TY - CHAP

T1 - Field-Based Assessment of Key Influence Factors for Throughput Time Prediction in Job Shop Manufacturing

AU - Reinhold, Jonas

AU - Rokoss, Alexander

AU - Schmidt, Matthias

N1 - Publisher Copyright: © IFIP International Federation for Information Processing 2026.

PY - 2026

Y1 - 2026

N2 - To determine delivery dates for customers, plan production capacities, and schedule and coordinate orders, it is of great importance for production planners to work with accurately planned Throughput Times for production orders. However, conventional estimation methods, which are based on fundamental statistical principles and expert knowledge, often fail to adequately account for the numerous factors that can lead to discrepancies between planned and actual outcomes. These discrepancies can have substantial ramifications. As production systems become more complex, the limitations of these conventional approaches have become increasingly evident. The employment of machine learning models holds promise in enhancing the precision of Throughput Time prediction. However, selecting the appropriate data is paramount for training a machine learning model. To achieve this objective, it is essential to identify the key factors that influence the prediction of planned Throughput Times. Despite its relevance, no systematic investigation has yet been conducted on these factors, especially in job shop manufacturing, where orders are processed according to the Make-to-Order principle. To address this research gap, data sets from five Small and Medium-sized Enterprises with job shop production are evaluated and key factors for determining planned Throughput Times are identified. Utilizing the Cross-Industry Standard Process for Data Mining, the characteristics of the individual steps involved in constructing a prediction model are elaborated.

AB - To determine delivery dates for customers, plan production capacities, and schedule and coordinate orders, it is of great importance for production planners to work with accurately planned Throughput Times for production orders. However, conventional estimation methods, which are based on fundamental statistical principles and expert knowledge, often fail to adequately account for the numerous factors that can lead to discrepancies between planned and actual outcomes. These discrepancies can have substantial ramifications. As production systems become more complex, the limitations of these conventional approaches have become increasingly evident. The employment of machine learning models holds promise in enhancing the precision of Throughput Time prediction. However, selecting the appropriate data is paramount for training a machine learning model. To achieve this objective, it is essential to identify the key factors that influence the prediction of planned Throughput Times. Despite its relevance, no systematic investigation has yet been conducted on these factors, especially in job shop manufacturing, where orders are processed according to the Make-to-Order principle. To address this research gap, data sets from five Small and Medium-sized Enterprises with job shop production are evaluated and key factors for determining planned Throughput Times are identified. Utilizing the Cross-Industry Standard Process for Data Mining, the characteristics of the individual steps involved in constructing a prediction model are elaborated.

KW - feature selection

KW - Production planning

KW - Throughput Time prediction

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=105015482717&partnerID=8YFLogxK

U2 - 10.1007/978-3-032-03550-9_14

DO - 10.1007/978-3-032-03550-9_14

M3 - Article in conference proceedings

AN - SCOPUS:105015482717

SN - 9783032035493

T3 - IFIP Advances in Information and Communication Technology

SP - 201

EP - 215

BT - Advances in Production Management Systems. Cyber-Physical-Human Production Systems

A2 - Mizuyama, Hajime

A2 - Morinaga, Eiji

A2 - Kaihara, Toshiya

A2 - Nonaka, Tomomi

A2 - von Cieminski, Gregor

A2 - Romero, David

PB - Springer Science and Business Media Deutschland

T2 - 44th IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2025

Y2 - 31 August 2025 through 4 September 2025

ER -