand, practitioners also feel that multi-services The business model has the opportunity to gain more room for bargaining and provide customers with more flexible prices. This result provides a development possibility for exploring and expanding the connotation of innovative business models related to hair salons in the future, and can also provide a reference for subsequent researchers and related industries. At the same time, suggestions for industry and future research are put forward from the perspective of academic research to successfully explore the key to the success of multi-service careers. It is expected to enhance the functional value and emotional value for customers, increase customers' willingness to consume and increase the unit price of consumption in stores. Finally, the research results can be of great reference value in improving the service quality and customer experience of hair salons in the future and are expected to further improve the competitiveness and market position of the hairdressing industry. [352] Prototype for traceability of supply chain with multiple Blockchains Mateen Ashraf (School of Engineering, University of Limerick, Limerick Ireland) and Cathal Heavey (School of Engineering, University of Limerick, Limerick Ireland & Confirm.ie). Abstract Blockchain applications within supply chain has been growing since blockchain was first introduced in 2008 by Satoshi Nakamoto. Blockchain provides persistent storage, encrypted and secure, immutable, decentralized along with faster settlement. This concept attracted attention from many prominent parties from academic and commercial circles as the realization came that blockchain holds the potential to revolutionize the supply chain industry. One of the features of blockchain is that each transaction is turned to a hash which is dependent on current and previous hash. After verification of transaction by the miners this is added to blockchain ledger in chronological order – each block is connected to the previous block as a result makes a blockchain along with storage of data on multiple computers provides data integrity and immutability. This inherent feature of blockchains can be used to address two major problems within supply chain industry. Firstly, collaboration and synchronisation of data across supply chain participants and traceability of products from its source to end destination. We, therefore, are looking to develop a Prototype for supply chain members. This will track products within supply chains to provide data integration and traceability using multiple blockchains and IoT sensors. The software will provide multiple dashboards to all actors in the supply chain journey e.g., manufacturers, suppliers, shippers, warehouse providers, distributors, and retailers. All these actors can view statuses of their products from information fetched from sensors and verify using blockchains along with saving new information and events on to the blockchain. We use a sensor by Sigfox which is a global cellular network for sending minimal data from IoT devices using Low Power Wide Area Network (LPWAN) communication. This prototype will also provide a multi-blockchain adaptor allowing Prototype application to connect with Ethereum, Solana, Tezos, Polkadot, Avalanche, Stellar blockchain. This multi blockchain Prototype for supply chain industry provides two-way communication to select the type of blockchain used by each participant and by saving and pulling data which eliminates the need of third party for verification of any of supply chain actors e.g., suppliers, manufacturers, shippers etc. User interfaces will provide separate views and functionality for supply chain actors and

2023 ICPR27 5 along with provision of authentication and authorization using selected a blockchain network. This prototype will also allow supply chain members to integrate with existing blockchain network if it has already been used. [419] Back-support exoskeleton impact on productivity and posture in order picking Gjulio Ashta (Università degli Studi di Padova), Daria Battini (Università degli Studi di Padova), Nicola Berti (Università degli Studi di Padova), Serena Finco (Università degli Studi di Padova) and Alessandro Persona (Università degli Studi di Padova). Abstract Despite the growing presence of automated solutions, manual order picking (OP) remains a time-, cost-, and labourintensive task in warehouses. Various assistive devices, including passive exoskeletons, have been developed to enhance workers' productivity and well-being. This study investigates the impact of a back support exoskeleton on productivity, ergonomics, and comfort in manual order picking. A laboratory experiment involving 53 volunteers is conducted in a blind protocol, collecting data on time, occupational risk scores, and subjective evaluations. Results indicate an increase in overall time performance for the active group but slowdowns for extensive movement subtasks, nearly unaffected deambulatory speed, and increased maximum values of the REBA index. Moreover, users perceived decreased performance and greater exertion when the exoskeleton was activated. [420] Developing a Framework for value creation in the context of digital transformation Rafael A. Fayet (Industrial and Systems Engineering, Pontifícia Universidade Católica do Paraná, Curitiba/Brazil), Edson Pinheiro de Lima (Industrial and Systems Engineering, Universidade Tecnológica Federal do Paraná, Curitiba/Brazil), Fernando Deschamps (Industrial and Systems Engineering, Pontifícia Universidade Católica do Paraná, Curitiba/Brazil) and Sérgio Gouvea da Costa (Industrial and Systems Engineering, Universidade Tecnológica Federal do Paraná, Curitiba/Brazil). Abstract The digital economy brings new and unpredictable competitive pressures. While many organizations are "digitized" in some value chain processes, in general, they need to take a more holistic approach to digital transformation. In this context, the scientific literature lacks understanding and theory on how digital transformation should be considered in operations strategy to create value for customers. This article presents a Systematic Review of the Literature (RSL) of the dimensions and characteristics of value that guide the operations strategy in the context of digital transformation and proposes a framework that operationalizes the flow of proposition, creation, delivery and capture of value. Content analysis was developed through the coding process focusing on value dimensions, competitive priorities and value characteristics considering the latest digital transformation approaches. In order to contribute to the organization of scientific knowledge, correlations were established to identify the variables related to the value used in the proposed framework. The context analysis identified the need for greater attention on how to define operations strategies of digital transformation and create value that meets market needs, exploring its internal resources and capabilities, supply chains, business models, organizational skills, among others. The proposed procedural framework establishes two main flows of value-related dimensions: the value innovation flow and the value co-creation flow. Define operations strategies of digital transformation as a way to create value for customers, through innovation flows and/or co-creation of value, can be identified as a competitive advantage for organizations. [500] Simulation Analysis for the Promotion of Organic Cotton With the Aim of Solving Social Issues in the Clothing Supply Chain Rei Kinoshita (Waseda University), Shunichi Ohmori (Waseda University) and Tomomi Kito (Waseda University). Abstract In India, where cotton production is the highest in the world, more than 10,000 farmers commit suicide every year due to high debts resulting from the purchase of fertilizer and pesticides. The health hazards caused by pesticides are also severe, and the average life expectancy of cotton farmers is only 35 years. To solve this problem, the government and

2023 ICPR27 6 other support organizations are promoting a shift from cotton cultivation using pesticides to organic cultivation. However, the effectiveness and sustainability of these support activities have not yet been verified, and the spread of organic cotton cultivation has not progressed sufficiently. In this study, we model the entire clothing supply chain (SC) consisting of multiple stakeholders based on a real case study using a method of systems dynamics (SD). Then, we examine whether the activities of support organizations are effective and sustainable. Furthermore, we will find out how to improve the supply chain by providing more effective support. The SC model we constructed consists of seven stakeholders (farmers, cotton merchants, spinning companies, fabric manufacturers, apparel manufacturers, retailers, and consumers), plus supporting organizations. In general, a system dynamics model consists of stocks, flows, and internal feedbacks. Here, stocks are parameters held by system components (amount of cotton, amount of funds, etc.), and flows represent the amount of change in stocks (e.g., amount of cotton produced). There are also variables (e.g., organic and non-organic cotton prices) that are numerical values calculated at every simulation turn. A farmer converts farmland from non-organic to organic when the sum of the expected profit from organic cultivation and the funds from the supporting organization exceeds the sum of the expected profit from non-organic cultivation and the cost of converting to organic cultivation. The respective prices of organic and non-organic cotton, which affect the farmer's expected profit, are determined by the amount of each produced and consumer demand. Consumer demand depends not only on price, but also on consumers' level of ethicality (i.e., how much they prefer organically grown cotton). The simulation results of this complex SD model show that the support currently provided is not necessarily effective or sustainable. Simply increasing consumer demand for organically grown cotton (i.e., increasing current support) would increase money for organic farmers but decrease revenue for non-organic farmers, who would then not have enough money to convert to organic production. In addition, promoting ethicality among consumers (i.e., preference for organically grown cotton despite its higher price) has led to an imbalance between supply and demand, resulting in a lack of farmland conversion from non-organic to organic. Based on our results, we argue that it is important that consumer demand be linked to increases/decreases in the amount of organic and non-organic cotton grown. Therefore, support activities need to be dynamically synced with changes in the SC, rather than financial assistance for conversion to organic cultivation or promotion of sales of organic cotton products, as is currently being done. This study further proposes a method to realize such assistance and verifies its effectiveness through simulation. [627] Supply network design for resilient manufacturing systems Stefan Minner (Technical University of Munich). Abstract Recent disruptions of supply chains have raised increasing attention for the design of resilient supply networks. Different dependent strategic and operational risks need to be considered when deciding about network design including locations and capacities and tactical decisions on supply base and safety stock placement. We present a multi-stage, multi-plant, multi-supplier supply network design optimization problem under different risk models and constraints. The scenariobased model is solved using large-scale sample average approximation using Benders decomposition and managerial insights are presented with regard to the use and size of different proactive and reactive risk mitigation strategies. Illustrative cases for automotive, pharmaceutical, and semiconductor supply chains will be discussed. [655] Machine learning algorithm-based parameters selection for meta-heuristic algorithm and its application to benchmark multi-objective flowshop scheduling problems. Vigneshwar Pesaru (University of Texas-Arlington). Abstract Combinatorial optimization problems (COP) are multifaceted set of problems with discrete decision variables and determinate exploration space. Typically for optimality, combinatorial optimization problems require exponential time to be solved. Hence COPs are generally classified as NP-hard class of optimization problems to be solved. With the recent advances in computational intelligence Metaheuristic (MH) algorithms are vastly used for solving COPs. In our recent investigation when applying the metaheuristic algorithm (non-dominated sorting genetic algorithm-II) to the benchmark (Taillard - 1993) flowshop problems, we identified the exceptional application of Machine learning (ML) algorithms for parameters selection in Metaheuristics. In this paper we attempted to leverage several ML algorithms for the selection of genetic operators of MH for the 90 benchmarks problems. We considered the minimization of flowtime and energy consumption as multi-objective criteria in the selection of best parameters of MH. Further we observed that ML

2023 ICPR27 7 algorithm-based parameters selection enhanced the MH performance in solution identification in lesser number of iterations. [698] Industrial Usage of Cold Rolled Materials in the Rollforming Process Himmet Kulavuz (KMC Kayseri Metal Center San. ve Tic. A.Ş / Meko Metal), Ali Burak Tekyalcin (KMC Kayseri Metal Center San. ve Tic. A.Ş / Meko Metal), Ismail Bogrekci (Aydın Adnan Menderes University) and Pinar Demircioglu (Munich Technical University). Abstract Roll forming process is a production method that is shaping the sheet metal strip station by station via cylindrical rollers to obtain the desired profile section. The prominent features of the roll forming process compared to other sheet metal forming processes are its high production volume and high production speed. Due to the wide usage area of sheet profiles, it is expected to produce profiles with distinct mechanical properties with the roll forming process. In recent times, the tendency that reduce weight without reducing inertia in systems and mechanisms where profiles are used has increased the demand in profiles with high mechanical properties. However, due to the issue of supply chain of high mechanical properties coils sheet material as well as requirement of special properties coils in most of sectors, the demand of coil sheet material whose mechanical values have been changed has arisen. In order to meet this demand, the production of profiles from coiled sheet material, whose mechanical values are improved by using cold rolling method, is examined within the scope of this study. The new cage structures of the material that is changed by reason of cold Rolling process new mechanical properties are determined. The material that has special mechanical properties has been used on roll forming process in order to reach desired section and the range of uses the sheet metal profiles are expanded. In the study, S355MC sheet material was used and its thickness was reduced by 0.1mm by cold rolling. By using the mechanical properties of S355MC materials before and after cold rolling, analysis was made for the production of rectangular profile in the FEA software. It was observed that the yield values of straight edges along the profile increased from 370MPa to 520MPa, and the mechanical properties of the profile increased as expected. [820] Research on Sustainable Management Strategy of Resource Rubber Recycling Industry Kai-Chao Yao (National Changhua University of Education), Hsi-Huang Hsieh (National Changhua University of Education), Ching-Hsin Wang (National Chin-Yi University of Technology) and Kim Hua Tan (Nottingham University Business School). Abstract The goal of innovative application of the resource rubber recycling industry is to design and manufacture products with the main spirit of waste reduction, recycling, and reuse. As a result, resources and materials can be recycled and made into raw materials or other substitute products to reduce waste generation, which has considerable substantial contributions to benefits of green energy, environmental benefits, economic benefits, and benefits of energy saving. This study first explored the innovative application and goal of the resource rubber recycling industry through literature and sorted out a total of 20 important impact indicators in four dimensions, including “benefits of green energy”, “environmental benefits”, “economic benefits”, and “benefits of energy saving”. Next, this study adopted the fuzzy delphi method (FDM) to integrate expert interviews and questionnaires to confirm key impact indicators as well as incorporated the fuzzy DEMATEL to conduct expert interviews and questionnaires. Besides, this study calculated the weight value of each index based on the corresponding relationship between the key dimensions and put forward the relevant industrial relationship of benefits and management implications by means of prioritization. The research results will help unite related industries to not only create better circular economy and new energy models but also maintain the ecological environment, in order to enhance the efficiency of resource reuse as well as move towards the goal of sustainable design and production.

2023 ICPR27 8 [874] Automating Test Cases for OPC UA Information Models Tonja Heinemann (University of Stuttgart), Oliver Riedel (University of Stuttgart) and Armin Lechler (University of Stuttgart). Abstract In a digitized production, devices are interconnected with each other. This is achievable with OPC UA, which is currently increasing in popularity. To accommodate different kinds of machinery, Companion Specifications (CSs) for OPC UA are being defined. To achieve connectivity at the installation of machinery equipment, interoperability and correct implementation of the CS interfaces must be guaranteed. Discovering faults as soon as possible can be achieved through testing. For OPC UA and CSs, a testing framework exists, but the test cases are written by hand. This is a drawback for CS standardization groups, that have to put in the effort to develop test cases. In this work, the prerequisites to automatically generate CS test cases are outlined. An approach for automatic test case generation is sketched, that synthesizes test cases based on these prerequisites. In a comparison to existing test cases, the possibilities and limits of autogenerating test cases are discussed. [920] Fully Automated Data Acquisition Methodology for Hybrid 3D Scanning Processes towards Cognitive Digital Twins Raul Matei (Fraunhofer Institute for Industrial Engineering - FhG IAO), Carmen Constantinescu (Fraunhofer Institute for Industrial Engineering - FhG IAO) and Daniela Popescu (Technical University of Cluj-Napoca). Abstract This paper presents our methodology for automated data acquisition and interpretation, aiming at a real-time update of the computational model of production multiscale Digital Twins with captured critical production parameters, in two complex 3D scanning processes: manual and fully automated. The methodology consists of four phases: 1) Identification of all critical process parameters; 2) Selection of the suitable technologies to collect data corresponding to the critical parameters; 3) Development of an IoT platform aiming at managing the captured data; 4) Processing the captured data as input for the computational model of the targeted real-time Digital Twin. It is prototyped in Gray Field digitalization, with defined critical production input parameters. To ensure the scientific robustness of the computational model, the methodology presents the management and pre-processing of collected data from the performed experiments, based on machine learning algorithms. An industrial validation plan, preliminary results, and future work conclude the paper. [939] The role of universities in increasing awareness of challenges related to Sustainable Development Goals. Case Study: Technical University of Cluj-Napoca Anca Stan (Technical University of Cluj-Napoca), Emilia Brad (Technical University of Cluj-Napoca), Ionut Adrian Chis (Technical University of Cluj-Napoca) and Costan-Vladut Trifan (Technical University of Cluj-Napoca). Abstract Universities play a vital role in raising awareness about the Sustainable Development Goals (SDGs) and the challenges associated with them, both locally and globally. They have the ability to educate and involve students, staff, and faculty in sustainability and encourage sustainable practices. Universities can also collaborate with various sectors such as the public, private and civil society to raise awareness and promote cross-sectoral partnerships to address the Sustainable Development Goals. They can also engage with the community through outreach and education programs to increase public awareness and adoption of sustainable practices. Additionally, universities can lead by example by implementing sustainable practices in their operations. In general, universities are essential in advancing sustainable development and tackling worldwide issues by creating consciousness about the SDGs. The Technical University of Cluj-Napoca has cognized the imperative to infuse the principle of sustainable development within its august institution. As a premier higher education establishment, the university possesses the erudite capability and potential to notably advance the ingenious resolution of ecological dilemmas through education, research, and practices that are sustainable. Although is has a long way to go, the university's strategy is primarily oriented towards sustainable and inclusive advancement, with the objective of fortifying institutional protocols, illuminating and elevating triumphant exemplars of sustainable and inclusive development practices.

2023 ICPR27 9 [946] Integration of Industry 4.0 concepts into Lean Six Sigma projects: A case study in the ready-mixed concrete industry. Juan Miguel Sepúlveda (University of Santiago - CHILE) and Tamara Droguett (Universidad Santiago de Chile). Abstract This paper presents a case study for the achievement of operational excellence in a company branch dedicated to the production and distribution of ready-mix concrete. Lean Six Sigma (LSS) methodology is selected to carry out the objective by following the DMAIC stages. The main quality attribute for customer satisfaction is product docility upon delivery at customer's site, which is related to the water/cement ratio of the mixture. For this, the focus is placed on quality deviations within the production and transportation cycle. The deviations in the tolerance of the unloaded product and the performance indicators were measured to construct a regression model to find the main process variables affecting variations. In the Improvement stage, it is implemented a new process control system with GPS, water flow sensors installed at the mixer trucks and an APP for the driver to help control the process by regulating the mixer pressure and the amount of water to keep the docility of the product while in movement. Over a period of six months, an improvement of the main performance indicators was achieved. [954] Artificial Intelligence-based ergonomic optimization of manufacturing workplaces through Real-Time Digital Twins Stefan Giosan (Fraunhofer Institute for Industrial Engineering - FhG IAO), Mihaela Palca (Fraunhofer Institute for Industrial Engineering - FhG IAO) and Carmen Constantinescu (Fraunhofer Institute for Industrial Engineering - FhG IAO). Abstract The authors propose a paradigm shift in improving the traditional ergonomics of workplace optimization by employing the novel concept of Real-Time Digital Twins. In this paper a human-centred methodology that continuously adapts workplace ergonomics by considering the Real-Time context of human workers’ capabilities, required to perform specific assembly and logistic processes is presented. The proposed methodology is structured in eight steps, having as the main core the activities grouped under 3D motion capturing, ergonomics simulation, and analysis. A manual scanning process performed with a coordinate measuring machine (CMM) is selected as the focused use case for the proposed research. The motion of the human worker when the manual scanning process is performed is captured with the support of stateof-the-art motion tracking technology, while its movement is processed employing powerful ergonomics simulation software and analysed with RULA (Rapid Upper Limb Assessment) ergonomic analysis reports. Experiments are performed by a group of specifically selected subjects covering different gender and anthropomorphic characteristics. The methodology is validated and demonstrated through hybrid experiments, performed physically in a real research environment, and complemented in the digital world by integrating 3D motion capturing and ergonomics simulations. Conclusions and measures for improvement regarding the optimization of workplace ergonomics are elaborated. Artificial Intelligence (AI) algorithms for data analysis and elaboration of recommendations for workplace ergonomics improvement represent the planned next steps of the group’s research. [986] DESIGN AND STIFFNESS OF A CNC MILLING ROUTER WITH RECONFIGURABLE ALUMINIUM STRUCTURE Claudiu Ioan Rusan (Technical University of Cluj-Napoca), Cornel Ciupan (Technical University of Cluj-Napoca), Mihai Ciupan (Technical University of Cluj-Napoca) and Dan Hurgoiu (Technical University of Cluj-Napoca). Abstract A company is considered profitable if it produces products that are competitive and that strike a balance between quality and cost. In today's market economy and globalized markets, it is possible to remain competitive if the products offered meet the quality requirements of customers and are sold at an appropriate cost, and this is possible if the company's machines are: reliable, offer a certain flexibility, have a low cost of manufacture or purchase, have low energy consumption, and can be easily reconfigured and adapted to new products. Accordingly, CNC-based systems, i.e., CNC

2023 ICPR27 10 routers, are rapidly adapting to the production requirements constrained by the current market and will continue to see increased use at the expense of high-cost CNC machines and especially conventional machine tools. The author focuses on the design and development of a high-performance Gantry CNC router that will have three kinematic translation axes, a reconfigurable structure made of commercial aluminium profiles and a 3-kW main milling spindle. The aim of the work is to generate new knowledge in how to build CNC routers and obtain their theoretical stiffness by FEA. [1051] Ontology Model for Mapping Terms and Relations in Plastic Manufacturing – A Case Study Riad Al Hasan Abir (Ohio University), Mandvi Malik Fuloria (Ohio University), Dusan Sormaz (Ohio University), Peter Adjei (Ohio University), Felix Asare (Ohio University), David Koonce (Ohio University) and Saruda Seeharit (Ohio University). Abstract The increasing demand for efficient and sustainable manufacturing processes has led to the development of new technologies and methodologies in the field of manufacturing. In modern manufacturing, ontology provides a standardized and structured way of organizing and sharing information. It enables better communication and collaboration between different entities involved in the manufacturing process. Acknowledging these benefits, we establish an ontology for manufacturing plastic components using the Industrial Ontology Foundry (IOF) framework. The IOF framework, derived from the Basic Formal Ontology (BFO), is an essential component of smart manufacturing. It facilitates semantic integration, enabling data exchange and reuse between different systems and organizations+ existing IOF framework. Our research then employs a case study approach to develop the ontology for plastic manufacturing, which involves three main stages: hopper loading, sheet extrusion, and thermal forming, each with specific process steps that must be followed to produce the final product. We aim to map and incorporate these required process steps into the current IOF framework, utilizing ontographs to illustrate the relationships between classes and subclasses. Ultimately, this ontology will enable better communication and collaboration between different entities involved in the manufacturing of plastic components, thereby improving the efficiency and effectiveness of the manufacturing process. [1071] Smart and resilient manufacturing: Machine learning approach to identify patterns of employee skill levels for cognitive assistance within inspection processes Johannes Wimmer (University of Stuttgart, Institute of Human Factors and Technology Management (IAT), Stuttgart, 70569 Germany), Christian-Oswaldo Saba-Gayoso (University of Stuttgart, Institute of Human Factors and Technology Management (IAT), Stuttgart, 70569 Germany) and Erdem Gelec (Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, 70569 Germany). Abstract Towards a resilient manufacturing using digital twins and cognitive assistant systems, human-focused and data-based approaches are essential. Work skill gaps and differences among employees still belong to the most common challenges that have a high impact in manual manufacturing processes. For measuring and inspecting of parts manually in between manufacturing process to secure perfect quality, it is essential that those measurements are performed in the correct way. Especially in manual processes, lack of data collection and usage for analysis purposes result in general in loss of competitiveness, whereas extracting data knowledge and reacting to skill level gaps immediately via algorithm predictions allows continuous improvement of measuring and inspection processes and employee skills. Hence, the main purpose of this study is to empirically explore and predict skill related patterns that affect process performance and the quality of 3D hand-based measurements for quality inspection tasks in the automotive industry context using machine learning approaches. The setting is a manual measuring and inspection station in the ARENA2036 in Stuttgart, Germany using a battery case as an example part for the performed measurements. The algorithms are selected following the human-in-the-loop-approach, where understanding of the statistical data and the involvement of the process experts to validate results should be addressed. In this sense, principal component analysis (PCA) and clustering algorithms are used in the first exploration stage of linear separability. Subsequently, predictions are done using multiple classification algorithms as K-Nearest Neighbours (KNN), Random Forest, Support Vector Machines (SVM), Naive-Bayes and Logistic Regression to evaluate accuracy. Thus, the novelty of this paper lies in validating predictions for

2023 ICPR27 11 qualification levels within the manual inspection context and promptly developing modules and guidelines for the use of data to enhance cognitive digital assistance systems for resilient factories. [1096] Sustainability orientation, Big Data and eco-innovation Jana Kunecova (Universitat de Girona), Andrea Bikfalvi (Universitat de Girona) and Pilar Marques (Universitat de Girona). Abstract Eco-innovation has gained prominence among manufacturing companies all over the world as environmental concerns among businesses become more widespread. Eco-innovation has become more well-known in the literature because it improves company competitiveness and the transition to a sustainable society (Carrillo-Hermosilla et al., 2010). Understanding the factors that drive eco-innovation could help policymakers create tools that would promote its growth and adoption in the economy's industrial sector (Sanni, 2018). Adopting a sustainability mindset encourages businesses to build superior sustainable practices and to effectively use the resources needed to create the right new green products, which results in superior green innovation performance (Cheng, 2020). Sustainable manufacturing practices are one of the key environmental initiatives (Abdul Rashid et al., 2008) which can be enhanced by the application of technologies such as Big Data. Big data has been proven to affect supply chain performance and innovation from an environmental and social standpoint (Dubey et al., 2017). It is crucial for businesses to adopt and create processes employing big data management skills to achieve supply chain sustainability performance given the high levels of environmental uncertainty (Janssen et al., 2017). Big data -technology, solutions and skills- can be useful during a variety of stages of the creation of green products, including the prospect identification, product development, testing, and launch phases (Bag et al., 2020). However, extant literature falls short of articulating how Big data use can affect the creation of innovative green products. Purpose of the study The main aim of the present study is to analyse the association between different intensities and types of Big Data use and the effect on the existence and types of eco-innovation with empirical data from manufacturing companies of several European countries. Research design The empirical evidence comes from the European Manufacturing Survey (EMS) 2018, an international survey combining innovation, production, organizational, and technological innovation in manufacturing. The data, comprising reported values by manufacturing establishments with at least 20 employees, is used for the purpose of the present analysis. Sustainability practices related to the technologies employed, certification, and services are analysed. Questions about the use of Big Data in companies relate to whether the company collects the data during the production process, and if the data is effectively being used and for what purpose. This information is then related to the question about new/improved green products in the last 3 years and their contribution to sustainability – whether there is an improvement related to environmental aspects during the use/disposal of the products, and whether this improvement is related to improved product lifespan, reduced energy consumption during their use, reduced environmental contamination (water, air, noise, oil), improved maintenance, improved recycling/recuperation, reduced health risks. Findings Results will be delivered in form of descriptive analysis and regression analysis for the studied relations. Preliminary results show that 43% of companies implementing Big Data have developed products with improved sustainable features. The sustainability practices that influence eco-innovation are the ones related to take-back services, and environmental certification. Use of Big Data shows to be moderating the relation between certified environmental system and green product innovation. Contribution This paper aims to contribute to the emerging body of knowledge analysing the effects of the latest technologies (digitalisation of manufacturing) on green manufacturing. The study on the factors influencing product ecoinnovation may help to better understand how to encourage this type of innovation among manufacturing companies. It also aims to better understand the determinants of digital technologies’ configurations conducive to green product innovation. Further, suggestions about the digital and green transition will be formulated. References Abdul Rashid, S. H., Evans, S., & Longhurst, P. (2008). A comparison of four sustainable manufacturing strategies. International Journal of Sustainable Engineering, 1(3), 214–229. https://doi.org/10.1080/19397030802513836 AbdulRashid, S. H., Sakundarini, N., Raja Ghazilla, R. A., & Thurasamy, R. (2017). The impact of sustainable manufacturing practices on sustainability performance: Empirical evidence from Malaysia. International Journal of Operations & Production Management, 37(2), 182–204. https://doi.org/10.1108/IJOPM-04-2015-0223 Bag, S., Wood, L. C., Xu, L., Dhamija, P., & Kayikci, Y. (2020). Big data analytics as an operational excellence approach to enhance sustainable supply chain performance. Resources, Conservation and Recycling, 153, 104559.

2023 ICPR27 12 https://doi.org/10.1016/j.resconrec.2019.104559 Carrillo-Hermosilla, J., del Río, P., & Könnölä, T. (2010). Diversity of ecoinnovations: Reflections from selected case studies. Journal of Cleaner Production, 18(10–11), 1073–1083. https://doi.org/10.1016/j.jclepro.2010.02.014 Cheng, C. C. J. (2020). Sustainability Orientation, Green Supplier Involvement, and Green Innovation Performance: Evidence from Diversifying Green Entrants. Journal of Business Ethics, 161(2), 393–414. https://doi.org/10.1007/s10551-018-3946-7 Dubey, R., Gunasekaran, A., Papadopoulos, T., Childe, S. J., Shibin, K. T., & Wamba, S. F. (2017). Sustainable supply chain management: Framework and further research directions. Journal of Cleaner Production, 142, 1119–1130. https://doi.org/10.1016/j.jclepro.2016.03.117 Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345. https://doi.org/10.1016/j.jbusres.2016.08.007 Sanni, M. (2018). Drivers of eco-innovation in the manufacturing sector of Nigeria. Technological Forecasting and Social Change, 131, 303–314. https://doi.org/10.1016/j.techfore.2017.11.007 [1247] Antecedents of supply chain resilience of logistics companies: an exploratory study Xinbing Gu (University of Nottingham Business School China, University of Nottingham Ningbo China), Hing Kai Chan (University of Nottingham Business School China, University of Nottingham Ningbo China), Dimple R. Thadani (University of Nottingham Business School China, University of Nottingham Ningbo China), Faith Ka Shun Chan (School of Geographical Sciences, Faculty of Science and Engineering, University of Nottingham Ningbo China) and Yi Peng (School of Government, Nanjing University). Abstract The operation and services of logistics companies are disrupted due to disasters and crises, which require them to be more resilient. However, limited research specifically explores how logistics companies develop supply chain resilience (SCR) and its antecedents to unpredictable disruptions. Exploratory research and thematic analysis were conducted to investigate the antecedents through semi-structured interviews with logistics managers and annual reports. Key findings reveal that logistics companies build and develop SCR through dynamic capability, resourcefulness, disruption orientation, policy instruments, and digital platform based on multi theories. This study further discussed the constructs of these antecedents in SCR practice. This study not only enriched dynamic capability theory and resource-based theory but linked the affordance theory, information processing theory, and policy instruments choice view to SCR. This study can contribute to the body of knowledge on logistics management and SCR and provide insights for logistics companies to build resilience and transfer operation solutions. [1418] Approach for Integration of Model-based Systems Engineering and Product Life Cycle Management Benjamin Schneider (Fraunhofer Institute for Industrial Engineering IAO), Josip Zilic (Institute of Human Factors and Technology Management IAT), Raul Ghedeon Matei (Institute of Human Factors and Technology Management IAT), Stefan Giosan (Fraunhofer Institute for Industrial Engineering IAO), Carmen Constantinescu (Fraunhofer Institute for Industrial Engineering IAO) and Mehmet Kürümlüoglu (Fraunhofer Institute for Industrial Engineering IAO). Abstract Advanced Systems Engineering (ASE) is a new paradigm for handling the complexity of todays and tomorrow’s products and services. Advanced systems, in the focus of the ASE paradigm, are supposed to be autonomous, dynamically networked, optimized socio-technical interaction and offer combinations of products and services by the means of product-service-systems. To handle the resulting complexity, the ASE paradigm describes model-based systems engineering (MBSE) as a suitable approach. To ensure a digital continuity, MBSE models and structures, created the early stages of product development, have to serve as a basis for further development steps and therefore need to be stored and managed in a central IT-system. A product lifecycle management (PLM) system represents such a system. This paper describes a concept for a prototype integration and bi-directional interface between a MBSE modeling environment and a commercial PLM system. The prototype development and evaluation of the interface is based on a use case from a research project with a background in automotive industry.

2023 ICPR27 13 [1488] Research on the Circular Economy Business Model of Intelligent Manufacturing of the Machine Tool Industry under the Digital Circular Economy Framework Ching-Hsin Wang (National Chin-Yi University of Technology), Chih-Han Chen (National Chung Hsing University), Ming-Lang Tseng (Asia University), Ming K Lim (University of Glasgow) and Anthony Shun Fung Chiu (De La Salle University). Abstract At present, digital technologies have been regarded as one of the important driving factors for the circular economy business model. Digital technologies support resource flow strategies as well as value creation and acquisition. This study took the digital circular economy as the framework and applied it to intelligent manufacturing of the machine tool industry. This study summarized literature and proposed several aspects: management innovation, business model innovation, digital technology, optimization of procurement process, life cycle assessment, capability maturity, product innovation, and traceability labeling. Next, various criteria were extended, and key factors were screened out via the fuzzy Delphi method (FDM) as well as the fuzzy Decision Making and Trial Evaluation Laboratory (Fuzzy DEMATEL). In addition, the causal relationship and importance ranking between the aspects and criteria were analyzed and perceived. Hence, effective application strategies and decision-making guidelines for circular economy business model of intelligent manufacturing of the machine tool industry proposed by this study are also beneficial for subsequent scholars to have more references when studying the digitization of circular economy. [1506] The Unintended Consequences of Digitalisation on Supply Chain Relationships Kim Hua Tan (University of Nottingham), Osamu Yoshie (Waseda University) and Leanne Chung (Cardiff University). Abstract A potential dark side of digitalisation is a drastic shift in supply chain ecosystems and power relationships between enterprises. Often, manufacturing SMEs need to relax various constraints in the digitalisation processes in order to facilitate collaboration and trust across organizational boundaries. This, in turn, will lead to a structural change in the power relationship between SMEs and large corporations, as well as its supply chain ecosystem of customers, suppliers, and subcontractors. Much has been written about the opportunities digitalisation offers the manufacturing SMEs in terms of efficiency, sustainability and international competitiveness. Nonetheless, a growing concern of managers is that even SMEs in a good position to benefit from digitalisation, these technologies may not necessarily work out positively for their supply chain relationships. Moreover, little is available in literature on how to support SMEs in coping with changing supply chain ecosystem and power relationships along the digitalisation processes. Hence, this paper investigates and compares SMEs digitalisation journey in the UK and Japan. examining how industrial boundaries are blurred and how value chain are integrated. Through a series of case studies, a model for analysing the phenomena in the supply chain ecosystem resulting from digitalization is proposed to explain the transformation of the industrial structure and power relationships. The actor-resource-activity model is used to analyse the cornerstone of the SMEs supply relationships and networks in the digital ecosystem. The findings of this research benefit SMEs in both UK and Japan to better understand how best to leverage digitalisation to overcome the power relationships dilemma across the industrial boundaries. The “dark side” of digitalisation on SC relationships in the longer term is worth worldwide attention, as this research could inform the next generation of SC ecosystem. [1774] An upper bound for the branch-and-bound algorithm for the blocking permutation flow shop with total tardiness criterion Thiago Kato (Universidade Tecnológica Federal do Paraná), Mauricio Iwama Takano (Universidade Tecnológica Federal do Paraná) and Marcelo Seido Nagano (Universidade de São Paulo). Abstract The use of an upper bound for the branch and bound algorithm proposed by Nagano, Takano, and Robazzi (2022) is proposed in this paper. The problem explored is a blocking-in-process permutation flow shop problem with total tardiness criterion, which is known to be NP-Hard for m≥2. The literature for this theme is scarce, therefore this article aims to fill this gap. To improve the algorithm, it is proposed the use of an initial solution that will be used as an upper

2023 ICPR27 14 bound for the problem. A database that contains 27 different classes of problems was used for the computational experiments. Each class of problems varies in number of jobs (n) and in number of machines (m). To generate the initial solution, different constructive heuristics will be analyzed and compared to each other. [1778] Ontology Modeling of Plan and their Conformance to Manufacturing Execution Arkopaul Sarkar (ENIT, LGP, INP University of Toulouse, France), Milos Drobnjakovic (National Institute of Standards and Technology), Sina Namaki Araghi (ENIT, LGP, INP University of Toulouse, France), Mohamed Hedi Karray (ENIT, LGP, INP University of Toulouse, France) and Dusan Sormaz (Ohio University, Department of Industrial and Systems Engineering). Abstract Semantic data interoperability has attracted a lot of attention in recent years, with ontologies being one of the major tools to accomplish the goal of seamless data and application integration in manufacturing, industry and supply chains. Among the most challenging issues are semantic relations between manufacturing plans and their realization in actual production. This paper provides a model based on the IOF approach to the subject. The initial discussion is devoted to the semantic modelling of intentions, desires, plans and actions, from which we propose the specification/process approach to modelling plans and their realizations. The developed ontological model of plans includes relations between specifications and future occurrences; models of sequences, hierarchy, and alternatives; and conformance of executed processes to their planned counterparts as given by specifications. The model is illustrated by several small examples implemented in OWL language. [1780] A Study on On-the-Job Training (OJT) operator allocation considering fatigue in cell manufacturing system Moe Endo (Ibaraki University) and Harumi Haraguchi (Ibaraki University). Abstract In labor-intensive cellular production systems, it is important to train operators efficiently because productivity depends on operator skills. In our previous study, we proposed a "skill index" to classify operator skills based on the time required for each task, and used this method to allocate operators with a primary focus on training. However, in actual workplaces, operators are expected to accumulate fatigue due to repetitive tasks, which affects their work time. Therefore, a new fatigue learning model was employed in this study. We proposed an operator allocation that takes fatigue into account and compared it with computer experiments in previous studies. As a result, we proposed a new proficiency curve that can represent the decrease in proficiency due to fatigue caused by repetitive tasks. To validate the proposed method, we also conducted an assembly experiment using a LEGO robot with the operator allocates obtained in the computer experiment and analyzed the assembly time for each operator. The results showed that the values of the skill index were the same for more than half of the tasks in the computer experiment and the assembly experiment, indicating that the proposed method is effective. [1815] Air Quality analysis in the surrounding environments using a LoRa Network Ionut Marian Dobra (Technical University of Cluj-Napoca), Adina Alexandra Dobra (Technical University of ClujNapoca), Vladut Alexandru Dobra (Technical University of Cluj-Napoca), Vlad Dacian Gavra (Technical University of Cluj-Napoca) and Silviu Folea (Technical University of Cluj-Napoca). Abstract This paper presents an analysis and a comparison of the air quality from different areas in different conditions from outdoor and indoor environments, so we could take actions that could improve our well-being or could signal to bigger institutes what exactly should be monitored in what area so we could have the best breathable air. The paper is organized in three parts, the first one describing the air quality analysis and used sensors for indoor and outdoor measurements. The second part is describing the data transmission using the LoRa Wan protocol with an STM32 gateway and end nodes, to transit the analyzed data to a gateway to be able to centralize and process the data. Also, we will check

2023 ICPR27 15 possible ranges for LoRa transmission in a big city. In the third part of the paper, we will present future development and points that will be checked with different sensors. [1819] Evaluating Precision and Repeatability of Industrial Robots Using Direct and Indirect Measurement Approaches Stelian Brad (Technical University of Cluj-Napoca), Vlad Florian (Technical University of Cluj-Napoca), Eyas Deeb (Technical University of Cluj-Napoca), Dragos Bartos (Technical University of Cluj-Napoca), Bogdan Balog (Technical University of Cluj-Napoca), Ovidiu Stan (Technical University of Cluj-Napoca) and Stefan Bodi (Technical University of Cluj-Napoca). Abstract This research embarks on a comprehensive investigation into the precision and repeatability of industrial robots, with a particular focus on a Universal Robot (UR) model. By deploying direct data collection methods and utilizing a highprecision CTrack 780 measurement equipment, a vast array of metrics including joint position, temperature, torque, velocity, and current were analyzed. This enabled the identification of correlations between joint temperature, joint current consumption, and robot precision, and yielded valuable insights into the robot's performance. Findings underline the negative influence of temperature fluctuations on absolute accuracy, calling for the implementation of automated adjustments within robot programming. The paper also outlines future directions, including the replication of the study on different machines and the development of machine learning models for real-time machine adjustments, aiming to enhance long-term performance and adaptability. [1864] Ontology-Based Engineering - an overview Joachim Lentes (Fraunhofer IAO). Abstract Since the publication of the well-known definition of an ontology as „an explicit specification of a conceptualization“ by Gruber (1993) and his subsequent work on an ontology for engineering mathematics (1994) thirty years of research and technical development have passed. In the contribution, an overview about essential work done to support the engineering of products and production systems by means of ontologies (I.e. Ontology-Based Engineering) is given based on an analysis of the literature. The literature review is done by using essential scientific literature databases. Identified literature ist classified in scientific work areas and directions for future work in the field of Ontology-Based Engineering concerning products and production systems are identified and described. Gruber, Thomas (1993): A translation approach to portable ontology specifications. In: Knowledge Acquisition, Volume 5, Issue 2, pages 199-220, https://doi.org/10.1006/knac.1993.1008. Gruber, Thomas; Olsen, Gregory (1994): An Ontology for Engineering Mathematics. In: Jon Doyle, Piero Torasso, & Erik Sandewall, Eds., Fourth International Conference on Principles of Knowledge Representation and Reasoning, Gustav Stresemann Institut, Bonn, Germany. [1893] Using the Delphi technique to develop a Data Science model for the Construction Industry Maury Melo (PUCPR - Pontifícia Universidade Católica do Paraná (PUCPR/PPGEPS)), Sergio E. Gouvea da Costa (Universidade Tecnológica Federal do Paraná (UTFPR-PB/PPGEPS)), Edson Pinheiro de Lima (Universidade Tecnológica Federal do Paraná (UTFPR-PB/PPGEPS)) and Fernando Deschamps (Pontifícia Universidade Católica do Paraná (PUCPR/PPGEPS) e Universidade Federal do Paraná (UFPR)). Abstract The advent of Industry 4.0 impelled the construction sector to think of technology as a technological business. The impact of this new reality will bring positive impacts, mainly in the operational context of the construction industry, providing opportunities for improvements in competitiveness and in the customization of products in this sector. To overcome the barriers of digital transformation, an infrastructure model was proposed for the implementation of a Data Science solution, in order to improve decision-making and improve the production process, giving decision-makers greater

2023 ICPR27 16 assertiveness in their predilections. In order to facilitate the company's technological change process, the research proposes a conceptual model to make the digital implementation successful. The Delphi technique with expert opinion was chosen for the model validation process mainly due to the lack of information on the subject. The technology infrastructure model validation process is normally the first activity developed for its implementation. In the context of this research, modeling and model validation are part of the organization's systems development methodology. The study adopted a qualitative exploratory research approach. The Delphi technique was used in order to obtain the opinion of specialists on several identified factors and components that make up the conceptual model. The selected experts were divided into two groups: Information Technology and Construction Engineers. The consensus of IT specialists sought approval of the infrastructure framework, and the agreement of Civil Engineers for the application of the presented model considering the challenges of digital transformation in this sector. The use of the Data Science approach in projects can help construction companies to improve project performance and reduce the risk of failure. This article unites Data Science theories and a new approach that facilitates solving old problems that negatively impacted projects in the construction industry sector. [1926] Increasing supply chain performance of a SME by increasing delivery function Claudiu Ioan Rațiu (Technical University Cluj Napoca) and Camelia Ioana Ucenic (Technical University Cluj Napoca). Abstract A higher supply chain performance is related with a better logistics. The prospective significant problems can be divided in smaller issues. Each issue can be analyzed and improved to obtain a better performance level. SMEs have limited resources and this approach allow them to become more competitive. This study analyses the delivery function for a small Romanian firm which intends to implement the QDCM model. Suitable planning and execution are mandatory for delivery. The delivery function has four parameters: on time delivery, variance of supplier lead time, backlog efficiency and transportation efficiency. Data was collected for six months from 2022. The average value of delivery function was compared with the thresholds specified by literature review. [1953] Simulation-based evaluation of optimized reconfigurable cellular manufacturing deploying a diversified workforce Julian Perwitz (Fraunhofer Austria), Thomas Sobottka (Fraunhofer Austria, TU Wien), Ádám Szaller (EPIC InnoLabs, SZTAKI) and Fazel Ansari (Fraunhofer Austria, TU Wien). Abstract This paper compares the performance of reconfigurable cellular manufacturing systems (RCMS) and linear systems when employing a diversified workforce with different work speeds. Due to demographic and labor market trends manufacturing companies increasingly struggle to find skilled personnel for efficient manufacturing operations usually organized in tact-driven production lines. Concepts for flexible manufacturing can foster the integration of a more diversified workforce. Yet, this potential is largely untapped in practice, as the benefits as well as the circumstances under which they can be realized are unknown. To address this gap, this paper proposes a simulation-based approach to compare the performance of reconfigurable cellular manufacturing systems (RCMS) and linear systems under different scenarios of workforce diversity. The approach is demonstrated within a dynamic multi-variant engine assembly use case, using discrete event simulation. The results confirm the feasibility of the method and point to the advantages of RCMS with different work speeds.

2023 ICPR27 17 [1958] Electromechanical Hinge for RS-485 Connection and Door Position Sensor in Data Center Racks Mustafa Can (Mesan Kilit A.Ş.), Engin Gunes (Mesan Kilit A.Ş.), Anıl Akdogan (Yıldız Technical University, Mechanical Engineering Department) and Ali Serdar Vanli (Yildiz Technical University, Mechanical Engineering Department). Abstract Technological developments create to needs data protection, monitoring and remote management have an important point. Plenty of industrial electromechanical locking systems are used in order to provide protection both access monitoring in data center server cabinets and server rooms. One of the important points in remote monitoring is to detective the position of the door. In current applications door position is monitored by wired door sensors or wireless reed-switch and magnets. Although this application methods have both many advantages and disadvantages, it is not useful. This research is to find an alternative and useful solution for the application areas of data center lock systems with a new application method based on the findings. This alternative and useful solution is offered for the application areas of data center lock systems. In consequence, thanks to this electromechanical hinge design will prevent not only cutting of the communication cables mounted on the cabinet doors but also will perceive from the safest point without requiring additional cost and labour. [1977] Requirements for the design of autonomous and stress-oriented Job Rotation Stefanie Findeisen (University of Stuttgart IAT, Institute of Human Factors and Technology Management). Abstract Manufacturing companies are facing an increasing shortage of skilled workers and an aging workforce in the future due to demographic developments. The benefits of Job Rotation as a tool for balancing load and stress in production work has been confirmed from a theoretical and practical perspective. Depending on the company, different challenges arise when planning and implementing a Job Rotation Concept. The operational matching of employees and workstations taking stress into account, is associated with a high level of planning and coordination. This paper presents an explorative study regarding the requirements for autonomous and stress-oriented Job Rotation based on a literature analysis and focus group survey. [2148] Application of Machine Learning and Time Series for Demand Forecasting: A Case Study in a Consumer Goods Company Guilherme Gomes (Federal Univertisy of Santa Catarina), Ana Lígia Vieira Rodrigues (Federal Univertisy of Santa Catarina), Marina Bouzon (Federal Univertisy of Santa Catarina) and Francielly Hedler Staudt (Federal Univertisy of Santa Catarina). Abstract A reliable Demand Planning process is the basis for the S&OP, "Sales and Operations Planning", which through a monthly cycle, gets plans in an integrated way integrating various business functions, providing a series of benefits such as inventory reduction, increased service level and revenue. Within S&OP, the starting point is the demand forecasting, if the accuracy of the forecast can be improved, consequently all supply chain can be well planned. It was noticed that advanced IT techniques like Machine Learning (ML) are rarely used in S&OP. And, from the literature perspective, it is perceived that there is still a gap of studies applying time series methods and machine learning in the S&OP process, so this research seeks to enhancement its application through the results obtained. The research focused on the first phase of the S&OP process called demand forecasting and its objective is to boost the accuracy of the forecast in a consumer goods company, through the implementation of time series models and machine learning. To this end, based on the methods used by Pavlyshenko (2019) and Schmidt (2021), this research defined the necessary steps to apply the methods of time series and machine learning in the process of predicting demand. The first step comprehend the company's sales histories collected over a five-year horizon generating more than 200,000 instances of data, in the sequence, an exploratory data analysis was subsequently performed in order to understand the demand patters of each product, including the coefficient of variation, which weighted by the volume obtained the value of 81% for the company’s portfolio. As the third stage of the research, the outliers were cleaned through the Winsorization method, which consists

2023 ICPR27 18 of replacing the outliers of the data by the superior or lower limit, which process resulted in a new coefficient of variation reduced to 55%. With the adjusted data, the historical base of 5 years was separated into 2 datasets, with the first 3 years used as a training dataset and the last 2 years as a test dataset. As the fifth stage of the research, the time series and machine learning models were tested using the training dataset as a basis, 30 models were used for each DFU (demand forecast unit). To select the best model by product, the accuracy in the test base was measured using the performance indicator known as "Mean Absolute Percentage Error" (MAPE). Knowing which model presented the best predictive performance in the test base, that is, lower MAPE for the DFU, the chosen method was applied considering the entire historical basis to predict the demand for the desired future horizon. After the forecasts are made, the demand realized versus the predicted is monitored, which will serve as a basis for continuous improvement of the process, alerting to opportunities for better calibration and testing of the proposed models. The results of the study were followed for the period of 4 months, from January-22 to April-22, and comparing the result with the some period in the previous year, 2021, it was found that there was a significant increase in the degree of predictability of the company studied, reducing the error indicator from approximately 60% to a new level of 41%, representing an improvement of 19 percentage points. This study focused on the application of machine learning and time series methods to increase the accuracy of the S&OP process. It has been proven that these models have significantly chances to achieve more attractive results in the demand planning process than naive models. In this case, 80% of the DFU’s presented best accuracy results for times series methods, 8% for linear and multiple regression, 4% for naïve methods and 7% for machine learning methods, which highlights the fact that without data from independent external variables with quality to correlate with demand, which was the case of the studied company, machine learning models are not so well used. [2164] A Carbon Reduction Evaluation Model for Industrial Processes Based on Circular Economy Paradigm Kuen-Suan Chen (Department of Industrial Engineering and Management, National Chin-Yi University of Technology), Kim Hua Tan (Operations & Innovation Management, Nottingham University Business School) and Chun-Min Yu (Department of Industrial Engineering and Management, National Chin-Yi University of Technology). Abstract In the face of global warming, the negative environmental impact triggered by product manufacturing has attracted a lot of attention. Corporate social responsibility has now become a business philosophy jointly driven by all companies. Governments and enterprises in various countries must carry their society and ecology responsibility in the process of pursuing their economic growth. Many studies have suggested that quality is a key factor for a good circular economy and the attainment of sustainable production. With robust quality processes, the rate of defective product output can be diminished and the number of shutdowns led by machine failures can be lowered. Hence, robust quality processes can reduce waste, save energy, reduce carbon emissions, and reduce manufacturing costs. Clearly, enhancing the process quality of products has become one of the important strategies for enterprises to shoulder their social responsibility and implement energy conservation and carbon reduction. This paper focuses on the carbon reduction evaluation of the industrial processing processes of the machine tool industry chain. The production data for the customer end of the machine tool industry chain will be collected, evaluated, and analyzed. The six sigma yield index is used as the processing capability index to evaluate the carbon reduction benefit of reducing the output of defective products in the production and after sale stages. The advantage of the fuzzy six sigma yield index model is that it can be integrated into the accumulated data experience, which is in line with the characteristics of companies pursuing a rapid response mechanism with small samples. The results show that the proposed carbon emission reduction fuzzy evaluation model not just evaluate the quality of processed products, but also the carbon reduction benefits in the production stage and after the products are sold. This paper provides specific operation guidelines for the machine tool industry to fulfill corporate social responsibilities, and actively gear towards the goal of net zero carbon emissions.

2023 ICPR27 19 [2241] Comparing different workforce strategies In mixed model assembly systems Francesca Catalano (Department of Management and Engineering, University of Padua), Nicola Berti (Università degli Studi di Padova), Ilenia Zennaro (Department of Management and Engineering, University of Padua) and Alessandro Persona (Department of Management and Engineering, University of Padua). Abstract Market needs are moving towards mass customisation, shorter lifecycles, and fluctuating demand. Industries need to react quickly to these changings, with flexible, dynamic, and easily reconfigurable assembly systems, especially in assembly-to-order systems, where customers’ order characteristics drive production. Workforce strategies allow to quickly adapt to frequent changes of the system. This paper presents a parametric model to compare fixed worker and walking worker strategy considering a balanced mixed-model assembly line. Some variables have been varied to compare the two strategies and define the best one in terms of productivity. Results show that if the line is well-balanced and task time variation is low, it is better to use fixed workers, as expected. But, if time variation is higher, penalty coefficient is low, or it is possible to have parallel workstations, the walking worker strategy could be better. [2308] Skill-and-Knowledge Sharing by Augmented Reality: HUB-CI Model Praditya Ajidarma (Bandung Institute of Technology) and Shimon Nof (Purdue University). Abstract Advancements and the adoption of productive human-robot collaborations have impacted every aspect of modern manufacturing. Although automation has been increasingly utilized on the shop floor for decades, a significant portion of manufacturing operations remains manual primarily due to the dominating ability of human operators to perform these operations well, compared to robots. These manual or semi-automated operations involve timely complex manipulations and reasoning, and depend highly on human labor skills, intelligence, and expertise. To prepare the workforce of the future, manufacturers are increasingly turning to augmented reality (AR) to prepare, train, guide, and refine the skills of their less-experienced workers by allowing skilled workers to collaboratively guide and mentor less-experienced workers, collaboratively and on the fly. In this paper, we propose a new skill-and-knowledge sharing model in manufacturing systems with a hub for collaborative intelligence (HUB-CI) that supports an effective learning protocol. The learning curves of operators are modeled as a function of time and the complexity of skills and knowledge, and analyzed for the learning quality. A case- study of assembly kitting workflow is used to conduct the experimental simulations and analyses. Several insights are drawn from the preliminary simulation analyses to inform the design of future AR technologies for skill and knowledge sharing among manufacturing workers, working collaboratively with robots on complex manipulation and reasoning tasks. [2320] Rescheduling with real time information and Industry 4.0 technologies for complex manufacturing systems Gonzalo Mejía (UNIVERSIDAD DE LA SABANA), Francisco Yuraszeck (Universidad Andrés Bello) and Daniel Rossit (Universidad Nacional del Sur). Abstract Scheduling in the Industry 4.0 is a complex task that requires not only quick and efficient responses but also real-time information that feeds the optimization algorithms. Scheduling in static and deterministic environments is rarely the case. As such, the performance of the quality of a schedule cannot be evaluated a priori. In this paper, we investigate the topic of real time re-scheduling in job shop environments subject to machine breakdowns and with real time diagnostics. We compare two scheduling algorithms under two metrics. The two scheduling algorithms are generic and can be adapted to a number of machine settings (flow shop, job shop). One of the algorithms is a beam search algorithm and the other one is a local search algorithm with re-starts. Total flow time and stability are the performance measures.

2023 ICPR27 20 [2361] Unravelling Eight Myths for Publishing Literature Reviews in the Domains of Operations and Supply Chain Management Rob Dekkers (Adam Smith Business School/University of Glasgow), Pauline Found (Cardiff Business School/Cardiff University) and Yang Cheng (Department of Materials and Production/Aalborg University). Abstract With literature reviews, particularly, protocol-driven ones, becoming more accepted in the domains of operations and supply chain management, eight myths that influence authors in their approach have been extracted from reviews on submissions to a special issue in the Journal of Manufacturing Technology Management. What to do about these myths, reflected in the comments of reviewers and guest editors, is at the heart of this paper. Some of the guidance here complements existing advice found in textbooks and publications in academic journals, whilst the discussion of other points leads to suggestions hardly addressed so far, even though they are essential to getting literature reviews published. Thus, the purpose of this paper is to encourage scholars to reflect on their approaches to literature reviews to increase chances of publication. [2453] Towards quantitative metrics for supply chain and company resilience assessment Martina Calzavara (University of Padua), Benedetta Baldi (University of Verona), Ivan Russo (University of Verona) and Daria Battini (University of Padua). Abstract Recent global events have demonstrated that supply chain resilience (SCRES) should represent a priority for academics and practitioners. However, there is still a lack of clarity related to the tools and metrics that allow managers to easily measure and estimate it. In fact, although some existing contributions propose metrics for assessing SCRES, they are usually keeping them at a qualitative level, without explaining how these should be calculated. Other contributions, instead, are defining formulas that can turn out to be difficult to be applied in practice, due to their complexity or the difficulty in getting the input data. Starting from a detailed analysis of the existing literature, this paper aims synthetizing and categorizing the already existing quantitative metrics and Key Performance Indicators (KPIs) presented in the literature, that can allow managers to measure supply chain and company resilience. The set of indicators refer to both operational aspects and to financial ones, and can measure reactive and/or a proactive supply chain resilience actions. [2511] Management of stakeholders based on the GRI Standards Isabella Gil (Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana), Isabela Dallabona (Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana), Pablo Carpejani (Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana), Edson Pinheiro de Lima (Department of Industrial and Systems Engineering, Federal University of Technology), Ubirata Tortato (Department of Industrial and Systems Engineering and Department of Business, Pontifical Catholic University of Parana), Sergio E. Gouvea da Costa (Department of Industrial and Systems Engineering, Federal University of Technology) and Fernando Deschamps (Department of Industrial and Systems Engineering, Federal University of Technology). Abstract In an increasingly competitive market, where sustainable development is gaining more and more prominence for the companies that employ it, it is necessary to understand and work with tools to progressively improve the image of the organization, especially considering stakeholders. First appearing in 2004, the term ESG can be summarized by practices adopted by companies in environmental, social, and governance aspects, always seeking to approach corporate sustainability. This article aims to understand how to implement ESG criteria in the development of stakeholder management model for different types of companies and business sizes. As a main result, the paper presents how organizations should manage their stakeholders based on the GRI Standards and also how they are executing this process based on sustainability reports.

2023 ICPR27 21 [2532] Current state of artificial intelligence based assistance systems in product development Benjamin Schneider (Fraunhofer Institute for Industrial Engineering IAO), Mehmet Kürümlüoglu (Fraunhofer Institute for Industrial Engineering IAO) and Oliver Riedel (Fraunhofer Institute for Industrial Engineering IAO). Abstract Advanced Systems Engineering (ASE) is a new paradigm for product creation. Companies currently face challenges in engineering regarding complexity of systems, need for interdisciplinary collaboration and steadily increasing demands form market and regulatory side. The ASE paradigm promotes the company specific application of ASE technologies such as model-based systems engineering, data continuity, software-defined-x, artificial intelligence (AI) and collaborative visualization. AI herby is a technology that can be applied in many different areas of product creation as a tool supporting humans in handling complex tasks or systems. While there are numerous applications in areas such as production, marketing or human resources today, applications in engineering are rather sparse. This might be due to the complexity of the engineering tasks or limited or highly varying data. This paper analyses the current state of AI-supported assistance systems based on in depth analysis of scientific literature as well as market offerings by software vendors identified via web search. The findings are summarized in different categories regarding specific tasks in the field of engineering. The categories are derived from literature and validated in expert workshops. Information provided for each identified assistance system are for example current readiness level, implementation effort, necessary data and type of interaction with the user. The findings are discussed and recommendations for bridging the gap between AI-research and industrial application, especially in SME environments are given. [2578] Due date assignment of multi-product in Make-to-order supply chains Haruki Takahashi (University of Tsukuba), Ibuki Shishido (University of Tsukuba) and Sumika Arima (University of Tsukuba). Abstract This paper introduces an optimization method of due date assignment (DDA) for make-to-order (MTO) production systems. The due date assignment (DDA) in MTO system takes an important role to balance and improve both the due date satisfaction and process efficiency as trade-offs for each other. The proposed method is to work for various load volume and order fluctuations as extended version of an existing DDA method which has considered the customers' importance. We examine the performance of the proposal DDA method to combine with Hybrid flowshop Scheduling (HFS) for a typical built-to-order production system. Actual company data and discrete event simulations are used for the numerical performance evaluation. In particular, the proposed DDA method can perform well with effective multiobjective HFS engine that can handle HFS problems of a practical scale of several hundred jobs. This is because the DDA method, adapted to the order characteristics of the real data, was able to reproduce the various load volume and order fluctuations better than proposed methods. [2645] Intelligent additive-subtractive manufacturing for resilient production Hans-Christian Möhring (IfW - University of Stuttgart). Abstract Additive manufacturing is a key enabler for fast reacting and therefore resilient production of complex components. A vast variety of materials can be processed and function integrated parts with dedicated geometrical and physical properties can be realized. In order to fulfill functional requirements, post treatment in terms of machining is necessary in most cases, leading to an additive-subtractive process chain. With the aim to achieve a first-part-right production, intelligent strategies, comprising process monitoring and data analytics, simulation and process control are required and have to be implemented throughout the entire process chain. This contribution introduces related intelligent approaches with an emphasis on the targeted application of artificial intelligence methods.

2023 ICPR27 22 [2681] Manual order picking and robotic mobile fulfilment systems performance comparison Nicola Berti (University of Padua), Francesca Catalano (University of Padua), Alessandro Persona (University of Padua) and Ilenia Zennaro (University of Padua). Abstract Automated order picking systems are often adopted to process more order lines compared to traditional and manual solutions. Among the most investigated Automated Storage and Retrieval Systems (AS/RS), Autonomous Mobile Robots (AMR) attracted considerable interest. Despite the reliability and excellent performance, the adoption of robotic systems is still largely debated, and the traditional manual solution is still attractive and preferable. This research aims to determine which aspects mostly affect the adoption of rack-moving mobile robots in the order picking system (OPS). The paper proposes a comparison between traditional picker-to-parts warehouse and AMR warehousing systems to determine convenience area in terms of process customers’ orders time minimization. A manual picker-to-parts system was compared to an AMR solution, and critical guidelines are presented for researchers and practitioners. [2736] Workforce individualization in Collaborative Assembly Line Balancing Niloofar Katiraee (University of Padova, Department of Management and Engineering), Ali Keshvarparast (University of Padova, Department of Management and Engineering), Serena Finco (University of Padova, Department of Management and Engineering) and Martina Calzavara (University of Padova, Department of Management and Engineering). Abstract Due to the advancement of Industry 4.0, many industries are looking for opportunities to utilize collaborative robots in assembly lines to perform tasks independently or assist human workers. Compared to the traditional usage of robots, Human-Robot Collaboration (HRC) can be considered a proper solution to improve the throughput of manual systems. However, successful implementation of HRC scenarios requires adapted decision support tools. Workforce diversity can be mentioned as one of the factors that should be included to investigate its impact on both the performance of the production system and on ergonomics risk since workers may have different features and capabilities. In this study, we propose collaborative assembly line balancing with cobots considering human-operator differences to investigate their impact on the production system and assess the ergonomic risk. [2748] Insights on the selection of international agribusiness markets using the Analytics Hierarchy Process Aymee Batista (University of Para State), Natan da Silva (University of Para State), Emilia Cardoso (University of Para State), Lianne Borja (Graduate Program in Environmental Sciences of University of Para State) and Renata Oliveira (University of Para State and Univeristy of Porto). Abstract This study presents an empirical investigation into the decision problem of identifying suitable international markets for a Brazilian livestock producer aiming to expand their business in compliance with the regulatory requirements of the European Union and Islamic countries. The study focuses on the top five pre-selected markets: Germany, Belgium, France, United Arab Emirates, and Turkey. Key Performance Indicators (KPIs) sourced from reliable entities such as The World Bank and the European Committee, covering the period from 2020 to 2023, were normalized and employed to evaluate the markets based on economic stability, logistics, culture, and market appeal criteria. The weights for these criteria were determined using the Analytic Hierarchy Process (AHP) methodology, employing a paired comparison survey to establish the hierarchy of criteria. The findings underscore the producer's priorities, highlighting the significance of economic stability and growth, particularly GDP growth. Additionally, logistics and operational difficulties emerged as crucial considerations, whereas cultural similarity and market appeal received relatively lower priority. This study showcases the practical applicability of the AHP method as a decision support tool in the livestock industry for international market selection, providing valuable insights for producers seeking expansion opportunities.

2023 ICPR27 23 [2777] Smart solution for a greenhouse with controlled bioclimate Claudiu Ioan Rațiu (Technical University Cluj Napoca) and Camelia Ioana Ucenic (Technical University Cluj Napoca). Abstract The field of agricultural production became agri-industrial along with the evolutions and revolutions produced in the industrial environment. The techniques and technologies implemented until now had as their main purpose the easing of physical effort through so-called mechanization. The spectacular developments produced in the last decades in the fields of informatics (hard and soft), sensors and data processing, chemistry and biochemistry or genetics, have begun to be transferred successfully and with spectacular effects in the agri-industrial environment. This is how applications have appeared, materialized through vegetable production farms with controlled microclimate, whose level of technology and especially computerization successfully competes with top applications in the industrial environment. These results are the result of effective collaboration and communication between specialists from apparently incompatible fields such as: agronomists, biologists, geneticists, hydraulics, automatists, and computer scientists. [2810] NEW ECOLOGICAL COMPOSITE MATERIALS FOR UPHOLSTERED FURNITURE INDUSTRY Ioan Cionca (Technical University of Cluj-Napoca), Emilia Ciupan (Technical University of Cluj-Napoca), Mihai Ciupan (Technical University of Cluj-Napoca) and Cornel Ciupan (Technical University of Cluj-Napoca). Abstract The paper presents a description of composite materials with an emphasis on natural fiber composite materials. It reviews industries such as car making and civil engineering which make up the largest consumers of these materials and it presents some of their specific applications. The authors research the use of natural fiber composite materials in furniture making and show the results of mechanical and water resistance testing of two blends of hemp fibers and natural based matrices. [2813] Production research in Romania: trends, needs and agenda Daniela Popescu (Technical University of Cluj-Napoca), Mihai Dragomir (Technical University of Cluj-Napoca), Sorin Popescu (Technical University of Cluj-Napoca), Calin Neamtu (Technical University of Cluj-Napoca) and Diana Dragomir (Technical University of Cluj-Napoca). Abstract An examination is performed on the future trends that are developing in the field of production research, that are pertinent to the academic and industrial needs in Romania. In the proposed approach, we include, for an analysis based on experience, the most relevant directions and investigations for research demarches, and we apply the strategic forecasting principles of Hoshin Kanri. The outcome of this process can form the basis of a future research agenda in the field of production, including topics related to manufacturing systems, digital transformation, innovative technologies, quality management and sustainability aspects. [2841] A Preliminary Study for Ontology-Based Controller in Factory Automation Yu-Ju Lin (Department of Industrial Engineering and Enterprise Information Tunghai University), Chi-Yuan Tseng (Department of Industrial Engineering and Enterprise Information Tunghai University), Chiao-Yueh Liu (Department of Industrial Engineering and Enterprise Information Tunghai University) and Chin-Yin Huang (Department of Industrial Engineering and Enterprise Information Tunghai University). Abstract ABSTRACT Since the 1970s, Programmable Logic Controller (PLC) as a typical example of sequence control system has gradually become the mainstream technology in factory automation. However, the inflexibility and low maintainability of PLC bring a difficulty to adopt a PLC in modern factory automation with agility to handle unexpected internal and external events. Recent research shows that ontology can provide better knowledge representation and reasoning, in

2023 ICPR27 24 comparison with expert system/knowledge-based system. Ontology provides high flexibility and maintainability when it is facilitated as a controller for factory automation. Examples could be to add a new machine to replace an old machine, or to add an urgent order with customized processes to the shop floor. This research proposes a preliminary ontology controller integrating with Internet of Things (IoTs), cloud computing, and sematic model and reasoners. The controller is implemented in a laboratory pick-and-place station. The results indicate the ontology-based controller can handle unexpected events without a need of interruption to adjust/re-code the program. This research intends to show a direction for the controller of factory automation. The success of the research paves a new way to develop a further large and complicated controller for factory automation. [2859] Online Sales Promotion with Time-dependent Coupons Kaiam Sou (Shanghai Jiao Tong University). Abstract For online retailers, sales promotion with coupons is one of the most important strategies for revenue management. Traditionally, the face value of a coupon is constant until it expires. In this study, we consider sales promotion with timedependent coupons, namely, the face value of a coupon depreciates with time until it becomes zero. We formulate a 0-1 integer program model for multi-period and multi-product promotion with time-dependent coupons, with the objective of determining the optimal coupon issuing strategy for the products in each period to maximize the total profits. We solve the model by solver and analyze the objective values under different parameters. The experimental results show that when the coupon duration is increased, higher profits can be obtained. [2888] How digital technologies improve supply chain resilience: The mediating role of supply chain transformative capability Giovanni Francesco Massari (Politecnico di Bari), Raffaele Nacchiero (Politecnico di Bari) and Ilaria Giannoccaro (Politecnico di Bari). Abstract To survive and secure more favourable business performance in dynamic environments under disruptions and shocks, supply chains (SCs) have to be resilient. To this regard, academists and practitioners agree that SCs need to develop specific dynamic capabilities and that the adoption of Digital Technologies (DTs) may play as enabling factor. Despite multiple studies on this topic, how DTs can improve SC resilience still requires further investigation. To fill this research gap, we investigate the SC transformative capabilities which can enable SC resilience. By adopting a microfoundational approach, based on dynamic capability theory, we identify the lower-order constructs that underlie SC transformative capabilities and formulate specific propositions on the relationship between them and DTs. [2925] NOVEL CIRCULAR SUPPLY CHAIN MODEL CONSIDERING THE REUSE OF RECOVERED PARTS IN MULTIPLE PRODUCTS Katsunari Ikezawa (Kanagawa University), Atsuya Kawai (Kanagawa University), Shingo Akasaka (Kanagawa University) and Jiahua Weng (Kanagawa University). Abstract Efforts to reduce resource waste and utilize limited resources effectively have been a primary concern in the manufacturing industry. Circular supply chain models were developed to collect and reuse products in the market. However, previous models have mainly focused on reusing collected parts for the same product. This paper proposes a novel model that reduces the wastage of parts by converting the recovered parts into different products according to their degree of functional deterioration. The conversion of electric-vehicle lithium-ion batteries to forklift and storage batteries was considered and the optimum number of cells per module that minimizes resource wastage was evaluated. Subsequently, the performance of the model was verified using an event simulator.

2023 ICPR27 25 [2928] Minimizing the number of operators with multi-skill levels in labor-intensive cells Takayuki Kataoka (Kindai University), Katsumi Morikawa (Hiroshima University) and Katsuhiko Takahashi (Hiroshima University). Abstract Cellular manufacturing (CM) has been popularly documented in the literature. Basically, the literature has two streams of concepts. One deals with machine-intensive cells and the other with labour-intensive cells. In the field of labour-intensive cells, a hierarchical approach based on multi-phases has been adopted in some previous studies even though it has some shortcomings. For example, as the configurations of operators are limited to maximize production rates in phase 1, the total number of operators cannot be decreased for production capacities in phase 2 even if there is a decrease in production rates caused by slack cells. In addition, operators are assigned to operations considering their skills by using another integer programming model in phase 3. However, it is difficult to solve the problem by integrating the multiphases because of non-linear. Furthermore, it is indispensable for an integrated model in the field of labour-intensive cells to define distinguished operators by simultaneously considering not only products, operations, cells, and operators but also their skills. In this paper, a new integrated mixed integer programming model without phases is proposed and compared with the 3- phase model with multi-skill levels. Firstly, the integrated model based on the 3-phases with multi-skill levels is redefined by three new decision variables. One is to consider products, operations, cells, operators, and their skills simultaneously and the other is to use the maximum and minimum value of cells where operators are assigned. Secondly, the proposed model is solved by linearizing the nonlinear equation that multiplies continuous and binary variables. Using comprehensive numerical experiments, we show some cases where the proposed model with multi-skill levels leads to fewer operators than the 3-phase model with multi-skill levels. References: Suer, G.A., “Optimal operator assignment and cell loading in labor-intensive manufacturing cells.” Computers & Industrial Engineering, 31, 155-158, 1996. Suer, G.A., Tummaluri, R.R., “Multi-period operator assignment considering skills, learning and forgetting in labour-intensive cells.” International Journal of Production Research, 46 (2), 469-493, 2008. [3096] Augmented reality for knowledge transfer and digitalization of processes in industrial maintenance Zsolt Levente Buna (Technical University of Cluj-Napoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania), Daniela Popescu (Technical University of Cluj-Napoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania), Vlăduț Astaloș (Technical University of Cluj-Napoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania), Călin Neamțu (Technical University of Cluj-Napoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania), Radu Comes (Technical University of Cluj-Napoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania) and Stefan Bodi (Technical University of ClujNapoca; 103-105 Muncii Blvd., 400641 Cluj-Napoca, Romania). Abstract The paper aims to present a workflow methodology for developing a mobile application based on augmented reality technology designed for various industrial maintenance scenarios. The pro-posed case study is focused on the disassembly and reassembly of a gear mechanism of a cutting machine, delivering to the user step by step animated instructions in four different working scenarios. The augmented reality application has been developed in Unity using Vuforia SDK for the AR capabilities and it offers an interactive experience available on a vast majority of Android de-vices. The proposed application emphasizes the importance and utility of integrating innovative technologies such augmented reality in digitalization of industrial processes as it represents an effective method for knowledge transfer in comparison with traditional solutions while keeping the implementation process relatively simple. An experiment was conducted with 60 students to study the knowledge transfer efficiency and the students` overall feedback regarding the effectiveness of MentAR. The results showed that the application has promising potential, and the UI design enables on demand knowledge database access as well as step-by-step guidance related to the maintenance tasks. The experimental testing enabled us to identify the advantages and dis-advantages of the application intended to be used on eye-wear glasses or tablets/smartphones.

2023 ICPR27 26 [3172] SUPPLY CHAIN STRATEGY AFTER THE COVID-19 PANDEMIC Ying Yang (Newcastle University). Abstract The COVID-19 pandemic exposed weaknesses in the global supply chains, highlighting the disruptions caused by suppliers on a global scale. As a response, many businesses realised the importance of re-evaluating their supply chain strategies to improve resilience and competitiveness. However, while supply management has been well studied in the literature as a result of increased implementations of offshoring and outsourcing strategies, it has not adequately addressed the challenges posed by the pandemic. This paper aims to investigate the competitiveness of international suppliers and regional suppliers and provide insights for businesses to develop suitable supply chain strategies for the post-COVID-19 era. The study employs the Analytical Hierarchy Process (AHP), a comprehensive decision-making methodology that incorporates multiple criteria, and collects data from ten chosen suppliers of British manufacturer. The AHP framework comprises four stages: identifying and assigning weights to supplier selection criteria, evaluating the performance of individual suppliers, and comparing the performance of the suppliers from China and Europe as a whole. Eight significant criteria are identified, and the findings suggest that, overall, the selected Chinese suppliers outperform their European counterparts, although one European supplier achieves the highest performance ranking. [3211] THE NEW DISRUPTION BY COVID-19 TO THE AUTOMOTIVE SUPPLY CHAIN AND RECOVERY STRATEGIES Weiyuan Li (Adam Smith Business School, University of Glasgow), Ming K. Lim (Adam Smith Business School, University of Glasgow) and James Wilson (Adam Smith Business School, University of Glasgow). Abstract The COVID-19 pandemic once led to a global supply chain disruption and had a significant impact. And this impact is still under research, with present studies mainly focused on the pharmaceutical and food supply chains. In order to clearly understand how COVID-19 has affected the global supply chain and prepare for the future, this research took China's automotive supply chain (ASC) as an example to explore the challenges brought to the manufacturing industry and the feasible response strategies. It defined the disruption as the New Disruption, as it has a broader range, a more prolonged period, and higher uncertainty. This research first extracted challenges from current literature, then conducted focus groups and one-on-one interviews with practitioners from Chinese ASC, to verify what challenges they have suffered in practice. Also, it investigated what strategies and methods they have implemented to address those challenges. Findings are that the challenges encountered by Chinese ASC are unique compared with previous studies, while no organisations have prepared for such a disruption in advance. [3222] The role played by digital technologies in implementing remanufacturing for the circular economy Larissa Taquetti (Universidade Federal do Oeste da Bahia), Fernando Deschamps (Pontifícia Universidade Católica do Paraná), Edson Pinheiro de Lima (Universidade Tecnológica Federal do Paraná) and Sergio E. Gouvea da Costa (Universidade Tecnológica Federal do Paraná). Abstract Once sustainability dilemmas significantly change the way the world works, the creation of new methods that reduce environmental impacts and influence values of social and cultural behaviors need to be intensified in corporate agendas. The circular economy is a promising approach to achieving sustainability, and the remanufacturing process is one of the most suitable practices, because it occupies the highest level of a product's recovery chain. Due to the implementation of circular economy principles requiring new strategies and business models, digital technologies are increasingly recognized as a powerful tool for the innovation projects needed to support the transition to the circular economy. However, there is still a lack of studies that address the integration of remanufacturing and digital transformation, and how digital technologies can impact the circular transition, allowing companies to better organize their resources and identify emerging opportunities for the sustainability of operations. This article reviews the literature that addresses this research gap. Key links between research streams and area insights are identified. The results suggest, although digital

2023 ICPR27 27 technologies are modifying business processes and models bringing potential benefits, there are risks from economic, social, technological, legal and political perspectives. It is expected that the implications resulting from this study will help organizations, professionals and researchers to successfully implement remanufacturing, driving the digital transformation towards sustainability. [3374] Challenges and stimulators for collection of Waste of Electrical and Electronic Equipment – on the way to a circular supply chain Karolina Werner-Lewandowska (Poznan University of Technology) and Paulina Golinska-Dawson (Poznan University of Technology). Abstract The sufficient volume of input is the crucial factor in establishing the cascade model for the recovery of Waste Electrical and Electronic Equipment (WEEE) in circular supply chains. Currently, there are many challenges to efficiently maintain the recovery targets required by the European Commission in the WEEE directive. In this paper, our objective is to identify, based on critical literature review, industry reports, expert opinions, media monitoring, the existing challenges in the collection of WEEE. We classify stimulators and challenges for the efficient collection of WEEE for recovery purposes. The identified challenges and stimulators for the WEEE collection are compiled into a list, which is then subject to the evaluation by experts. The expert survey uses the Likert scale to assess the relevance of factors. The results of the expert survey are subjected to statistical analysis to assess the consensus of expert’s opinions. In this way, the most relevant challenges and stimulators are defined to achieve a sufficient level of WEEE collection for the development of recovery processes, and transition towards circular supply chains. [3434] Optimizing the configuration of labor-intensive production lines with the minimum number of workers Katsumi Morikawa (Hiroshima University), Keisuke Nagasawa (Hiroshima University), Katsuhiko Takahashi (Hiroshima University) and Takayuki Kataoka (Kindai University). Abstract Multiple products are produced by preparing several cells. Each cell generates a production line to accept some products. The line comprises multiple asynchronous stations, each requiring one or more workers. The productivity of a station can be increased by allocating more workers. Reallocation of workers within the same cell is allowed when the line arranges for the following product. The primary objective is to find the minimum number of workers to satisfy the demand for all products under the product- and the station-dependent processing times. Three mixed-integer programming models are developed to find the allocation of products and workers among cells and to optimize the sequence. For the cyclic production condition, the allocation of workers is fixed, assuming no setup time between products. Numerical experiments indicate that the inefficiency in productivity by fixing the worker allocation is offset by reducing the idle state by cycling production. [3507] Modeling of Disassembly Parts Selection for Recycling, GHG Saving Rates and Cost using Linear Physical Programming Yuki Kinoshita (Kindai University), Hiromasa Ijuin (The University of Electro-Communications) and Tetsuo Yamada (The University of Electro-Communications). Abstract Material recycling contributes not only material circulation but also reduction of greenhouse gas (GHG) emissions since GHG emissions for a virgin material production can be saved by using recycled materials. Partial disassembly is required to maximize GHG saving rate defined as the saved volumes of GHG emissions by recycling, and recycling rate, and to minimize cost. This study proposes a multi-objective disassembly parts selection for cost, recycling and GHG saving rates using Linear Physical Programming (LPP). The LPP is one of the effective methods for solving multi-objective problems, and can obtain one satisfactory solution by expressing preferences of the decision maker (DM) for each objective. The

2023 ICPR27 28 proposed model was validated using a vacuum cleaner consisted of 23 parts, and found one satisfactory solution, which had over 70.00[%] and 80.36[%] of recycling and GHG saving rates and achieved over 50% reduction of total cost compared to complete disassembly. [3549] Formalization the Expert Knowledge of Human-Bound and Individual Processes in Manufacturing Manja Mai-Ly Pfaff (Fraunhofer-Institut for Machine Tools and Forming Technology IWU), Tim Wunderlich (Fraunhofer-Institut for Machine Tools and Forming Technology IWU), Ken Wenzel (Fraunhofer-Institut for Machine Tools and Forming Technology IWU) and Steffen Ihlenfeldt (Fraunhofer-Institut for Machine Tools and Forming Technology IWU). Abstract Around 40% of employees stay in companies for more than ten years. During this time, people gain valuable experience and build up expertise. Therefore, expert knowledge is one of a company's most valuable assets. However, it is threatened by employee turnover, e.g., as a result of illness or retirement. Human intervention is often essential for complex manufacturing and maintenance tasks despite increasing automation, where the related expert knowledge needs to be sufficiently documented. Ontologies are already successfully applied for knowledge representation in other domains, proving promising for formalizing expert knowledge in production. This paper shows how a workflow, including machine-specific specifications, can be modelled ontologically, using the commissioning process of special-purpose machines as a case study. Applying the Basic Formal Ontology as a top-level and the Common Core Ontologies as midlevel ontologies, we present a reference ontology for expert-bound processes in production. Each machine is designed individually for the customer's production requirements. The machine-specific documents, such as the circuit diagram and the work instructions, often vary considerably. At the same time, changes in the work processes, e.g., in the case of new findings due to the natural human learning process, must also be represented. The result is a reference ontology for the formalization and generalization of individual expert knowledge for human-bound processes in manufacturing based on existing standards in the sense of interoperability. [3565] Support methodology for infrastructure decision-making in a smart city Pedro Palominos (University of Santiago of Chile), Diego Cabrera (University of Santiago of Chile), David Perez (University of Santiago of Chile), Luis Quezada (University of Santiago of Chile), Astrid Oddershede (University of Santiago of Chile) and Juan Barrientos (University of Santiago of Chile). Abstract The growth and development of cities has become a major challenge throughout the world. Starting with a growing migration from rural areas to the cities, which entails new challenges in terms of demand for space, resources and facilities, among others [1]. Even in some cities, the rapid growth of the population in urban areas has caused various problems of pollution, attacks, and increased demand for energy and sanitation services [2]. Because of the above, city authorities can no longer invest in issues such as mobility, pollution, health or new infrastructures, among others, with traditional top-down decision-making approaches, without considering the public. citizens, since many times the projects fail by not considering the expectations and desires of the beneficiaries, in this case the citizens. For this reason, a methodology for decision-making is proposed, based on the Smart Cities paradigm [3], which seeks to detect the needs of the different stakeholders, to thereby reduce the times in the project design process, to the instead of balancing the different in-terests of groups in a community. This methodology is based on a survey that captures the needs of the communities, to later formalize it using the ELECTRE Method in conjunction with other stakeholders. This methodology was applied to a neighborhood in the city of Santiago de Chile, generating solutions to the main problems faced by residents of the sector.

2023 ICPR27 29 [3575] Collaborative robotic assembly line balancing to increase workers productivity and safety Ali Keshvarparast (University of Padua), Daria Battini (University of Padua), Amir Pirayesh (KEDGE Business School) and Olga Battaïa (KEDGE Business School). Abstract Human-robot collaboration is an efficient solution to boost assembly line productivity while guaranteeing the flexibility of the assembly line. Collaborative robots (Cobots) may be employed to work alongside humans at the same workstation under safety conditions. As a consequence, the use of cobots increases the number of possible system configurations and work organisations. However, since cobots may have different performances while working independently or in collaboration with human operators, their functional characteristics have to be taken into account such as arm lengths, capability to lift weights, or speed of movements. In addition, there are some safety protocols that do not allow cobots to work with full operation speed in presence of workers to avoid the possible collusions. In this research, to address these issues, a new cost-oriented mixed-integer linear model was proposed to optimize the total cost of the assembly line, including the cost of cobots, humans, and stations. The diversity of workers was modelled with their experience level and skills to perform tasks, while the diversity of cobots was modelled with different task times, full-speed operations, and their capability to perform tasks. An automotive industry case study was used to analyse and validate the suggested model. The results show that considering resource diversity is important for a correct estimation of the system performance. [3602] STRATEGIC WORKFORCE PROGNOSIS IN AUTOMOTIVE R&D Michael Hertwig (Fraunhofer-Institute for Industrial Engineering IAO), Wolfgang Beinhauer (Fraunhofer-Institute for Industrial Engineering IAO), Daniel Borrmann (Fraunhofer-Institute for Industrial Engineering IAO), Pascal Westerbeck (ZF Friedrichshafen AG) and Florian Herrmann (Fraunhofer-Institute for Industrial Engineering IAO). Abstract The automotive industry is undergoing a profound transformation, induced by the digitalization of processes, products, and the shift to electromobility. Work and co-operation are transforming, and along with these, the necessary job profiles and skills are changing. In fact, the restructuring of competences represents the main challenge for the automotive industry, along with coping with the technical changes. An important element of the necessary workforce transformation is a forecast of future competence demands resulting from future market shares and position in the value-chain. The established approach to strategic workforce planning at OEMs is to break down the corporate strategy into value-added areas and core tasks, followed by an analysis of the required volume of work and the skills and competencies needed. For suppliers, predicting future workforce demand is considerably more difficult, since they are affected by even greater uncertainties of the market, as they have less control over the value chain. Future market shares are even more difficult to estimate due to the restructuring of the supply chains and associated disruptive changes. Finally, it is easier for OEMs to link their workforce forecasts to projected sales of their vehicles which are more predictable as market shares in the overall market. Moreover, for research and development, future sales figures are even less relevant when it comes to predicting the skills needed for the technical development of future products. In this paper, a method is presented to forecast the personnel and competence requirements in the R&D division of an automotive supplier. Since future sales figures are of little relevance to the R&D departments, the approach chosen by the authors is based on an analysis of strategic development projects. For this purpose, the R&D agenda is derived from the corporate strategy and broken down to development plans and individual projects. The core point of the personnel demand forecast is therefore not the number of units as in manufacturing as with OEMs, but individual development projects. R&D projects are characterized according to their intrinsic complexity, the competencies required for their implementation, the typical composition of project teams and expected synergy effects. Other influencing factors such as efficiency gains from digital development tools, global megatrends and general efficiency improvements are also taken into account in the model. The assumptions made about the impact of the influencing factors are summarized in parameterized scenarios, which enable a scenariobased forecast of personnel and competence requirements in development corridors in the current decade. The newly created model was evaluated using past data for which actual personnel development was already available and could be used as a reference.

2023 ICPR27 30 [3621] Dynamic Facility Location with Flexible Capacity in Vehicle-to-Vehicle Delivery and Crowdsourcing: A Deep Reinforcement Learning Approach Keonwoo Park (Department of Industrial Engineering, Seoul National University), Changseong Ko (Department of Industrial and Management Engineering, Kyungsung University) and Ilkyeong Moon (Department of Industrial Engineering, Seoul National University). Abstract Recently, the focus on last-mile delivery has brought attention to novel delivery systems such as vehicle-to-vehicle and crowdsourcing. However, the problem of simultaneously locating facilities and expanding or reducing capacity during the implementation of these systems has not been investigated. We propose a dynamic facility location model with flexible capacity that utilizes vehicle-to-vehicle delivery and crowdsourcing, powered by deep reinforcement learning. The proposed model aims to optimize facility location and capacity decisions in a dynamic environment by considering various factors, such as demand patterns, traffic conditions, and facility operating costs. Our approach leverages deep reinforcement learning to optimize the sequential location and capacity allocation decisions in real-time, resulting in efficient and effective decisions in a dynamic environment. To evaluate the performance of the proposed model, we conducted experiments using both real-world data and artificially-generated data. The computational results show that the proposed model outperforms existing facility location models in terms of efficiency and adaptability to dynamic conditions. [3629] A Conceptual Framework for Combining Design Thinking and Lean Structures Nathalia Chamie (PUCPR), Fernando Deschamps (PUCPR), Sergio Eduardo Gouvêa da Costa (UTFPR) and Edson Pinheiro de Lima (UTFPR). Abstract Within the need of growth and organizational sustainability, small and medium size enterprises (SMEs) seek for methods to support process optimization and performance. The purpose of this paper is to propose a conceptual framework combining Design Thinking and Lean structures to aid SMEs on how to better seek and implement improvements towards business strategy and performance. This exploratory research analyzes the relationship between the tougher SMEs improvement implementation barriers - reduced resources, change resistance and low organizational maturity - and methods to better structure and apply Lean tools . The research design was limited to Scopus’ papers published exclusively in high impact journals. Results emphasize the important role of Lean designed practices to SMEs competitive advantages and how a combination of structures, as Design Thinking could be applied on to this enterprises scenario. Further research might consider the impact of digital transformation and green Lean particularities that drive SMEs future. [3688] NUMERICAL CALCULATION OF PNEUMATIC LOSSES IN A ROTARY VANE COMPRESSOR Mihai Ciupan (Technical University of Cluj-Napoca) and Claudiu Ioan Rusan (Technical University of Cluj-Napoca). Abstract Positive displacement pneumatic compressors rely on suitable sealing in order to achieve the required pressure and work efficiently. Most rotary vane compressors rely on a thin film of hydraulic oil that forms between the vanes and the stator, the vanes and the rotor, the vanes and the end plates and the rotor and the end plates to seal and minimize air losses. If the clearance between the different components is too large a significant amount of air escapes and the compressor does not achieve sufficient outlet pressure. If it is too small machining costs increase and it is also possible for the blades to get locked and damage the compressor. The paper calculates the amount of air lost through internal leakage in a vane compressor based on the clearances between the components. A method for these calculations is proposed using the computational fluid dynamics tool Solidworks Flow and the data is extrapolated to the entire sealing surface.

2023 ICPR27 31 [3732] Uncovering the Neglected Nexus: Exploring the Interplay between Resilience and Working Capital Strategies in Supply Chains Christiaan de Goeij (Windesheim University of Applied Sciences) and Luca Gelsomino (University of Groningen). Abstract Covid-19 and geopolitical tensions show that there are several working capital disruptions impacting supply chains, which are to a large degree neglected in supply chain resilience literature. Resilience and working capital strategies are both investigated in supply chain literature, however they are not looked at in conjunction. Based on focus group research with 17 participants representing 14 different companies, we studied trade-offs and overlaps between both. This data is currently being enriched by case study research. Based on the disruptions companies faced, we have first of all studied to which degree the resilience strategies redundancy, collaboration, flexibility and agility were used, and second, which intersections exist with working capital approaches. Redundancy was the most popular resilience approach among participants. Most companies increased their own inventories and requested suppliers to increase inventories to deal with scarcity of materials. However, for many of them this resulted in significant working capital challenges. The agility approach was used by companies to speed up delivery and production processes, but for example not to speed up the Order-to-Cash process. While a majority of participating companies suffered from higher inventory levels and/or extended payment terms, such working capital issues were only to a limited degree taken into account in their resilience approach. A lack of collaboration on such issues, both internally between departments such as purchasing and finance, and externally between supply chain partners, further reinforced disruptions. Based on the (preliminary) results we identify three key themes at the intersection between resilience and working capital strategies in supply chains: 1) interdepartmental inventory and payments management, 2) inter-company collaborative working capital management and 3) financial process improvement. [3792] INTERNAL PROCESS OPTIMIZATION FOR E-COMMERCE D2C Geovana Pagnoncelli (Pontifica Universidade Católica do Paraná), Pablo Carpejani (Pontificia Universidade Católica do Paraná), Edson Pinheiro de Lima (Department of Industrial and Systems Engineering, Federal University of Technology), Ubirata Tortato (Department of Industrial and Systems Engineering and Department of Business, Pontifical Catholic University of Parana), Sergio E Gouvea da Costa (Department of Industrial and Systems Engineering, Federal University of Technology) and Fernando Deschamps (Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana). Abstract Corporations increasingly need to improve their processes to face challenges. Therefore, companies that do not manage their strategic planning and operations constantly end up wasting opportunities. In this circumstance, it is necessary to use performance measures that manage the conservation of processes related to the admitted strategies. Based on this, a study was carried out applied in the area of D2C, in the sectors of Logistics and PCP in the home appliance company, in order to reduce the time in some daily processes and use the Power BI tool to manage indicators that contain information on consumer interactions in relation to Logistics service and the availability of products for the consumer in the virtual store. For this, the DMAIC methodology was used, which is capable of assisting in the structured resolution of problems, seeking root causes and providing actions that contribute to the elimination and reduction of problems. To achieve the objective, some quality tools such as A3, 5W2H, 5 Whys, Brainstorming, Ishikawa Diagram, Stratification, Benchmarking, and Indicators were used. With this, the lack of connection between all the data that is collected made it possible to identify that it is an impacting factor in the problem. In this way, it was proposed to use the Power BI to create a dashboard with indicators that contain all the information requested daily. The solution developed also included a reduction in the performance of activities, being approximately 75% in both sectors. The application was validated by stakeholders and provided a more strategic vision for the company in decision-making and, consequently, adding value to end consumers.

2023 ICPR27 32 [3797] INNOVATIVE GRIPPER DESIGN FOR ELECTRICAL CABINET ASSEMBLY IN LEAN ROBOTICS Stelian Brad (Technical University of Cluj-Napoca), Bogdan Balog (Technical University of Cluj-Napoca), Vlad Florian (Technical University of Cluj-Napoca), Eyas Deeb (Technical University of Cluj-Napoca) and Ovidiu Stan (Technical University of Cluj-Napoca). Abstract This paper addresses the challenges and advancements in automatic wire manipulation, particularly focusing on deformable linear objects (DLOs) like wires and cables. Key areas of emphasis include gripper design and manipulation techniques for flexible components during wire insertion tasks. The study incorporates inventive design techniques like TRIZ. The CSDT framework provides a structured approach to manage and explore innovation vectors for achieving desired end results. The integration of the gripper model resulting from the methodology will finalize the continuation of the reinforcement learning algorithm, allowing for further optimization and adaptation in the path planning process. [3802] OPTIMAL REPEATED PRODUCT MANAGEMENT WITH CUSTOMERS’ PREFERENCES Arik Sadeh (HIT Holon Institute of Technology). Abstract The study deals with the product management of repeated products such as smartphones, cars, and healthcare devices. These products within a given technology have limited lifecycles mainly because of the following model—for example, the Galaxy S5 was marketed in 2014 and faded out as Galaxy S6 launched in 2015. The study deals with when to stop selling a given product and initiate another repeated product, together with the R&D period and timing issues. In the proposed model, decision-makers face a given technology and have a single product in the market to satisfy consumers’ needs. The objective function is to maximize the discounted net cash flow or the equivalent annuity for a given period, e.g., a year, a decade, etc. The first-order conditions for optimality are provided along with economic and management interpretation. The initiation of the following product functionally depends on the current product termination. Managerial and operational constraints are included, such as the need to begin R&D just before the termination of the existing product, market share constraints, etc. The resulting rules are expressed in managerial terms, for example, level of cash flow and average cash flow. as a function of the peak of cash flow, cumulative cash flow, level of cash flow, and the length of the R&D period. A numerical example illustrates the rules and the solution. [3803] A novel method for increasing the accuracy of the WAAM process technology Stefan Bodi (Technical University of Cluj-Napoca), Amina Vildanova (Technical University of Cluj-Napoca), Zsolt Levente Buna (Technical University of Cluj-Napoca), Calin Neamtu (Technical University of Cluj-Napoca), Radu Comes (Technical University of Cluj-Napoca), Daniela Popescu (Technical University of Cluj-Napoca) and Raul-Silviu Rozsos (Technical University of Cluj-Napoca). Abstract Wire Arc Additive Manufacturing, a 3D printing technology that uses metal as base material, rose in popularity over the past few years due to the overall maturing of the 3D printing concept and the possibility of rapidly obtaining prototypes in an ever-growing competitive industrial environment. Yet, there are still a lot of shortcomings when employing this technology, that limit its applicability. This paper proposes a novel methodology that improves the accuracy of the WAAM process (employing an industrial robot) and the product’s fidelity in relation to the 3D model, by scanning each printed layer and actively adjusting the printing / welding parameters (e.g. material feeding speed, robot speed, accuracy, acceleration, feedstock radius, material type, etc.), such that the prototype’s shape will have a lower deviation in relation to the reference 3D model.

2023 ICPR27 33 [4051] A Proposed Ontological OPC-UA in Semiconductor Cleanroom Monitoring System Yu-Ju Lin (Department of Industrial Engineering and Enterprise Information Tunghai University), Ting-Yi Weng (Department of Industrial Engineering and Enterprise Information Tunghai University), Ting-Kai Liang (Department of Industrial Engineering and Enterprise Information Tunghai University) and Chin-Yin Huang (Department of Industrial Engineering and Enterprise Information Tunghai University). Abstract With the development of Industry 4.0 and the rapid changes in various facilities, demands, and internal/external environment, it is necessary to have common platform and language for communication and co-work. OPC-UA (Open Platform Communications Unified Architecture) is a platform-independent protocol that allows communication between different hardware/software systems. OPC-UA uses a client-server architecture, where a client can communicate with one or more servers to access data or perform operations. However, the application of OPC-UA is domain-related. To construct an OPC-UA protocol platform for industrial communications requires domain and OPC-UA experts to apply the domain Companion Specifications of OPC-UA. The match of various domain companion specifications may become a problem when the project includes various domains. By applying ontology to specify the elements of OPC-UA, this research standardizes OPC-UA. The standardized ontological OPC-UA is more readable, adaptable, and transferable. To demonstrate the advantages of ontological OPC-UA, this research applied it to a semiconductor cleanroom monitoring system. The results show the quick adaptability and expandability of the ontological OPC-UA. [4191] METHODOLOGY FOR AN AGGREGATE READINESS LEVEL ASSESSMENT OF INNOVATIVE TECHNOLOGIES Laura-Florentina Boanță (University POLITEHNICA from Bucharest), Alexandru Marin (University POLITEHNICA from Bucharest), Miron Zapciu (University POLITEHNICA from Bucharest) and Bogdan-George Rânea (University POLITEHNICA from Bucharest). Abstract In this paper, the authors propose a methodology for an „aggregate” readiness level (AgRL) assessment of an innovative material, i.e., a novel asphalt concrete with constituents and waste derived from recycled glass and plastics. The methodology expands on the well-known Technology Readiness Level (TRL) assessments, with a method for assessing TRL, in combination with Market Readiness Level (MRL), Regulatory Readiness Level (RRL), Acceptance Readiness Level (ARL), Organizational Readiness Level (ORL), and Commercial Readiness Index (CRI), taking into account their reciprocal influences, as a new and original criterion of evaluating the maturity level, as a precondition for research organizations developing innovative technologies, in any technology take-up pilot aiming to be successful, by accessing investment funding, public or private. [4282] Seru Production System: Model Proposal for Loading Problem in Multi-Skilled Worker Environment Emre Bilgin Sari (Dokuz Eylul University), Dusan Sormaz (Ohio University) and Omar Alhawari (Ohio University). Abstract The Seru Production System (SPS) is Japanese cellular production system, that originated with the needs of improvements in assembly process of electronic industry. Distinct from conventional production systems, SPS has lots of autonomous production units called as seru. In these serus, there is the opportunity for producing different kind of products, this helps to increase the resilience of the production system. And also, the workers are working in these seru, supposed to be cross-trained, so they can be capable to complete the manufacturing process by themselves. From this point, in this study, it is founded a seru loading model in multi-skilled worker environment. In the model, it is aimed to decide which worker and product type will be assigned to which seru in a production environment where the number of seru, the number of workers and the number of product types are predetermined. In the proposed model, it is assumed that, workers product completion times vary according to their skill weights.

2023 ICPR27 34 [4322] Patterns for Resilient Workforce Structures and Strategies in Manufacturing Stefan Gerlach (Fraunhofer Institute for Industrial Engineering IAO) and Moritz Hämmerle (Fraunhofer Institute for Industrial Engineering IAO). Abstract A resilient setup of manufacturing facilities covers all factors: buildings, equipment, processes, supply chains and human labour. Especially the utilization of human labour and resources is a key success factor for manufacturing companies to maintain their business responsiveness and competitiveness in volatile market environments. To build resilience in human labour is a strategic task which has to reflect different criteria, e.g. cost of labour and releasing of staff, developing and securing of skills and training, shortage of labour market, time for onboarding. Therefore, “firing and hiring” of employees may be a measure in case of disturbed markets or supply chains. But it must not be the solely and appropriate answer in every case. This paper highlights the requirements for resilient human resource allocation and describes rules and patterns to analyze and build resilience into human labour in manufacturing companies. The patterns give operational advice to experts how to analyze the requirements and to compare them with specific solutions. As a result, companies are able to develop and establish long term strategies for human resource allocation. The rationale is based on selected HRinstruments, which needs a longer time up to several years for preparation to utilize them. Therefore, adjusted strategies for human resource allocation are needed. [4478] Developing employees for smart human-machine collaboration in manufacturing: an Industry 5.0 perspective Djerdj Horvat (Fraunhofer Institute for Systems and Innovation Research ISI), Angela Jäger (Fraunhofer Institute for Systems and Innovation Research ISI), Christian Lerch (Fraunhofer Institute for Systems and Innovation Research ISI) and Heidi Heimberger (Fraunhofer Institute for Systems and Innovation Research ISI). Abstract Fast growing digital technologies, for instance Internet of Things, cloud computing, 5G networks or artificial intelligence, have been successfully enabling the integration of virtual and physical space, so called CPS connectivity, bringing manufacturing closer to fourth industrial revolution (Industry 4.0). While process optimization has received significant attention in Industry 4.0, the vital role played by human resources in such systems has been greatly overlooked. Leading by this issue, the emergence of Industry 5.0 in the literature is driven by the need to leverage the unique creativity of human experts and collaborate with powerful, smart, and accurate machinery to achieve resource-efficient and userpreferred manufacturing. Although Industry 5.0 is still just a political concept, it has garnered interesting conceptualization in research. However, concrete empirical operationalizations are missing for analytical testing and empirical monitoring of assumptions and interesting developments. To address this gap, our research aims to investigate various combinations of usage of manufacturing technologies and competence management practices concerning human-machine collaboration. Using the latest data from the European Manufacturing Survey 2022 conducted in Germany, we focus on examining specific organizational practices, advanced technology usage, and human resource measures related to competence development. The data encloses a cross section of the manufacturing sector of Germany based on approximately 1,300 manufacturing firms. Our study offers first empirical insights into various patterns of firms by examining the manufacturing sector in its variety with companies of different sizes. Through this research, we aim to contribute to both manufacturing literature and practices by highlighting the importance of employees' competences in collaboration with machines and emphasizing the significance of their continuous development for enabling the Industry 5.0.

2023 ICPR27 35 [4485] KNOWLEDGE CAPITALIZATION IN AUTOMOTIVE RESEARCH & DEVELOPMENT OFFSHORE BRANCHES Marius Gal (Lucian Blaga University of Sibiu), Lucian Lobont (Lucian Blaga University of Sibiu) and Claudiu Kifor (Lucian Blaga University of Sibiu). Abstract The development of products from the research and development centers has entered an era with fiercer competition, accentuated by the client's desire for increased quality, the constant innovation demanded by the market, the rapid development dictated by the business management and reasonable prices dictated by the stakeholders. Offshoring can help companies to increase the international exposure, to find new talents and support the company to maximize the effectiveness of its employees. The paper proposes a framework which can be used in automotive software companies in the process of offshoring, to keep the projects quality under control, from the first steps, until the project is delivered to the customer It is imperative that the software product, that is developed or tested in another part of the world than the home country of the company, fulfill the internal standards, the quality standards required by the customer and the standard that was created by the team. [4505] Process analysis in dynamic production environments Nikolas Zimmermann (Fraunhofer IAO). Abstract Many external uncertainties and volatilities, such as bottlenecks in supply and fluctuations in sales volumes, require a rapid response and flexibility in capacity and order planning within a factory control system. Responsiveness and flexibility require decision-making processes that can be efficiently supported and, if necessary, quickly adapted. However, this requirement is often hindered by many inefficient processes for data provision. Processes often involve media discontinuities because the relevant data and information flow along a heterogeneous IT-system landscape. Dynamic changes forces companies to adapt concepts and working methods to the external conditions. Rigidly defined business processes seem to leave little room for reaction. This raises the question of the usefulness of traditional modelling approaches in a dynamic environment. In this context, existing concepts and approaches for process modelling and approaches for dealing with dynamics are examined and typified. [4517] Design Artefact for Selecting a Digital Transformation Project Portfolio Reginaldo Carreiro Santos (Pontifícia Universidade Católica do Paraná), Edson Pinheiro de Lima (Universidade Tecnológica Federal do Paraná), Fernando Deschamps (Pontifícia Universidade Católica do Paraná), Sergio E. Gouvea da Costa (Universidade Tecnológica Federal do Paraná), Elias Ribeiro da Silva (University of Southern Denmark) and José Luís Martinho (Coimbra Polytechnic - ISEC). Abstract This paper is contextualised from an organizational problem within a relevant food industry, which consists of the lack of a practical and structured procedure to support managers in selecting a project portfolio within a digital transformation journey. Design Science is adopted as the research strategy and the main objective is to assess the developed design artefact to address the problem of the studied company, considering its specific context. Therefore, it is an inductive approach that flows from the observed organisational issue to the development and application of a proper solution. The purpose is to bring a normative statement, focused on aiding managers and specialists in the decision-making process. A consolidated Design Science Research methodology was adopted to guide the research. A developing case was performed in the analysed company to demonstrate and evaluate the artefact's feasibility. As encompassed in a normative science, the main implication of this paper is practice. The design artefact is aimed to test and evaluate a prescriptive solution as a policy proposition, and not merely describe or generalise facts. Besides, its theoretical contribution is the integration of the designed artefact, namely a multi-criteria decision-aiding procedure, with the Cambridge Process Approach.

2023 ICPR27 36 [4747] Hidden in the Corner? A Systems Approach to the Z-diagram for Capturing Timeliness of Manufacturing Rob Dekkers (Adam Smith Business School/University of Glasgow) and L. J. Lekkerkerk (Department of Business Administration/Radboud University). Abstract Timeliness, aka reliability of delivery, is a key concern for manufacturing management and topic of research. Some have proposed mathematical approaches to capture timeliness, and related it to throughput time and performance of manufacturing systems. The point of our study here is that a simple diagram in existence for decades can offer insight into the behaviour of manufacturing systems and overcome some of the difficulties associated with measuring timeliness. By visually capturing cumulative input and output, the Z-diagram indicates throughput time and work-inprogress including variations over time. Furthermore, a systems-theoretical perspective defines how this diagram can be used for managing manufacturing. An illustrative example follows to demonstrate the points made. These deliberations set the scene for a proposed research agenda and implications for practice. [4839] System dynamic modelling is supporting the urban production paradigm Michael Hertwig (Fraunhofer IAO). Abstract Production in the direct proximity of residential areas causes conflicts. On the one hand, they are in competition with residential real estate development; on the other hand, hey are often viewed as undesirable by residents. However, manufacturing companies contribute significantly to the maintenance of prosperity. Cities are therefore called upon not to allow existing value-added structures to migrate. To this end, a correspondingly attractive framework must be created. At the same time, production companies are under increasing pressure in terms of sustainability and workforce development. To act in a more ecologically and socially responsible manner, manufacturing companies need to be further advanced. In addition to sustainability, companies must not only eliminate negative influences in the future but should also fulfill their local responsibility. Nevertheless, further development is surrounded by economic framework conditions. Therefore, the increase in value for the company in all dimensions of urban production must be presented from a sustainability perspective as well as from a human-factors perspective. By developing a long-term perspective, in which the holistic contribution of further development measures becomes visible, strategic goals can be addressed. In this article, we will look at how it is possible to combine a holistic approach with a long-term view. For this purpose, different modeling approaches are compared with each other to prioritize a suitable methodology. Since the development of a system dynamic model is costly and time-consuming, a procedural proposal is presented that supports small and medium-sized enterprises in the usability of system dynamic modeling in order to anchor urban production measures in strategic planning. The outlined procedure is intended to support an evaluation of urban production approaches regarding sustainability and environmental compatibility. The outlook will highlight what needs to take place to assess the applicability of the approach. In addition, it will be discussed how the approach can strategically support manufacturing companies in the transformation to near-urban, ecologically safe, and socially compatible production. [4931] Order and rack scheduling problem with multiple workstations and rack reassignment in the robotic mobile fulfillment system Yong Jae Kim (Incheon National University) and Byung Soo Kim (Incheon National University). Abstract We study the robotic mobile fulfillment system, a special parts-to-picker based order picking system where robots bring racks to pickers at workstations. In particular, we address an order and rack scheduling problem with multiple workstations, rack reassignment, and SKU exhaustion in each rack. The objective function is to minimize the makespan. We developed a mixed-integer linear programming for this problem. Also, we proposed dispatching rule-based metaheuristic algorithms for efficient solutions to large problems. To verify the performance of the meta-heuristic algorithms, we compared it to the optimal solution obtained from the mixed-integer linear programming for small-sized problem

2023 ICPR27 37 instances. To evaluate the performance of the meta-heuristic algorithms, we compared it to a tight lower bound and other heuristics for large-sized problem instances. [4932] IMPORTANCE-PERFORMANCE MATRIX APPLICATION IN ORDER TO PRIORITIZE PERFORMANCE OBJECTIVES IN SUSTAINABLE OPERATIONS David Grudzien (FAE Centro Universitário), Edson Pinheiro (Universidade Tecnológica Federal do Paraná), Fernando Deschamps (Pontifícia Universidade Católica do Paraná), Sergio Eduardo Gouvêa (Universidade Tecnológica Federal do Paraná) and Darcleia Forlin (Universidade Tecnológica Federal do Paraná). Abstract The main purpose of this study is to apply the importance-performance matrix to identify the improvement priorities that need to be applied to sustainable performance objectives. These sustainable performance objectives include the five traditional and economic objectives: Cost, Quality, Dependability, Speed, and Flexibility. The environmental dimension includes the Consumption of natural resources, Emissions, Environmental Regulation, and Waste and reverse management. In the social dimension: Exposure to occupational risk agents, and both internal and external engagement. Each sustainable performance objective is judged by two criteria: the importance given by customers and performance against competitors. After judging by these two criteria, it becomes possible to prioritize sustainable performance objectives from different zones: appropriate, improve, urgent action, and excess. Testing in two Brazilian factories, this empirical investigation identifies some patterns. More specifically, the economic performance objectives of Quality, Reliability, Cost, and Speed lie with the highest priorities. Flexibility remains secondary. The four environmental performance objectives remain in the status of qualifiers and lie in the improvement zone. Finally, regarding the social performance objectives, the performance objectives are considered satisfactory [5124] Industrial symbiosis, a key strategy for building resilient and sustainable supply chains Maxime Bouillon (Windesheim University of Applied Sciences), Luca Mattia Gelsomino (University of Groningen) and Christiaan de Goeij (Windesheim University of Applied Sciences). Abstract Companies are under rising pressure to be more resilient in face of major crises. In the last two and a half years profound supply chain disruptions, from the Covid-19 pandemic to the Ukrainian conflict or the Evergreen shipping vessel blocking the Suez Canal have transformed supply chain structure and influenced perception and management policies related to supply chain risk. Calls to address vulnerability and resilience seem today more relevant than ever. At the same time, most supply chains are under pressure from stakeholders to better monitor and improve sustainability. Recent years have brought up the realisation that sustainability goes beyond reputational considerations but has clear and direct implication for supply chain performance. To face such challenges, interest has resurged among academia and practitioners towards industrial symbiosis. In this paper, we want to progress knowledge related to the use of industrial symbiosis in the making of more resilient and sustainable supply chains. To achieve this, we performed an in-depth single case study to analyse the impact of industrial symbiosis on supply chain resilience and sustainability. In doing so, we aim to paint a clear picture of 1. current understanding of the interconnection between industrial symbiosis, resilience, and sustainability within corporates part of the case study, 2. their strategy to develop those concepts forwards and 3. existing tensions/collaborations to achieve those goals. Different companies are interviewed individually, where the relationship between resilience and sustainability and its perspective on industrial symbiosis collaboration are investigated more in-depth. While still undergoing, expected results will identify empirically based archetypes of connection between industrial symbiosis, resilience and sustainability. This will further clarify the impact industrial symbiosis has on extended networks.

2023 ICPR27 38 [5142] Implications of Biomimetic Functional Trait Diversity on the Potential Operational Resilience of BPO Firms Manuel Christian Jr Orias (De la Salle University), Jose Edgar Mutuc (De La Salle University) and Anthony Shun Fung Chiu (De La Salle University). Abstract Organizations suffer economic losses from operational disruptions (see Rice and Caniato, 2003, Chopra and Sodhi, 2004, Greising and Johnsson, 2007, Essuman et al., 2020, Haraguchi and Lall, 2015, Pettit et al., 2013). Thus, there is a pressing imperative to determine alternative modalities of enhancing the operational resilience of organizations that will allow firms to become “naturally” more tolerant to operational disruptions. In ecosystem research, functional diversity, functional redundancy, response diversity and adaptive capacity have been argued and proved to promote ecosystem stability and resilience against natural and anthropogenic perturbations (see Nystrom and Folke, 2001; Folke et al, 2002; Mouillot et al, 2005; De Bello et al., 2007; Naeem, 1998; Galland et al, 2020; Walker, 1995; Desjardins et al, 2015; Elmqvist et al, 2003; Baskett et al, 2014; Leslie and McCabe, 2013; Mori et al, 2013, etc.). Owing to ecological thinking which supposes that organizational ecosystems function similarly as biological systems (Mars et al, 2012), it is hypothesized that firms can similarly leverage on functional trait diversity to enhance their potential operational resilience. To substantiate such postulation, this paper presents a framework for evaluating the implications of altering the biomimetic functional trait diversity of a firm on its adaptive capacity for sustaining its production during a potential operational disruption. Considering the resilience exemplified by business process outsourcing (BPO) firms at the onset of the COVID-19 pandemic, the examination is performed in the context of BPO operations. [5252] A hybrid multi-agent system for analysis of efficiency in collaborative human-robot order picking systems Kyung Jun Min (Department of Industrial Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-gu, Seoul 03722, Republic of Korea), Byung Do Chung (Department of Industrial Engineering, Yonsei University, 50 Yonsei-Ro, Seodaemun-gu, Seoul 03722, Republic of Korea) and Gyusung Cho (Department of Port Logistics System, Tongmyoung University, 428 Sinseon-ro, Nam-gu, Busan 48520, Republic of Korea). Abstract Recently, order-picking systems that involve collaboration between humans and AGVs have been operating. In these systems, safety issues can arise between humans and AGVs, and efficiency may decrease due to congestion in the workspace. This study used agent-based modeling to investigate the effect of humans' role in a collaborative humanrobot order picking system. Also, we developed a centralized and decentralized agent architecture depending on the characteristics of either human or AGV agents. Human roles in collaborative order picking systems are categorized into three levels: (i) moving with an AGV and loading items from the shelf to the AGV, (ii) collecting items by moving on a short path depending on the situation, and loading them onto the AGV, and (iii) delivering items to the drop point for lightweight items. We have examined the validity and efficiency of three policies. Furthermore, we aim to improve worker safety and efficiency in logistics warehouses. [5424] ADVANCED ENERGY MANAGEMENT: GUIDELINES TO THE INDUSTRIAL MANAGEMENT Caio Cesar Ferreira (PUCPR), Nathalia Juca Monteiro (PUCPR/UEPA), Sergio E. Gouvea Da Costa (UTFPR), Fernando Deschamps (PUCPR) and Edson Pinheiro De Lima (UTFPR). Abstract Effective energy management has become increasingly crucial for companies due to the need to enhance competitiveness in the face of resource scarcity. By investing in energy management (EM), companies can reduce energy consumption and costs. One way to achieve this is by implementing an energy management system (EnMS), which can serve as a blueprint for companies looking to incorporate EM practices. However, integrating EnMS with digital technologies can be challenging. To help organizations structure their EnMS with the support of digital technologies, this paper proposes guidelines based on a thorough content analysis of literature and expert interviews. In total, 23

2023 ICPR27 39 guidelines were proposed across four dimensions, and they were refined by specialists with at least five years of experience in the industry, consulting, or academia. These guidelines can help organizations of any size or sector optimize their EM practices by effectively integrating EnMS and digital technologies. The guidelines were successfully applied in three departments of an automotive company and related to the company's current practices. By implementing them, organizations can reduce their energy consumption and costs while enhancing their competitiveness. [5443] Production line measurements as predictors: towards geometric quality assurance for assembly Deborah Gabriela Sarria Garcia (PUC PR), Anis Assad Neto (PUC PR), Alexandre Surkus-Castro (PUC PR) and Fernando Deschamps (PUC PR). Abstract Many companies are already collecting data on the geometry of the components that they manufacture for use in the assembly of their products. This data can be used for better control of quality assurance by predicting the cumulative effect of geometric deviations that can have an impact on assembly. However, this data is not standardized, it is collected at different points in the process and stored in different systems. Therefore, a strategy is needed to facilitate using these geometric data by applying algorithms that allow using them to improve product assembly. To address this problem, in this article, we propose a technological framework to predict the quality defects that may occur in the production process, based on geometric deviations, using inspection data collected on the production line. This allows a virtual simulation of the assembly of the vehicles to prevent quality defects that may affect the assembly of parts on the vehicle. [5472] Strategies for digital transformation in SMEs: the impact of timing and speed Qijun Zhou (University of West of England), Abdul Ali (University of Greenwich) and Honglan Yu (University of Huddersfield). Abstract Digital transformation (DT) has emerged as a critical concept in organizational contexts, driven by the adoption and implementation of digital technologies. This paper aims to explore the challenges faced by small and medium-sized enterprises (SMEs) in successfully implementing digital transformation and identify strategic options available to them. Preliminary results from a systematic literature review is presented, focusing on empirical evidence on the integration of digital technologies in SMEs. The review revealed key challenges in strategizing for digital transformation and categorized them accordingly. Additionally, the study shedding light on digital strategies for SMEs. The findings contribute to the understanding of why some DT initiatives fail and provide insights into how SMEs can leverage digital transformation for success. By addressing the challenges and exploring strategic options, this research aims to assist SMEs in navigating the complex process of digital transformation. [5489] The Data Journey: Proposal of Formal Representation of Mapping and Characterizing Data Agents, Assets and States from Data Generation to Data Serving in a Data-Centered Operating Environment Alexandre Surkus-Castro (Pontifical Catholic University of Parana – Brazil), Fernando Deschamps (Pontifical Catholic University of Parana – Brazil), Edson Pinheiro De Lima (UTFPR), Deborah Gabriela Sarria Garcia (Pontifical Catholic University of Paraná - Brazil), Anis Assad Neto (Pontifical Catholic University of Paraná) and Sergio E Gouvea da Costa (Universidade Tecnologica Federal do Parana). Abstract In the ever-expanding landscape of data-intensive operations, such as Data Analytics, Machine Learning, and Deep Learning, where massive computational resources and extensive data are essential, the need for reliable and consistent attributes for data and operating agents becomes paramount. While frameworks like Archimate® and BPMN enable functional and semantic representation of organizational architecture and processes at a higher abstraction level, the data context lacks a comprehensive framework capable of capturing its components' semantic and functional aspects,

2023 ICPR27 40 from minor to big data infrastructures. This paper proposes a novel ontological framework that addresses this gap, offering a semantic and practical representation approach for data-centric business contexts. The proposed model encompasses the entire data journey, starting from data generation, moving through various stages of maturity within the value chain, and culminating in its utilization by consumer business platforms. The framework aims to be vendoragnostic and intelligible across diverse organizations and technological ecosystems, fostering interoperability and collaboration. By leveraging this ontological framework, organizations can enhance data operations, ensuring reliability, consistency, and compliance while facilitating effective communication and decision-making within and between entities. [5573] CERTIFICATION AND DIGITAL TECHNOLOGIES FOR THE SUSTAINABLE SUCCESS OF MANUFACTURING COMPANIES Galina Robertsone (Riga Technical University, Institute for Quality Engineering, Faculty of Engineering Economics and Management), Janne Heilala (The University of Turku, Department of Mechanical and Materials Engineering) and Eduards Lapiņš (Riga Technical University, Riga Business School). Abstract Abstract The adoption of new methods for production management and control based on advanced technologies, including artificial intelligence (AI) and robotics, creates the conditions for more productive and defect-free manufacturing. Minimization of human errors, the introduction of technological innovations, digitalization, and digital transformation, combined with the application of well-known production management and control methods, certification, and continuous improvement, lead to smart factories. However, the speed of digital technologies adoption varies by companies and industries. In this article, the authors explore the degree of technology adoption for management and control of production processes in manufacturing companies in Latvia and Finland, its impact on the quality of end products, the relation between the applicability of traditional management and control methods and companies’ motivation to implement technological innovations. [5642] ASSESSMENT AND ROADMAP I4.0: A CASE STUDY IN A SMEs COMPANY IN THE CONSUMER GOODS SECTOR Ana Lígia Vieira Rodrigues (Federal Univertisy of Santa Catarina), Guilherme Gomes (Federal Univertisy of Santa Catarina) and Marina Bouzon (Federal Univertisy of Santa Catarina). Abstract The digital transformation and the 4.0 movement have been printing important challenges for Industrial Management, which needs to adapt its internal processes to promote faster responses and with controlled costs in order to maintain competition in the face of this new scenario. However, before investing in new digital technologies, industries need to understand what their initial situation is and what their organizational needs are, especially SMEs (Small and Medium Enterprises), which have more difficulty in extracting the maximum potential from Industry 4.0. A proper transformation plan as well as the measurement of your maturity index is essential for a complete and successful transformation. The literature shows that there is still a lack of studies focusing on applying maturity assessment models in SMEs, as they are more adherent to the scenario of large corporations. Thus, the present research makes an evaluation of the maturity models developed and selects the one that can best be applied in the case study. Through a search in the literature bases Scopus and WEB of Science, the main maturity models that can be applied in SMEs were listed. Through an evaluation of the models and guidelines of other research, the maturity model of IMPULS was selected, which is based on 6 dimensions: Smart Manufacturing, Smart Products, Data-driven services and Smart Operation, Strategy and Organization and Employees, to be applied in the case study. The objective of this research was to define a maturity assessment model for SMEs and apply it in a company in the consumer goods sector, as well as to draw a roadmap for the implementation of I4.0 considering the company's business strategies. After the application of the questionnaire with 23 questions, the studied company reached a total score of 1.152 and was classified in level 1 of maturity, that is, the company is considered a beginner. When compared to other companies in the same segment, 40.9% of the companies that answered the questionnaire were also classified in level 1. In this sense, before starting the design of the roadmap, it was defined what the strategy of I4.0 for the company focusing on the next 3 years of work. A roadmap was designed focusing on the 3 main dimensions that appear with gaps in the evaluation that are: strategy and organization, smart manufacturing and smart operations. As implications of the work, in relation to theory, this article did a research and

2023 ICPR27 41 presented the main maturity models available and that can be applied for SMEs to measure their maturity level in I4.0. As practical contributions, after assessing maturity and visualizing the main gaps, the company has a feasible roadmap to be implemented and reap the benefits of I4.0 [5645] The impact of IoT tools on drivers' driving style and CO2 emissions Michal Adamczak (Poznan School of Logistics), Adrianna Tobola-Walaszczyk (Poznan School of Logistics), Piotr Cyplik (Faculty of Engineering Management, Poznan University of Technology) and Maciej Torz (Rentis S.A.). Abstract Cars and the related emissions of exhaust gases, including CO2, are one of the topics raised in discussions on nature protection. Reducing gas emissions from cars can be implemented in a variety of ways. One way is to change the driving style of drivers. Focusing your driving style on eco-driving allows you to reduce fuel consumption and thus reduce greenhouse gas emissions into the atmosphere. The study analyzed the impact of a tool based on IoT technology on driver behaviour. The developed application allows to provide drivers information about driving style according to 15 parameters. The application and the analytical system collect data from the sensor installed in the vehicle. The entire fleet of cars rented by one of the companies operating on the Central and Eastern European market was equipped with sensors. The analysis covered the driving of drivers who expressed their willingness to use the application and the hints it directs to them. The study used statistical methods of data analysis, data visualization and group comparison with the verification of statistical hypotheses. The conducted analysis allowed to conclude that drivers who downloaded the application and declared their willingness to use the instructions sent by the system drive less aggressively, which means they consume less fuel and emit less greenhouse gases into the atmosphere. It can therefore be concluded that solutions based on IoT technology may contribute to reducing fuel consumption and greenhouse gas emissions, including CO2. [5675] Strategic Corporate Sustainability Management (ESG) based on the materiality process: A systematic literature review Isabela Dallabona (Department of industrial and systems engineering, Pontifical Catholic University of Paraná), Isabella Gil (Department of industrial and systems engineering, Pontifical Catholic University of Paraná), Pablo Carpejani (Department of industrial and systems engineering, Pontifical Catholic University of Paraná), Edson Pinheiro de Lima (Department of Industrial and Systems Engineering, Federal University of Technology), Ubirata Tortato (Department of Industrial and Systems Engineering and Department of Business, Pontifical Catholic University of Parana), Sergio E. Gouvea da Costa (Department of Industrial and Systems Engineering, Federal University of Technology) and Fernando Deschamps (Department of Industrial and Systems Engineering, Pontifical Catholic University of Parana). Abstract Corporate sustainability has been a popular topic amongst organizations since the 90s, but now it’s even more in the spotlight due to the UN’s sustainable development goals/2030 Agenda. To keep track of the sustainability “issues”, a lot of different development measurement systems have been created, and companies use the Global Reporting Initiative to adopt those practices, report their results, coordinate ESG indicators, and others. This paper aims to understand the connection between material topics and companies' sustainability reports, based on stakeholders' demands. The technique used is the systematic review and qualitative analysis (content analysis), with support from ATLAS.ti software. The results show how organizations use material themes to manage their stakeholders via analysis of sustainability reports. They also present a categorization of the central studies present in the literature

2023 ICPR27 42 [5683] Impact of the digital twin on operations management: a systematic literature review André Silva de Souza (Pontifícia Universidade Católica do Paraná), Clarissa Figueredo Rocha (Pontifícia Universidade Católica do Paraná), Fernando Deschamps (Pontifícia Universidade Católica do Paraná), Sergio E. Gouvea da Costa (Universidade Tecnológica Federal do Paraná) and Edson Pinheiro de Lima (Universidade Tecnológica Federal do Paraná). Abstract The digital transformation plays a critical role for organizations to not only survive. It is a requirement that involves industries that need to be competitive in a global and dynamic market. To keep them competitive, it is necessary to align business strategy and operations management, being aware of the opportunities that the application of digital technologies offers to obtain competitive advantages. The adoption of such technologies can positively impact organizational performance in different dimensions, such as innovation, sustainability, flexibility, quality, costs, speed, and reliability. With the objective to evaluate the influence of digital technologies, and more specifically the Digital Twin (DT) solution, this paper presents a systematic literature review (SLR) focused on the application of DT as a technology for companies to achieve competitiveness in their businesses. The results of this SLR identifies and characterizes the influence of DT use in the business models (products, services, data and product service data systems) of different organizations. [5777] Introducing IoT in VSM to Realize Digital Lean Production Yu-Ju Lin (Department of Industrial Engineering and Enterprise Information Tunghai University), Tai-Yu Lu (Department of Industrial Engineering and Enterprise Information Tunghai University), Wei-Che Ko (Department of Industrial Engineering and Enterprise Information Tunghai University) and Chin-Yin Huang (Department of Industrial Engineering and Enterprise Information Tunghai University). Abstract Industry 4.0 has revolutionized manufacturing with various techniques including Internet of Thing, Cyber-Physical System, etc. On the other hand, Lean Production has been widely applied to improve the productivity of a manufacturing shop floor. Various kaizen approaches of Lean Production concern communication with physical media, e.g., Kanban and Andon. However, it is necessary to concern the digitalization of Lean Production in the era of Industry 4.0. The Internet of Things (IoTs) is a typical technology for information transmission in Industry 4.0. Introducing IoT to Lean Production to replace the traditional physical communication media can provide a quick digital transformation of Lean Production. This research proposes to apply IoT in Value Stream Mapping (VSM), an important tool to illustrate Lean Production, to digitalize Lean Production. Each of the IoT techniques has been designated a special icon in the VSM. To demonstrate the utilization of the IoT techniques in the VSM, a case study on a ball and cylindrical roller manufacturer has been investigated in this research. [5847] An optimal configuration and operation model for hybrid MTS/MTO production systems with multiple products and multiple machines Ryusei Toshima (Hiroshima University), Keisuke Nagasawa (Hiroshima University), Katsumi Morikawa (Hiroshima University) and Katsuhiko Takahashi (Hiroshima University). Abstract This study considered the optimal configuration and operation of hybrid MTS/MTO production systems. Previous studies have investigated the optimal operation of hybrid production systems using Markov decision process (MDP), but the optimal configuration of hybrid production systems has not been investigated. Therefore, this study proposed an optimal configuration and operation model based on the optimal operation model for hybrid MTS/MTO production systems. The model minimised the total cost of all production systems for producing one or two kinds of products, MTS and/or MTO products, by allocating products and hybrid machines to each production system. In addition, the proposed model

2023 ICPR27 43 considered a multi-period reconfiguration of the hybrid production systems. Numerical investigations showed the characteristics of the configured and reconfigured hybrid production systems. [5856] Decision model for attaining the relevant factors affecting port projects assessment. Astrid Oddershede (University of Santiago of Chile), Luis Quezada (UNIVERSITY OF SANTIAGO OF CHILE), Cecilia Montt (Universidad de Santiago de Chile) and Pedro Palominos (Universidad de Santiago de Chile). Abstract This work refers to the application of a decision methodology to determine and prioritize the factors that most affect the evaluation of port sector projects. As a complement to the traditional methods based only on economic criteria to evaluate port sector projects, the study has used a multi-criteria methodology that includes the opinion of experts in port and environmental impact analysis. It incorporates qualitative factors to decide the importance of these factors in each proposed project. By applying the Analytical Hierarchy Process (AHP) it has been possible to obtain a synthesis of the relative importance in percentage terms of all the elements considered. The methodology allows the multidisciplinary work of a team of experts and to recognize the weighted importance of the evaluation elements that each of them must deal with. By subjecting these elements to a comparative and prioritization process, agreement is finally reached on the influence or weight that each element or factor contributes to the assessment. The participation of the experts made it possible to identify the environmental factors and disturbances derived from the execution of port projects. However, as future work it is advisable to incorporate into the model the existing admissible ranges or standards that act as elements of the environmental variables. For this purpose, it will be necessary to broaden the scope of experts to analyse in depth the permissible ranges. [5857] Sustainable and resilient closed-loop supply chain network design with disruptions Keisuke Nagasawa (Hiroshima University), Katsumi Morikawa (Hiroshima University) and Katsuhiko Takahashi (Hiroshima University). Abstract Recently, the growing interest in the environmental and social impact of production activities has led to a demand for sustainable production activities that take into account the triple bottom line (economic, environmental, and social) in all companies, including not only economic profit but also efforts to address environmental issues and develop local communities. It is also necessary to consider the outbreak of pandemics and disasters, such as the recent COVID-19, and the risk of impact and disruption until the damage is contained. CLSC (Closed-Loop Supply Chain) has been researched for sustainable production activities with the development of supply chain design that considers environmental aspects. While most conventional supply chain design has focused on the forward flow, CLSC focuses additionally on the reverse flow. In the CLSC, the collected products are reused to decrease environmental impact by reducing waste, reduce potential costs, and also benefit from the sale of recycled products to secondary markets. Previous studies include the design of CLSCs that are sustainable with regard to the spread of COVID-19 and changes of demand, and that also take into account the risk of infection. The proposed model considers three aspects of the triple bottom line at three points and impacts in production activities: the first is the cost of establishing and transporting the facility; the second is the environmental impact of production and transportation at the facility; the third is the infection impact that depends on the duration, product, facility, and amount of transportation; and the fourth is the environmental impact of the facility. A proposed multi-objective mixed-integer linear programming (MILP) model was solved. The results of several numerical experiments confirm the usefulness of the proposed model. A sensitivity analysis of the proposed model was also conducted.

2023 ICPR27 44 [5904] Comparative Analysis between different types of Seru Production System and Assembly Line Ciro-Alberto Amaya (Universidad de los Andes) and Melissa Pájaro (Universidad de los Andes). Abstract The implementation of the Seru production system has meant a significant improvement for several Japanese manufacturing companies. Currently, different types of Seru are identified: Divisional, Rotary and Yatai. In this study, different production metrics were compared between the assembly line and the different types of Seru. For this purpose, a worker allocation model was developed for the divisional Seru and the assembly line. Subsequently, experiments were conducted in a laboratory setup, where different production systems were implemented. Finally, simulations of these experiments were developed over a longer time horizon. From the results, a positive impact of the implementation of the different types of Seru on cycle time, throughput and in-process inventory was observed. [5935] Establishing a comprehensive quality management for the use of crowd-based mechanisms by SMEs Kirsten Lange (Department of Quality and Process Management, Kassel University), Robert Refflinghaus (Department of Quality and Process Management, Kassel University), Anna Hupe (Business Information Systems, Kassel University) and Ulrich Bretschneider (Business Information Systems, Kassel University). Abstract This paper describes a concept to adapt the design of quality management in SMEs to better exploit the potential of crowdworking. It shows how the quality management, especially of SMEs, can be organised to make efficient and targeted use of work perfomed by crowd-based mechanisms and continue to ensure that customer requirements are met. The different forms of crowd-based mechanisms will be discussed. Barriers of SMEs in adopting crowdsourcing and strategies to overcome them will be identified. A modular reference process model for the quality-oriented use of crowdworking in SMEs will be developed. In addition, concrete recommendations for action to adapt or supplement the design of quality management in SMEs for the efficient and effective use of crowd-based mechanisms will be given. [6013] Exploring models' interoperability in digital thread and twin: a proposal for an ontologydriven approach based on intra-and cross-organizational enterprise integration Arkopaul Sarkar (ENIT), Zhengyu Liu (ENIT), Sina Namaki Araghi (ENIT), Bernard Archimede (ENIT), Rebecca Arista (AIRBUS) and Mohamed Hedi Karray (ENIT). Abstract The increasing adoption and investment in Model-Based Systems Engineering (MBSE) and Digital Thread and Digital Twin (DT&T) demand interoperability among models across the organization. Model interoperability is a critical challenge in the development of DT&T due to the diversity of models and standards used in different phases of product or system design life cycle (PLC/SDLC). Moreover, this challenge is further complicated by the complexity of the differences among various organizations and enterprises. This paper proposes an ontology-driven approach to address the challenge of model interoperability and emphasizes the significance of intra-and cross-organizational Enterprise Integration (EI) and trustworthy decision-making in the development of DT&T. The proposed approach investigates models' interoperability, transformation, and portability and introduces potential research avenues to add explainability throughout the digital model's lifecycle. Furthermore, the paper emphasizes the need for standard evaluation criteria or measurements to evaluate the maturity of EI and to compare different model translation and portability methodologies.

2023 ICPR27 45 [6148] Application of performance and network indicators in supply chain resilience Michele Martignago (Department of Management and Engineering, University of Padua), Martina Calzavara (Department of Management and Engineering, University of Padua), Niloofar Katiraee (Department of Management and Engineering, University of Padua), Dmitry Ivanov (Department of Business and Economics, Berlin School of Economics and Law) and Daria Battini (Department of Management and Engineering, University of Padua). Abstract The turbulent years of pandemic and geopolitical tensions, product shortages and freight bottlenecks have had an impact on supply chains (SCs), with deep uncertainties still prevailing and a growing interest in supply chain resilience (SCRE). SCRE can be achieved in different ways; however, there is a lack of clarity in quantifying the impact of different supply network design practices on SCRE, with numerous and sometimes contradictory indicators. In addition, the literature provides few application cases for testing the impact of strategic choices on resilience. By calculating some key indicators (entropy, connectivity coefficient, node and arc connectivity), different SC configurations in the context of an industrial case study are compared. The results show that some effects deserve to be highlighted to understand the design of efficient and responsive supply networks with an appropriate level of resilience. [6233] Profit maximization problem for closed loop supply chain with subsidy Daisuke Hirotani (Prefectural University of Hiroshima) and Syunsuke Nakao (Prefectural University of Hiroshima). Abstract In this paper, closed loop supply chain is considered. Closed loop supply chain includes not only producing, but also collecting and remanufacturing. In the previous paper, closed loop supply chain with and without government subsidy are considered. Also, the model includes manufacturer, retailer, customer and recycler, and the optimal price are derived to maximize the profit. However, in that paper, the case of subsidy to the customer is only considered. If subsidy is given to another, it may maximize the total profit. Purpose of this paper is modeling the case when subsidy is given to manufacturer, retailer and recycler, respectively. Also, comparing these models to maximize the profit in the numerical analysis. [6370] A two-phase branch and bound algorithm for three-machine flow shop scheduling with reworks under overlapped queue time limits Hyeon-Il Kim (Department of Industrial Engineering, Hanyang University) and Dong-Ho Lee (Department of Industrial Engineering, Hanyang University). Abstract This study addresses a three-machine flow shop scheduling problem in which each job must be reworked after a rework setup is done when one of its overlapped queue time limits is violated. The problem is to determine the start times of jobs on each machine and rework setups/operations, if occur, with the objective of minimizing makespan. According to additional rework setup/operations, the problem can be decomposed into two sub-problems: process route selection and resulting reentrant flow shop scheduling with reworks. After representing the problem as a mixed integer programming model, a two-phase branch and bound algorithm is proposed that gives near optimal solutions, in which the process route of each job is generated using a two-level tree and the resulting flow shop schedules are determined using an active schedule based branch and bound algorithm. Computational experiments were done on various randomly generated test instances and the results are reported.

2023 ICPR27 46 [6418] The Facility Layout Problem of Marine Current Turbines against Uncertainty Rudi Nurdiansyah (National Taiwan University), I-Hsuan Hong (National Taiwan University), Jack Su (University of New Mexico) and Nai-Lun Pan (National Taiwan University). Abstract We propose a scenario-based robust Bi-level Optimization model for solving the Marine Current Turbine Installation problem (BO-MCTIP) against uncertainty of environment parameters. In the proposed model, the upper-level determines the number of Marine Current Turbines (MCTs) and their locations, and the lower-level determines how to connect the installed MCTs. We first solve all scenarios in the BO-MCTIP model followed by the min-max relative regret decision rule to find the robust solution. The result of a test case in Cook Inlet, Alaska indicates that the robust solution performs well across all scenarios in the BO-MCTIP model. [6455] Variable neighborhood search algorithms for system-level configuration selection in reconfigurable single part flow lines Hyeon-Il Kim (Department of Industrial Engineering, Hanyang University), Ae-Jin Youn (Department of Industrial Engineering, Hanyang University), Seung-Hyun Lee (Department of Industrial Engineering, Hanyang University) and Dong-Ho Lee (Department of Industrial Engineering, Hanyang University). Abstract This study addresses system-level configuration selection for the reconfigurable single part flow lines that consist of parallel identical machines at each serial stage. For a given demand of a part type, the problem is to determine the number of stages, the number of machines of the same type at each stage and the assignment of operations to each stage to satisfy the demand, precedence relations among the operations and space limitation. A new nonlinear integer programming model is developed that minimizes the sum of machine and operation costs. Then, due to the limited applications of the optimal mathematical programming approach, a variable neighborhood search (VNS) algorithm is proposed that generates an initial solution using a greedy algorithm and improves it using a shaking and a local search improvement methods. To test the performance of the VNS algorithm, computational experiments were done on a number of test instances, and the results show that the VNS algorithm outperforms the existing genetic algorithm in both solution quality and computation time. Moreover, a general variable neighborhood search (GVNS) algorithm is proposed that enlarges the search space using a variable neighborhood descent (VND) method, and it is shown from computational experiments that the GVNS algorithm outperforms the VNS algorithm significantly. [6505] The 3As for Theory: The Curious Case of Theory of Agency for Supply Chain Finance Rob Dekkers (Adam Smith Business School/University of Glasgow), Ronald de Boer (Windesheim University of Applied Sciences), Luca Mattia Gelsomino (University of Groningen), Christiaan de Goeij (Windesheim University of Applied Sciences) and Qijun Zhou (Business School/University of Greenwich). Abstract During the analysis of studies on supply chain finance using the theory of agency, we discovered that theory available to scholars for this subject of study is not always used in an appropriate manner, and even not, when it should have been. This led us to frame the use of theory by 3As (awareness, appropriation, advancements). This framework is used in this paper to relate findings from the analysis of studies to its use for supply chain finance and to demonstrate how it informed setting a research agenda related to theory of agency. The proposed framework may also be useful for appraisal of other theories in use for the domains of operations and supply chain management.

2023 ICPR27 47 [6521] Increasing resilience by quantifying the value of collaborative negotiations Frederik Weber (PRISM CENTER, Purdue University) and Shimon Nof (PRISM CENTER Purdue University). Abstract A negotiation process is crucial for establishing agreements between suppliers and customers in a network. Price negotiations are essential but often face challenges due to hidden information. This paper introduces a novel collaborative negotiation method that addresses hidden information in supply networks, promoting trust. The method encourages the disclosure of operation costs by incorporating them into auction bids alongside estimated profits. The study explores the benefits of the newly developed CAP through a numerical analysis involving 60 bidders following a static strategy. Two scenarios are considered: comparing single bidding strategy performance to a non-collaborative baseline scenario and introducing an additional collaboration level with a penalizing factor. Results demonstrate cost savings and a preference for collaborative bidding parties, increasing the resilience of a supply network. [6531] How can Deep Tier Finance support Supply Chain Sustainability? Elisa Medina (Politecnico di Milano), Luca Mattia Gelsomino (University of Groningen), Antonella Moretto (Politecnico di Milano) and Federico Caniato (Politecnico di Milano). Abstract Abstract The interest in Supply Chain Finance (SCF) has increased both from an academic and a practitioner viewpoint, but research on SCF solutions that go beyond the first supply chain (SC) tier is still scarce. Moreover, the increasing interest in Sustainable Supply Chain Management (SSCM) practices at multiple SC tiers calls for research and actions oriented to solving issues (e.g., goal misalignment and information asymmetry) arising when focal companies extend sustainability. SCF solutions that go beyond the first supply tier, defined as Deep Tier Finance (DTF) have been developed, but research has not explored how these solutions can support SC sustainability. This paper contributes by identifying two models of DTF solutions, describing how DTF can be used as an incentive to align goals in SCs and by improving information sharing. [6536] Trends within creativity support tools: How to improve remote collaboration within new product development? Reto Wechner (Fraunhofer Institute for Industrial Engineering (IAO)), Verena Lisa Kaschub (GSaME University of Stuttgart), Michael Gräf (University of Stuttgart - Institute of Human Factors and Technology Management (IAT)), Benjamin Wingert (Fraunhofer Institute for Industrial Engineering (IAO)), Matthias Bues (Fraunhofer Institute for Industrial Engineering (IAO)) and Oliver Riedel (Fraunhofer Institute for Industrial Engineering (IAO)). Abstract The importance for the generation of new, innovative, technical products has increased significantly in recent years. This derives from an increase in competition. However, the development of innovative, technical products is time-consuming and depends to a certain level on the creativity of engineers. CSTs can help product developers to be more creative within the development and design task and can support them all along the product development process. Furthermore, such tools can enable remote collaboration to fit within the future challenges of new product development. In this paper, a comprehensive examination of 947 creativity-related publications from the ACM Digital Library and ScienceDirect Library, using a keyword search, is conducted. The results show an overview of identified tools based on criteria proposed in the paper. The analysis is used to identify trends of CSTs for future technical product development, regarding especially aspects of local and remote collaboration. These trends show current development activities as well as existing fields of action for CSTs within technical product development, to further increase creativity.

2023 ICPR27 48 [6719] Application of Alternate Routes to Reduce Risk of Job Tardiness in Manufacturing Schedules Saruda Seeharit (Ohio University), Dusan Sormaz (Ohio University), Mohammad Milad Omar (Ohio University) and Mandvi Malik Fuloria (Ohio University). Abstract This paper reports on an application of alternate routings on manufacturing systems performance, defined as risk of completing production plans. In previous work an integrative methodology was developed in which process planning system IMPlanner was integrated with an FMS simulation module. IMPlanner’s rule-based process selection system performed knowledge intensive task of generating alternative processing options for each feature for parts in production plan. In this paper an updated simulation model using Simio was developed and Simio’s scheduling capability was utilized in order to determine the reduction of risk of job tardiness in the production plans when alternate routings are considered. Methodology is explained and new simulation model described. The experiments were performed on several case studies from machining domain, with a number of alternate routings as input parameters, and different due dates as performance criteria. Results from the updated simulation model were compared with the previous study in which only resource utilization were analysed. The level of risk reduction with alternate routings is quantified. [6746] Holistic Methodology for real-time optimization of holistic production through comprehensive and Cognitive Digital Twins Raul Matei (Fraunhofer Institute for Industrial Engineering - FhG IAO), Stefan Giosan (Fraunhofer Institute for Industrial Engineering - FhG IAO), Cristian Roiban (Fraunhofer Institute for Industrial Engineering - FhG IAO) and Carmen Constantinescu (Fraunhofer Institute for Industrial Engineering - FhG IAO). Abstract The authors present a methodology for real-time optimization of holistic production by bringing in a synergy of 3D capturing, Product Lifecycle Management (PLM), and discrete event simulation technologies. The core of the methodology is represented by such called Comprehensive and Cognitive Digital Twins and consists of three main phases: 1) 3D based Real-Time data acquisition; 2) Digital Twin development, and PLM-based production data management; 3) Propagation of the Real-Time captured data to the Digital Twin of comprehensive production area and 4) Enhancement of the production Digital Twin with temporal dimension towards the realization of Cognitive Digital Twin. The methodology is validated in a manufacturing scenario, which implements the quality measurement process of critical parts in discrete event manufacturing instantiated in the automotive industry. The paper concludes the research results by highlighting the encountered challenges of integrating these powerful technologies and envisioning the research's next steps. [6826] Integration of Knowledge Reasoning and Deep Learning to Predict the Removal Rate of CMP in Semiconductor Manufacturing Yu-Ju Lin (Department of Industrial Engineering and Enterprise Information Tunghai University), Chih-Chia Lai (Department of Industrial Engineering and Enterprise Information Tunghai University), Yu-Shuo Tseng (Department of Industrial Engineering and Enterprise Information Tunghai University) and Chin-Yin Huang (Department of Industrial Engineering and Enterprise Information Tunghai University). Abstract Semiconductor manufacturing consists of a large number of high-precision manufacturing processes, of which Chemical Mechanical Polishing (CMP) is one of the critical processes. The purpose of CMP is to remove excess deposits on the surface of the silicon wafer during the manufacturing. CMP can ensure that the subsequent processes can continue to stack up, in the meantime to ensure the yield of the wafer. Because the process variables that affect the removal rate of excess deposits are highly diverse, researchers have proposed various methods to predict the removal rate, such as physics-based or data-driven models. However the physics model lacks flexibility for on-site adjustments, whereas the data-driven takes much time on training the black box model. By integrating ontology inference system built based on the experts’ knowledge and deep learning on the open on-site data, this research aims to build a system with visible and

2023 ICPR27 49 modifiable CMP process inferences based on the experts’ knowledge and with the function of accurate prediction based on deep learning. The experimental analysis of this research shows the mean square error on the training set can be reduced by 80%, compared with the traditional approaches. The outcome of our research indicates that experts’ knowledge can effectively increase the accuracy of prediction on the removal rate of CMP. [6832] PRIORITIZATION OF RISKS CATHEGORIZED UNDER ISO 31,000 IN CONSTRUCTION PROJECTS USING THE ANALYTICAL HIERARCHY PROCESS Luis Quezada (Universidad de Santiago de Chile), Matias Contreras (Universidad de Santiago de Chile), Astrid Oddershede (University of Santiago of Chile) and Pedro Palominos (Universidad de Santiago de Chile). Abstract This paper addresses the problems generated in the process of risk management in engineering projects, specifically in construction projects, which are affected by different risks (internal and external) and the lack of a method to categorize and prioritize these variables. The purpose of this research is linked to the need to manage the risks associated with the efficient management of an engineering contract from its inception to its completion. From the perspective of contract management, the risks of a project can affect it positively or negatively, which is why it is important to know how to identify them and to manage them. This study proposes the identification of risks through the use of ISO 31,000 and also considers qualitative aspects in the analysis by using a multi-criteria approach and applying the analytic hierarchy process (AHP). By using these models, risks can be managed, starting by identifying risks using the PMI (Project Management Institute) risk breakdown structure (RBS), and then designing a matrix to identify risks using ISO 31,000 standards. Once the risks have been categorized and classified, these variables are ranked using AHP. The results of this investigation provide a framework for risk management in any engineering project. The use of different models for risk management allows complementing the analysis of these variables, such as the use of ISO 31,000 to analyse these variables and the use of the AHP matrix model to rank these variables. It is concluded that both models are complementary to each other so it is feasible to implement both models in engineering projects. [7053] Process improvement and real-time defect inspection for statistical process control Yong-Jun Im (Kyonggi University), Yeon-Soo Kim (Kyonggi University), Huyen Vu Thi Thu (Kyonggi University) and Tai-Woo Chang (Kyonggi University). Abstract Korean small and medium-sized enterprises (SMEs) are becoming increasingly interested in smart factories. However, there is still a lack of cases where technologies such as real-time quality judgment using AI technology have been applied. This study is being conducted on the printing process of an actual SME in Korea. By identifying the problems in the product flow within the current process, the process is redesigned and a conveyor for inspection is constructed. A realtime defect detection method using actual products is employed, utilizing the YOLO deep learning model. Through model training, defects in the products can be detected and stored in a database. Summarizing the collected data and applying statistical quality control principles allows for the assessment of the manufacturing process. This approach enables the detection of process abnormalities and variations in product quality caused by abnormal factors, with the information being transmitted to the Manufacturing Execution System (MES). Additionally, techniques such as the OC curve and other statistical process control methods are used for integrated process control, providing valuable insights into the decisionmaking process [7071] Unsupervised Anomaly Detection considering Angle-wise Relational Function in Latent Space of Time Series Data Yunseon Byun (Korea University) and Jun-Geol Baek (Korea University). Abstract Time series signals are collected continuously in a wide range of fields such as stock prices, weather, and manufacturing industries. In particular, manufacturing systems such as steel, semiconductors, and electronics collect time series data to

2023 ICPR27 50 monitor the occurrence of abnormal patterns. In actual, abnormal patterns are rare because engineers control the quality of production strictly. Also, the distinction between normal and abnormal may not be clear because many false alarms and noises are contained. Due to this difficulty, it is important to monitor the signal of the manufacturing process and increase the accuracy of abnormal detection. Previous studies have been conducted using the One-class Support Vector Machine, Isolation Forest, and Autoencoder, but the studies assume that we know the label of the normal pattern. Since the engineers should add labels manually, it is too hard to obtain proper label information. Therefore, we propose the unsupervised anomaly detection method using the relational function of embedding vectors in latent space for time series signals. After segmented signals on a window are embedded into a latent space through an encoder, we identify the different patterns using an angle-wise relational function between embedding vectors. If some vectors have a narrow angle compared to other vectors, this is likely to correspond to an abnormal pattern because the distance on the latent space is far. In this step, only the filtered normal pattern is entered into the decoder to learn the autoencoder structure. Finally, the anomaly score is calculated based on the reconstruction error of the learned model. If it deviates from the threshold set by the bootstrap of normal data, it is determined to be an abnormal pattern. The proposed model was validated with cwru bearing data and concluded that it is effective for anomaly detection even though label information has not been used. The proposed model is meaningful in that it has improved anomaly detection performance in consideration of data issues in the actual industrial field. [7214] Decision-making model for packing substandard agricultural products in D2C Kotomichi Matsuno (Waseda University), Takahiro Ohno (Waseda University) and Yoshikuni Edagawa (Waseda University). Abstract In recent years, Japanese farmers are gradually increasing their use of direct sales channels to deliver agricultural products directly to consumers to increase their profits. In addition, with the widespread use of the Internet, e-commerce is being put into practice in agriculture. However, the share of direct sales in the total volume of agricultural products shipped is still very small. At present, most of the farmers send their harvested agricultural products to Japan Agricultural Cooperative in order to focus on cultivation activities and ensure stable income. On the other hand, many substandard agricultural products are returned to the farmers due to the strict purchasing standards set by Japan Agricultural Cooperative, especially for fruits such as peaches, the color, sweetness, shape, etc. are strictly controlled by sorting machines and visual inspection. As a result, problems such as decreased income for agricultural producers and increased food loss due to waste disposal are on the rise. Selling substandard agricultural products directly to consumers through D2C platforms is considered a good approach to solve such problems. However, the variation in the condition of substandard products is quite large. Packaging based on individual experience results in a significant difference in product value per package, which can lead to customer dissatisfaction. In this study, we focus on the direct sale of substandard peaches, assuming that attributes such as color and fruit spots are quantified by new sorting machines. To improve customer satisfaction, a decision-making model for packaging peaches that maximizes the value of each package while minimizing the difference in product value between packages is proposed. We also propose a mechanism for adjusting the inventory level of each type of package based on sales, considering multiple packaging patterns to help farmers increase their sales. [7232] The Digital Transformation in the Energy Sector to Meet Decarbonization Future Goals: Literature Review Tamer Abdulghani (Brandenburg University of Technology Cottbus-Senftenberg), Nizar Abdelkafi (Politecnico di Milano), Hans-Ruediger Lange (Brandenburg University of Technology Cottbus-Senftenberg) and Herwig Winkler (Brandenburg University of Technology Cottbus-Senftenberg). Abstract Digital transformation has been implemented across all industries, with a special focus on the energy sector in order to improve the business operations. It has the potential to optimize existing business models and develop new ones based on different technologies and thus, make them fit for the future by reducing greenhouse gas emissions and meeting decarbonization future goals. The purpose of this paper is to discuss the significance of various digital technologies in the stages of the energy value chain from previous studies by using a systematic literature review to identify in what ways they have been implemented in the energy sector. By reviewing the applications of digital technologies such as Big Data,