Application of time series data mining for the prediction of transition times in production
Günther Schuh, Andreas Gützlaff, Frederick Sauermann, Theresa Theunissen · Procedia CIRP · 2020
Transition times between value-adding operations often account for more than 90 % of lead times in workshop productions and vary greatly from order to order. However in day-to-day business, they are treated as static master data or only roughly estimated. One approach for a more precise prediction of future transition times of production orders is time series data mining. This paper presents a first application approach as basis for improving the quality of production planning. Based on empirical findings, future research issues are derived.