Towards a Technology-Oriented Theory of Production

Schuh, Günther; Brettel, Malte; Reuter, Christina; Bendig, David; Dölle, Christian; Friederichsen, Niklas; Hauptvogel, Annika; Kießling, Thomas; Potente, Till; Prote, Jan-Philipp; Weber, Anja; Wolff, Bartholomäus

doi:10.1007/978-3-319-47452-6_12

Cited by 4 publications

(1 citation statement)

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“…[4] In those, a high planning reliability plays a central role in achieving a high logistical performance. [5] In principle, companies can positively influence the delivery reliability by measures such as additional shifts, employee overtimes, short-term outsourcing or targeted overcapacities. However, those measures come at high costs.…”

Section: Introductionmentioning

confidence: 99%

Impact of Modeling Production Knowledge for a Data Based Prediction of Transition Times

Schuh

Prote

Hünnekes

et al. 2019

IFIP Advances in Information and Communication Technology

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An increasing demand for customer-specific products is a major challenge for manufacturing companies. In many cases, companies attempt to satisfy this demand by increasing the number of product variants. In those companies, cost-oriented production processes have to be transformed into flexible workshop or island production structures in order to be able to produce this variety. This leads to an increasing complexity of production and subsequently planning. In order to reliably meet due dates, it is necessary to improve the quality of planning. This paper presents an approach for predicting transition times, the times between two production steps, by employing machine learning methods. In particular, the influence of the modelling of production knowledge of experienced employees on the prediction quality compared to a pure optimization of the methods' parameters is investigated.

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Section: Introductionmentioning

confidence: 99%