Predictive Maintenance (PdM) is a data-driven maintenance strategy thatanticipates equipment failures before they occur by analysing assetcondition, operational data, and historical patterns. Unlike reactive ortime-based preventive maintenance, PdM enables organisations tointervene only when failure risk is high—minimising downtime, cost, andoperational disruption.This handbook provides a practical, implementation-focused guide fororganisations seeking to adopt Predictive Maintenance. It covers end-toend processes, enabling technologies, analytics approaches, andorganizational considerations, along with proven ROI frameworks tosupport executive buy-in and long-term success.IntroductionPREDICTIVE MAINTENANCE IMPLEMENTATIONHANDBOOK: PROCESSES, TOOLS & ROI FRAMEWORKSPredictive Maintenance transforms maintenancefrom a cost center into a strategic value driver.Business Value of Predictive MaintenanceKey BenefitsReduced Unplanned DowntimeLower Maintenance CostsExtended Asset LifeImproved Safety & ComplianceHigher Operational EfficiencyTypical Impact Benchmarks20–40% reduction in maintenance costs30–50% reduction in unplanned downtime10–20% increase in asset availability
Reactive MaintenanceFix assets after failure occurs. High downtime andunpredictable costs.Preventive MaintenanceTime- or usage-based maintenance regardlessof actual condition.Condition-Based Maintenance (CBM)Maintenance triggered by sensor thresholds andinspections.Predictive Maintenance (PdM)Failure probability predicted using analytics andmachine learning.Prescriptive MaintenanceSystems recommend optimal maintenanceactions and timing automatically.Organisations typically progress through the following maturity stages:Predictive Maintenance Maturity ModelPREDICTIVE MAINTENANCE IMPLEMENTATIONHANDBOOK: PROCESSES, TOOLS & ROI FRAMEWORKS
Phase 1: Strategy & Asset SelectionPredictive Maintenance Implementation RoadmapIdentify high-criticality assets based on downtime impact, safety risk,and repair costDefine business objectives (cost reduction, uptime, safety, compliance)Establish success metrics and governance structurePREDICTIVE MAINTENANCE IMPLEMENTATIONHANDBOOK: PROCESSES, TOOLS & ROI FRAMEWORKSPhase 2: Data Readiness AssessmentReview historical maintenance, failure, and operational dataAssess sensor availability and data qualityIdentify data gaps and integration requirementsPhase 3: Data Acquisition & InfrastructureDeploy condition-monitoring sensors (vibration, temperature, pressure,current)Integrate SCADA, PLC, historian, and CMMS/EAM systemsEstablish secure data pipelines (edge, on-premise, or cloud)Phase 4: Analytics & Model DevelopmentPerform exploratory data analysisSelect appropriate modeling techniquesTrain, validate, and test predictive modelsPhase 5: Pilot DeploymentApply PdM models to a limited asset setValidate alerts against real-world outcomesRefine thresholds and workflows
Predictive Maintenance Implementation RoadmapTigernix Pte LtdTel: +(65) 6760 6647 / +(65) 6760 6012Email: [email protected]: 21, Woodlands Close, #05-47 PrimzBizhub, Singapore 737854www.tigernix.comPREDICTIVE MAINTENANCE IMPLEMENTATIONHANDBOOK: PROCESSES, TOOLS & ROI FRAMEWORKSPhase 6: Integration & ScalingIntegrate predictions with maintenance workflowsAutomate work order generationScale across additional assets and sitesPhase 7: Continuous OptimisationMonitor model performanceIncorporate feedback loopsContinuously retrain modelswith new dataTechnology Stack & ToolsData SourcesData InfrastructureAnalytics & AIVisualisation & ActionAnalytics & Modeling ApproachesRule-Based ThresholdsStatistical AnalysisMachine Learning ModelsTime-Series ForecastingRemaining Useful Life (RUL)Predictive Maintenance is astrategic capability thatenables organisations to shiftfrom reactive maintenanceto proactive, data-drivenasset management. Byfollowing a structuredimplementation roadmap,selecting the right tools, andapplying a clear ROIframework, organisations canunlock measurableoperational and financialbenefits while building afoundation for futureintelligent operations.