Analysis and Approximation of a JIT Production Line: A Comment

In: Decision sciences, Band 21, Heft 3, S. 660-669

Abstract

ABSTRACTA recent Decision Sciences article by Jordan [9] presented a Markov‐chain model of a just‐in‐time (JIT) production line. This model was used to estimate average inventories and production rates to find the optimal number of kanbans. Results for expected production rate were found to be consistently lower than those obtained by Huang, Rees, and Taylor [8] in a previous Decision Sciences article. Jordan attributed this unexpected outcome to some procedural problems in Huang et al.'s simulation methodology. In this paper, Markov‐numerical analysis is used to compare the performance of Jordan's and Huang et al.'s methods of production control. Simulation analysis is then used to determine the effects of finite withdrawal cycle times. Results show that, for equal numbers of kanbans, Huang et al.'s two‐card method of production control provides substantially greater expected production rates than Jordan's method. These results suggest that the Jordan model should not be applied to the problem of setting kanban numbers on manual JIT lines. Finally, we comment on the efficiency of Jordan's iterative method to obtain performance measures of tandem queues.