A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application
Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and...
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MDPI AG
2025-07-01
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| Series: | Machines |
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| Online Access: | https://www.mdpi.com/2075-1702/13/7/602 |
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| author | Valentina De Simone Valentina Di Pasquale Joanna Calabrese Salvatore Miranda Raffaele Iannone |
| author_facet | Valentina De Simone Valentina Di Pasquale Joanna Calabrese Salvatore Miranda Raffaele Iannone |
| author_sort | Valentina De Simone |
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| description | Predicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs. |
| format | Article |
| id | doaj-art-3f47309203ca44278c435faf2c0707cd |
| institution | DOAJ |
| issn | 2075-1702 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-3f47309203ca44278c435faf2c0707cd2025-08-20T03:08:01ZengMDPI AGMachines2075-17022025-07-0113760210.3390/machines13070602A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study ApplicationValentina De Simone0Valentina Di Pasquale1Joanna Calabrese2Salvatore Miranda3Raffaele Iannone4Department of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyMotortecnica S.r.l., 84099 San Cipriano Picentino, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyDepartment of Industrial Engineering, University of Salerno, 84084 Fisciano, ItalyPredicting workload for tasks in manufacturing is a complex challenge due to the numerous variables involved. In small- and medium-sized enterprises (SMEs), this process is often experience-based, leading to inaccurate predictions that significantly impact production planning, order management, and consequently the ability to meet customer deadlines. This paper presents an approach that leverages machine learning to enhance workload prediction with minimal data collection, making it particularly suitable for SMEs. A case study application using supervised machine learning models for regression, trained in an open-source data analytics, reporting, and integration platform (KNIME Analytics Platform), has been carried out. An Automated Machine Learning (AutoML) regression approach was employed to identify the most suitable model for task workload prediction based on minimising the Mean Absolute Error (MAE) scores. Specifically, the Regression Tree (RT) model demonstrated superior accuracy compared to more traditional simple averaging and manual predictions when modelling data for a single product type. When incorporating all available product data, despite a slight performance decrease, the XGBoost Tree Ensemble still outperformed the traditional approaches. These findings highlight the potential of machine learning to improve workload forecasting in manufacturing, offering a practical and easily implementable solution for SMEs.https://www.mdpi.com/2075-1702/13/7/602supervised learningAutoMLworkload predictionplanningSMEs |
| spellingShingle | Valentina De Simone Valentina Di Pasquale Joanna Calabrese Salvatore Miranda Raffaele Iannone A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application Machines supervised learning AutoML workload prediction planning SMEs |
| title | A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application |
| title_full | A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application |
| title_fullStr | A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application |
| title_full_unstemmed | A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application |
| title_short | A Supervised Machine Learning-Based Approach for Task Workload Prediction in Manufacturing: A Case Study Application |
| title_sort | supervised machine learning based approach for task workload prediction in manufacturing a case study application |
| topic | supervised learning AutoML workload prediction planning SMEs |
| url | https://www.mdpi.com/2075-1702/13/7/602 |
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