paper focuses on models and solution approachesfor planning and scheduling in supplychains. It describes several classes of planning andscheduling models that are currently being used insystems that optimize supply chains. It discusses thearchitecture of decision support systems that havebeen implemented in industry and the problems thathave come up in the implementation and integrationof systems in supply chains. In the implementationsconsidered, the total cost in the supply chain has to beminimized, i.e., the stages in the supply chain do notcompete in any form with one another, but collaboratein order to minimize total costs. This paper focusesprimarily on how to integrate medium term planningmodels (e.g., lot sizing models) and detailed schedulingmodels (e.g., job shop scheduling models) into asingle framework.A medium term production planning model typicallyoptimizes several consecutive stages in a supplychain (i.e., a multi-echelon model), with each stagehaving one or more facilities. Such a model is designedto allocate the production of the different productsto the various facilities in each time period, whiletaking into account inventory holding costs and transportationcosts. A planning model may make a distinctionbetween different product families, but usuallydoes not make a distinction between differentproducts within a family. It may determine the optimalrun length (or, equivalently, batch size or lot size)of a given product family when a decision has beenmade to produce such a family at a given facility. Ifthere are multiple families produced at the same facility,then there may be setup costs and setup times.The optimal run length of a product family is afunction of the trade-off between the setup costand/or setup time and the inventory carrying cost.The main objectives in medium term planning involveinventory carrying costs, transportation costs,tardiness costs, and the major setup costs. However,in a medium term planning model, it is typically notcustomary to take the sequence dependency ofsetup times and setup costs into account. The sequencedependency of setups is difficult to incorporatein an integer programming formulation and canincrease the complexity of the formulation significantly.A short term detailed scheduling model is typicallyonly concerned with a single facility, or, at most, with

PRODUCTION AND OPERATIONS MANAGEMENT POMSVol. 13, No. 1, Spring 2004, pp. 77–92issn 1059-1478 _ 04 _ 1301 _ 077$1.25 © 2004 Production and Operations Management Society77a single stage. Such a model usually takes more detailedinformation into account than a planningmodel. It is typically assumed that there are a givennumber of jobs and each one has its own parameters(including sequence-dependent setup times and sequence-dependent setup costs). The jobs have to be

scheduled in such a way that one or more objectivesare minimized, e.g., the number of jobs that areshipped late, the total setup time, and so on.Clearly, planning models differ from schedulingmodels in a number of ways. First, planning modelsoften cover multiple stages and optimize over amediumterm horizon, whereas scheduling models are usuallydesigned for a single stage (or facility) and optimize overa short term horizon. Second, planning models use moreaggregate information, whereas scheduling models usemore detailed information. Third, the objective to beminimized in a planning model is typically a total costobjective and the unit in which this is measured is amonetary unit; the objective to be minimized in a schedulingmodel is typically a function of the completiontimes of the jobs and the unit in which this is measuredis often a time unit. Nevertheless, even though there arefundamental differences between these two types ofmodels, they often have to be incorporated into a singleframework, share information, and interact extensivelywith one another.Planning and scheduling models may also interactwith other types of models, such as long term strategicmodels, facility location models, demand managementmodels, and forecasting models; these modelsare not discussed in this paper. The interactions withthese other types of models tend to be less intensiveand less interactive. In what follows, we assume that thephysical settings in the supply chain have already beenestablished; the configuration of the chain is given, andthe number of facilities at each stage is known.Supply chains in the various industries are often notvery similar and may actually give rise to differentsets of issues and problems. This paper considers applicationsof planning and scheduling models in supplychains in various industry sectors. A distinction ismade between two types of industries, namely thecontinuous manufacturing industries (which includethe process industries) and the discrete manufacturingindustries (which include, for example, automotiveand consumer electronics). Each one of these two maincategories is subdivided into several subcategories.This categorization is used because of the fact that theplanning and scheduling procedures in the two maincategories tend to be different. We focus on the frameworksin which the planning and scheduling modelshave to be embedded; we describe the type of informationthat has to be transferred back and forth betweenthe modules and the kinds of optimization thatis done within the modules.There is an extensive literature on supply chainmanagement. Many papers and books focus on supplychain coordination; a significant amount of thiswork has an emphasis on inventory control, pricingissues, and the value of information; see Simchi-Levi,Kaminsky, and Simchi-Levi (2000), Chopra andMeindl (2001), and Stadtler and Kilger (2000). There isalso an extensive literature on production planningand scheduling theory. A significant amount of research

has been done on the solution methods applicableto planning and scheduling models; see Shapiro(2001). Planning models and scheduling models haveoften been studied independently from one another inorder to obtain elegant theoretical results. Planningmodels are often based on (multi-echelon) inventorytheory and lot sizing; see Zipkin (2000), Kimms (1997),Drexl and Kimms (1997), Muckstadt and Roundy(1993), and Dobson (1987, 1992). Scheduling modelstypically focus on how to schedule a number of jobs ina given machine environment in order to minimizesome objective. For general treatises on scheduling,see Bhaskaran and Pinedo (1992), Brucker (1998),Pinedo (2002), and Pinedo and Chao (1999). For applicationsof scheduling to supply chain management,see Hall and Potts (2000) and Lourenco (2001). Someresearch has been done on more integrated models inthe form of hierarchical planning systems; this researchhas resulted in frameworks that incorporateplanning and scheduling; see Bowersox and Closs(1996), Barbarosoglu and Ozgur (1999), Dhaenens-Flipo and Finke (2001), Shapiro (2001), and Miller(2002). For examples of descriptions of successful industrialimplementations, see Haq (1991), Arntzen,Brown, Harrison, and Trafton (1995), Hadavi (1998),and Shepherd and Lapide (1998).This paper is organized as follows. The second se