PRODUCTION AND OPERATIONS MANAGEMENT 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: An Overview 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. TIt first gives an overview of the 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 mal run length (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 there are multiple families produced at the same fa- have come up in the implementation and integration cility, then there may be setup costs and setup times. of systems in supply chains. In the implementations The optimal run length of a product family is a considered, the total cost in the supply chain has to be function of the trade-off between the setup cost minimized, i.e., the stages in the supply chain do not and/or setup time and the inventory carrying cost. compete in any form with one another, but collaborate The main objectives in medium term planning in- in order to minimize total costs. This paper focuses volve inventory carrying costs, transportation costs, primarily on how to integrate medium term planning tardiness costs, and the major setup costs. However, models (e.g., lot sizing models) and detailed schedul- in a medium term planning model, it is typically not ing models (e.g., job shop scheduling models) into a 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. signed to allocate the production of the different prod- A short 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 designed for a single stage (or facility) and optimize over order to obtain elegant theoretical results. Planning a short term horizon. Second, planning models use more 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 with other types of models, such as long term strategic 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, and less 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 Supply chains in the various industries are often not 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 and consumer electronics). Each one of these two main 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- works in which the planning and scheduling models 2. Supply Chain Settings and 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.