Reducing bottle‐necks in a manufacturing system with automatic data collection and discrete‐event simulation

Abstract

Purpose Seeks to present a methodology for working with bottle‐neck reduction by using a combination of automatic data collection and discrete‐event simulation (DES) for a manufacturing system. Design/methodology/approach In the DES model, the bottle‐neck was identified by studying the simulation runs based on the collected automatic data from the different machines in the manufacturing system. Findings A case study showed an improvement of the availability in one machine from 58.5 to 60.2 percent. This single alteration with a minimum of investment resulted in a 3 percent increase of the overall output in the manufacturing system consisting of 11 numerically controlled machines and six other stations. A new simulation run was performed one year after the first study in order to see how the improvement work has progressed with the suggested method. The method resulted in an increase of 6 percent in overall output. Originality/value It could be assumed that machines in future manufacturing systems will provide automatic data. The data can then be used for DES models when identifying bottle‐necks in a manufacturing system.

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