Manufacturing Technology 2026, 26(1):53-62 | DOI: 10.21062/mft.2026.010

Machine Learning Regression Approaches for Manufacturing Cost and Time Prediction: A Comprehensive Review

Michal Matějka ORCID...1, Milan Dian ORCID...2, Jan Lhota ORCID...1, Theodor Beran ORCID...1, Vojtěch Hlinák ORCID...
1 Faculty of Mechanical Engineering, CTU in Prague, Karlovo náměstí 13, 121 35, Prague 2 – Nové město. Czech Republic
2 Faculty of Mechanical Engineering, J. E. Purkyne University in Usti nad Labem. Pasteurova 3334/7, 400 01 Usti nad Labem. Czech Republic

Today, machine learning regression methods are quietly but fundamentally transforming cost and time estimation in manufacturing: from early pricing to labor planning to operational order management. This survey offers a comprehensive map of approaches - from linear models, to tree ensembles (RF, GBM, XGBoost) and shallow neural networks, to multi-target and tensor regressions that can exploit data structure across BOM items and sequences of operations. With an emphasis on SME conditions, we show how to reconcile three often conflicting requirements of practice: accuracy, explainability, and integration into existing data flows (MES/ERP). The paper presents a comparative taxonomy of methods, recommended validation practices (MAE, RMSE, MAPE, R² including confidence intervals) and a pragmatic adoption trajectory: from regularized multiple regressions to tree models to multi-output formulations sharing re-presentations across operations. Consolidated findings show that modern learners consistently outperform traditional baselines when supported by careful flag engineering, drift management, and data standardization. As a major research-application contribution, we propose a unified multi-objective framework for simultaneous cost and time prediction that combines domain (queueing/simulation) features with data-driven regression to enable transparent decision making in pricing and capacity planning. The study thus creates a bridge between theory and manufacturing practice and invites the reader to systematically but achievably deploy ML in everyday decision making.

Keywords: Machine Learning, Regression, Cost Estimation, Time Prediction, Manufacturing

Received: September 5, 2025; Revised: February 28, 2026; Accepted: March 6, 2026; Prepublished online: March 20, 2026; Published: March 21, 2026  Show citation

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Matějka M, Dian M, Lhota J, Beran T, Hlinák V. Machine Learning Regression Approaches for Manufacturing Cost and Time Prediction: A Comprehensive Review. Manufacturing Technology. 2026;26(1):53-62. doi: 10.21062/mft.2026.010.
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    References

    1. BODENDORF, F., FRANKE, J. (2021). A machine learning approach to estimate product costs in the early product design phase: a use case from the automotive industry. In: Procedia CIRP, Vol. 100, pp. 643 - 648. Elsevier B. V. Netherlands. ISSN 2212-8271. Go to original source...
    2. IŞIK, K., ALPTEKIN, E., S. (2022). A benchmark comparison of Gaussian process regression, support vector machines, and ANFIS for man-hour prediction in power transformers manufacturing. In: Procedia Computer Science, Vol. 207, pp. 2567 - 2577. Elsevier B. Netherlands. ISSN 1877-0509. Go to original source...
    3. BODENDORF, F., MERKL, P., FRANKE, J. (2021). Intelligent cost estimation by machine learning in supply management: A structured literature review. In: Computers and Industrial Engineering, Vol. 160. Elsevier Ltd. United Kingdom. ISSN 0360-8352. Go to original source...
    4. LIU, B., JIANG, Z., H. (2005). The man-hour estimation models & its comparison of interim products assembly for shipbuilding. In: International Journal of Operations Research, Vol. 2, No. 1, pp. 9 - 14. In-derscience Enterprises. United Kingdom. ISSN 1745-7645.
    5. HUR, M., LEE, S., K., BONGSEOK, K., CHO, S., LEE, Do., LEE, Da. (2015). A study on the man-hour prediction system for shipbuilding. In: Journal of Intelligent Manufacturing, Vol. 26, No. 6, pp. 1267 - 1279. Springer. Netherlands. ISSN 0956-5515. Go to original source...
    6. YU, T., CAI, H. (2015). The Prediction of the Man-Hour in Aircraft Assembly Based on Support Vector Machine Particle Swarm Optimization. In: Journal of Aerospace Technology and Management, Vol. 7, No. 1, pp. 19 - 30. Departamento de Ciencia e Technologia Aeroespacial. Brazil. ISSN 1984-9648. Go to original source...
    7. WEN, J., LI, S., LIN, Z., HU, Y., HUANG, CH. (2012). Systematic literature review of machine learning based software development effort estimation models. In: Information and Software Technology, Vol. 54, No. 1, pp. 41 - 59. Elsevier. Netherlands. ISSN 0950-5849. Go to original source...
    8. QIN, J., HU, F., LIU, Y., WITHERELL, P., WANG, C., C., L., ROSEN, D., W., SIMPSON, T., W., LU, Y., TANG, Q. (2022). Research and application of machine learning for additive manufacturing. In: Additive Manufacturing, Vol. 52. Elsevier. Netherlands. ISSN 2214-8604. Go to original source...
    9. CHAN, S., L., LU, Y., WANG, Y. (2018). Data-driven cost estimation for additive manufacturing in cybermanufacturing. In: Journal of Manufacturing Systems, Vol. 46, pp. 115 - 126. Elsevier. Netherlands. ISSN 0278-6125. Go to original source...
    10. DENG, S., YEH, T., S. (2011). Using least squares support vector machines for the airframe structures manufacturing cost estimation. In: International Journal of Production Economics, Vol. 131, No. 2, pp. 701 - 708. Elsevier. Netherlands. ISSN 0925-5273. Go to original source...
    11. SAJADFAR, N., MA, Y. (2015). A hybrid cost estimation framework based on feature-oriented data min-ing approach. In: Advanced Engineering Informatics, Vol. 29, No. 3, pp. 633 - 647. Elsevier. United Kingdom. ISSN 1474-0346. Go to original source...
    12. GUNDUZ, M., UGUR, L., O., OZTURK, E. (2011). Parametric cost estimation system for light rail transit and metro trackworks. In: Expert Systems with Applications, Vol. 38, No. 3, pp. 2873 - 2877. Elsevier. United Kingdom. ISSN 0957-4174. Go to original source...
    13. HAMRANI, A., AGARWAL, A., ALLOUHI, A., MCDANIEL, D. (2024). Applying machine learning to wire arc additive manufacturing: a systematic data-driven literature review. In: Journal of Intelligent Manufacturing, Vol. 35, No. 6, pp. 2407 - 2439. Springer. Netherlands. ISSN 0956-5515. Go to original source...
    14. NGUYEN, T., H., V., HUANG, P., M., CHIEN, CH., F., CHANG, CH., K. (2023). Digital transformation for cost estimation system via meta-learning and an empirical study in aerospace industry. In: Computers and Industrial Engineering, Vol. 184. Elsevier. United Kingdom. ISSN 0360-8352. Go to original source...
    15. NING, F., SHI, Y., CAI, M., XU, W., ZHANG, X. (2020). Manufacturing cost estimation based on the machining process and deep-learning method. In: Journal of Manufacturing Systems, Vol. 56, pp. 11 - 12. Elsevier. Netherlands. ISSN 0278-6125. Go to original source...
    16. ELMOUSALAMI, H., H. (2020). Artificial Intelligence and Parametric Construction Cost Estimate Modelling: State-of-the-Art Review. In: Journal of Construction Engineering and Management, Vol. 146, No. 1. American Society of Civil Engineers. United States. ISSN 0733-9364. Go to original source...
    17. SHAMIM, M., M., I., HAMID, A., B., A., NYAMASVISVA, T., E., RAFI, N., S., B. (2025). Advance-ment of Artificial Intelligence in Cost Estimation for Project Management Success: A Systematic Review of Machine Learning, Deep Learning, Regression, and Hybrid Models. Online. Modelling. Vol. 2025, no. 6, article 35. ISSN 2673‑3951. https://doi.org/https://doi.org/10.3390/ modelling6020035. Go to original source...
    18. HENNEBOLD, CH., KLÖPFER, K., LETTENBAUER, P., HUBER, M. (2022) Machine Learning based Cost Prediction for Product Development in Mechanical Engineering. Online. Procedia CIRP (55th CIRP Conference on Manufacturing Systems). Article 107, s. 264-269. ISSN 2212-8271. https://doi.org/10.1016/j.procir.2022.04.043. Go to original source...
    19. LIU, J., ZHU, C., LONG, Z., LIU, Y. (2021). "Tensor Regression", Foundations and Trends® in Machine Learning. 14. No. 4, pp 379-565. Hanover, MA 02339: now Publishers. ISBN 978-1-68083-887-9. http://dx.doi.org/10.1561/2200000087. Go to original source...
    20. LIU, M., L., ZHANG, J., QIN, S., ZHANG, K., WANG, S., et al. (2025). A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing work-shops. Online. Measurement. Vol. 2025, no. 242, article 116231. ISSN 0263-2241. https://doi.org/https://doi.org/10.1016/j.measurement.2024.116231. Go to original source...
    21. NIAZI, A., DAI, J., S., BALABANI, S., SENEVIRATNE, L., (2021). "Product Cost Estimation: Technique Classification and Methodology Review." ASME. J. Manuf. Sci. Eng. May 2006; 128(2): 563-575. https://doi.org/10.1115/1.2137750 Go to original source...
    22. BREIMAN, L., FRIEDMAN, J., OLSHEN, R., A., STONE, C., J. (1984). Classification and Regression Trees (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781315139470 Go to original source...
    23. MONDON, C. (1984). Classification and regression trees.
    24. BREIMAN, L. Random Forests. (2001). Machine Learning 45, 5-32 https://doi.org/10.1023/A:1010933404324 Go to original source...
    25. FRIEDMAN, J. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29, 1189-1232. http://dx.doi.org/10.1214/aos/1013203451 Go to original source...
    26. TIANGI, CH., GUESTRIN, C. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '16). Association for Computing Machinery, New York, NY, USA, 785-794. https://doi.org/10.1145/2939672.2939785 Go to original source...
    27. LESZCZYŃSKI, Z., JASIŃSKI, T. (2020). Comparison of Product Life Cycle Cost Estimating Models Based on Neural Networks and Parametric Techniques-A Case Study for Induction Motors. Sustaina-bility. 12. 8353. 10.3390/su12208353. Go to original source...
    28. KO, H., S., SEO, H., G. (2025). A Machine Learning Approach for Manufacturing Cost Estimation. Online. Roč. 2024, s. 181-214. ISSN 1598-978X. Dostupné z: https://doi.org/https://doi.org/10.31507/KJMAR.2024.12.24.3.181. [cit. 2025-07-26]. Go to original source...
    29. NING, F., QU, H., SHI, Y., CAI, M., Xu, W. (2022). Feature-Based and Process-Based Manufacturing Cost Estimation. Machines. 10. 319. 10.3390/machines10050319. Go to original source...
    30. HE, D., SUN, S., XIE, L. (2023). Multi-target regression based on multi-layer sparse structure and its application in warships scheduled maintenance cost prediction. Applied Sciences, 13(1), 435. Go to original source...
    31. BROWN, B. (2025). AI-Driven Cost Estimation Models for Complex Manufacturing Systems. Online. Roč. 2025. Dostupné z: https://www.researchgate.net/publication/393661904.
    32. MÄHLKVIST, S., EJENSTAM, J., KYPRIANIDIS, K. (2023). Cost-Sensitive Decision Support for In-dustrial Batch Processes. Online. Sensors. Vol. 2023, no. 23, article 9464. ISSN 1424-8220. Dostupné z: https://doi.org/https://doi.org/10.3390/s23239464. Go to original source...
    33. LIU, M., L., ZHANG, J., QIN, S., ZHANG, K., WANG, S., et al. (2025). A multi-target regression-based method for multiple orders remaining completion time prediction in discrete manufacturing work-shops. Online. Measurement. Vol. 2025, no. 242, article 116231. ISSN 0263-2241. Dostupné z: https://doi.org/https://doi.org/10.1016/j.measurement.2024.116231. Go to original source...
    34. ANTONENKO, E. (2023). Multi-target Learning and Prediction : Novel Methods and Applications. Artificial Intelligence. [cs.AI]. Institut Polytechnique de Paris. ISBN NNT : 2023IPPAX098.
    35. TSALLIS, CH., PAPAGEORGAS, P., PIROMALIS, D., MUNTEANU, R., A. (2025). Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions. Online. Applied Sciences. 2025, No. 15, article 4898. ISSN 2076‑3417. Available: https://doi.org/https://doi.org/10.3390/ app15094898. Go to original source...
    36. ZHANG, M., SUN, Z., ZHENG, W. (2025). Fault Diagnosis of Electric Motor Rotor System Based on Feature Exreaction & CNN-BiGRU-Attention. Manufacturing Technology. Vol. 25, No. 4. ISSN 1213-2489. DOI: 10.21062/mft.2025.048 Go to original source...
    37. CÉZOVÁ, E. (2025). Demonstration of Neural Network in Prediction of Bearing Lifetime. Manufacturing Technology. Vol. 25, No. 2. ISSN 1213-2489. DOI: 10.21062/mft.2025.017 Go to original source...
    38. LUSI, N., FIVERIATI, A., AFANDI, A., WEDARMA, G., N., B., C., S., YULIANDOKO, H., DARSIN, M., QUTABA, S. (2023). Predictive Modelling On Machining Performance of ECDM Using Artificial Neural Network and Particle Swarm Optimization. Manufacturing Technology. Vol. 23, No. 5. ISSN 1213-2489. DOI: 10.21062/mft.2023.076 Go to original source...

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