A methodology to customize pull control systems
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A methodology to customize pull control systems EGA Gaury1,2*, JPC Kleijnen1 and H Pierreval2 1
Tilburg University, Tilburg, The Netherlands; and 2Equipe de Recherche en Syste`mes de Production de l’Institut Franc¸ais de Me´canique Avance´e, Aubie`re, France The objective of this paper is to develop a methodology for customizing pull control systems, replacing traditional control systems such as Kanban, Conwip, and Base Stock. The basic principle is to start with a pull control system that connects each stage of a given production line with each preceding stage; next, the optimization of the corresponding simulation model shows which of these potential control loops are actually implemented. The result of this methodology may be one of the traditional systems, but it may also be one of the following three new types: (1) the total line may be decomposed into several segments, each with its own classic control system, (2) the total line or its segments may combine different traditional systems, or (3) the line may be controlled through a new type of control system. Indeed, these three systems are found in a simulation experiment with a set of twelve production lines. These twelve lines are selected through a statistical design with ten factors that characterize production lines. Keywords: production control systems; heuristic optimization; simulation
Introduction In practice, production and inventory control has a major effect on the performance of a manufacturing company, notably its inventories, production delays, and makespan, which affects the company’s competitiveness. There are two general types of control strategy, namely push versus pull. We limit ourselves to pull control, which uses information about product deliveries to control production (for details see the next section). We focus on customized control systems that may replace traditional control systems such as Kanban, Conwip, and Base stock. To achieve such customization we start with a system that connects each production stage (of the given production line) with each preceding stage; next, we optimize the corresponding simulation model to determine which control loops actually need to be implemented. We further limit our study to production lines processing a single part type. Gstettner and Kuhn1 identify three basic pull systems, namely Kanban, Conwip, and Base stock. Those authors also consider the ‘serial’ combination of these pull systems within a production line. In the next section we describe these three basic systems and possible serial combinations. We call these serial combinations, segmented systems. We shall also consider joint systems, that is, combinations of control systems on the same segment of a production system. Kimura and Terada2 claim that a pull system has multiple objectives, namely minimize Work In Process’ (WIP)
*Correspondence: EGA Gaury, API: TCR AVA 3 61, Technocentre Renault, 1 Avenue du Golf, F-78288 Guyanc
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