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While there is considerable pressure to improve the operation of supply chains, their inherent complexity can make modeling a supply chain a difficult task. This difficulty is compounded when the need to model the effects of product and process design changes are also considered. Yet there could be considerable benefits in designing supply chains taking into account the operation of the supply chain as well as the design of the product and the design of the manufacturing processes used in the supply chain. This paper aims to address the central research issue of developing a methodology that can assist a manager in making decisions by modeling both the operation of a supply chain design and the effects of product and process design decisions. The proposed methodology, called Product Chain Decision Model (PCDM), is presented in this paper. This uses an abstracted network to model supply chain systems. Finally, an industry example of joint supply chain and product design is described to illustrate the application of PCDM. #

Three problem areas exist in designing and implementing a kanban controlled JIT system: the identification of flow lines problem, the flow line loading problem and the operational control problem. This paper addresses the operational control problem. A general N-stage serial production system is modeled as a discrete time Markov process. Capacity constraints, stochastic machine reliability and demand variability are included. The model is illustrated by a 3-stage system, describing the effects of the number of kanbans, the machine reliability, the demand variability and safety stock requirements on the performance of a kanban controlled pull system.kanban system, production/inventory, Markov process

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