Details
| Translated title of the contribution | Modellbasierter Ansatz zur Bewertung der Planungsqualität in der Produktionslogistik |
|---|---|
| Original language | English |
| Pages (from-to) | 115077-115089 |
| Number of pages | 13 |
| Journal | IEEE ACCESS |
| Volume | 9 |
| Issue number | 9 |
| Publication status | Published - 13 Aug 2021 |
Abstract
For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model's application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system.
Keywords
- Production management, data integration, production planning, schedules, stability
ASJC Scopus subject areas
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In: IEEE ACCESS, Vol. 9, No. 9, 13.08.2021, p. 115077-115089.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Model-Based Approach for Assessing Planning Quality in Production Logistics
AU - Lucht, Torben
AU - Mütze, Alexander Carolus Erich
AU - Kämpfer, Tim
AU - Nyhuis, Peter
N1 - Funding Information: This work was funded by Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in part ‘‘SFB 871/3’’ - 119193472 and in part ‘‘Systematic analysis of the effect of production planning and control processes on logistical objectives’’ - 434659386.
PY - 2021/8/13
Y1 - 2021/8/13
N2 - For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model's application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system.
AB - For manufacturing companies, reliable production planning and scheduling not only is the basis for efficient order processing but at the same time is an essential prerequisite for the integration and coordination of all participants along the entire supply chain. At the same time, the increasing delegation of planning activities to dynamic software solutions leads to increasing intransparency regarding the planning behavior. It thus becomes increasingly difficult to identify and address inefficiencies or problems caused by the planning processes within industrial supply chains. This paper presents an easy-to-use method for describing, visualizing and analyzing scheduling behavior in manufacturing companies requiring only very few data. In addition, an overview of key planning quality indicators (KPQIs) to be considered in the evaluation of the planning quality is given and structured along the assessment dimensions of plan stability and planning accuracy. The specific application at a maintenance, repair and overhaul (MRO) service provider for complex capital goods demonstrates the benefits and insights to be gained from the model's application, especially in highly dynamic market environments. Using machine learning, characteristic planning patterns can also be statistically determined with the developed description logic and KPQI system.
KW - Production management
KW - data integration
KW - production planning
KW - schedules
KW - stability
UR - http://www.scopus.com/inward/record.url?scp=85113918343&partnerID=8YFLogxK
U2 - 10.1109/access.2021.3104717
DO - 10.1109/access.2021.3104717
M3 - Article
VL - 9
SP - 115077
EP - 115089
JO - IEEE ACCESS
JF - IEEE ACCESS
SN - 2169-3536
IS - 9
ER -