TY - CHAP

T1 - Machine Learning-Supported Planning of Lead Times in Job Shop Manufacturing

AU - Kramer, Kathrin

AU - Wagner, Carsten

AU - Schmidt, Matthias

PY - 2020

Y1 - 2020

N2 - In order to ensure adherence to schedules, knowledge of planned lead times (LT) is crucial for success. In practice, however, rigid planning methods are often used which cannot adequately reflect constantly changing environmental influences (e.g. fluctuations in the daily workload). Particularly in job shop production, precise planning of LT is difficult to implement. This paper therefore examines whether existing machine learning (ML) approaches, in particular supervised learning methods, in production planning can support LT scheduling in job shop production to generate added value. The paper enhances existing research by comparing deep artificial neural networks with ensemble methods (e.g. random forest, boosting decision trees). The applied approach bases on the Cross Industry Standard Process for Data Mining (CRISP-DM), which was created by a consortium of companies. Finally, the evaluation through an exemplary job shop production shows that the present work contributes to mastering the planned LT. In particular, the ML model, boosting decision trees and deep artificial neural networks show significant improvements in planning quality. This practical reference has not yet been addressed comprehensively in the literature.

AB - In order to ensure adherence to schedules, knowledge of planned lead times (LT) is crucial for success. In practice, however, rigid planning methods are often used which cannot adequately reflect constantly changing environmental influences (e.g. fluctuations in the daily workload). Particularly in job shop production, precise planning of LT is difficult to implement. This paper therefore examines whether existing machine learning (ML) approaches, in particular supervised learning methods, in production planning can support LT scheduling in job shop production to generate added value. The paper enhances existing research by comparing deep artificial neural networks with ensemble methods (e.g. random forest, boosting decision trees). The applied approach bases on the Cross Industry Standard Process for Data Mining (CRISP-DM), which was created by a consortium of companies. Finally, the evaluation through an exemplary job shop production shows that the present work contributes to mastering the planned LT. In particular, the ML model, boosting decision trees and deep artificial neural networks show significant improvements in planning quality. This practical reference has not yet been addressed comprehensively in the literature.

KW - Engineering

KW - Machine learning

KW - Production planning & control approaches

KW - Job shop production

KW - Lead times

UR - http://www.scopus.com/inward/record.url?scp=85090170979&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-57993-7_41

DO - 10.1007/978-3-030-57993-7_41

M3 - Article in conference proceedings

SN - 978-3-030-57992-0

VL - 1

T3 - IFIP Advances in Information and Communication Technology

SP - 363

EP - 370

BT - Advances in Production Management Systems. The Path to Digital Transformation and Innovation of Production Management Systems - IFIP WG 5.7 International Conference, APMS 2020, Proceedings

A2 - Lalic, Bojan

A2 - Marjanovic, Ugljesa

A2 - Majstorovic, Vidosav

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer

CY - Cham

T2 - International IFIP WG 5.7 Conference on Advances in Production Management Systems

Y2 - 30 August 2020 through 3 September 2020

ER -