TY - GEN
T1 - Simple Analysis of Planning Quality in Production Logistics
AU - Hiller, Tobias
AU - Osterkamp, Lena
AU - Vinke, Lea
AU - Holtsch, Patrick
AU - Mütze, Alexander
AU - Nyhuis, Peter
N1 - Funding Information:
Acknowledgment. This project is funded by the German Federal Ministry of Education and Research, as part of the Aviation Research and Technology Program of the Lower Saxony Ministry of Economics, Labor, Transport and Digitalization (funding code ZW 1 - 80157862).
PY - 2023/9/14
Y1 - 2023/9/14
N2 - On-time delivery is one of the most critical performance characteristics of manufacturing companies. To remain competitive, companies must constantly strive to optimize their logistical performance. Poor on-time delivery has complex causes that are difficult to identify due to the many logistical interdependencies. Increasing market volatility, complex products and production processes, and individual customer requirements further complicate the situation. Digitalization has led to more and more data being available, which requires additional capabilities in data analysis. In order to obtain a fundamental overview of planning quality in production, this paper presents two simple descriptive models. These models can visualize the progression of different KPIs for measuring the planning quality along different production steps. In addition, they allow conclusions to be drawn about the extent to which specific product characteristics have an influence on the planning quality. A case study evaluates the models using a real data set from a maintenance service provider. As production is a complex process that cannot be perfectly planned, these models help to fundamentally understand planning errors and provide a basis for further exploration.
AB - On-time delivery is one of the most critical performance characteristics of manufacturing companies. To remain competitive, companies must constantly strive to optimize their logistical performance. Poor on-time delivery has complex causes that are difficult to identify due to the many logistical interdependencies. Increasing market volatility, complex products and production processes, and individual customer requirements further complicate the situation. Digitalization has led to more and more data being available, which requires additional capabilities in data analysis. In order to obtain a fundamental overview of planning quality in production, this paper presents two simple descriptive models. These models can visualize the progression of different KPIs for measuring the planning quality along different production steps. In addition, they allow conclusions to be drawn about the extent to which specific product characteristics have an influence on the planning quality. A case study evaluates the models using a real data set from a maintenance service provider. As production is a complex process that cannot be perfectly planned, these models help to fundamentally understand planning errors and provide a basis for further exploration.
KW - Data Science
KW - Logistics Performance
KW - Production Planning and Control
UR - https://www.scopus.com/pages/publications/85174437131
U2 - 10.1007/978-3-031-43670-3_50
DO - 10.1007/978-3-031-43670-3_50
M3 - Conference contribution
AN - SCOPUS:85174437131
SN - 9783031436697
SN - 9783031436727
T3 - IFIP Advances in Information and Communication Technology
SP - 722
EP - 734
BT - Advances in Production Management Systems
A2 - Alfnes, Erlend
A2 - Romsdal, Anita
A2 - Strandhagen, Jan Ola
A2 - von Cieminski, Gregor
A2 - Romero, David
PB - Springer Science and Business Media Deutschland GmbH
T2 - IFIP WG 5.7 International Conference on Advances in Production Management Systems, APMS 2023
Y2 - 17 September 2023 through 21 September 2023
ER -