Applied Machine Learning for Production Planning and Control: Overview and Potentials

IFAC-PapersOnLine, vol. 55, pp. 2629–2634

Abstract

Manufacturing companies are under constant pressure to increase efficiency and to achieve logistical objectives. Improving production planning and control (PPC) has significant impact on these efforts. At the same time, increasing complexity and dynamics of PPC environments make PPC more difficult. One way to cope with this situation is the application of machine learning (ML) methods. In this article, we therefore address the current state of PPC-ML research and show, based on the Aachen PPC model, in which PPC tasks and subtasks ML is already applied and to what degree the task is covered by ML. The analysis is limited to core and cross-sectional tasks of the Aachen PPC model, procurement and network tasks are not included. Furthermore, a broad analysis of the targeted data mining, business and logistic objectives is conducted. In addition, we also identify motivations which prompted researchers to apply ML in PPC.

Authors 2

1. Oliver Antons corresponding Aachen Chair of Management Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Management Science, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany

2. Julia C. Arlinghaus corresponding

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto von Guericke University, Universitätsplatz 2, 39106 Magdeburg, Germany