Authors:
Konstantin Muehlbauer
;
Stephan Schnabel
and
Sebastian Meissner
Affiliation:
Technology Center for Production and Logistics Systems, Landshut University of Applied Sciences, Am Lurzenhof 1, Landshut, Germany
Keyword(s):
Data Science, Decision Support Systems, Internal Logistics, Key Performance Indicators, Process Analysis.
Abstract:
Due to the use of planning and control systems and the integration of sensors in the material flow, a large amount of transaction data is generated by logistics systems in daily operations. However, organizations rarely use this data for process analysis, problem identification, and process improvement. This article presents a knowledge-based, data-driven approach for transforming low-level transaction data obtained from logistics systems into valuable insights. The procedure consists of five steps aimed at deploying a decision support system designed to identify optimization opportunities within logistics systems. Based on key performance indicators and process information, a system of interdependent effects evaluates the logistics system’s performance in individual working periods. Afterward, a machine learning model classifies unfavorable working periods into predefined problem classes. As a result, specific problems can be quickly analyzed. By means of a case study, the functiona
lity of the approach is validated. In this case study, a trained gradient-boosting classifier identifies predefined classes on previously unseen data.
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