Early Indication System for Critical Situations in Job-Shop Production

Job-shop production can be very complex and extremely volatile, especially when part variability is high and orders require an increasingly large amount of processing steps. Achieving a manufacturing performance that is robust against volatilities is of great importance in such systems. We therefore develop an early indication system to detect potentially problematic situations (e. g. processing delays) in a job-shop and to forecast a set of key performance indicators with the aim of increasing the robustness of manufacturing performance by improved reaction possibilities. We compare the results predicted by the early indication system with a discrete-event simulation. Our findings show that 67.8% of the processes that developed issues in the simulated environment were detected by the system beforehand, while the schedule deviation forecasting had an error rate of 34.4%. (C) 2014 Elsevier B.V.