Discrete-event simulation: Difference between revisions

Revision as of 12:06, 16 December 2020

Revision as of 14:56, 11 January 2021

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===Diagnosing process issues===

Simulation approaches are particularly well equipped to help users diagnose issues in complex environments. The [[Theorytheory of Constraintsconstraints]] illustrates the importance of understanding bottlenecks in a system. Identifying and removing bottlenecks allows improving processes and the overall system. For instance, in manufacturing enterprises bottlenecks may be created by excess inventory, [[overproduction]], variability in processes and variability in routing or sequencing. By accurately documenting the system with the help of a simulation model it is possible to gain a bird’s eye view of the entire system.

A working model of a system allows management to understand performance drivers. A simulation can be built to include any number of [[performance indicator]]s such as worker utilization, on-time delivery rate, scrap rate, cash cycles, and so on.