Improving the Planning Quality in Practice with Artificial Intelligence

Leonhard Czarnetzki, David Karnok, Johannes Breitschopf, Matthias Karner, Milot Gashi, Thomas Mambrini, Catherine Laflamme, Viola Gallina, Wilfried Sihn

Abstract

<p>Accurate production planning is economic interest of manufacturing companies. Reducing the work-inprogress levels, the lead time or control efforts with the simultaneous increase of utilization and adherence to schedule might lead to instantaneous cost reduction and to increased competitiveness on long-term. In the era of digitization various artificial intelligence-based methods have been investigated by the scientific community to improve these key performance indicators. In this paper the results of a joint research project dealing with planning quality improvement with the help of Machine Learning (ML) are summarized. The results of two use case studies investigating the application and suitability of different planning approaches in the semiconductor and steel industries are presented and considerations regarding the practical application of ML assisted planning approaches are discussed.</p>

Keywords

Production scheduling, Production planning and control, Master data, Dynamic data, Optimization, Industry, Innovation and Infrastructure

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IMEKO-TC10-2023-004.pdf

DOI

10.21014/tc10-2023.004

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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