<div class="csl-bib-body">
<div class="csl-entry">LIngitz, L., Gallina, V., Breitschopf, J., Finamore, L., & Sihn, W. (2023). Quality in production planning: Definition, quantification and a machine learning based improvement method. In <i>4th International Conference on Industry 4.0 and Smart Manufacturing (ISM 2022)</i> (pp. 358–365). Elsevier. https://doi.org/10.1016/j.procs.2022.12.231</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/154035
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dc.description.abstract
The reliability of production plans drops drastically within several days after plan creation. The reasons for the deviation between planning and execution are manifold. Causes can be e.g., uncertainties, inaccurate or insufficient planning data (e.g., data quality and availability), inappropriate planning and control models and systems or unforeseeable events, leading to high control effort in order to reach the desired logistical KPI's, such as due date reliability or lead time. The paper addresses this problem with machine learning and states a methodological approach to measure and improve the quality in production planning. Although the term “planning quality” (PQ) has been used several times by the scientific community, a standard definition of PQ in the field of production planning and a clear distinction to other similar concepts like “robust planning” is still missing. Furthermore, PQ and its application within the production planning process are evaluated with a real industrial use case from the steel manufacturing industry.
en
dc.language.iso
en
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dc.relation.ispartofseries
Procedia computer science
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dc.rights.uri
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.subject
production planning
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dc.subject
scheduling resilience
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dc.subject
machine learning
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dc.subject
planning quality prediction
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dc.title
Quality in production planning: Definition, quantification and a machine learning based improvement method
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dc.type
Inproceedings
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dc.type
Konferenzbeitrag
de
dc.rights.license
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
en
dc.rights.license
Creative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International