Authors:
Kaja Balzereit
;
Nehal Soni
and
Andreas Bunte
Affiliation:
Fraunhofer IOSB, Industrial Automation branch, Fraunhofer Center for Machine Learning, Lemgo, Germany
Keyword(s):
Explainability, Prediction, Regression Analysis, Industrial Application of AI.
Abstract:
The order picking process in a manufacturing supermarket is central in many industrial productions as it ensures that the items required for production are provided at the right time. However, the order picking process itself often is a black box, i.e., the time it takes to pick an order and the dependencies in the process that influence the time usually are not exactly known. In this work, we highlight the potentials of creating explainable predictions of order picking times using Artificial Intelligence methods. The prediction is based on the analysis of a historic database and on a linear regression analysis that learns the dependencies in the data. From this prediction, (1) the potential of identifying features having a high and a low influence on the order picking time, (2) the potential of optimizing the order picking process itself, and (3) the potential of optimizing depending processes are identified. For prediction, we utilize the regression methods LASSO and Decision Tree.
These methods are compared with regard to their interpretability and usability in industrial manufacturing.
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