Simulation study demonstrates superior throughput and lower lead times using optimization-based inventory classification in batch manufacturing, indicating improved operational control and revenue.
This paper studies the effects of integrating four inventory classification methods into constant work-in-process (CONWIP) systems in a multistage batch production. The inventory classification methods are multiple closed-loop CONWIP (MCC), parallel CONWIP (PC), analytical hierarchy procedure, and weighted linear optimization (WLO). Discrete-event simulation models were built. A full-factorial experimental design was employed. Response surface methodology generated suitable regression models for performance comparison of different ABC analysis methods. In the second stage of experiment, the ABC analysis methods from the first stage were fine-tuned and compared. PC and WLO are the most preferred methods because of higher total outputs, lower work-in-process levels, and shorter flow times compared with the other two methods. However, the revenue accrued through WLO is higher than that accrued via PC. This study complements existing literature on price setting in manufacturing organizations by delineating classification of finished goods based on both exogenous and production factors.
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Prakash et al. (2017) studied this question.
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