Details

Autor(en) / Beteiligte

• Syberg, Marius
• West, Nikolai
• Lenze, David
• Deuse, Jochen

Titel

Framework for predictive sales and demand planning in customer-oriented manufacturing systems using data enrichment and machine learning

Ist Teil von

• Procedia CIRP, 2023, Vol.120, p.1107-1112

Ort / Verlag

Elsevier B.V

Erscheinungsjahr

2023

Link zum Volltext

Quelle

Electronic Journals Library

Beschreibungen/Notizen

• Companies with Make-to-Order (MTO) manufacturing have always faced the conflict of meeting a large volume of individual customer orders on time while remaining as flexible as possible. Unlike Make-to-Stock (MTS) manufacturing, planning the production requirements for MTO is a challenging task since incoming orders may vary in time and quantity, while also being subject to a number of variables. In some cases, the delivery time allocated by the customer can be less than the required Order Lead Time to fulfil the order. Manufacturers can respond to this with either with approached from production management or from data science. This paper presents a framework to leverage the benefit of both domains. We conduct a literature review and present the results of an expert workshop. We propose criteria for a suitable data enrichment and the application of Machine Learning (ML) methods in sales and demand forecasting. In conclusion, the proposed framework helps to equip manufacturing companies with a structured strategy for data management and to utilize the benefit of ML for sales and demand forecasting.