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Effect of product mix on multi-product pull control
Affiliation:1. Key Laboratory of Low-Grade Energy Utilization Technologies and Systems, Chongqing University, Ministry of Education, Chongqing 40003, China;2. Institute of Engineering Thermophysics, Chongqing University, Chongqing 400030, China;1. The Turkish Air Force Academy, İstanbul, Turkey;2. Energy Institute, İstanbul Technical University, İstanbul 34357, Turkey;3. Industrial Engineering Department, İstanbul Technical University, İstanbul 34357, Turkey;1. Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan;2. Department of Biochemistry, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan;3. Department of Internal Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan;4. Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung, Taiwan
Abstract:Product mix influences the performance of pull production control strategy in multi-product manufacturing systems. The complexity of product mix on the performance of a manufacturing system is primarily based on the characteristics of the demand and production control strategies. Demands are mainly characterised by volume and product-type while production control strategy is characterised by material release time, part flow, inventory control and throughput times. In multi-product systems, pull production control strategy operates dedicated or shared Kanban allocation policy. This paper examines the performance of the Generalised Kanban Control Strategy (GKCS), Extended Kanban Control Strategy (EKCS) and Basestock Kanban-CONWIP (BK-CONWIP) control strategy operating Shared Kanban Allocation Policies (S-KAP) or Dedicated Kanban Allocation Policies (D-KAP) for a healthcare parallel/serial assembly line with setup times. A simulation based multi-objective optimisation technique was adopted to examine the effect of different product mixes on the strategies and policies. A ranking and selection technique for multiple systems was used to screen the performance of the strategies. It was shown that product mix variability in a system influence the inventory levels of the pull control strategies examined. However, the performances of the strategies vary with strategies operating S-KAP having better inventory control than strategies operating D-KAP. Similarly, BK-CONWIP outperformed its alternatives.
Keywords:Healthcare manufacturing  Pull production control strategies  Kanban allocation policies  Multi-product systems  Assembly line  Product mix
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