Dergiler / Turkish Journal of Forecasting / 2026 / Volume: 10 Issue: 2

Analysis of Manufacturing Industry Capacity Utilization Rate Forecasts Using Decision Tree-Based Algorithms

Enes Filiz(BALIKESİR ÜNİVERSİTESİ)0000-0002-8006-9467
Sayfa
—
Yayın Tarihi
2026-09-14
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

These variables were determined to be ‘Manufacture of electrical equipment’, ‘Non-Durable Consumer Goods’, ‘Investment Goods’, and ‘Durable Consumer Goods’, respectively.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In the globalizing world order, different sectors are affected by global events.
📊
Temel Bulgular & Veri:In this context, this study aims to forecast the future values of Türkiye's manufacturing industry capacity utilization rate.
💡
Sonuç & Katkı:These variables were determined to be ‘Manufacture of electrical equipment’, ‘Non-Durable Consumer Goods’, ‘Investment Goods’, and ‘Durable Consumer Goods’, respectively.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2013 Verileri
⚙️Değişkenler:Forecasting · Machine Learning · Decision Trees

Abstract

In the globalizing world order, different sectors are affected by global events. The manufacturing sector is one of the most critical sectors for both individual countries and the world at large. The development of this sector alleviates the economic burden on countries and helps them gain economic independence. In this context, this study aims to forecast the future values of Türkiye's manufacturing industry capacity utilization rate. To this end, monthly data from June 2013 to December 2024 were used to forecast all months of 2025. The Random Tree and Random Forest Algorithms, which are machine learning algorithms, were employed for this forecasting process. As a result of the study, the Random Tree algorithm showed a better forecasting performance with an accuracy of 83.48%. Furthermore, the Correlation Attribute Feature Selection Algorithm was used to identify the variables affecting the prediction of Türkiye's manufacturing industry capacity utilization rate. These variables were determined to be ‘Manufacture of electrical equipment’, ‘Non-Durable Consumer Goods’, ‘Investment Goods’, and ‘Durable Consumer Goods’, respectively.

🔖

Bu Makaleye Atıf Yap

Filiz, E. (2026). Analysis of Manufacturing Industry Capacity Utilization Rate Forecasts Using Decision Tree-Based Algorithms. Turkish Journal of Forecasting, Volume: 10 Issue: 2. https://doi.org/10.34110/forecasting.1863623

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (56)

  1. Baba Dauda, A., & Shettima, M. (2023). Comparative Analysis of Cluster and Random Tree Algorithm Based on Students Performance Outcome. Fane-Fane International Multidisciplinary Journal, 7(2 November), 335-341.
  2. Berndt, E. R., & Hesse, D. M. (1986). Measuring and assessing capacity utilization in the manufacturing sectors of nine OECD countries. European Economic Review, 30(5), 961-989.
  3. Birant, K. U., Ghasemkhani, B., Varlıklar, Ö., & Birant, D. (2025). A new machine learning method for rainfall classification: temporal random tree. PeerJ Computer Science, 11, e3022.
  4. Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
  5. Breiman, L., Friedman, J. H., Stone, C. J., & Olshen, R. A. (1984). Classification algorithms and regression trees. Classification and Regression Trees, 246-279.
  6. Canbolat, Y. B., Chelst, K., & Garg, N. (2007). Combining decision tree and MAUT for selecting a country for a global manufacturing facility. Omega, 35(3), 312-325.
  7. Central Bank of the Republic of Türkiye Web Page. (2025). Türkiye manufacturing industry capacity utilization rate. Access date: 05.01.2026: https://evds2.tcmb.gov.tr/index.php?/evds/serieMarket/#collapse_2.
  8. Chen, P., Li, F., & Wu, C. (2021). Research on intrusion detection method based on Pearson correlation coefficient feature selection algorithm. In Journal of Physics: Conference Series (Vol. 1757, No. 1, p. 012054). IOP Publishing.
  9. Chen, T., Sampath, V., May, M. C., Shan, S., Jorg, O. J., Aguilar Martin, J. J.,. & Calaon, M. (2023). Machine learning in manufacturing towards industry 4.0: From ‘for now’to ‘four-know’. Applied Sciences, 13(3), 1903.
  10. Chen, X. W., & Liu, M. (2005). Prediction of protein–protein interactions using random decision forest framework. Bioinformatics, 21(24), 4394-4400.
  11. Cheng, Z., Liu, J., Li, L., & Gu, X. (2020). The effect of environmental regulation on capacity utilization in China’s manufacturing industry. Environmental Science and Pollution Research, 27(13), 14807-14817.
  12. Chopra, V., & Priyadarshi, D. (2019). Role of machine learning in manufacturing sector. Int. J. Recent Technol. Eng, 8(4), 2320-2328.
  13. Corrado, C., & Mattey, J. (1997). Capacity utilization. Journal of Economic Perspectives, 11(1), 151-167.
  14. Dahal, N. P., & Shakya, S. (2022). A comparative analysis of prediction of student results using decision trees and random forest. Journal of Trends in Computer Science and Smart Technology, 4(3), 113-125.
  15. Emmoh, P. U., Eke, C. I., & Moses, T. (2025). A feature selection and scoring scheme for dimensionality reduction in a machine learning task. Journal of the Nigerian Society of Physical Sciences, 2273-2273.
  16. Filiz, E., & Öz, E. (2017). Classification of BIST-100 index’ changes via machine learning methods. Marmara University Journal of Economic and Administrative Sciences, 39(1), 117-129.
  17. Filiz, E., & Öz, E. (2019). Finding the Best Algorithms and Effective Factors in Classification of Turkish Science Student Success. Journal of Baltic Science Education, 18(2), 239-253.
  18. Forsman, H. (2011). Innovation capacity and innovation development in small enterprises. A comparison between the manufacturing and service sectors. Research policy, 40(5), 739-750.
  19. Geng, N., & Jiang, Z. (2009). A review on strategic capacity planning for the semiconductor manufacturing industry. International journal of production research, 47(13), 3639-3655.
  20. Ghasemkhani, B., Balbal, K. F., Birant, K. U., & Birant, D. (2025). Ordinal random tree with rank-oriented feature selection (ORT-ROFS): a novel approach for the prediction of road traffic accident severity. Mathematics, 13(2), 310.
  21. Greenwood, J., Hercowitz, Z., & Huffman, G. W. (1988). Investment, capacity utilization, and the real business cycle. The American Economic Review, 402-417.
  22. Hassani, I. E., Mazgualdi, C. E., & Masrour, T. (2019). Artificial intelligence and machine learning to predict and improve efficiency in manufacturing industry. arXiv preprint arXiv:1901.02256.
  23. Jagannathan, G., Pillaipakkamnatt, K., & Wright, R. N. (2009, December). A practical differentially private random decision tree classifier. In 2009 IEEE international conference on data mining workshops (pp. 114-121). IEEE.
  24. Kneller, R., & Stevens, P. A. (2006). Frontier technology and absorptive capacity: Evidence from OECD manufacturing industries. Oxford Bulletin of Economics and Statistics, 68(1), 1-21.
  25. Koulinas, G., Paraschos, P., & Koulouriotis, D. (2020). A Decision Trees-based knowledge mining approach for controlling a complex production system. Procedia Manufacturing, 51, 1439-1445.
  26. Li, B. H., Hou, B. C., Yu, W. T., Lu, X. B., & Yang, C. W. (2017). Applications of artificial intelligence in intelligent manufacturing: a review. Frontiers of Information Technology & Electronic Engineering, 18(1), 86-96.
  27. Li, D., Yang, Y., Chang, X., & Huang, X. (2025, January). Research on Multistage Production Decision-Making Strategy Based on Decision Tree Algorithm. In Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering (pp. 166-171).
  28. Liu, P., Huang, S. H., Mokasdar, A., Zhou, H., & Hou, L. (2014). The impact of additive manufacturing in the aircraft spare parts supply chain: supply chain operation reference (scor) model based analysis. Production planning & control, 25(13-14), 1169-1181.
  29. Mao, Y., Holmström, J., & Cheng, Y. (2025). Applying machine learning to predict production capacity for engineer-to-order products: Learning from wind turbine industry. Technological Forecasting and Social Change, 219, 124295.
  30. Mayr, A., Kißkalt, D., Meiners, M., Lutz, B., Schäfer, F., Seidel, R.,. & Franke, J. (2019). Machine learning in production–potentials, challenges and exemplary applications. Procedia CIRP, 86, 49-54.
  31. Mubarak, M. F., Ghobakhloo, M., Evans, R., Jucevicius, G., Prestianawati, S. A., & Mubarik, M. (2025). Metaverse adoption in the manufacturing industry: impact on social and environmental sustainability performance. Asia-Pacific Journal of Business Administration, 17(4), 915-934.
  32. Prajwala, T. R. (2015). A comparative study on decision tree and random forest using R tool. International journal of advanced research in computer and communication engineering, 4(1), 196-199.
  33. Price, S. (1995). Aggregate uncertainty, capacity utilization and manufacturing investment. Applied Economics, 27(2), 147-154.
  34. Rai, R., Tiwari, M. K., Ivanov, D., & Dolgui, A. (2021). Machine learning in manufacturing and industry 4.0 applications. International Journal of Production Research, 59(16), 4773-4778.
  35. Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review. Partners Universal International Innovation Journal, 2(3), 147-171.
  36. Ray, S. C., Mukherjee, K., & Wu, Y. (2006). Direct and indirect measures of capacity utilization: a non‐parametric analysis of US manufacturing. The Manchester School, 74(4), 526-548.
  37. Realyvásquez-Vargas, A., Arredondo-Soto, K. C., Carrillo-Gutiérrez, T., & Ravelo, G. (2018). Applying the Plan-Do-Check-Act (PDCA) cycle to reduce the defects in the manufacturing industry. A case study. Applied Sciences, 8(11), 2181.
  38. Saidi, R., Bouaguel, W., & Essoussi, N. (2018). Hybrid feature selection method based on the genetic algorithm and pearson correlation coefficient. In Machine learning paradigms: theory and application (pp. 3-24). Cham: Springer International Publishing.
  39. Salman, H. A., Kalakech, A., & Steiti, A. (2024). Random forest algorithm overview. Babylonian Journal of Machine Learning, 2024, 69-79.
  40. Shea, J. (1993). Do supply curves slope up?. The Quarterly Journal of Economics, 108(1), 1-32.
  41. Simon-Oke, O. O., & Awoyemi, O. V. (2010). Manufacturing capacity utilization and industrial development in Nigeria: An assessment (1976–2005). African Research Review, 4(2).
  42. Singh, J., & Aggarwal, P. K. (2024). Comparative Analysis of Productivity Growth of Different Industries: An-Empirical Evidence from India. Meerayan Journal, 24 (8), 103-129
  43. Singh, M., Rathi, R., & Singh Kaswan, M. (2022). Capacity utilization in industrial sector: a structured review and implications for future research. World Journal of Engineering, 19(3), 310-328.
  44. Solke, N. S., Shah, P., Sekhar, R., & Singh, T. P. (2022). Machine learning-based predictive modeling and control of lean manufacturing in automotive parts manufacturing industry. Global Journal of Flexible Systems Management, 23(1), 89-112.
  45. Sugianela, Y., & Ahmad, T. (2020, February). Pearson correlation attribute evaluation-based feature selection for intrusion detection system. In 2020 International Conference on Smart Technology and Applications (ICoSTA) (pp. 1-5). IEEE.
  46. Usuga Cadavid, J. P., Lamouri, S., Grabot, B., Pellerin, R., & Fortin, A. (2020). Machine learning applied in production planning and control: a state-of-the-art in the era of industry 4.0. Journal of Intelligent Manufacturing, 31(6), 1531-1558.
  47. Wang, H., & Li, B. (2021). Environmental regulations, capacity utilization, and high-quality development of manufacturing: An analysis based on Chinese provincial panel data. Scientific reports, 11(1), 19566.
  48. Wuest, T., Weimer, D., Irgens, C., & Thoben, K. D. (2016). Machine learning in manufacturing: advantages, challenges, and applications. Production & manufacturing research, 4(1), 23-45.
  49. Xie, B., Yang, C., Song, W., Song, L., & Wang, H. (2023). The impact of environmental regulation on capacity utilization of China’s manufacturing industry: An empirical research based on the sector level. Ecological Indicators, 148, 110085.
  50. Xie, W., Zou, Y., Guo, H., & Wang, Y. (2024). Digital innovation and core competence of manufacturing industry: Moderating role of absorptive capacity. Emerging Markets Finance and Trade, 60(1), 185-202.
  51. Xu, T. (2024). Recent advances in Rapidly-exploring random tree: A review. Heliyon, 10(11).
  52. Yeo, B., & Grant, D. (2018). Predicting service industry performance using decision tree analysis. International Journal of Information Management, 38(1), 288-300.
  53. Zhang, M., Zhao, Y., & Tian, Y. (2024, May). Research on Production Capacity Utilization Index Based on Electricity Big Data. In 2024 IEEE 10th Conference on Big Data Security on Cloud (BigDataSecurity) (pp. 135-138). IEEE.
  54. Zhao, C., Zhu, Y., Du, Y., Liao, F., & Chan, C. Y. (2022). A novel direct trajectory planning approach based on generative adversarial networks and rapidly-exploring random tree. IEEE Transactions on Intelligent Transportation Systems, 23(10), 17910-17921.
  55. Chen, T., Sampath, V., May, M. C., Shan, S., Jorg, O. J., Aguilar Martin, J. J.,. & Calaon, M. (2023). Machine learning in manufacturing towards industry 4.0: From ‘for now’to ‘four-know’. Applied Sciences, 13(3), 1903.
  56. Mayr, A., Kißkalt, D., Meiners, M., Lutz, B., Schäfer, F., Seidel, R.,. & Franke, J. (2019). Machine learning in production–potentials, challenges and exemplary applications. Procedia CIRP, 86, 49-54.