Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches
This article explores the application of process mining techniques to dynamically observe operations and enhance production planning efficiency in assemble-to-order (ATO) manufacturing systems using a hybrid approach. The study addresses critical challenges in managing the repetitive and complex wor...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10870119/ |
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| author | Ufuk Celik Yuksel Yurtay |
| author_facet | Ufuk Celik Yuksel Yurtay |
| author_sort | Ufuk Celik |
| collection | DOAJ |
| description | This article explores the application of process mining techniques to dynamically observe operations and enhance production planning efficiency in assemble-to-order (ATO) manufacturing systems using a hybrid approach. The study addresses critical challenges in managing the repetitive and complex workflows characteristic of ATO models. It highlights inefficiencies that impact productivity, lead times, and process flow. By combining direct flow graphs, inductive visual miner, and state chart workflows with a recursive algorithm, the proposed hybrid methodology identifies bottlenecks with repetitive works, analyzes variant impacts, and uncovers non-value-added activities. As a case study, the manufacturing processes of an automotive company employing an ATO strategy were analyzed. The hybrid approach achieved a fitness value of 92.5% with 77% precision, significantly improving upon the initial life cycle miner algorithm results (fitness value: 79.5%). Insights derived from the analysis informed actionable recommendations to optimize production workflows. This study demonstrates the potential of hybrid process mining techniques for understanding and improving ATO systems, offering a dynamic and scalable framework for addressing inefficiencies in similar production models. |
| format | Article |
| id | doaj-art-e1005fd6b1024de4a49a0b40a41f615f |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e1005fd6b1024de4a49a0b40a41f615f2025-02-11T00:00:38ZengIEEEIEEE Access2169-35362025-01-0113238662387610.1109/ACCESS.2025.353817110870119Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid ApproachesUfuk Celik0https://orcid.org/0000-0003-3063-6272Yuksel Yurtay1https://orcid.org/0000-0003-1814-3432Department of Management Information Systems, Faculty of Applied Sciences, Bandirma Onyedi Eylul University, Balikesir, TürkiyeDepartment of Computer Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya, TürkiyeThis article explores the application of process mining techniques to dynamically observe operations and enhance production planning efficiency in assemble-to-order (ATO) manufacturing systems using a hybrid approach. The study addresses critical challenges in managing the repetitive and complex workflows characteristic of ATO models. It highlights inefficiencies that impact productivity, lead times, and process flow. By combining direct flow graphs, inductive visual miner, and state chart workflows with a recursive algorithm, the proposed hybrid methodology identifies bottlenecks with repetitive works, analyzes variant impacts, and uncovers non-value-added activities. As a case study, the manufacturing processes of an automotive company employing an ATO strategy were analyzed. The hybrid approach achieved a fitness value of 92.5% with 77% precision, significantly improving upon the initial life cycle miner algorithm results (fitness value: 79.5%). Insights derived from the analysis informed actionable recommendations to optimize production workflows. This study demonstrates the potential of hybrid process mining techniques for understanding and improving ATO systems, offering a dynamic and scalable framework for addressing inefficiencies in similar production models.https://ieeexplore.ieee.org/document/10870119/Business intelligenceprocess miningprocess analysisproduction planning |
| spellingShingle | Ufuk Celik Yuksel Yurtay Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches IEEE Access Business intelligence process mining process analysis production planning |
| title | Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches |
| title_full | Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches |
| title_fullStr | Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches |
| title_full_unstemmed | Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches |
| title_short | Improvement of Assemble-to-Order Model Processes With Process Mining: Dynamic Analysis and Hybrid Approaches |
| title_sort | improvement of assemble to order model processes with process mining dynamic analysis and hybrid approaches |
| topic | Business intelligence process mining process analysis production planning |
| url | https://ieeexplore.ieee.org/document/10870119/ |
| work_keys_str_mv | AT ufukcelik improvementofassembletoordermodelprocesseswithprocessminingdynamicanalysisandhybridapproaches AT yukselyurtay improvementofassembletoordermodelprocesseswithprocessminingdynamicanalysisandhybridapproaches |