Modeling the impact of anchoring in capacity adjustments to work-in-process behavior in a two stage production system: A system dynamics approach
College
Gokongwei College of Engineering
Department/Unit
Industrial Engineering
Document Type
Article
Source Title
Procedia - Social and Behavioral Sciences
Volume
57
First Page
566
Last Page
574
Publication Date
2012
Abstract
Past researches have focused on managing Work-In-Process (WIP) through the use of WIP control policies such as Kanban, CONWIP, and DBR. These WIP policies assume that decision makers are rational and are able to follow the optimality conditions stated by these policies. Behavioral Operations Management suggests that decision makers are not “rational” decision makers and possess limited or bounded rationality. One particular behavioral element among decision makers that is related to bounded rationality is the use of “anchoring” in decision making. This paper focuses on exploring the impact of changes in anchoring for a two-stage production system by an upstream decision maker who is responsible for capacity adjustments in order to maintain flow of products. The decision maker is theorized to adapt anchoring and adjustment based on the amount of work in process in the production system. Three anchors are tested, namely, a.) Anchoring on output, b.) Anchoring on input, and c.) No anchors. Using system dynamics approach, initial finding from the simulation suggest that WIP variability increases when changes in anchoring for capacity adjustment is based on downstream information rather than upstream. However, when anchors are taken out as an option for capacity adjustments, the WIP variability achieves a more stable pattern.
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Recommended Citation
Beng Hui, D. T. (2012). Modeling the impact of anchoring in capacity adjustments to work-in-process behavior in a two stage production system: A system dynamics approach. Procedia - Social and Behavioral Sciences, 57, 566-574. Retrieved from https://animorepository.dlsu.edu.ph/faculty_research/14546
Disciplines
Industrial Engineering
Keywords
Process control; Production control; System theory; Decision making
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