Matching Supply with Demand An Introduction to Operations Management (1)

Solutions to Selected Practice Problems 481

Part g
Equation (17.1) in the text gives the buy-back price that coordinates the supply chain (that
is, maximizes the supply chain’s profit). That buy-back price is $1 ϩ $28 Ϫ ($28 Ϫ $20) ϫ
($28 Ϫ $6)/($28 Ϫ $7.5) ϭ $20.41. Note, the publisher’s buy-back price is actually higher
than the wholesale price because the publisher needs to subsidize Dan’s shipping cost to
return books: Dan’s net loss on each book returned is $20 Ϫ (20.41 Ϫ 1) ϭ $0.59.

Chapter 18

Q18.1 (Bauxite to New Zealand)

Total emissions on the journey is 1,400,000 ϫ 38.2 ϭ 53,480,000 kgs CO2. The journey
transports 300,000 ϫ 3,000 ϭ 900,000,000 tonne kms. So emissions are 53,480,000 kgs
CO2/900,000,000 tonne kms ϭ 0.059 kgs CO2 per tonne km.

Glossary

A B

abandoning Refers to flow units leaving the process back order If demand occurs and inventory is not avail-
because of lengthy waiting times. able, then the demand can be back-ordered until inventory
becomes available.
activity Value-adding steps in a process where resources
process flow units. back-order penalty cost The cost incurred by a firm per
back order. This cost can be explicit or implicit (e.g., lost
activity on node (AON) representation A way to goodwill and future business).
graphically illustrate the project dependencies in which
activities correspond to nodes in a graph. balancing resources Attempting to achieve an even utili-
zation across the resources in a process. This is equivalent
activity time The duration that a flow unit has to spend at to minimizing idle time by reallocating work from one
a resource, not including any waiting time; also referred to resource to another.
as service time or processing time.
base stock level Also known as the order-up-to level. In the
A/F ratio The ratio of actual demand (A) to forecasted implementation of an order-up-to policy, inventory is ordered
demand (F). Used to measure forecast accuracy. so that inventory position equals the base stock level.

Andon cord A cord running adjacent to assembly lines batch A collection of flow units.
that enables workers to stop production if they detect
a defect. Just like the jidoka automatic shut-down of batch flow operation Those processes where flow units
machines, this procedure dramatizes manufacturing prob- are batched to benefit from scale economies of production
lems and acts as a pressure for process improvements. and/or transportation. Batch flow operations are known to
have long flow times.
appointment Predefined times at which the flow unit sup-
posedly enters the process; used to reduce variability (and batch ordering A firm batch orders when it orders in inte-
seasonality) in the arrival process. ger multiples of a fixed batch size. For example, if a firm’s
batch size is 20 cases, then the firm orders either 0, 20, 40,
assemble-to-order Also known as make-to-order. A 60, . . . cases.
manufacturing system in which final assembly of a product
only begins once a firm order has been received. Dell Inc. bid price With bid price control, a bid price is assigned to
uses assemble-to-order with personal computers. each segment of capacity and a reservation is accepted only
if its fare exceeds the bid prices of the segments of capacity
asset turns See capital turns. that it uses.

assignable causes of variation Those effects that result in bid price control A method for controlling whether or not
changes of the parameters of the underlying statistical distri- to accept a reservation. This method explicitly recognizes
bution of the process. Thus, for assignable causes, a change that not all customers paying the same fare for a segment of
in process performance is not driven simply by common- capacity are equally valuable to the firm.
cause variation.
blocking The situation in which a resource has completed
attribute-based control charts A special form of control its work on a flow unit, yet cannot move the flow unit to the
chart that only distinguishes between defective and nonde- next step (resource or inventory) downstream as there is not
fective items. Such control charts should be used if it is not space available.
possible to capture the quality conformance of a process
outcome in one variable. booking limit The maximum number of reservations that
are allowed for a fare class or lower.
authorization level For a fare class, the percentage
of capacity that is available to that fare class or lower. bottleneck The resource with the lowest capacity in the
An authorization level is equivalent to a booking limit process.
expressed as a percentage of capacity.
buckets A booking limit is defined for a bucket that contains
availability The proportion of a time a process (single multiple fare class–itinerary combinations.
resource or buffer) is able to either process (in the case of a
resource) or admit (in the case of a buffer) incoming flow units. buffer Another word for inventory, which is used
especially if the role of the buffer is to maintain a certain
average labor utilization Measures the percentage of throughput level despite the presence of variability.
paid labor time that is spent on actual production as opposed
to idle time; measures the efficiency of the process as well buffer inventory Allows resources to operate independent
as the balance of work across workers. from each other, thereby avoiding blocking and starving (in
which case we speak of a decoupling buffer).
482

Glossary 483

buffer-or-suffer principle The inherent tension between conservation-of-matter law A law that states that, on average,
inventory and flow rate. In a process that suffers from setup the flow into a system must equal the flow out of the system,
times or variability, adding inventory can increase the flow rate. otherwise the quantity within the system will not be stable.

bullwhip effect The propagation of demand variability up consignment inventory Inventory that is kept at a cus-
the supply chain. tomer’s location but is owned by the supplier.

business model Articulates how a firm aims to create a consolidated distribution The practice of delivering from
positive net utility to the customer while making a profit. a supplier to multiple locations (e.g., retail stores) via a dis-
Typically involves choices with respect to the process tribution center.
timing, the process location, and the process standardization.
continuous process A process in which the flow unit con-
business model innovation A substantial shift in a firm’s tinuously flows from one resource to the next; different from
business model either relative to others in the industry or to discrete process, in which the flow units are separate entities.
its previous practice.
contract manufacturer A firm that manufactures or
buy-back contract A contract in which a supplier agrees assembles a product for another firm. Contract manufacturers
to purchase leftover inventory from a retailer at the end of typically manufacture products from multiple competitors,
the selling season. they are generally responsible for procurement, but they do
not design or distribute the products they assemble.
C
control charts Graphical tools to statistically distinguish
capability index The ratio between the tolerance level and between assignable and common causes of variation. Con-
the actual variation of the process. trol charts visualize variation, thereby enabling the user to
judge whether the observed variation is due to common
capacity Measures the maximum flow rate that can be causes or assignable causes.
supported by a resource.
control limits Part of control charts that indicate to what
capacity-constrained A process for which demand extent a process outcome falls in line with the common
exceeds the process capacity. cause variation of the process versus being a result of an
assignable cause. Outcomes above the upper control limit
capacity pooling The practice of combining multiple (UCL) or below the lower control limit (LCL) indicate the
capacities to deliver one or more products or services. presence of an assignable cause.

capital turns The ratio between revenues and invested cost of direct labor Measures the per-unit cost of labor,
capital; measures how much capital is needed to support a which includes both the labor content (the actual labor going
certain size of a company. into completing a flow unit) and the idle time that occurs
across all workers per completed flow unit.
carbon dioxide equivalent (CO2e) For a given gas, the
weight of CO2 that has the equivalent warming potential as critical path A project management term that refers to all
1 kilogram of that gas. those activities that—if delayed—would delay the overall
completion of the project.
carbon footprint The total creation of carbon dioxide
equivalents an organization is responsible for. Can be critical ratio The ratio of the underage cost to the sum of the
analyzed in the form of scope 1, 2, or 3. overage and underage costs. It is used in the newsvendor model
to choose the expected profit-maximizing order quantity.
channel assembly The practice in the PC industry of hav-
ing final assembly completed by a distributor (e.g., Ingram cross docking The practice of moving inventory in a
Micron) rather than the manufacturer (e.g., IBM). distribution facility from the inbound dock directly to the
outbound loading dock without placing the inventory in
channel stuffing The practice of inducing retailers to storage within the distribution facility.
carry more inventory than needed to cover short-term needs.
cycle inventory The inventory that results from receiving
coefficient of variation A measure of variability. Coef- (producing) several flow units in one order (batch) that are
ficient of variation ϭ Standard deviation divided by the then used over a time period of no further inflow of flow units.
mean; that is, the ratio of the standard deviation of a random
variable to the mean of the random variable. This is a rela- cycle time The time that passes between two consecutive
tive measure of the uncertainty in a random variable. flow units leaving the process. Cycle time ϭ 1/Flow rate.

collaborative planning, forecasting, and replenishment D
A set of practices designed to improve the exchange of infor-
mation within a supply chain. decision tree A scenario-based approach to map out the
discrete outcomes of a particular uncertainty.
common causes of variation Constant variation reflect-
ing pure randomness in the process. Such causes are hence a decoupling inventory See buffer inventory.
result of pure randomness as opposed to being the result of
an assignable cause.

484 Glossary

demand pooling combining demands from multiple improvement along one dimension can occur only if the
sources to reduce overall variability. level of another dimension is reduced.

discovery-driven planning A process that emphasizes electronic data interchange (EDI) A technology standard
learning about unknown variables related to a project for the communication between firms in the supply chain.
with the goal of deciding whether or not to invest further
resources in the project. elimination of flow units Discarding defective flow units
instead of reworking them.
demand aggregation The idea to supply multiple demand
streams with the same resource and thereby generate e-lot system A decoupling buffer that is tracked to detect
economies of scale. any systemic variation that could suggest a defect in the
process; it is thereby a way to direct managerial attention
demand-constrained A process for which the flow rate is toward defects in the process that would otherwise be hid-
limited by demand. den by inventory without immediately losing flow rate.

demand-pull An inventory policy in which demand trig- empirical distribution function A distribution function
gers the ordering of replenishments. constructed with historical data.

density function The function that returns the probability EOQ (economic order quantity) The quantity that mini-
the outcome of a random variable will exactly equal the mizes the sum of inventory costs and fixed ordering cost.
inputted value.
erlang loss formula Computes the proportion of time
dependency matrix Describes the dependencies among a resource has to deny access to incoming flow units in
the activities in a project. a system of multiple parallel servers and no space for
inventory.
design specifications Establish how much a process out-
come is allowed to vary before it is labeled a defect. Design expected leftover inventory The expected amount of
specifications are driven by customer requirements, not by inventory left over at the end of the season when a fixed
control limits. quantity is chosen at the start of a single selling season with
random demand.
distribution function The function that returns the prob-
ability the outcome of a random variable will equal the expected lost sales The expected amount of demand that
inputted value or lower. is not satisfied when a fixed quantity is chosen at the start of
a single selling season and demand is random.
diversion The practice by retailers of purchasing product
from a supplier only to resell the product to another retailer. Expected Marginal Seat Analysis A technique devel-
oped by Peter Belobaba of MIT to assign booking limits to
diverters Firms that practice diversion. multiple fare classes.

double marginalization The phenomenon in a supply expected sales The expected amount of demand that is
chain in which one firm takes an action that does not opti- satisfied when a fixed quantity is chosen at the start of a
mize supply chain performance because the firm’s margin is single selling season and demand is random.
less than the supply chain’s total margin.
exponential distribution Captures a random variable with
DuPont model A financial framework built around the distribution Prob{x < t} ϭ 1 Ϫ exp(Ϫt/a), where a is the
idea that the overall ROIC of a business can be decomposed mean as well as the standard deviation of the distribution.
into two financial ratios, the margins and the asset turns. If interarrival times are exponentially distributed, we speak
of a Poisson arrival process. The exponential distribution
E is known for the memoryless property; that is, if an expo-
nentially distributed service time with mean five minutes
earliest completion time (ECT) The earliest time a project has been going on for five minutes, the expected remaining
activity can be completed, which can be computed as the duration is still five minutes.
sum of the earliest start time and the duration of the activity.
external setups Those elements of setup times that can
earliest start time (EST) The earliest time a project be conducted while the machine is processing; an important
activity can start, which requires that all information element of setup time reduction/SMED.
providing activities are completed.
F
economies of scale Obtaining lower cost per unit based
on a higher flow rate. Can happen, among other reasons, FCFS (first-come, first-served) Rule that states that flow
because of a spread of fixed cost, learning, statistical reasons units are processed in the order of their arrivals.
(pooling), or the usage of dedicated resources.
fences Restrictions imposed on a low-fare class to prevent
Efficient Consumer Response The collective name given high-fare customers from purchasing the low fare. Examples
to several initiatives in the grocery industry to improve the include advanced purchase requirements and Saturday night
efficiency of the grocery supply chain. stay over.

efficient frontier All locations in a space of performance FIFO (first-in, first out) See FCFS.
measures (e.g., time and cost) that are efficient, that is,

Glossary 485

fill rate The fraction of demand that is satisfied; that is, implied utilization The workload imposed by demand of
that is able to purchase a unit of inventory. a resource relative to its available capacity. Implied utiliza-
tion ϭ Demand rate/Capacity.
flexibility The ability of a process to meet changes in
demand and/or a high amount of product variety. incentive conflicts In a supply chain, firms may have conflict-
ing incentives with respect to which actions should be taken.
flow rate (R) Also referred to as throughput. Flow rate
measures the number of flow units that move through independent arrivals A requirement for both the waiting
the process in a given unit of time. Example: The plant time and the loss formulas. Independent arrivals mean that the
produces at a flow rate of 20 scooters per hour. Flow probability of having an arrival occur in the next x minutes
rate ϭ Min{Demand, Capacity}. is independent of how many arrivals have occurred in the
last y minutes.
flow time (T) Measures the time a flow unit spends in
the process, which includes the time it is worked on at vari- information turnaround time (ITAT) The delay
ous resources as well as any time it spends in inventory. between the occurrence of a defect and its detection.
Example: A customer spends a flow time of 30 minutes on
the phone in a call center. innovation A novel match between a solution and a need
that creates value.
flow unit The unit of analysis that we consider in process
analysis; for example, patients in a hospital, scooters in a in-stock probability The probability all demand is satis-
kick-scooter plant, and callers in a call center. fied over an interval of time.

forecast The estimate of demand and potentially also of integrated supply chain The supply chain considered as a
the demand distribution. single integrated unit, that is, as if the individual firms were
owned by a single entity.
forward buying If a retailer purchases a large quantity
during a trade promotion, then the retailer is said to interarrival time The time that passes between two con-
forward buy. secutive arrivals.

G internal setups Those elements of setup times that can
only be conducted while the machine is not producing.
gamma distribution A continuous distribution. The sum Internal setups should be reduced as much as possible
of exponential distributions is a gamma distribution. This and/or converted to external setups wherever possible
is a useful distribution to model demands with high coeffi- (SMED).
cients of variation (say about 0.5).
inventory (I) The number of flow units that are in the pro-
Gantt chart A graphical way to illustrate the durations cess (or in a particular resource). Inventory can be expressed
of activities as well as potential dependencies between the in (a) flow units (e.g., scooters), (b) days of supply (e.g.,
activities. three days of inventory), or (c) monetary units ($1 million of
inventory).
H
inventory cost The cost a process incurs as a result of
heijunka A principle of the Toyota Production System, inventory. Inventory costs can be computed on a per-unit
proposing that models are mixed in the production process basis (see Exhibit 2.1) or on a per-unit-of-time basis.
according to their mix in customer demand.
inventory level The on-hand inventory minus the number
hockey stick phenomenon A description of the demand of units back-ordered.
pattern that a supplier can receive when there is a substantial
amount of order synchronization among its customers. inventory policy The rule or method by which the timing
and quantity of inventory replenishment are decided.
holding cost rate The cost incurred to hold one unit of
inventory for one period of time. inventory position The inventory level plus the number
of units on order.
horizontal pooling Combining a sequence of resources
in a queuing system that the flow unit would otherwise visit inventory turns How often a company is able to turn over
sequentially; increases the span of control; also related to its inventory. Inventory turns ϭ 1/Flow time, which—based
the concept of a work cell. on Little’s Law—is COGS/Inventory.

I Ishikawa diagram Also known as fishbone diagram or
cause–effect diagram; graphically represents variables that
idle time The time a resource is not processing a flow are causally related to a specific outcome.
unit. Idle time should be reduced as it is a non-value-adding
element of labor cost. J

ikko-nagashi An element of the Toyota Production System. jidoka In the narrow sense, a specific type of machine
It advocates the piece-by-piece transfer of flow units (transfer that can automatically detect defects and automatically
batches of one). shut down itself. The basic idea is that shutting down the
machine forces human intervention in the process, which in
turn triggers process improvement.

486 Glossary

just-in-time The idea of producing units as close as possi- M
ble to demand, as opposed to producing the units earlier and
then leaving them in inventory or producing them later and machine-paced line A process design in which flow units
thereby leaving the unit of demand waiting. Just-in-time is a are moved from one resource to another by a constant speed
fundamental part of the Toyota Production System as well as dictated by the conveyor belt. There is typically no inven-
of the matching supply with demand framework postulated tory between the resources connected by a conveyor belt.
by this book.
make-to-order A production system, also known as
K assemble-to-order, in which flow units are produced
only once the customer order for that flow unit has been
kaizen The continuous improvement of processes, typically received. Make-to-order production typically requires
driven by the persons directly involved with the process on wait times for the customer, which is why it shares many
a daily basis. similarities with service operations. Dell Inc. uses make-to-
order with personal computers.
kanban A production and inventory control system in
which the production and delivery of parts are triggered by make-to-stock A production system in which flow units
the consumption of parts downstream (pull system). are produced in anticipation of demand (forecast) and then
held in finished goods inventory.
key performance indicator An operational variable with
a strong marginal impact on ROIC (value driver) that is used margin arithmetic Equations that evaluate the percentage
as an indicator of current operational performance. increase in profit as a function of the gross margin and the
percentage increase in revenue.
L
marginal cost pricing The practice of setting the whole-
labor content The amount of labor that is spent on a flow sale price to the marginal cost of production.
unit from the beginning to the end of the process. In a purely
manual process, we find labor content as the sum of all the materials requirement planning A system to control the
activity times. timing and quantity of component inventory replenishment
based on forecasts of future demand and production schedules.
labor productivity The ratio between revenue and labor
cost. maximum profit In the context of the newsvendor model,
the expected profit earned if quantity can be chosen after
labor utilization How well a process uses the labor observing demand. As a result, there are no lost sales and no
involved in the process. Labor utilization can be found based leftover inventory.
on activity times and idle times.
mean The expected value of a random variable.
latest completion time (LCT) The latest time a project
activity has to be completed by to avoid delaying the overall mismatch cost The sum of the underage cost and the
completion time of the project. overage cost. In the context of the newsvendor model, the
mismatch cost is the sum of the lost profit due to lost sales
latest start time (LST) The latest time a project activity and the total loss on leftover inventory.
can start without delaying the overall completion time of the
project. mixed model production See heijunka.

lead time The time between when an order is placed and MRP jitters The phenomenon in which multiple firms
when it is received. Process lead time is frequently used as operate their MRP systems on the same cycle, thereby creat-
an alternative word for flow time. ing order synchronization.

lean operations An operations paradigm built around the muda One specific form of waste, namely waste in the form
idea of waste reduction; inspired by the Toyota Production of non-value-adding activities. Muda also refers to unnecessary
System. inventory (which is considered the worst form of muda), as
unnecessary inventory costs money without adding value and
line balancing The process of evenly distributing work can cover up defects and other problems in the process.
across the resources of a process. Line balancing reduces
idle time and can (a) reduce cycle time or (b) reduce the multiple flow units Used in a process that has a mix of
number of workers that are needed to support a given products or customers flowing through it. Most computa-
flow rate. tions, including the location of the bottleneck, depend on the
mix of products.
Little’s Law The average inventory is equal to the average
flow rate times the average flow time (I ϭ R ϫ T). N

location pooling The combination of inventory from negative binomial distribution A discrete distribu-
multiple locations into a single location. tion function with two parameters that can independently
change the mean of the distribution as well as the standard
loss function A function that returns the expected deviation. In contrast, the Poisson distribution has only one
number of units by which a random variable exceeds the parameter and can only regulate its mean.
inputted value.

Glossary 487

nested booking limits Booking limits for multiple fare out-of-stock When a firm has no inventory.
classes are nested if each booking limit is defined for a
fare class or lower. With nested booking limits, it is always overage cost In the newsvendor model, the cost of pur-
the case that an open fare class implies all higher fare chasing one too many units. In other words, it is the increase
classes are open and a closed fare class implies all lower in profit if the firm had purchased one fewer unit without
fare classes are closed. causing a lost sale (i.e., thereby preventing one additional
unit of leftover inventory).
newsvendor model A model used to choose a single
order quantity before a single selling season with stochastic overbooking The practice of accepting more reservations
demand. than can be accommodated with available capacity.

nonlinear The relationship between variables if the graph P
of the two variables is not a straight line.
pallet Literally the platform used (often wood) by a fork-
normal distribution A continuous distribution function lift to move large quantities of material.
with the well-known bell-shaped density function.
par level Another name for the order-up-to level in the
no-show A customer that makes a reservation but cancels order-up-to model.
or fails to arrive for service.
Pareto principle Principle that postulates that 20 percent
O of the causes account for 80 percent of all problems (also
known as the 80-20 rule).
on allocation A product whose total amount demanded
exceeds available capacity. period In the order-up-to model, time is divided into peri-
ods of time of equal length. Typical period lengths include
on-hand inventory The number of units currently in one day, one week, and one month.
inventory.
phantom orders An order that is canceled before delivery
on-order inventory Also known as pipeline inventory. is taken.
The number of units of inventory that have been ordered but
have not been received. pipeline inventory The minimum amount of inventory
that is required to operate the process. Since there is a mini-
one-for-one ordering policy Another name for an order- mum flow time that can be achieved (i.e., sum of the activity
up-to policy. (With this policy, one unit is ordered for every times), because of Little’s Law, there is also a minimum
unit of demand.) required inventory in the process. Also known as on-order
inventory, it is the number of units of inventory that have
options contract With this contract, a buyer pays a price been ordered but have not been received.
per option purchased from a supplier and then pays an
additional price later on to exercise options. The supplier is point-of-sale (POS) Data on consumer transactions.
responsible for building enough capacity to satisfy all of the
options purchased in case they are all exercised. Poisson distribution A discrete distribution function
that often provides an accurate representation of the num-
order batching A cause of the bullwhip effect. A firm ber of events in an interval of time when the occurrences
order batches when it orders only in integer multiples of of the events are independent of each other. In other
some batch quantity. words, it is a good distribution to model demand for slow-
moving items.
order inflation The practice of ordering more than desired
in anticipation of receiving only a fraction of the order due Poisson process An arrival process with exponentially
to capacity constraints upstream. distributed interarrival times.

order synchronization A cause of the bullwhip effect. poka-yoke A Toyota technique of “fool-proofing” many
This describes the situation in which two or more firms sub- assembly operations, that is, by making mistakes in assem-
mit orders at the same moments in time. bly operations physically impossible.

order-up-to level Also known as the base stock level. In the pooling The concept of combining several resources
implementation of an order-up-to policy, inventory is ordered (including their buffers and their arrival processes) into
so that inventory position equals the order-up-to level. one joint resource. In the context of waiting time problems,
pooling reduces the expected wait time.
order-up-to model A model used to manage inventory
with stochastic demand, positive lead times, and multiple preference fit The firm’s ability to provide consumers
replenishments. with the product or service they want or need.

origin-destination control A revenue management sys- price protection The practice in the PC industry of
tem in the airline industry that recognizes passengers that compensating distributors due to reductions in a supplier’s
request the same fare on a particular segment may not be wholesale price. As a result of price protection, the price a
equally valuable to the firm because they differ in their itin- distributor pays to purchase inventory is effectively always
erary and hence total revenue. the current price; that is, the supplier rebates the distributor

488 Glossary

whenever a price reduction occurs for each unit the distributor protection level The number of reservations that must
is holding in inventory. always be available for a fare class or higher. For example,
if a flight has 120 seats and the protection level is 40 for
precedence relationship The connection between two the high-fare class, then it must always be possible to have
activities in a project in which one activity must be com- 40 high-fare reservations.
pleted before the other can begin.
pull system A manufacturing system in which production
priority rules Used to determine the sequence with is initiated by the occurrence of demand.
which flow units waiting in front of the same resource are
served. There are two types of priority rules: service time push system A manufacturing system in which production
independent (e.g., FCFS rule) and service time dependent is initiated in anticipation of demand.
(e.g., SPT rule).
Q
process Resources, inventory locations, and a flow that
describe the path of a flow unit from its transformation as quality at the source The idea of fixing defects right
input to output. when and where they occur. This is a fundamental idea of
the Toyota Production System. Fixing defects later on in the
process analysis Concerned with understanding and process is difficult and costly.
improving business processes. This includes determining the
location of the bottleneck and computing the basic perfor- quantity discount Reduced procurement costs as a result
mance measures inventory, flow rate, and flow time. of large order quantities. Quantity discounts have to be
traded off against the increased inventory costs.
process capability The tolerance level of the process rela-
tive to its current variation in outcomes. This is frequently quantity flexibility contracts With this contract, a buyer
measured in the form of the capability index. provides an initial forecast to a supplier. Later on the buyer
is required to purchase at least a certain percentage of
process capacity Capacity of an entire process, which is the initial forecast (e.g., 75 percent), but the buyer also is
the maximum flow rate that can be achieved in the process. allowed to purchase a certain percentage above the forecast
It is based on the capacity of the bottleneck. (e.g., 125 percent of the forecast). The supplier must build
enough capacity to be able to cover the upper bound.
process flow diagram Maps resources and inventory and
shows graphically how the flow unit travels through the pro- quartile analysis A technique to empirically analyze
cess in its transformation from input to output. worker productivity (e.g., in the form of their processing
times) by comparing the performance of the top quartile
process utilization To what extent an entire process uses with the performance of the bottom quartile.
its capacity when supporting a given flow rate. Process
utilization ϭ Flow rate/Process capacity. queue The accumulation of flow units waiting to be
processed or served.
processing time The duration that a flow unit has to spend
at a resource, not including any waiting time; also referred queuing system A sequence of individual queues in
to as activity time or service time. which the outflow of one buffer/server is the inflow to the
next buffer/server.
product pooling The practice of using a single product to
serve two demand segments that were previously served by Quick Response A series of practices in the apparel indus-
their own product version. try used to improve the efficiency of the apparel supply chain.

production batch The collection of flow units that are R
produced within a production cycle.
random variable A variable that represents a random event.
production cycle The processing and setups of all flow For example, the random variable X could represent the number
units before the resource starts to repeat itself. of times the value 7 is thrown on two dice over 100 tosses.

production smoothing The practice of smoothing pro- range of a sample The difference between the highest and
duction relative to demand. Used to manage seasonality. In the lowest value in the sample.
anticipation of a peak demand period production is main-
tained above the current flow rate, building up inventory. R-bar charts Track the variation in the outcome of a pro-
During the peak demand, production is not increased sub- cess. R-bar charts require that the outcome of a process be
stantially, but rather, the firm satisfies the gap by drawing evaluated based on a single variable.
down previously accumulated inventory.
reactive capacity Capacity that can be used after useful
productivity ratio The ratio between revenue as a mea- information regarding demand is learned; that is, the capac-
sure of output and some cost (for example labor cost) as a ity can be used to react to the learned demand information.
measure of input.
resource The entity of a process that the flow unit has to
project A temporary (and thus nonrepetitive) operation visit as part of its transformation from input to output.
consisting of a set of activities; different from a process,
which is a repetitive operation. returns policy See buy-back contract.

Glossary 489

revenue management Also known as yield management. service time The duration that a flow unit has to spend at
The set of tools used to maximize revenue given a fixed a resource, not including any waiting time; also referred to
supply. as activity time or processing time.

revenue-sharing contracts With this contract, a retailer setup cost Costs that are incurred in production whenever
pays a supplier a wholesale price per unit purchased plus a a resource conducts a setup and in transportation whenever a
fraction of the revenue the retailer realizes from the unit. shipment is done. Setup costs drive batching. It is important
to include only out-of-pocket costs in the setup costs, not
rework An approach of handling defective flow units that opportunity costs.
attempts to invest further resource time into the flow unit in
the attempt to transform it into a conforming (nondefective) setup time The duration of time a resource cannot
flow unit. produce as it is either switched from one setting to the
other (e.g., from producing part A to producing part B, in
rework loops An iteration/repetition of project or process which case we speak of a changeover time) or not available
activities done typically because of quality problems. for production for other reasons (e.g., maintenance step).
Setup times reduce capacity and therefore create an incen-
ROIC Return on invested capital, defined as the ratio tive to produce in batches.
between financial returns (profits) and the invested capital.
setup time reduction See SMED.
round-up rule When looking for a value inside a table, it
often occurs that the desired value falls between two entries shortage gaming A cause of the bullwhip effect. In situ-
in the table. The round-up rule chooses the entry that leads ations with a capacity constraint, retailers may inflate their
to the larger quantity. orders in anticipation of receiving only a portion of their
order.
S
single segment control A revenue management system in
safety inventory The inventory that a firm holds to pro- the airline industry in which all passengers on the same seg-
tect itself from random fluctuations in demand. ment paying the same fare class are treated equally.

salvage value The value of leftover inventory at the end six sigma In its narrow sense, refers to a process capabil-
of the selling season in the newsvendor model. ity of two. This means that a process outcome can fall six
standard deviations above or below the mean and still be
scale economies Cost savings that can be achieved in within tolerance (i.e., still not be a defect). In its broader
large operations. Examples are pooling benefits in waiting meaning, refers to quality improvement projects that are
time problems and lower per-unit setup costs in batch flow using statistical process control.
operations.
slack time The difference between the earliest completion
scope 1 emissions All direct emissions of an organization, time and the latest completion time; measures by how much
such as fuel burned in its own trucks, or oil burned in its own an activity can be delayed without delaying the overall
machines. project.

scope 2 emissions All emissions of an organization associ- SMED (single minute exchange of dies) The philosophy
ated with purchased electricity, heat, steam, or other forms of of reducing setup times instead of just finding optimal batch
energy. sizes for given setup times.

scope 3 emissions All emissions of an organization, includ- span of control The scope of activities a worker or a
ing emissions created upstream in the value chain (suppliers) resource performs. If the resource is labor, having a high
as well as downstream in the value chain (customers using the span of control requires extensive training. Span of control
product or service). is largest in a work cell.

seasonal arrivals Systemic changes in the interarrival times specification levels The cut-off points above (in the case
(e.g., peak times during the day, the week, or the year). of upper specification level) and below (in the case of lower
specification level) which a process outcome is labeled a
seasonal inventory Arises if the flow rate exceeds the defect.
demand rate in anticipation of a time period when the
demand rate exceeds the flow rate. SPT (shortest processing time) rule A priority rule that
serves flow units with the shortest processing time first. The
second buy An opportunity to request a second replen- SPT rule is known to minimize the overall waiting time.
ishment, presumably after some demand information is
learned. standard deviation A measure of the absolute variability
around a mean. The square of the standard deviation equals
service level The probability with which a unit of incom- the variance.
ing demand will receive service as planned. In the context
of waiting time problems, this means having a waiting time standard normal A normal distribution with mean 0 and
less than a specified target wait time; in other contexts, this standard deviation 1.
also can refer to the availability of a product.

490 Glossary

starving The situation in which a resource has to be idle inventory reduction (just-in-time, kanban), mixed model pro-
as there is no flow unit completed in the step (inventory, duction (heijunka), and reduction of setup times (SMED).
resource) upstream from it.
trade promotion A temporary price discount off the whole-
stationary arrivals When the arrival process does not sale price that a supplier offers to its retailer customers.
vary systemically over time; opposite of seasonal arrivals.
transactional efficiency Measures how easy it is to do
statistical process control (SPC) A set of statistical tools business with a firm. Consists of the subdimension customer
that is used to measure the capability of a process and to effort and the elapsed time between need articulation and
help monitor the process, revealing potential assignable need fulfillment.
causes of variation.
transfer batch A collection of flow units that are trans-
stochastic An event that is random, that is, its outcome ferred as a group from one resource to the next.
cannot be predicted with certainty.
trunk inventory The inventory kept by sales representatives
stockout Occurs if a customer demands a unit but a unit of in the trunk of their vehicles.
inventory is not available. This is different from “being out
of stock,” which merely requires that there is no inventory tsukurikomi The Toyota idea of integrating quality
available. inspection throughout the process. This is therefore an
important enabler of the quality-at-the-source idea.
stockout probability The probability a stockout occurs
over a predefined interval of time. turn-and-earn An allocation scheme in which scarce
capacity is allocated to downstream customers proportional
supply chain The series of firms that deliver a good or to their past sales.
service from raw materials to customer fulfillment.
U
supply chain efficiency The ratio of the supply chain’s
actual profit to the supply chain’s optimal profit. underage cost In the newsvendor model, the profit loss
associated with ordering one unit too few. In other words,
supply-constrained A process for which the flow rate is it is the increase in profit if one additional unit had been
limited by either capacity or the availability of input. ordered and that unit is sold.

sustainable operations An operations paradigm built universal design/product A product that is designed to
around the idea of not depleting or destroying scarce serve multiple functions and/or multiple customer segments.
resources, including the atmosphere, water, materials, land,
and people. unknown unknowns (unk-unks) Project management
parlance to refer to uncertainties in a project that are not
T known at the outset of the project.

takotei-mochi A Toyota technique to reduce worker idle utilization The extent to which a resource uses its capac-
time. The basic idea is that a worker can load one machine ity when supporting a given flow rate. Utilization ϭ Flow
and while this machine operates, the worker—instead of rate/Capacity.
being idle—operates another machine along the process
flow. V

tandem queue A set of queues aligned in a series so that value chain See supply chain.
the output of one server flows to only one other server.
value curve A graphical depiction of the key attributes of
target wait time (TWT) The wait time that is used to a product or service. Can be used to compare a new business
define a service level concerning the responsiveness of a model with an incumbent solution. Common attributes are
process. the transactional efficiency, the preference fit, as well as
price and quality.
tasks The atomic pieces of work that together constitute
activities. Tasks can be moved from one activity/resource to value driver An operational variable that has a strong
another in the attempt to improve line balance. marginal effect on the ROIC.

throughput See flow rate. variance A measure of the absolute variability around a
mean. The square root of the variance equals the standard
tolerance levels The range of acceptable outcomes of a deviation.
process. See also design specifications.
vendor-managed inventory The practice of switching
Toyota Production System A collection of practices control of inventory management from a retailer to a supplier.
related to production, product development, and supply chain
management as developed by the Toyota Motor Corporation. virtual nesting A revenue management system in the
Important elements discussed in this book are the idea of per- airline industry in which passengers on different itineraries and
manent improvement (kaizen), the reduction of waste (muda), paying different fare classes may nevertheless be included in
the same bucket for the purchase of capacity controls.

Glossary 491

virtual pooling The practice of holding inventory in worker-paced line A process layout in which a worker
multiple physical locations that share inventory informa- moves the flow unit to the next resource or buffer when he or
tion data so that inventory can be moved from one location she has completed processing it; in contrast to a machine-paced
to another when needed. line, where the flow unit moves based on a conveyor belt.

W workload The request for capacity created by demand.
Workload drives the implied utilization.
waiting time The part of flow time in which the flow unit
is not processed by a resource. X

waiting time formula The average wait time, Tq, that a X-bar charts Track the mean of an outcome of a process.
flow unit spends in a queue before receiving service. X-bar charts require that the outcome of a process be evalu-
ated based on a single variable.
waste An abstract word that refers to any inefficiencies
that exist in the process; for example, line imbalances, Y
inadequate batch sizes, variability in service times, and so
forth. Waste can be seen as the distance between the current yield management Also known as revenue management.
performance of a process and the efficient frontier. Waste is The set of tools used to maximize revenue given a fixed
called “muda” in the Toyota Production System. supply.

win–win A situation in which both parties in a negotiation yield of a resource The percentage of flow units pro-
are better off. cessed correctly at the resource. More generally, we also can
speak of the yield of an entire process.
work cell A resource where several activities that
were previously done by separate resources (workers, Z
machines) are combined into a single resource (team of
workers). Work cells have several quality advantages, as zero-sum game A game in which the total payoff to all
they have a short ITAT; they are also—by definition— players equals a constant no matter what outcome occurs.
more balanced.
z-statistic Given quantity and any normal distribution, that
work in process (WIP) The inventory that is currently in quantity has a unique z-statistic such that the probability the
the process (as opposed to inventory that is finished goods outcome of the normal distribution is less than or equal to
or raw material). the quantity equals the probability the outcome of a standard
normal distribution equals the z-statistic.

References

Abernathy, F. H., J.T. Dunlop, J. Hammond, and D.Weil. Chase, R. B., and N. J. Aquilano. Production and Opera-
A Stitch in Time: Lean Retailing and the Transformation of tions Management: Manufacturing and Services. 7th ed.
Manufacturing—Lessons from the Apparel and Textile Indus- New York: Irwin, 1995.
tries. New York: Oxford University Press, 1999.
Chopra, S., and P. Meindl. Supply Chain Management:
Antonio Moreno, Christian Terwiesch, “Pricing and Strategy, Planning and Operation. 2nd ed. Upper Saddle
Production Flexibility: An Empirical Analysis of the U.S. River, NJ: Pearson Prentice Hall, 2004.
Automotive Industry,” Working Paper.
Cross, R. “An Introduction to Revenue Management.” In
Anupindi, R., S. Chopra, S. D. Deshmukh, J. A. Van Handbook of Airline Economics, ed. D. Jenkins, pp. 453–58.
Mieghem, and E. Zemel. Managing Business Process New York: McGraw-Hill, 1995.
Flows. Upper Saddle River, NJ: Prentice Hall, 1999.
Cross, R. Revenue Management: Hard-Core Tactics for
Bartholdi, J. J., and D. D. Eisenstein. “A Production Market Domination. New York: Broadway Books, 1997.
Line That Balances Itself.” Operations Research 44,
no. 1 (1996), pp. 21–34. De Groote, X. Inventory Theory: A Road Map. Unpublished
teaching note. INSEAD. March 1994.
Beatty, S. “Advertising: Infinity and Beyond? No Supply
of Toys at Some Burger Kings.” The Wall Street Journal, Diwas Singh KC and Christian Terwiesch, An Econometric
November 25, 1996, p. B-10. Analysis of Patient Flows in the Cardiac Intensive Care
Unit, Manufacturing and Service Operations Management,
Belobaba, P. “Application of a Probabilistic Decision Model msom.1110.0341; published online before print September 2,
to Airline Seat Inventory Control.” Operations Research 37, 2011.
no. 2 (1989), pp. 183–97.
Drew, J., B. McCallum, and S. Roggenhofer. Journey to
Bohn, R. E., and R. Jaikumar. “A Dynamic Approach to Lean: Making Operational Change Stick. New York: Pal-
Operations Management: An Alternative to Static Optimiza- grave Macmillan, 2004.
tion.” International Journal of Production Economics 27,
no. 3 (1992), pp. 265–82. Feitzinger, E., and H. Lee. “Mass Customization at Hewlett-
Packard: The Power of Postponement.” Harvard Business
Bohn, R. E., and C.Terwiesch. “The Economics of Yield- Review 75 (January–February 1997), pp. 116–21.
Driven Processes.” Journal of Operations Management
18 (December 1999), pp. 41–59. Fisher, M. “What Is the Right Supply Chain for Your Prod-
uct.” Harvard Business Review 75 (March–April) 1997,
Breyfogle, F. W. Implementing Six Sigma. New York: John pp. 105–16.
Wiley & Sons, 1999.
Fisher, M., K. Rajaram, and A. Raman. 2001. “Optimizing
Brown, A., H. Lee, and R. Petrakian. “Xilinx Improves Its Inventory Replenishment of Retail Fashion Products.” Manu-
Semiconductor Supply Chain Using Product and Process facturing and Service Operations Management 3, no. 3
Postponement.” Interfaces 30, no. 4 (2000), p. 65. (2001), pp. 230–41.

Brynjolfsson, E., Y. Hu, and M. D. Smith. “Consumer Fisher, M., and A. Raman. “Reducing the Cost of Demand
Surplus in the Digital Economy: Estimating the Value of Uncertainty through Accurate Response to Early Sales.”
Increased Product Variety.” Management Science 49, no. 11 Operations Research 44 (1996), pp. 87–99.
(2003), pp. 1580–96.
Fujimoto, T. The Evolution of a Manufacturing System at
Buzzell, R., J. Quelch, and W. Salmon. “The Costly Toyota. New York: Oxford University Press, 1999.
Bargain of Trade Promotion.” Harvard Business Review 68,
no. 2 (1990), pp. 141–49. Gans, N., G. Koole, and A. Mandelbaum. “Telephone Call
Centers:Tutorial, Review, and Research Prospects.” Manu-
Cachon, G. “Supply Chain Coordination with Contracts.” facturing & Service Operations Management 5 (2003),
In Handbooks in Operations Research and Management pp. 79–141.
Science: Vol. 11. Supply Chain Management, I: Design,
Coordination, and Operation, ed. T. Kok and S. Graves. Gaur, V., M. Fisher, A. Raman. “An Econometric Analysis
Amsterdam: North-Holland, 2004. of Inventory Turnover Performance in Retail Services.”
Management Science 51. (2005). pp. 181–194.
Cannon, J., T. Randall, and C. Terwiesch. “Improving Earn-
ings Prediction Based on Operational Variables: A Study Geraghty, M., and E. Johnson. “Revenue Management
of the U.S. Airline Industry.” Working paper, The Wharton Saves National Rental Car.” Interfaces 27, no. 1 (1997),
School and The Eccles School of Business, 2007. pp. 107–27.

Chan Kim, W., R. Mauborgne. Blue Ocean Strategy. Hall, R. W. Queuing Methods for Services and Manufactur-
Harvard Business School Press. 2005. ing. Upper Saddle River, NJ: Prentice Hall, 1997.

492

References 493

Hansell, S. “Is This the Factory of the Future.” New York Loch C. H., A. DeMeyer, and M. T. Pich, Managing the
Times, July 26, 1998. Unknown: A New Approach to Managing High Uncertainty
and Risk in Projects, John Wiley & Sons, 2006.
Harrison, M. J., and C. H. Loch. “Operations Management
and Reengineering.” Working paper, Stanford University, Magretta, J. 1998. “The Power of Virtual Integration: An
1995. Interview with Dell Computer’s Michael Dell.” Harvard
Business Review 76 (March–April 1998), pp. 72–84.
Hayes, R. H., and S. C. Wheelwright. “Link Manufacturing
Process and Product Life Cycles.” Harvard Business Review, McGill, J., and G. van Ryzin. “Revenue Management:
January–February 1979, pp. 133–40. Research Overview and Prospects.” Transportation Sci-
ence 33, no. 2 (1999), pp. 233–56.
Hayes, R. H., S. C. Wheelwright, and K. B. Clark. Dynamic
Manufacturing: Creating the Learning Organization. New McWilliams, G., and J. White. “Others Want to Figure
York: Free Press, 1988. out How to Adopt Dell Model.” The Wall Street Journal,
December 1, 1999.
Hillier, F. S., and G. J. Lieberman. Introduction to Opera-
tions Research. 7th ed. New York: McGraw-Hill, 2002. Moreno, Antonio, Christian Terwiesch, Pricing and Produc-
tion Flexibility: An Empirical Analysis of the US Auto-
Holweg, M., and F. K. Pil. The Second Century: Reconnecting motive Industry, Working paper at the Kellogg School of
Customer and Value Chain through Build-to-Order, Moving Management and at the Wharton School Cannon et al is still
beyond Mass and Lean Production in the Auto Industry. New a working paper.
ed. Cambridge, MA: MIT Press, 2005.
Motorola. “What Is Six Sigma?” Summary of Bill Weisz’s
Hopp, W. J., and M. L. Spearman. Factory Physics I: Foun- videotape message, 1987.
dations of Manufacturing Management. New York: Irwin/
McGraw-Hill, 1996. Nahmias, S. Production and Operations Analysis. 5th ed.
New York: McGraw-Hill, 2005.
Jordon, W., and S. Graves. “Principles on the Benefits of
Manufacturing Process Flexibility.” Management Science 41 Ohno, T. Toyota Production System: Beyond Large-
(1995), pp. 577–94. Scale Production. Productivity Press, March 1, 1998.

Juran, J. The Quality Control Handbook. 4th ed. New York: Olivares, Marcelo, Christian Terwiesch, and Lydia Cassorla,
McGraw-Hill, 1951. “Structural Estimation of the Newsvendor Model: An Appli-
cation to Reserving Operating Room Time,” Management
Juran, J. Juran on Planning for Quality. New York: Free Science, Vol. 54, No. 1, 2008 (pp. 45–55).
Press, 1989.
Padmanabhan,V., and I. P. L. Png. “Returns Policies: Make
Karmarkar, U. “Getting Control of Just-in-Time.” Harvard Money by Making Good.” Sloan Management Review, Fall
Business Review 67 (September–October 1989), pp. 122–31. 1995, pp. 65–72.

Kaufman, L. “Restoration Hardware in Search of a Papadakis, Y. “Operations Risk and Supply Chain Design.”
Revival.” New York Times, March 21, 2000. Working paper. The Wharton Risk Center, 2002.

Kavadias S., C. H. Loch, and A. DeMeyer, “DragonFly: Pasternack, B. “Optimal Pricing and Returns Policies for
Developing a Proposal for an Uninhabited Aerial Vehicle Perishable Commodities.” Marketing Science 4, no. 2 (1985),
(UAV),” Insead case 600-003-1. pp. 166–76.

Kimes, S. “Revenue Management on the Links I: Applying Petruzzi, N., and M. Dada. “Pricing and the Newsvendor
Yield Management to the Golf-Course Industry.” Cornell Problem: A Review with Extensions.” Operations Research 47
Hotel and Restaurant Administration Quarterly 41, no. 1 (1999), pp. 183–94.
(February 2000), pp. 120–27.
Porter, M., M. Kramer. Creating Shared Value: How to
Kimes, S., R. Chase, S. Choi, P. Lee, and E. Ngonzi. Reinvent Capitalism and Unleash a Wave of Innovation and
“Restaurant Revenue Management: Applying Yield Man- Growth. Harvard Business Review. Jan-Feb, 2011.
agement to the Restaurant Industry.” Cornell Hotel and
Restaurant Administration Quarterly 39, no. 3 (1998), Porteus, E. Stochastic Inventory Theory. Palo Alto, CA:
pp. 32–39. Stanford University Press, 2002.

Koller, T., M. Goedhart, and D. Wessels. Valuation. 4th ed. Ramstad, E. “Koss CEO Gambles on Inventory Buildup:
New York: John Wiley & Sons, 2005. Just-in-Time Production Doesn’t Always Work.” The Wall
Street Journal, March 15, 1999.
Lee, H. “Effective Inventory and Service Management
through Product and Process Redesign.” Operations Sakasegawa, H. “An Approximation Formula
Research 44, no. 1 (1996), pp. 151–59. Lq ϭ ␣␤␳ (1 Ϫ ␳).” Annals of the Institute of Statistical
Mathematics 29, no. 1 (1977), pp. 67–75.
Lee, H., V. Padmanabhan, and S. Whang. “The Bullwhip
Effect in Supply Chains.” MIT Sloan Management Review 38, Sechler, B. “Special Report: E-commerce, behind the
no. 3 (1997), pp. 93–102. Curtain.” The Wall Street Journal, July 15, 2002.

494 References

Silver, E., D. Pyke, and R. Peterson. Inventory Ulrich, K. T., and S. Eppinger, Product Design and Devel-
Management and Production Planning and Scheduling. opment, 5th ed., McGraw Hill Irwin, 2011.
New York: John Wiley & Sons, 1998.
Simchi-Levi, D., P. Kaminsky, and E. Simchi-Levi. Design- Upton, D. “The Management of Manufacturing Flexibility.”
ing and Managing the Supply Chain: Concepts, Strategies, California Management Review 36 (Winter 1994), pp. 72–89.
and Case Studies. 2nd ed. New York: McGraw-Hill, 2003.
Upton, D. “What Really Makes Factories Flexible.” Har-
Simison, R. “Toyota Unveils System to Custom-Build Cars in vard Business Review 73 (July–August 1995), pp. 74–84.
Five Days.” The Wall Street Journal, August 6, 1999.
Vitzthum, C. “Spain’s Zara Cuts a Dash with ‘Fashion on
Smith, B., J. Leimkuhler, and R. Darrow. “Yield Manage- Demand.’ ” The Wall Street Journal, May 29, 1998.
ment at American Airlines.” Interfaces 22, no. 1 (1992),
pp. 8–31. Wadsworth, H. M., K. S. Stephens, and A. B. Godfrey.
Modern Methods for Quality Control and Improvement.
Stevenson, W. Operations Management. 8th ed. New York: John Wiley & Sons, 1986.
McGraw-Hill/Irwin, 2006.
Weatherford, L. R., and S. E. Bodily. “A Taxonomy and
Stringer, K. “As Planes Become More Crowded, Travelers Research Overview of Perishable-Asset Revenue Man-
Perfect Getting ‘Bumped.’ ” The Wall Street Journal, March agement: Yield Management, Overbooking and Pricing.”
21, 2002. Operations Research 40, no. 5 (1992), pp. 831–43.

Talluri, K., and G. van Ryzin. The Theory and Practice Whitney, D. Mechanical Assemblies: Their Design, Manufac-
of Revenue Management. Boston: Kluwer Academic ture, and Role in Product Development. New York: Oxford
Publishers, 2004. University Press, 2004.

Terwiesch, Christian, and Karl T. Ulrich, Innovation Tour- Whitt, W. “The Queuing Network Analyzer.” Bell System
naments: Creating and Selecting Exceptional Technology Journal 62, no. 9 (1983).
Opportunities, Harvard Business School Press, 2009.
Womack, J. P., D. T. Jones, and D. Roos. The Machine That
Terwiesch, C. “Paul Downs Cabinet Maker.” Teaching case Changed the World: The Story of Lean Production. Reprint
at The Wharton School, 2004. edition. New York: Harper Perennial, 1991.

Terwiesch, C., and C. H. Loch. “Pumping Iron at Cliffs and Zipkin, P. Foundations of Inventory Management.
Associates I: The Cicored Iron Ore Reduction Plant in Trini- New York: McGraw-Hill, 2000.
dad.” Wharton-INSEAD Alliance case, 2002.
Zipkin, P. “The Limits of Mass Customization.” Sloan Man-
Tucker, A. L. “The Impact of Operational Failures on Hos- agement Review, Spring 2001, pp. 81–87.
pital Nurses and Their Patients.” Journal of Operations
Management 22, no. 2 (April 2004), pp. 151–69.

Index of Key
“How to” Exhibits

Exhibit Title Page

2.1 Calculating inventory turns and per-unit inventory costs 22
3.1 Steps for basic process analysis with one type of flow unit 49
3.2 Steps for basic process analysis with multiple types of flow units 50
4.1 Time to process a quantity X starting with an empty process 60
4.2 Summary of labor cost calculations 63
5.1 Summary of calculations for a critical pat analysis 88
5.2 Summary of different uncertainty levels in a project 93
6.1 How to create an ROIC tree 106
7.1 Finding a good batch size in the presence of setup times 123
7.2 Finding the economic order quantity 131
8.1 Summary of waiting time calculations 163
9.1 Using the Erlang loss formula 189

Newsvendor Model A process for using historical A/F ratios to choose a mean and standard deviation 249
12.1 for a normal distribution forecast
A process for evaluating the probability demand is either less than or equal to Q 250
12.2 (which is F(Q)) or more than Q (which is 1 Ϫ F(Q)) 254
A procedure to find the order quantity that maximizes expected profit in the newsvendor model 257
12.3 Expected lost sales evaluation procedure 258
12.4 Expected sales, expected leftover inventory, and expected profit evaluation procedures 259
12.5 In-stock probability and stockout probability evaluation 260
12.6 A procedure to determine an order quantity that satisfies a target in-stock probability
12.7

Order-up-to Model

14.1 How to convert a demand distribution from one period length to another 297
14.2 In-stock probability and stockout probability evaluation in the order-up-to model 301
14.3 Expected back order evaluation for the order-up-to model 302
14.4 Evaluation of expected on-hand inventory, and pipeline/expected
on-order inventory in the order-up-to model 303
14.5 How to choose an order-up-to level S to achieve an in-stock probability target
in the order-up-to model 305

Revenue Management

16.1 Evaluating the optimal protection level for the high fare or the optimal booking limit 359
16.2 for the low fare when there are two fares and revenue maximization is the objective 363
The process to evaluate the optimal quantity to overbook

495

Summary of Key Notation
and Equations

Chapter 2: Process Flow Average flow rate Average time

Little,s Law: Average inventory

Chapter 3: Capacity/Bottleneck Analysis

Implied utilization ϭ Capacity requested by demand
Available capacity

Chapter 4: Labor Content

Flow rate ϭ Min{Available input, Demand, Process capacity}

Cycle time ϭ 1
Flow rate

Cost of direct labor ϭ Total wages
Flow rate

Idle time across all workers at resource i Cycle time (Number of workers
at resource i) Processing time at resource i

Average labor utilization Labor content

Labor content Total idle time

Chapter 7: Batching

Capacity given batch size Batch size

Setup time Batch size Time per unit

Recommended batch size Flow rate Setup time

1 Flow rate Time per unit

Economic order quantity 2 Setup cost Flow rate
Holding cost

Chapter 8: Waiting Time Systems

m ϭ number of servers
p ϭ processing time
a ϭ interarrival time

496

Summary of Key Notation and Equations 497

CVa ϭ coefficient of variation for interarrivals
CVp ϭ coefficient of variation of processing time

Utilization u p

am

Processing time Utilization 2(m 1) 1 Q CVa2 2 CVp2 R
m 1 Utilization
Tq Q R Q R

Flow time T Tq p
Inventory in service I p m u
Inventory in the queue Iq Tq /a
Inventory in the system I I p Iq

Chapter 10: Quality

Yield of resource ϭ Flow rate of units processed correctly at the resource
Flow rate

Chapter 12: Newsvendor

Q ϭ order quantity

Cu ϭ Underage cost
Co ϭ Overage cost
␮ ϭ Expected demand

␴ ϭ Standard deviation of demand

F(Q) ϭ Distribution function

⌽(Q) ϭ Distribution function of the standard normal

L(Q) ϭ Loss function

L(z) ϭ Loss function of the standard normal distribution

Critical ratio Cu
Co Cu

A F ratio ϭ Actual demand
Forecast

Expected profit-maximizing order quantity: F(Q) Cu
Co Cu

z-statistic or normalized order quantity: z Q
Qϭ ϩzϫ

498 Summary of Key Notation and Equations

Expected lost sales with a standard normal distribution ϭ L(z)
Expected lost sales with a normal distribution ϭ ϫ L(z)

In Excel: L(z) ϭ *Normdist(z, 0,1, 0) Ϫ z*(1 Ϫ Normdist(z))
Expected lost sales for nonnormal distributions ϭ L(Q) (from loss function table)

Expected sales ϭ Ϫ Expected lost sales
Expected leftover inventory Q Expected sales
Expected profit [ (Price Cost) Expected sales]

[ (Cost Salvage value) Expected leftover inventory]
In-stock probability ϭ F(Q)

Stockout probability 1 In-stock probability
In Excel: In-stock probability ϭ Normdist(z)

In Excel: z ϭ Normsinv(Target in-stock probability)

Chapter 13: Reactive Capacity

Mismatch cost (Co Expected leftover inventory) (Cu Expected lost sales)
Maximum profit Expected profit
Maximum profit ϭ (Price Ϫ Cost) ϫ

Coefficient of variation ϭ Standard deviation/Expected demand

Chapter 14: Order-up-to Model

l ϭ Lead time

S ϭ Order-up-to level

Inventory level On-hand inventory Back order
Inventory position On-order inventory Inventory level
In-stock probability 1 Stockout probability
Prob{Demand over (l 1) periods S}

z-statistic for normalized order quantity: z ϭ S Ϫ

Expected back order with a normal distribution ϭ ϫ L(z)

In Excel: Expected back order ϭ *(Normdist(z, 0,1, 0) Ϫ z*(1 Ϫ Normdist (z)))

Expected back order for nonnormal distributions ϭ L(S ) (from loss function table)

Summary of Key Notation and Equations 499

Expected inventory S Expected demand over l 1 periods
Expected back order

Expected on-order inventory = Expected demand in one period × Lead time

Chapter 15: Pooling

Expected pooled demand ϭ 2 ϫ

Standard deviation of pooled demand ϭ 2 ϫ (1 ϩ Correlation) ϫ

Coefficient of variation of pooled demand ϭ 1 (1 ϩ Correlation) ϫ Q R
2

Chapter 16: Revenue Management

Protection level: Critical ratio Cu rh rl
Co Cu rh

Low-fare booking limit Capacity Q

Overbooking: Critical ratio Cu rl
Cost per bumped customer
Co Cu rl

Index

General index for Cachon, Matching Supply Assembly operations; see also Bullwhip effect, 8, 373–384
with Demand Batching causes and consequences of,
373–376
A analysis of, 56–58 forward buying, 378–382
line balancing; see Line balancing order batching, 377–378
A/F ratio, 246, 248 Asset, 16 order synchronization, 376–377
Activities (steps), 10–11 Assignable causes of variation, 201 reactive and overactive
Activity-on-node (AON) Attribute control charts, 210–211 ordering, 382–383
Authorization level, 358 shortage gaming, 383–384
representation, 82–84 Automobile industry; see Toyota trade promotions, 378–382
Activity time, 11, 57 defined, 374
Agriculture, fishing, and forestry, Production System (TPS) mitigating strategies, 384–389
Average labor utilization, 62–63, eliminating pathological incen-
sustainability in, 404–405 tives, 385–386
Airline industry, 2 66, 69 sharing information, 384–385
smoothing product flow, 385
in 2010, 111 B vendor-managed inventory,
available seat miles (ASMs), 107 386–388
customer segmentation, 360, 364 Back-order, 292 production smoothing, 388–389
innovation and value creation, expected, 301–302
Business model innovation, 410, 414
412–413 Back-order penalty cost, 305 customer value curve, 414–417
load factor, 107 Barilla, 373 demand side of, 414–417
multiple fair classes, 364–365 Base stock level, 293 process location, 419–421
origin-destination (O-D) control, Base stock model, 293; see also process standardization, 421–422
process timing, 418–419
365–366 Order-up-to inventory model supply side of, 417–422
overbooking, 353, 361–363 Batch, 115 unsuccessful model, 422
productivity ratios, 108 Batch-flow processes, 28–29 value creation and, 412–414
revenue management; see Revenue Batching, 114; see also Setup time Zipcar and Netflix, 410–412

management batch size and setup times, Buy-back contracts, 392–395
revenue passenger miles 121–123 Buy-back price, 394

(RPMs), 106 buffer or suffer, 135, 194, 233 C
ROIC tree, 107 choosing batch size, 123, 137
supply and demand match, 2 inventory and, 118–120 Call centers
US Airways vs. Southwest, summary of, 136 analysis of, 4, 147
Beblobaba, P., 357 eliminating inefficiencies, 5
109–111 Best Buy, 22 labor productivity vs.
variation in capacity purchase, 365 Bid price, 367 responsiveness, 5
yields/efficiency/cost, 108 Bid-price control, 366 operations tools and, 4–5
Andon cord, 231–232 Blocking, 192–193 processing times in, 156
Applied Materials, 29 Booking limits, 355–361 redesign and improved frontier, 6
Appointment systems, 174 authorization level, 358 staffing plans, 165–169
Arrival process; see also Waiting time nesting booking limits, 356 waiting time example, 145–147
analyzing of, 149–155 optimal protection level, 359
demand/arrival process, 155 Bottlenecks, 32, 38, 44, 56, 63 Campbell’s Soup, 24, 26, 39, 287,
exponential interarrival times, in a multiproduct case, 45 381, 387
nested booking limits, 356
153–155 Brands, sustainability and, 405–406 Capacity, 15, 32, 135
nonexponential interarrival Buckets, 366 booking limits, 355–361
Buffer or suffer principle, 135, 194, 233 bottleneck/nonbottleneck step, 121
times, 155 Buffers, 35, 190 calculation of, 40
reducing variability, 174–175 resource blocked/starved, 192
stationary arrivals, 151–153 role of, 192–194
summary of, 155 size of, 189
Arrival time, 149
Assemble-to-order, 270
Assembly line, 27, 222

500

Index 501

defined, 58 Continuous process flows, 134 Decision tree, 91–92
impact of setups on, 115–118 Continuous product replenishment, 387 Decoupling inventory/buffers, 23, 26, 193
increasing Continuous replenishment, 387 Defects, 215
Contracts
by adding workers, 67–69 variability and, 218
by line balancing, 53–66 buy-back, 392–395 Delayed differentiation, 338–340
line replication, 67 options, 395–396 Dell Computer, 1, 270, 277, 410,
scaling up, 66–67 price protection, 397
task specialization, 69–71 quantity discount, 395 418–419
as perishable, 360 quantity flexibility (QF), 396–397 Demand
pooling with flexible manufacturing, revenue sharing, 396
Control charts, 237 arrival process, 155
341–347 attribute, 210–211 expected, 248
process, 38, 60 construction of, 202–205 forecasting of, 250, 363–364
reactive, 8, 270 generic example, 202 matching supply with, 1–2, 228–231
requested capacity, 68 service setting example, 205–208 seasonal, 23
as restrictive, 360 Cost of direct labor, 61, 63–64, stochastic, 23, 26
utilization of, 41–43 supply-demand mismatches, 2–3,
variation in, 364 166–167
Capacity constrained, 16 Cost of goods sold (COGS), 20, 22 10, 270–73
Capacity controls, revenue Cost per unit, 131 Demand aggregation, 419
Costs Demand-constrained processes, 39
management, 353 Demand distributions, inventory
Capacity utilization, 41–43 back-order penalty, 305
Capital investment, labor productivity fixed, 99, 101 management system, 295–298
fixed vs. variable, 105 Demand-pull strategy, 375, 379–380
vs., 73 inventory, 21 Deming, W. E., 200, 202
Capital turns, 98 labor, 63 Dependency matrix, 81–82, 91
Carbon capture and sequestration mismatch cost, 271, 273–275, 282 Design specifications
ordering, 308–311
(CCS), 406 overage, 251 process capability and, 208–210
Carbon dioxide equivalent, 402 setup, 126–130 target value, 208
Carbon footprint, 404 variable, 99 tolerance level, 208
Cause-effect diagrams, 237 CPFR (collaborate planning, forecasting, Direct labor, cost of, 61, 63–64, 166–167
Certainty, 93 Discounts
Chaining, 235, 344 and replenishment, 385 quantity, 395
Changeover time; see Setup time Crash activities, 93 trade promotions, 378–382, 386
Channel stuffing, 378 Critical path, 11, 23, 82–84 Discovery-driven planning, 92
Circored plant example, 32–38, 44 Discrete distribution functions, 244
finding of, 84–85 Distribution, 244
completed process flow diagram, 38 overlap of activities, 93–94 consolidated, 333–338
Cisco, 240 summary of calculations for, 88 continuous, 244
Cleveland Cliffs, 32 Critical ratio, 252, 274–275, 359 demand, 295–298
Climate change, 401 Customer impatience, throughput loss, discrete, 244
Coefficient of variation, 149, 160, exponential, 153
189–191 standard normal, 248–249, 298
274, 282 Customer segments, 360, 364 Distribution centers, Medtronic’s
interarrival time, 160 Customer value curve, 414–417
as a measure of variability, 155, example, 324–325
preference fit, 415 Diversion, 382
157 price, 415 Diverters, 382
pooled demand, 329 quality, 415 Doig, S., 96
processing time, 159 transactional efficiency, 415 Double marginalization, 391
product pooling and, 322 Cycle inventory, 23, 25–26, 309 Downs, P., 96
of a random variable, 149 Cycle time, 59, 62–63 DuPont model, 98–99
Collaborative planning, 385
Common causes of variation, 200 D E
Computer numerically controlled
Daimler, G., 28, 410 Earliest completion time (ECT), 84,
(CNC), 114 Days of supply, 20 86, 88
Consignment inventory, 288 De Groote, X., 23
Consolidated distribution, 333–338 De Meyer, A., 81 Earliest start time (EST), 84, 86, 99
Consumer electronics, 22
Continuous distribution functions, 244

502 Index

Economic order quantity model Financial data Hillier, F. S., 189
(EOQ), 114, 310–311 analyzing operations based on, Hockey stick phenomenon, 377
106–111 Hopp, W. J., 162
finding the, 131 DuPont model, 98–99 Horizontal distance, 17
formula for, 130 Human resource practices, 236–237
observations related to, 130–134 Finished goods inventory (FIG), 17, 58
scale economies in, 132 First-come, first-service (FCFS) rule, 173 I
setup and inventory costs, 126–130 First-in, first-out (FIFO) basis, 231, 290
Economic value created, 96 Fishbone (Ishikawa) diagrams, IBM, 384
Economies of scale, 23, 127, 132 Idle time, 60–63
impact of pooling, 169–172 237–38
Efficiency frontier, 4–6 Fisher, M., 21–22 basic calculations, 62
Efficient consumer response, 271, 384 “Five Whys,” 237 IKEA, 419
Electronic commerce, 325–326 Fixed costs, 99, 101 Implied utilization, 43–44, 48, 186
Electronic data interchange (EDI), 385 Flexibility, 234–236 In-stock probability, 258, 299, 301
End-of-period inventory level, 294–295 Incentive conflicts, 373
Energy multi-task flexibility, 235
carbon footprint, 404 Flexible manufacturing, capacity sunglasses supply chain example,
emissions, 404 389–392
global warming/climate change, 401 pooling with, 341–347
greenhouse gas, 402 Flow interruptions, 114, 134–135 Individual inventories, 320
sustainability and, 401–404 Flow rate, 15, 39, 49, 60, 63, 99 Individual territories, 320
Enterprise resource planning (ERP), 229 Information sharing, 384–385
Erlang, A. K., 187 inventory levels, 234 Information turnaround time
Erlang loss formula, 187, 189 Flow rate (throughput), 38
Excess capacity, 56 Flow times, 15–16, 18–20, 162 (ITAT), 232
Exit option, 91–92 Flow units, 15 Inputs, 15
Expected back order, 301–302 Integrated supply chain, 390
Expected demand, 248 eliminating, 214, 216 Interarrival time, 149, 153–54
Expected demand-supply mismatch multiple types of, 44–48, 50 Internal setup, 126
Ford, H., 27, 125, 222–223, 232, International Motor Vehicle Program
cost, 271
Expected inventory in days-of- 340, 410 (IMVP), 225
Ford Motor Corporation, 222–223 Inventory, 15–16, 18, 20, 121
demand, 321 Forecasting, 385
Expected leftover inventory, 257–258 back-order, 292
Expected lost sales, 255–257 key managerial lessons of, 259–262 base stock level, 293
Expected on-hand inventory, 302–303 Forward buying, 378–382 batching and, 118–120
Expected on-order inventory, 303 Fujimoto, T., 230–231 buffers, 35
Expected profit, 257–258 consignment, 288
Expected profit-maximizing order G costs, 21
cycle, 23, 25, 309
quantity, 250–254 Gans, N., 190–191 days of supply, 20
Expected sales, 256, 258 Gantt, H., 11 decoupling/buffers, 23, 26, 193
Exponential distribution, 153 Gantt diagram, 11–12 end-of-period level, 294–295
Exponential interarrival times, 153–155 expected on-order, 302–303
External setup, 126 creation of, 84–85 finished goods inventory (FIG),
Gaur, V., 21–22
F General Electric, 202 17, 58
General Motors, 225–226, 341 holding costs, 21
Fences, 360 Gilbreth, F., 226 on-hand, 292
Fill rate, 258 Gilbreth, L., 226 on-order, 292
Fill-up, 230 Global warming, 401 order-up-to level, 293
Finance; see also Return on invested Goedhart, M., 96 pipeline, 23–24, 303
Graves, S., 341 reasons for holding, 23–27
capital (ROIC) Greenhouse gas (GHG), 402 reduction, 233–234
economic value, 96 Gross margins, 21–22 safety, 23, 26–27, 309
operations and, 96–97 seasonal, 23–25
return/profits, 99 inventory turns and, 23 spoilage of, 290
stockout probability, 258, 299, 301
H trunk, 288

Hayes, R. H., 27–28
Heijunka, 119

Index 503

turns, 20 Labor productivity, 108 Milestones, 91–92
vendor-managed inventory (VMI), capital investment vs., 73 Mismatch cost, 271, 273–275, 282

386–387 Latest completion time (LCT), 86, 88 reducing with make-to-order,
waste and, 226 Latest start time (LST), 86, 88 276–277
work-in-process (WIP), 15, 17, Lead time, 292
Lead time pooling, 336 Mixed-model production, 119
58, 101 Lean operations, 222; see also Toyota Monitor Sugar, 25
Inventory costs, 19–23 Monte Carlo simulation, 90
Inventory turns, 19–23, 98 Production System (TPS) Motion, 226
Lean transformation, 237–238 Motorola, 202
calculation of, 22 Levi Strauss, 408 MRP jitters, 377
gross margin and, 23 Liability, 16
retail segments, 21 Liebermann, G. J., 189 N
Iron ore plant, 2 Line balancing, 63, 172
Ishikawa diagrams (fishbone Nesting booking limits, 356
graphical illustration of, 67 Net profit equation, 354
diagrams), 237–38 highly specialized line, 71 Net utility, 412, 414
increasing capacity by, 63–66 Netflix, 410–412, 416, 420
J scale up to higher volume, 66–71 Newsvendor model, 240–243, 390;
Little, J. D. C., 18
JetBlue, 413 Little’s Law, 7, 16–20, 118–119, see also Performance measures
Jidoka concept, 231–32 A/F ratio, 246
JIT (just-in-time) methods, 224, 146, 227 demand forecasting, 243–250
expected waiting time, 189 demand-supply mismatch cost,
226, 271 flow time, 19, 161, 163
implement pull systems, 229–231 formula for, 18 271–273
matching supply with demand, pipeline inventory, 24, 303 equations, 262
Location pooling, 319–326 expected profit-maximizing order
228–231 Loch, C. H., 32–35, 80–81
one-unit-at-a-time flow, 228–229 Loss function quantity, 250–254
produce at customer demand, 229 Erlang loss formula, 187, 189 introduction to, 243
Job shop process, 27–28 expected lost sales, 255–257 managerial lessons, 259–262
Jones, D. T., 225 Lower specification level (LSL), 208 mismatch costs, 271–273
Juran, J. M., 202, 211 Lurgi AG, 32 O’Neill Inc. example, 240–250
order quantity, 259–260
K M performance measures, 254–258
round-up rule, 253
Kaizen, 224, 237 Machine-paced process layout, 59–60 summary of, 262
Kanban-based pull, 230 McDonald’s, 421–422 Nike, 405
Kanban system, 224, 228, 230, McKinsey, 227, 237, 405 Nintendo, 1–2
Make-to-order, 270 Nonexponential interarrival times, 155
233, 294 Novacruz’s Xootr scooter, 56–57
Kavadias, S., 80–81 reducing mismatch costs, 276–277 current process layout, 58
Keeling, C., 401 Make-to-order production, 228, lifecycle demand trajectory, 57
Keeling curve, 401
Kohl’s Corporation, 19–22 230–231 O
Koller, T., 96 Make-to-stock, 270
Koole, G., 190–91 Mandelbaum, A., 190–191 Ohno, T., 226
Koss Corp., 277 Margin, 98–99 On-hand inventory, 292
KPI tree (key performance indicators), Margin arithmetic, 354 On-order inventory, 292
Marginal cost pricing, 391 One-for-one ordering policy, 294
97 Material, sustainability and, 404 One-unit-at-a-time flow, 228–229
Kulicke & Soffa, 29 Materials requirement planning Operational improvements, valuing of,

L (MRP) system, 229, 377 103–106
MRP jitters, 377 Operations
Labor content, 60–63 Matsushita, K., 237
defined, 60 Maximum profit, 272, 282 analyzing based on financial data,
Medtronic’s supply chain, 288–291 106–111
Labor cost calculations, summary of, 63 Memoryless property, 154
finance and, 96–97

504 Index

Operations management, sustainability Papadakis, Y., 236 Probability, Erlang loss formula, 187
and, 406–409 Parallel work cells, 71 Process analysis, 10
Pareto diagram, 211
Operations management tools, 3–4 Pathological incentives, 385–386 activities (steps), 10–11
eliminating inefficiencies, 5 Patient log, 13 activity times/processing times, 11
labor productivity vs. People, sustainability and, 405 demand-constrained processes, 39
responsiveness, 5 Per-unit inventory costs, 21–22 elements of, 35
Percentage change in profit, 354 Gantt chart, 11–12
Options contracts, 395–396 Performance measures, 254–258 high-volume production, 68
Order batching, 377–378 hospital example, 10–14
Order frequency, 308 expected back order, 301–302 multiple types of flow units, 50
Order inflation, 383 expected leftover inventory, 257 one type of flow unit, 49
Order quantity expected lost sales, 255–257 process resources, 13
expected on-hand inventory, summary of, 49
choosing, 259–261 supply-constrained processes, 39
service objective and, 259 302–303 waiting time, 13
Order synchronization, 376–377 expected on-order inventory, 303 Process boundaries, 35
Order-up-to inventory model, 8, expected profit, 257–258 Process capability, design
expected sales, 256
287–288 fill rate, 258 specifications and, 208–210
choosing demand distribution, in-stock probability, 258–260, Process capability index, 209
Process capacity, 32, 38, 60; see also
295–298 299–301
choosing service level, 304–308 initial input parameters and, 255 Capacity
controlling ordering costs, 308–311 pipeline inventory, 303 drivers of, 100
defined, 287 stockout probability, 258–259, Process flow
design and implementation, impact of yield and defects on,
299–301
291–294 Period, 291 214–218
end-of-period level, 294–295 Phantom orders, 384 interruptions to, 134–135
expected back order, 301–302 Pipeline inventory, 23–24, 303 Process flow diagrams, 15, 32
expected on-hand inventory, Planning software, 229 first step in, 36
Plant, property, and equipment how to draw, 33–38
302–303 multiple product types, 45
fill rate, 258 (PP&E), 101 Process improvement, 56, 218–220
in-stock and stockout probability, Poisson arrival process, 153, 160 Process location, 419–421
Poisson distribution, 296, 298, Process management
299–301 inventory turns, 19–23
managerial insights, 311–313 320, 322 Little’s Law, 16–19
Medtronic example, 287–291 coefficient of variation of, 322 types of processes, 28
performance measures, 299–303 Poka-yoke, 231 Process performance
pipeline inventory/expected Pooled inventory, 320 three measures of, 15–16
Pooled territory, 320 variability and its impact, 144
on-order inventory, 303 Pooling; see also Risk pooling Process resources, 13
service target, 304 Process standardization, 421–422
stockout probability, 299, 301 strategies Process time, 57
summary of key notations/ benefits of, 171 Process timing, 418–419
concept of, 168 Process utilization, 41–43
equations, 314 economies of scale, 169–172 Process yield, 215
Order-up-to-level inventory, 293, 375 impact of, 169–172 Processing time, 11, 57
Ordering costs, 308–311 Medtronic’s field inventory, Processing time variability, 155–157
Out of stock, 299 reduction of, 175–176
Outputs, 15 320–324 Procter & Gamble, 374, 386
Overage cost, 251 product pooling, 326–332 Product assortment, 340
Overall Equipment Effectiveness virtual pooling, 323–324 Product flow smoothing, 385
Potential iteration, 91 Product life cycle, 307
(OEE) framework, 227 Preference fit, 415 Product line rationalization, 332
Overbooking, 353, 361–363 Presbyterian Hospital, Philadelphia, Product mix, 45
Product pooling, 326–332
optimal quantity, 363 10–14, 27 Product-process matrix, 27–29
Overprocessing, 226 Price adjustments, 2
Overproduction, 226 Price protection, 397
Overreactive ordering, 382–383 Principles of Scientific Management

P (Taylor), 237
Priority rules, 172–173
Pacemakers, 2
Panasonic, 237

Index 505

Product variety, setup times and, Return on invested capital (ROIC), Service levels
124–125 96–97 choosing appropriate, 304–308
in supply chains, 287
Production cycle, 115 for airline, 107 waiting time and, 164–165
Production smoothing, 388–389 building an ROIC tree, 98–102, 106
Production volume, 99 equation for, 99 Service objective
Production yield, 290 for fixed costs, 101 order quantity and, 259–261
Productivity ratios, 108 fixed vs. variable costs, 105 order-up-to level, 304–305
Profits, 412 improvement of, 103
Project, 80 invested capital, 102 Service time, 57
Project completion time, 83–84 Return (profits), 99 Service-time-dependent priority
Project management, 80 Returns policy, 392
Revenue, 99 rules, 172
accelerating projects, 92–94 Revenue management, 353 Service-time-independent priority
critical path, 11, 82–85 capacity controls and, 353
dealing with uncertainty, 88–92 demand forecasting, 363–364 rules, 173
Gantt charts, 11, 84–85 dynamic decisions, 364 Setup costs
motivating example, 80–82 effective segmenting of
project completion time, 83–84 EOQ model, 126–130
slack time, 85–88 customers, 364 inventory costs and, 126–130
Project scope, 93 implementation of, 363–367 Setup time, 114
Protection levels/booking limits, 353, margin arithmetic, 353–354 choosing batch size, 121–123
multiple fare classes, 364 impact on capacity, 115–118, 120
355–361 overbooking, 361–363 product variety, 124–125
Pull systems, 228–231, 294 protection levels and booking reduction of, 125–126
Push system, 294 Setup time reduction, 125–126
limits, 355–361 Shewhart, W. A., 200, 202
Q reservations coming in groups, 364 Shingo, S., 125
software implementation, 365 Shortage gaming, 383–384, 386
Qualitative strategy, 3–4 variability in available capacity, 364 Shortest processing time (SPT) rule,
Quality at the source, 217 variation in capacity purchase,
Quality management, 198, 231–233; 172–172
365–367 Simson, R., 277
see also Toyota Production Revenue sharing, 396 Single-leg segment, 365
System (TPS) Rework, 214–216, 226 Single minute exchange of die
Quantitative model, 3–4 Rework loops, 91
Quantity discounts, 395 Risk pooling strategies, 8, 319 (SMED), 125–126
Quantity flexibility (QF) contracts, Single-segment control, 365
396–397 capacity pooling, 341–347 Six-sigma capability, 198, 209–210
Quick response, 270–271 consolidated distribution, 333–338 Slack time, 85–88
reactive capacity, 277–281 delayed differentiation, 338–340
flexible manufacturing, 341–347 availability of resources, 87
R lead time pooling, 333–340 computation of, 85–88
location pooling, 319–326 potential start delay, 87
Radio Shack, 22 Medtronic’s field inventory, Southwest Airlines, 106–111, 410,
Raman, A., 21–22
Ramstad, E., 277 320–324 412–413
Randall, T., 96 product pooling, 326–332 Spearman, M. L., 162
Random activity times, 88–91 Roos, D., 225 Spoilage, 290
Rates, 16 Round-up rule, 253 Standard deviation
Raw materials, 17
Reactive capacity, 8, 270 S of a Poisson distribution, 297
variability measurement, 149
quick response with, 277–281 Safety inventory, 23, 26–27, 309 Standard normal distribution, 248–249
Reactive ordering, 382–383 Sakasegawa, H., 162 Standard Normal Distribution
Replenishment, 385 Salvage value, 243
Research and development (R&D), 92 Scope 1/2/3 emissions, 404 Function Table, 248–250
Resources (labor and capital), 15 Scrapped units, 214, 217 Standardization of work, 236
Seasonal demand, 23 Starving, 192–192
yield of, 215 Seasonal inventory, 23–25 Stationary arrivals, 151–153
Retailing, 2, 21 Seasonality, 26, 151–52
Serial queuing, 191 test for, 152
Statistical process control, 198, 200

objective of, 202
Stevenson, W., 233
Stochastic demand, 23, 26
Stockout, 299
Stockout probability, 258, 299, 301

506 Index

Supermarket pull, 230 Three-sigma capability, 209 US Airways, 109–111
Supply chain Throughput loss, 183; see also Utilization

controlling ordering costs, 308–311 Flow rate capacity, 41–43
managerial insights, 311–313 buffers, 192–194 defined, 41, 49
Medtronic’s example, 288–291 customer impatience, 189–191 implied, 43–44, 48, 186, 188
service levels and lead times, 287 Erlang loss formula, 189 labor, 62–63, 66
sources of cost in, 72 several resources, 191–194 process, 41–43
Supply chain management, 373 simple process, 185–188 of resources, 162
allocation of supply chain profit, 392 Throughput rate, 15 Utilization profile, 42
bullwhip effect; see Bullwhip effect Time to process a quantity, 60
buy-back contracts, 392–395 Tolerance level, 208 V
incentive conflicts, 373, 389–392 Tools of operations management,
options contracts, 395–396 Value creation, innovation and,
price protection, 397 3–4 412–414
quantity discounts, 395 Toyota Production System (TPS), 6–7,
quantity flexibility contracts, Value curve, 416
69, 72–73, 119, 222 Variability
396–397 architecture of, 224–225
revenue sharing, 396 flexibility, 234–236 analyzing arrival, 149–155
vendor managed inventory, General Motors plant vs., 226 in available capacity, 364
history of Toyota, 222–223 defects and, 218
386–387 human resource practices, 236–237 measuring of, 147–149
Supply chain optimal quantity, 390 inventory reduction, 233–234 problem types, 194
Supply-constrained processes, 39 JIT, 228–231 process performance and, 144
Supply-demand match/mismatches, muda (waste), 69, 72, 225–228 processing time variability,
pull systems, 231
1–3, 10 quality at the source, 217–218 155–157
Supply-demand mismatches, 2–3, standardization of work, 236 reduction of, 174–176, 236
variability reduction, 236 sequence of resources, 191–194
270–273 waste reduction, 224 sources of, 147–149
Sustainable business practices, 401 Trade promotions, 378–382, 386 street vendor example, 184
Sustainable operations, 8, 401 Transactional efficiency, 415 throughput loss and; see Throughput
Transport, 226
agriculture, fishing, and forestry, Triage step, 172 loss
404–405 Trunk inventory, 288 waiting time; see Waiting time
Tucker, A. L., 227 Variable costs, 99
background to, 401–405 Turn-and-earn, 386 Variation
brands and, 405–406 assignable causes of, 201
business case for, 405–406 U common causes of, 200
energy, 401–404 controlling, 199–200
material, 404 Ulrich, K., 199, 412 types of, 200–202
operations management and, Uncertainty, 319 Vendor-managed inventory (VMI),

406–409 dealing with, 88–92 386–387
people, 405 decision tree/milestones/exit Vertical distance, 17
water, 404 Virtual nesting, 366
option, 91–92 Virtual pooling, 323–324
T potential iteration/rework loops,
W
Takotei-mochi, 235 91, 93
Takt time, 229 random activity times, 88–91 Waiting time, 13, 144, 226
Tandem queues, 192 uncertainty levels in a project, 93 arrival process analysis, 149–155
Target value, 208 unknown unknowns (unk-unks), calculations for, 163
Target wait time (TWT), 164 capacity-related, 144
Task durations, 65 92–93 causes of, 144
Task specialization, 69–70 Universal design, 327, 332 customer loss, 191
Taylor, F., 226, 237 Unknown unknowns (unk-unks), economic consequences of, 165
Term paper syndrome, 93
Terwiesch, C., 32–35, 412 92–93
Test points, 217–218 Upper specification level (USL), 208

Index 507

pooling, 169–172 Water, sustainability and, 404 Y
predicting for multiple resources, Webvan, 422
Weighted average cost of capital Yield management; see Revenue
161–164 management
predicting for one resource, 157–161 (WACC), 96
priority rules, 172–173 Wessels, D., 96 Yield of resource, 215
service levels, 164–165 Wheelwright, S. C., 27–28
staffing plan, 165–169 Whitney, D., 72 Z
target wait time (TWT), 164 Whitt, W., 162
types of, 144 Wiggle room; see Slack time Z-statistic, 249–250
variability problems, 144 Womack, J. P., 225 Zero defects/breakdowns, 225,
Waiting time problems, 144 Work-in-process (WIP), 15, 17, 58, 101
Walmart, 20–21, 386 Worker-paced line, 59–60 227, 231
Waste (muda), 69, 72, 222, 224, 226 Working capital, 101 Zero-sum game, 391
seven sources of, 225–228 Workload, 43–44 Zipcar, 410–412, 416, 420