Use of DES to develop a decision support system for lot size decision-making in manufacturing companies
There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Production and Manufacturing Research: An Open Access Journal |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21693277.2022.2092564 |
| _version_ | 1811238612799324160 |
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| author | Lukas Budde Shuangqing Liao Roman Haenggi Thomas Friedli |
| author_facet | Lukas Budde Shuangqing Liao Roman Haenggi Thomas Friedli |
| author_sort | Lukas Budde |
| collection | DOAJ |
| description | There exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions. |
| first_indexed | 2024-04-12T12:45:42Z |
| format | Article |
| id | doaj.art-0f1b9e149c754707aaa4e791cca356a1 |
| institution | Directory Open Access Journal |
| issn | 2169-3277 |
| language | English |
| last_indexed | 2024-04-12T12:45:42Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Production and Manufacturing Research: An Open Access Journal |
| spelling | doaj.art-0f1b9e149c754707aaa4e791cca356a12022-12-22T03:32:38ZengTaylor & Francis GroupProduction and Manufacturing Research: An Open Access Journal2169-32772022-12-0110149451810.1080/21693277.2022.2092564Use of DES to develop a decision support system for lot size decision-making in manufacturing companiesLukas Budde0Shuangqing Liao1Roman Haenggi2Thomas Friedli3ITEM, Institute of Technology Management at the University of St. Gallen, St. Gallen, SwitzerlandIPEK, Eastern Switzerland University of Applied Sciences, Rapperswil, SwitzerlandIPEK, Eastern Switzerland University of Applied Sciences, Rapperswil, SwitzerlandITEM, Institute of Technology Management at the University of St. Gallen, St. Gallen, SwitzerlandThere exists a wide range of optimization models in the Operations Management (OM) community to solve complex problems such as lot sizing. However, their practical performance is often criticized due to the complexity of implementation and insufficient applicability in real-world decision processes. These theory-driven approaches are either simple to compute, but only focus on single aspects of the decision without being able to capture the practical problem comprehensively, or are complex computational models with limited practicability. We apply a Design Science Research approach to resolve this issue and show how lot size decision-making models should be designed to thoroughly support managers. Our innovative model combines Discrete Event Simulation (DES) with OM methods and is developed and tested in a case study in the metal processing industry. Results reveal that the model is suitable to provide transparency about effects and a range of efficient solutions.https://www.tandfonline.com/doi/10.1080/21693277.2022.2092564Lot sizesmart manufacturingdigital technologiesdecision-support systemsDesign Science Research |
| spellingShingle | Lukas Budde Shuangqing Liao Roman Haenggi Thomas Friedli Use of DES to develop a decision support system for lot size decision-making in manufacturing companies Production and Manufacturing Research: An Open Access Journal Lot size smart manufacturing digital technologies decision-support systems Design Science Research |
| title | Use of DES to develop a decision support system for lot size decision-making in manufacturing companies |
| title_full | Use of DES to develop a decision support system for lot size decision-making in manufacturing companies |
| title_fullStr | Use of DES to develop a decision support system for lot size decision-making in manufacturing companies |
| title_full_unstemmed | Use of DES to develop a decision support system for lot size decision-making in manufacturing companies |
| title_short | Use of DES to develop a decision support system for lot size decision-making in manufacturing companies |
| title_sort | use of des to develop a decision support system for lot size decision making in manufacturing companies |
| topic | Lot size smart manufacturing digital technologies decision-support systems Design Science Research |
| url | https://www.tandfonline.com/doi/10.1080/21693277.2022.2092564 |
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