NOTE: Exercises below may link to supporting files in a GitHub repository. If you follow such links and (at the GitHub website) right-click a file and choose “Save link as…”, it will appear to download a desired file but may in fact fail. A failure will be discovered when trying to open the downloaded file, usually in MATLAB, and learning that it is not in fact a MATLAB script, function, or SimEvents simulation model.
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A production line has four serial workstations. The number of servers at each workstation is [1, 2, 6, 2] with exponentially-distributed processing times of [2, 5, 10, 3] hours. The purpose of this exercise is to visualize differences between Push and CONWIP control paradigms for releasing work into a make-to-stock system, in which the external demand process is separated from the release of work.
BIG PICTURE: This should be a straightforward exercise to visualize differences between the Push and the CONWIP control paradigms for releasing work into a production line. The particular numbers chosen here make the CONWIP paradigm look great - an expected result is that peak demand backorders, average cycle time, and cycle time variability are all lower. However, that is not a general result - while the CONWIP paradigm is generally better at controlling WIP and cycle times, it involves a fundamental tradeoff between demand backorders and finished goods inventory, and if the CONWIP level is chosen too small then it may perform worse than the Push paradigm at controlling demand backorders and hence the fill rate (the fraction of demands filled without backorder).
A production line has four serial workstations. Each workstation has a single server with exponentially-distributed processing rates averaging [1/2, 2/5, 6/10, 2/3] jobs/hour. The purpose of this exercise is to visualize differences between Push and CONWIP control paradigms for releasing work into a make-to-stock system, in which the external demand process is separated from the release of work. Demands arrive at a rate of 0.37/hour with no variability, corresponding to a bottleneck utilization of 92.5%.
BIG PICTURE: This exercise is designed to illustrate a tradeoff inherent in the CONWIP paradigm - demand backorders versus CONWIP and Finished Goods Inventory levels. By choosing the CONWIP level at the minimum possible number for a certain throughput, it is expected that demand backorders (and fill rate, the fraction of demands filled without backorder) will suffer. If minimizing backorders and their wait times is important, then a natural remedy with the CONWIP paradigm is to increase the CONWIP level (which should increase the Finished Goods Inventory level).