Details
| Original language | English |
|---|---|
| Pages (from-to) | 1480-1486 |
| Number of pages | 7 |
| Journal | Procedia CIRP |
| Volume | 130 |
| Early online date | 27 Nov 2024 |
| Publication status | Published - 2024 |
| Event | 57th CIRP Conference on Manufacturing Systems 2024, CMS 2024 - Povoa de Varzim, Portugal Duration: 29 May 2024 → 31 May 2024 |
Abstract
The manufacturing industry faces an increasing demand for high product customization and high delivery performance in a volatile market environment. To meet these market demands, companies are reliant on high-performing production logistics. In case of underperformance, root causes must be identified, and appropriate countermeasures must be initiated to avoid jeopardizing customer satisfaction. However, complex logistic cause-effect relationships often obscure the root causes of underperformance. Existing approaches to logistic modeling enable a root cause analysis based on generally valid cause-effect relationships in production logistics. Due to increasing data availability, Data Analytics (DA) and Machine Learning (ML) enable context-specific analysis at a high level of granularity and individuality. Systematically integrating DA/ML with logistic models promises a high potential for revealing actual root causes, thus enabling data-driven and target-oriented countermeasure derivation. This paper presents a production monitoring and control framework for identifying the root causes of logistics underperformance and deriving target-oriented countermeasures that systematically combines the benefits of logistic models and DA/ML. The developed framework comprises a hierarchical target system, a data architecture, and a catalog of countermeasures combined into a monitoring and control procedure. In a case study, the root causes of delayed material availability in the procurement of an electrical machinery manufacturer are analyzed using logistic models and DA/ML to demonstrate the potential of hybrid analysis.
Keywords
- Cause-Effect Relationships, Data Analytics, Logistic Key Performance Indicators, Production Logistics
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Industrial and Manufacturing Engineering
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In: Procedia CIRP, Vol. 130, 2024, p. 1480-1486.
Research output: Contribution to journal › Conference article › Research › peer review
}
TY - JOUR
T1 - Production Monitoring and Control Framework for data-driven improvement of Logistics Performance
AU - Welzel, Kira
AU - Kulaszewski, Dario
AU - Mütze, Alexander
AU - Lucht, Torben
AU - Nyhuis, Peter
AU - Schmidt, Matthias
N1 - Publisher Copyright: © 2024 The Authors. Published by Elsevier B.V.
PY - 2024
Y1 - 2024
N2 - The manufacturing industry faces an increasing demand for high product customization and high delivery performance in a volatile market environment. To meet these market demands, companies are reliant on high-performing production logistics. In case of underperformance, root causes must be identified, and appropriate countermeasures must be initiated to avoid jeopardizing customer satisfaction. However, complex logistic cause-effect relationships often obscure the root causes of underperformance. Existing approaches to logistic modeling enable a root cause analysis based on generally valid cause-effect relationships in production logistics. Due to increasing data availability, Data Analytics (DA) and Machine Learning (ML) enable context-specific analysis at a high level of granularity and individuality. Systematically integrating DA/ML with logistic models promises a high potential for revealing actual root causes, thus enabling data-driven and target-oriented countermeasure derivation. This paper presents a production monitoring and control framework for identifying the root causes of logistics underperformance and deriving target-oriented countermeasures that systematically combines the benefits of logistic models and DA/ML. The developed framework comprises a hierarchical target system, a data architecture, and a catalog of countermeasures combined into a monitoring and control procedure. In a case study, the root causes of delayed material availability in the procurement of an electrical machinery manufacturer are analyzed using logistic models and DA/ML to demonstrate the potential of hybrid analysis.
AB - The manufacturing industry faces an increasing demand for high product customization and high delivery performance in a volatile market environment. To meet these market demands, companies are reliant on high-performing production logistics. In case of underperformance, root causes must be identified, and appropriate countermeasures must be initiated to avoid jeopardizing customer satisfaction. However, complex logistic cause-effect relationships often obscure the root causes of underperformance. Existing approaches to logistic modeling enable a root cause analysis based on generally valid cause-effect relationships in production logistics. Due to increasing data availability, Data Analytics (DA) and Machine Learning (ML) enable context-specific analysis at a high level of granularity and individuality. Systematically integrating DA/ML with logistic models promises a high potential for revealing actual root causes, thus enabling data-driven and target-oriented countermeasure derivation. This paper presents a production monitoring and control framework for identifying the root causes of logistics underperformance and deriving target-oriented countermeasures that systematically combines the benefits of logistic models and DA/ML. The developed framework comprises a hierarchical target system, a data architecture, and a catalog of countermeasures combined into a monitoring and control procedure. In a case study, the root causes of delayed material availability in the procurement of an electrical machinery manufacturer are analyzed using logistic models and DA/ML to demonstrate the potential of hybrid analysis.
KW - Cause-Effect Relationships
KW - Data Analytics
KW - Logistic Key Performance Indicators
KW - Production Logistics
UR - http://www.scopus.com/inward/record.url?scp=85215014214&partnerID=8YFLogxK
U2 - 10.1016/j.procir.2024.10.270
DO - 10.1016/j.procir.2024.10.270
M3 - Conference article
AN - SCOPUS:85215014214
VL - 130
SP - 1480
EP - 1486
JO - Procedia CIRP
JF - Procedia CIRP
SN - 2212-8271
T2 - 57th CIRP Conference on Manufacturing Systems 2024, CMS 2024
Y2 - 29 May 2024 through 31 May 2024
ER -