Minimum Inventory Variability Dispatching Policies (Mivp)

This paper illustrates the use of discrete event stochastic simulation modeling to compare two scheduling (dispatching) policies for machines in a factory when a machine becomes available for processing. The two policies are first-in-first-out (FIFO) and Minimum Inventory Variability Policies (MIVP), both control the items in the queue (buffers) in front of the machine or resource . The simulation model is run with FIFO for each queue for 100 days to establish a baseline set of data. This baseline cycle time and work-in-progress (WIP) data are collected for comparison to MIVP. The only change between the model runs is that the queues in the model are switched to run the rule set of MIVP. With discrete event simulation modeling, the user can play “what if” scenarios without expended a lot of capital . The results from simulation give the user an additional input in making decisions. Examples of such a simulator use include the analysis of machine utilization, queue statistics, mean cycle time and mean WIP and production throughput, etc. This analysis can serve to push the bottleneck capacity to its limit, setup and test scheduling rules and preventive maintenance schedules, and determine personnel (operator) availability requirements. Thus, a good simulator allows for the investigation of complex “what-if” scenarios at a minimal cost, high speed, and without disturbing the normal production. The System Model The following figure and specification was taken from a test-bed designed by Karl Kempf, Manufacturing Systems Principal Scientist, of the INTEL Corporation. This test-bed is an example of a very small section in the Semiconductor FAB and is referred to as a Mini-FAB . The Mini-FAB included two products and test wafers with their process flows (production recipes) of six steps utilizing three different machine sets. There was one re-entrant step at each machine group, steps 4, 5 and 6. The machine groups emulate (in a small scale) Diffusion-C1 (2 Page 553.1 machines Ma and Mb), Implant-C2 (2 machines Mc and Md) and Photolithography-C3 (1 machine Me). Machines Ma and Mb in the Diffusion bay used predictive machine controllers (a PID controller compared to an H-infinity controller) to establish when the machines were to be taken down for emergency maintenance. Predictive controllers are being researched for yield improvement to determine a potential failure prior to its occurrence so the product being processed is not scraped. Machines Mc and Md in the Implant bay used historical data of MeanTime-Before-Failure (MTBF) and Mean-Time-To-Repair (MTTR) as the emergency maintenance. All machines had a specific Preventive Maintenance Schedule (PM) with rules for when the machines could be taken off line for PM. Diffusion Photolithography Implant C1 C3 C2 Ma Mb Mc Md Me S2