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PRODUCTIONANDOPERATIONSMANAGEMENT POMS
Vol. 13, No. 1, Spring 2004, pp. 77–92
issn 1059-1478 04 1301 077$1.25 ©2004 Production and Operations Management Society
Planning and Scheduling in Supply Chains:
AnOverview of Issues in Practice
Stephan Kreipl • Michael Pinedo
SAP Germany AG & Co.KG, Neurottstrasse 15a, 69190 Walldorf, Germany
Stern School of Business, New York University, 40 West Fourth Street, New York, New York 10012
his paper gives an overview of the theory and practice of planning and scheduling in supply chains.
TItfirstgivesanoverviewofthe various planning and scheduling models that have been studied in
the literature, including lot sizing models and machine scheduling models. It subsequently categorizes
the various industrial sectors in which planning and scheduling in the supply chains are important;
these industries include continuous manufacturing as well as discrete manufacturing. We then describe
how planning and scheduling models can be used in the design and the development of decision
support systems for planning and scheduling in supply chains and discuss in detail the implementation
of such a system at the Carlsberg A/S beerbrewer in Denmark. We conclude with a discussion on the
current trends in the design and the implementation of planning and scheduling systems in practice.
Key words: planning; scheduling; supply chain management; enterprise resource planning (ERP) sys-
tems; multi-echelon inventory control
Submissions and Acceptance: Received October 2002; revisions received April 2003; accepted July 2003.
1. Introduction taking into account inventory holding costs and trans-
This paper focuses on models and solution ap- portation costs. A planning model may make a dis-
proaches for planning and scheduling in supply tinction between different product families, but usu-
chains. It describes several classes of planning and ally does not make a distinction between different
scheduling models that are currently being used in products within a family. It may determine the opti-
systems that optimize supply chains. It discusses the malrunlength(or, equivalently, batch size or lot size)
architecture of decision support systems that have of a given product family when a decision has been
been implemented in industry and the problems that made to produce such a family at a given facility. If
have come up in the implementation and integration there are multiple families produced at the same fa-
of systems in supply chains. In the implementations cility, then there may be setup costs and setup times.
considered, the total cost in the supply chain has to be The optimal run length of a product family is a
minimized, i.e., the stages in the supply chain do not function of the trade-off between the setup cost
competeinanyformwithoneanother,butcollaborate and/or setup time and the inventory carrying cost.
in order to minimize total costs. This paper focuses The main objectives in medium term planning in-
primarily on how to integrate medium term planning volve inventory carrying costs, transportation costs,
models (e.g., lot sizing models) and detailed schedul- tardiness costs, and the major setup costs. However,
ing models (e.g., job shop scheduling models) into a in a mediumtermplanningmodel,itistypicallynot
customary to take the sequence dependency of
single framework. setup times and setup costs into account. The se-
A medium term production planning model typi- quence dependency of setups is difficult to incorpo-
cally optimizes several consecutive stages in a supply rate in an integer programming formulation and can
chain (i.e., a multi-echelon model), with each stage increase the complexity of the formulation signifi-
having one or more facilities. Such a model is de- cantly.
signedtoallocatetheproductionofthedifferentprod- Ashort term detailed scheduling model is typically
ucts to the various facilities in each time period, while only concerned with a single facility, or, at most, with
77
Kreipl and Pinedo: Planning and Scheduling in Supply Chains
78 Production and Operations Management 13(1), pp. 77–92, © 2004 Production and Operations Management Society
a single stage. Such a model usually takes more de- There is an extensive literature on supply chain
tailed information into account than a planning management. Many papers and books focus on sup-
model. It is typically assumed that there are a given ply chain coordination; a significant amount of this
number of jobs and each one has its own parameters work has an emphasis on inventory control, pricing
(including sequence-dependent setup times and se- issues, and the value of information; see Simchi-Levi,
quence-dependent setup costs). The jobs have to be Kaminsky, and Simchi-Levi (2000), Chopra and
scheduled in such a way that one or more objectives Meindl(2001), and Stadtler and Kilger (2000). There is
are minimized, e.g., the number of jobs that are also an extensive literature on production planning
shipped late, the total setup time, and so on. and scheduling theory. A significant amount of re-
Clearly, planning models differ from scheduling search has been done on the solution methods appli-
models in a number of ways. First, planning models cable to planning and scheduling models; see Shapiro
often cover multiple stages and optimize over a medium (2001). Planning models and scheduling models have
term horizon, whereas scheduling models are usually often been studied independently from one another in
designedforasinglestage(orfacility)andoptimizeover order to obtain elegant theoretical results. Planning
ashorttermhorizon.Second,planningmodelsusemore models are often based on (multi-echelon) inventory
aggregate information, whereas scheduling models use theory and lot sizing; see Zipkin (2000), Kimms (1997),
more detailed information. Third, the objective to be Drexl and Kimms (1997), Muckstadt and Roundy
minimized in a planning model is typically a total cost (1993), and Dobson (1987, 1992). Scheduling models
objective and the unit in which this is measured is a typically focus on how to schedule a number of jobs in
monetary unit; the objective to be minimized in a sched- a given machine environment in order to minimize
uling model is typically a function of the completion some objective. For general treatises on scheduling,
times of the jobs and the unit in which this is measured see Bhaskaran and Pinedo (1992), Brucker (1998),
is often a time unit. Nevertheless, even though there are Pinedo (2002), and Pinedo and Chao (1999). For appli-
fundamental differences between these two types of cations of scheduling to supply chain management,
models, they often have to be incorporated into a single see Hall and Potts (2000) and Lourenco (2001). Some
framework, share information, and interact extensively research has been done on more integrated models in
with one another. the form of hierarchical planning systems; this re-
Planning and scheduling models may also interact search has resulted in frameworks that incorporate
withothertypesofmodels,suchaslongtermstrategic planning and scheduling; see Bowersox and Closs
models, facility location models, demand manage- (1996), Barbarosoglu and Ozgur (1999), Dhaenens-
ment models, and forecasting models; these models Flipo and Finke (2001), Shapiro (2001), and Miller
are not discussed in this paper. The interactions with (2002). For examples of descriptions of successful in-
these other types of models tend to be less intensive dustrial implementations, see Haq (1991), Arntzen,
andless interactive. In what follows, we assume that the Brown, Harrison, and Trafton (1995), Hadavi (1998),
physical settings in the supply chain have already been and Shepherd and Lapide (1998).
established; the configuration of the chain is given, and This paper is organized as follows. The second section
the number of facilities at each stage is known. describes and categorizes some of the typical industrial
Supplychainsinthevariousindustriesareoftennot settings. The third section discusses the overall frame-
very similar and may actually give rise to different works in which planning models as well as scheduling
sets of issues and problems. This paper considers ap- models have to be embedded. The fourth section de-
plications of planning and scheduling models in sup- scribes a standard mixed integer programming formu-
ply chains in various industry sectors. A distinction is lation of a planning model for a supply chain. The fifth
made between two types of industries, namely the section covers a typical formulation of a scheduling
continuous manufacturing industries (which include problem in a facility in a supply chain. The sixth section
the process industries) and the discrete manufacturing describes an actual implementation of a planning and
industries (which include, for example, automotive scheduling software system at the Danish beerbrewer
andconsumerelectronics).Eachoneofthesetwomain Carlsberg A/S. The last section presents the conclusions
categories is subdivided into several subcategories. and discusses the impact of the Internet on decision
This categorization is used because of the fact that the support systems in supply chains.
planning and scheduling procedures in the two main
categories tend to be different. We focus on the frame- 2. Supply Chain Settings and
works in which the planning and scheduling models
have to be embedded; we describe the type of infor- Configurations
mation that has to be transferred back and forth be- This section gives a concise overview of the various
tween the modules and the kinds of optimization that types of supply chains. It describes the differences in
is done within the modules. the characteristics and the parameters of the various
Kreipl and Pinedo: Planning and Scheduling in Supply Chains
Production and Operations Management 13(1), pp. 77–92, © 2004 Production and Operations Management Society 79
categories. It first describes the various different in- single machine and parallel machine scheduling mod-
dustry groups and their supply chain characteristics els. If it operates according to mts, then it may follow
and then discusses how the different planning and a so-called s-S or Q-R inventory control policy. If it is
scheduling models analyzed in the literature can be amixtureofmtoandmts,thentheschedulingpolicies
used in the management of these chains. One can become a mixture of inventory control and detailed
make a distinction between two types of manufactur- scheduling rules.
ing industries, namely: Discrete Manufacturing. The discrete manufacturing
(I) Continuous manufacturing industries (e.g., the industry sector is quite diverse and includes the auto-
process industries), motive industry, the appliances industry, and the pc
(II) Discrete manufacturing industries (e.g., cars, industry. From the perspective of planning and sched-
semiconductors). uling, a distinction can be made between three differ-
These two industry sectors are not all-encompassing; ent types of operations in this sector. The reason for
theborderlinesaresomewhatblurryandmayoverlap. making such a distinction is based on the fact that
However, planning and scheduling in continuous planning and scheduling in these three segments are
manufacturing (the process industries) often have to quite different.
deal with issues that are quite different from those in (II-a) Primary converting operations (e.g., cutting
discrete manufacturing. and shaping of sheet metal),
Continuous Manufacturing. Continuous manufactur- (II-b) Main production operations (e.g., production
ing (process) industries often have various types of of engines, pcbs, wafers), and
different operations. The most common types of op- (II-c) Assembly operations (e.g., cars, pcs).
erations can be categorized as follows: Primary Converting Operations in Discrete Manufac-
(I-a) Main processing operations, turing (II-a). Primary converting operations are some-
(I-b) Finishing or converting operations. what similar to the finishing operations in the process
Main Processing Operations in Continuous Manufac- industries. These operations may typically include
turing (I-a). The main production facilities in the pro- stamping, cutting, or bending. The output of this op-
cess industries are, for example, paper mills, steel eration is often a particular part that is cut and bent
mills, aluminum mills, chemical plants, and refineries. into a given shape. There are usually few operations
Inpaper,steel,andaluminummills,themachinestake doneonsuchanitem,andtheroutinginsuchafacility
in the raw material (e.g., wood, iron ore, alumina) and is relatively simple. The final product of a primary
produce rolls of paper, steel, or aluminum, which converting facility is usually not a finished good, but
afterwards are handled and transported with special- basically a part or piece made of a single material
ized material-handling equipment. Machines that do (boxes, containers, frames, stamped body parts of cars,
the main processing operations typically have very andsoon).Examplesofthetypesofoperationsinthis
high startup and shutdown costs and usually work category are stamping plants that produce body parts
around the clock. A machine in the process industries for cars, and plants that produce epoxy boards of
also incurs a high changeover cost when it has to various sizes for the facilities that produce Printed
switch over from one product to another. Various Circuit Boards. The planning and scheduling proce-
methodologies can be used for analyzing and solving dures under II-a may be similar to those under I-b.
the models for such operations, including cyclic However, they may be here more integrated with the
scheduling procedures and Mixed Integer Program- operations downstream.
ming approaches. MainProduction Operations in Discrete Manufacturing
Finishing Operations in Continuous Manufacturing (II-b). The main production operations are those op-
(I-b). Many process industries have some form of fin- erations that require multiple different operations by
ishing operations that do some converting of the out- different machine tools, and the product (as well as its
put of the main production facilities. This converting parts) may have to follow a certain route through the
usually involves cutting of the material, bending, fold- facility going through various work centers. Capital
ing, and possibly painting or printing. These opera- investments have to be made in various types of ma-
tions often (but not always) produce commodity-type chine tools (lathes, mills, chip fabrication equipment).
items, for which the producer has many clients. For For example, in the semiconductor industry, wafers
example, a finishing operation in the paper industry typically have to undergo hundreds of steps. These
mayproduce cut size paper out of the rolls that come operations include oxidation, deposition, and metalli-
from the paper mill. The paper finishing business is zation, lithography, etching, ion implantation, pho-
often a mixture of Make-To-Stock (mts) and Make-To- toresist stripping, and inspection and measurements.
Order (mto). If it operates according to mto, then the It is often the case that certain operations have to be
scheduling is based on customer due dates and se- performed repeatedly and that certain orders have to
quence-dependent setup times. This leads often to visit certain workcenters in the facility several times,
Kreipl and Pinedo: Planning and Scheduling in Supply Chains
80 Production and Operations Management 13(1), pp. 77–92, © 2004 Production and Operations Management Society
i.e., they have to recirculate through the facility. In Table 1
semiconductorandPrintedCircuitBoardmanufactur- Product
ing, the operations are often organized in a job shop Sector Processes Time horizon Clock-speed differentiation
fashion. Each order has its own route through the (I-a) planning long-medium low very low
system, its own quantity (and processing times), and (I-b) planning/scheduling medium/short medium/high medium/low
its own committed shipping date. An order typically (II-a) planning/scheduling medium/short medium very low
represents a batch of identical items that requires se- (II-b) planning/scheduling medium/short medium medium/low
quence-dependent setup times at many operations. (II-c) scheduling short high high
Assembly Operations in Discrete Manufacturing (II-c).
The main purpose of an assembly facility is to put
different parts together. An assembly facility typically There are some basic differences between the pa-
does not alter the shape or form of any one of the rameters and operating characteristics of the facilities
individual parts (with the possible exception of the in the two main categories described above. Several of
painting of the parts). Assembly operations usually do these differences have an impact on the planning and
notrequiremajorinvestmentsinmachinetools,butdo scheduling processes, including the differences in (i)
requireinvestmentsinmaterialhandlingsystems(and the planning horizon, (ii) the clock-speed, and (iii) the
possibly robotic assembly equipment). An assembly level of product differentiation.
operation may be organized in workcells, in assembly (i) The planninghorizonincontinuousmanufactur-
lines, or according to a mixture of workcells and as- ing facilities tends to be longer than the planning
semblylines. For example, pcs are assembled in work- horizon in the discrete manufacturing facilities. In
cells, whereas cars and TVs are typically put together continuous as well as in discrete manufacturing the
in assembly lines. Workcells typically do not require planning horizons tend to be shorter more down-
any sequencing, but they may be subject to learning stream in the supply chain.
curves. In assembly operations that are set up in a line, (ii) The so-called “clock-speed” tends to be higher
the sequencing is based on grouping and spacing heu- in a discrete manufacturing facility than in a continu-
ristics combined with committed shipping dates. The ous manufacturing facility. A high clock-speed im-
schedules that are generated by the grouping and spac- plies that existing plans and schedules often have to be
ing heuristics typically affect not only the throughput of changed or adjusted; that is, planning and scheduling
the line, but also the quality of the items produced. is more reactive. In continuous as well as in discrete
Supplychainsinbothcontinuousanddiscreteman- manufacturing, the clock-speed increases the more
ufacturing may have, besides the stages described downstream in the supply chain.
above,additionalstages.Inasupplychaininaprocess (iii) In discrete manufacturing, there may be a sig-
industry, there may be a stage preceding Stage I-a in nificant amount of mass customization and product
whichtherawmaterialisbeinggatheredatitspointof differentiation. In continuous manufacturing, mass-
origination (which may be a forest or a mine) and customization does not play a very important role.
taken to the main processing operations. There may The number of SKUs in discrete manufacturing tends
also be a distribution stage following stage I-b. A to be significantly larger than the number of SKUs in
company may have its own distribution centers in continuous manufacturing. The number of SKUs
different geographical locations, where it keeps cer- tends to increase more downstream in the supply
tain SKUs in stock for immediate delivery. The com- chain.
pany may also ship directly from its manufacturing These operating characteristics are summarized in
operations to customers. A supply chain in a discrete Table1.Becauseofthesedifferences,theplanningand
manufacturing industry also may have other types of scheduling issues in each one of the sectors can be
stages. There may be a stage preceding stage II-a in very different. Table 2 presents a summary of the
which raw material is being collected at a supplier model types that can be used in the different catego-
(which may be an operation of the type I-b) and ries as well as the corresponding solution techniques.
broughttoaprimaryconvertingoperation.Theremay Note that problems that have continuous variables
also be a stage following stage II-c which would con- mayleadtoMixedIntegerProgramming(mip)formu-
sist of distribution operations (e.g., dealerships). lations, whereas problemsthathaveonlydiscretevari-
Supplychainsinbothcontinuousanddiscreteman- ables may lead to pure Integer Programming (ip)
ufacturing may have several facilities at each one of formulations (or Disjunctive Programming formu-
the stages, each one feeding into several facilities at lations). However, a discrete problem in which certain
stages downstream. The configuration of an entire variablesassumelargevalues(i.e.,thenumberofunits
chain may be quite complicated: For example, there to be produced) may be replaced by a continuous
may be assembly operations that produce subassem- problem, resulting in a Mixed Integer Programming
blies that have to be fed into a production operation. formulation rather than a pure Integer Programming
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