Demand forecasting application with regression and artificial intelligence methods in a construction machinery company


Demand forecasts are used as input to planning activities and play an important role in the management of fundamental operations. Accurate demand forecasting is an important information for many organizations. It provides information for each stage of inventory management. In this study, multiple linear regression analysis, multiple nonlinear regression analysis, artificial neural networks and support vector regression were applied in a production facility that produces spare parts of construction machinery. The aim of the study is to forecast the number of spare parts requested in the future period by the customer as close as possible. As the input variables in the developed models, the sales amounts of the past years belonging to the manifold product group, which is one of the important spare parts of the construction machinery, number of construction machines sold in the world, USD exchange rate and monthly impact rate are used as input variables. The inputs of the model are designed according to construction machinery sector. In the model, monthly impact rate enables us to create more robust model. In addition, the estimation results have high accuracy by systematic parameter design of artificial intelligence methods. The data of the 9 years (from 2010 to 2018) were used in the application. Demand forecasts were conducted for 2018 to compare actual values. In forecasts, artificial neural network and support vector regression produced better results than regression methods. In addition, it was found that support vector regression forecasting produced better results in comparison to artificial neural network.


This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8


  1. Abbasimehr, H., Shabani, M., & Yousefi, M. (2020). An optimized model using LSTM network for demand forecasting. Computers & Industrial Engineering, 143, 1–13.

    Article  Google Scholar 

  2. Adamowski, J., Fung Chan, H., Prasher, S. O., Ozga-Zielinski, B., & Sliusarieva, A. (2012). Comparison of multiple linear and nonlinear regression, autoregressive integrated moving average, artificial neural network, and wavelet artificial neural network methods for urban water demand forecasting in Montreal (p. 48). Water Resources Research: Canada.

    Google Scholar 

  3. Aengchuan, P., & Phruksaphanrat, B. (2018). Comparison of fuzzy inference system (FIS), FIS with artificial neural networks (FIS+ANN) and FIS with adaptive neuro-fuzzy inference system (FIS+ANFIS) for inventory control. Journal of Intelligent Manufacturing, 29, 905–923.

    Article  Google Scholar 

  4. Akay, M., Ozsert, G., & George, J. (2014). Prediction of maximal oxygen uptake using support vector machines from submaximal data. Dokuz Eylül University Faculty of Engineering, Journal of Science and Engineering, 16(48), 42–48.

    Google Scholar 

  5. Aktepe, A., Yanik, E., & Ersöz, S. (2019). An application of demand forecasting with artificial neural networks in construction machinery sector. In: Proceedings of 10th International Symposium on Intelligent Manufacturing and Service Systems, pp. 78–87.

  6. Alon, I., Qi, M., & Sadowski, R. J. (2001). Forecasting aggregate retail sales: A comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services, 8, 147–156.

    Article  Google Scholar 

  7. Amirkolaii, K. N., Baboli, A., Shahzad, M. K., & Tonadre, R. (2017). Demand forecasting for irregular demands in business aircraft spare parts supply chains by using artificial intelligence (AI). IFAC-PapersOnLine, 50(1), 15221–15226.

    Article  Google Scholar 

  8. Alves, S., Dias, M., L., D., Neto, A., R., R., & Freire, A. L. (2017). Evolutionary support vector regression via genetic algorithms: A dual approach, In: IWANN 2017, Part I, LNCS 10305, 85–97

  9. Ay S. (2016). Forecasting sales quantity of a factory by using fuzzy linear and quadratic model, (Master’s thesis), Başkent University, Ankara, Turkey

  10. Bacchetti, A., & Saccani, N. (2012). Spare parts classification and demand forecasting for stock control: Investigating the gap between research and practice. Omega, 40, 722–737.

    Article  Google Scholar 

  11. Ballı, M.T. (2014). Demand forecasting with artificial neural networks and its application in the food sector (Master’s thesis), Yildiz Technical University, İstanbul, Turkey.

  12. Boser, B., Guyon, I., & Vapnik V. (1992). A training algorithm for optimal margin classifiers, In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, 144–152.

  13. Canias ERP, (2019),, Date of access: 15.12.2019.

  14. Chawla, A., Singh, A., Lamba, A., Gangwani, N., & Soni, U. (2019). Demand forecasting using artificial neural networks—a case study of american retail corporation. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds), Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, 697, Springer, Singapore.

  15. Cherkassky, V., & Ma, Y. (2002). Selecting the loss function for robust linear regression. Date of access 15 Feb 2020.

  16. Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273–297.

    Google Scholar 

  17. Dahl, C. M., & Hylleberg, S. (2004). Flexible regression models and relative forecast performance. International Journal of Forecasting, 20, 201–217.

    Article  Google Scholar 

  18. Daş, M., Balpetek, N., Kavak Akpınar, E., & Akpınar, S. (2019). Investigation of wind energy potential of different provinces found in Turkey and establishment of predictive model using support vector machine regression with the obtained results. Journal of the Faculty of Engineering and Architecture of Gazi University, 34(4), 2203–2214.

    Google Scholar 

  19. Demren D. (2011). Electrical load demand forecasting application using support vector machines (Master’s thesis). İstanbul Technical University, İstanbul, Turkey.

  20. Frank, C., Garg, A., Sztandera, L., & Raheja, A. (2003). Forecasting women’s apparel sales using mathematical modeling. International Journal of Clothing Science and Technology, 15(2), 107–125.

    Article  Google Scholar 

  21. Girma, H. (2009). A tutorial on support vector machine, University of Ljubljana,, Date of access: 10.12.2019.

  22. García, V., Sánchez, J. S., Rodríguez-Picón, L. A., & Méndez-GonzálezOchoa-Domínguez, L. C. H. (2019). Using regression models for predicting the product quality in a tubing extrusion process. Journal of Intelligent Manufacturing, 30, 2535–2544.

    Article  Google Scholar 

  23. Guo, H., Wang, X., & Gao, Z. (2017). Uncertain linear regression model and its application. Journal of Intelligent Manufacturing, 28, 559–564.

    Article  Google Scholar 

  24. Hamilton, J. D. (2001). A parametric approach to flexible nonlinear inference. Econometrica, 69, 537–573.

    Article  Google Scholar 

  25. Hua, Z., & Zhang, B. (2006). A hybrid support vector machines and logistic regression approach for forecasting intermittent demand of spare parts. Applied Mathematics and Computation, 181(2), 1035–1048.

    Article  Google Scholar 

  26. Huang, T. M., Kecman, V., & Kopriva, I. (2006). Kernel based algorithms for mining huge data sets: supervised, semi-supervised, and unsupervised learning, Studies in Computational Intelligence, 17. Berlin, Heidelberg: Springer.

    Google Scholar 

  27. Huang, Y., Wang, H., Xing, G., & Sun, D. (2010). A hybrid grey relational analysis and support vector machines approach for forecasting consumption of spare parts. 2010 International Conference on Artificial Intelligence and Education (ICAIE) (pp. 602–605). China: Hangzhou.

    Google Scholar 

  28. Karapinar, H.C., Altay, A., & Kayakutlu, G. (2016). Churn detection and prediction in automotive supply industry, In: Proceedings of the Federated Conference on Computer Science and Information Systems, 1349–1354.

  29. Kargul, A., Glaese, A., Kessler, S., & Günthner, W. A. (2016). Heavy equipment demand prediction with support vector machine regression towards a strategic equipment management. International Journal of Structural and Civil Engineering Research, 6, 137–143.

    Google Scholar 

  30. Kuo, R. J., Tseng, Y. S., & Chen, Z. Y. (2016). Integration of fuzzy neural network and artificial immune system-based back-propogation neural network for sales forecasting using qualitative and quantitative data. Journal of Intelligent Manufacturing, 27, 1191–1207.

    Article  Google Scholar 

  31. Mansur, A., & Kuncoro, T. (2012). Product inventory predictions at small medium enterprise using market basket analysis approach—neural networks. Procedia Economics and Finance, 4, 312–320.

    Article  Google Scholar 

  32. Li, D., Fang, Y. H., Liu, C. W., & Juang, C. J. (2012). Using past manufacturing experience to assist building the yield forecast model for new manufacturing processes. Journal of Intelligent Manufacturing, 23, 857–868.

    Article  Google Scholar 

  33. MATLAB, (2019)., Date of access: 20.01.2020.

  34. Merkuryeva, G., Valberga, A., & Smirnov, A. (2018). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 3–10.

    Article  Google Scholar 

  35. Muhasebe News, (2019). Average rate of the exchange,, Date of access: 10.09.2019.

  36. Müller, K. R., Smola, A. J., Ratsch, G., Schölkopf, B., Kohlmorgen, J., & Vapnik, V. (1997). Predicting time series with support vector machines. International conference on artificial neural networks, Springer, Berlin, 1327, 999–1004.

    Google Scholar 

  37. Osuna, E., Freund, R., & Girosi, F. (1997). Support vector machines: Training and applications. Massachusetts Institute of Technology and Artificial Intelligence Laboratory and Center for Biological and Computational Learning Department of Brain and Cognitive Sciences, A.I. Memo No. 1602, C.B.C.L Paper No. 144.

  38. Öztemel, E. (2016). Yapay Sinir Ağları, 4. Basım, Papatya Yayıncılık, İstanbul.

    Google Scholar 

  39. Persson, H., & Wilhelmsson, E. (2018). The impact of omni-channel retailing on demand planning for new products at IKEA, (Master Thesis), The University of Lund, Sweden

  40. Qian, Z., Shenyang, L., Zhijie, H., & Chen, Z. (2017). Prediction model of spare parts consumption based on engineering analysis method. Procedia Engineering, 174, 711–716.

    Article  Google Scholar 

  41. Philips, D. (2019). Steady signs of market strength,, Date of access: 5.10.2019.

  42. Reynolds, J., Ahmad, M., Rezgui, Y., & Hippolyte, J. (2019). Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm. Applied Energy, 235, 699–713.

    Article  Google Scholar 

  43. Rosienkiewicz, M. (2013). Artificial intelligence methods in spare parts demand forecasting. Logistics and Transport, 2(18), 41–50.

    Google Scholar 

  44. Sarı, M. (2016). Artificial neural networks and sales demand forecasting application in an automotive company, (Master’s thesis). Sakarya University, Sakarya, Turkey.

  45. Snap, S. (2007). How to understand spare parts planning by different industries,, Date of access: 01.07.2020.

  46. Silva, N., Ferreira, M., Silva, C., & Neto, P. (2017). Improving supply chain visibility with artificial neural networks. Procedia Manufacturing, 11, 2083–2090.

    Article  Google Scholar 

  47. Smola, A. J., & Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing, 14, 199–222.

    Article  Google Scholar 

  48. SPSS 21 Software,, Date of access: 01.12.2019.

  49. Sönmez, O., & Zengin, K. (2019). Yiyecek ve İçecek İşletmelerinde Talep Tahmini: Yapay Sinir Ağları ve Regresyon Yöntemleriyle Bir Karşılaştırma (Demand forecasting in food and beverage enterprises: A comparison via artificial neural networks and regression methods). European Journal of Science & Technology, Special Issue, 302–308.

  50. STATISTA, Global construction equipment market size between 2014 and 2019 by region., Date of access: 10.10.2019.

  51. Tanyaş, M., Baskak, M. (2012), Üretim Planlama ve Kontrol, İrfan Yayıncılık, İstanbul.

  52. TUIK, (2019), Türkiye İstatistik Kurumu (TÜİK),, Date of Access: 10.09.2019.

  53. Vapnik, V., & Chervonenkis, A. (1964). On a perceptron class. Avtomat i telemekh, 25(1), 112–120.

    Google Scholar 

  54. Vapnik, V., & Chervonenkis, A. (1974). Theory of Pattern Recognition: Statistical Problems of Learning. Moscow: Russia, Nauka.

    Google Scholar 

  55. Vapnik, V. (1979). Estimation of Dependences Based on Empirical Data. Moscow: Russia, Nauka.

    Google Scholar 

  56. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer.

    Google Scholar 

  57. Vapnik, V. (2000). The Nature of Statistical Learning Theory (Vol. 2, p. 138). Berlin p: Springer.

    Google Scholar 

  58. Vijai, P., & Sivakummar, B. (2018). Performance comparison of techniques for water demand forecasting. Procedia Computer Science, 143, 258–266.

    Article  Google Scholar 

  59. Vhatkar, S., & Dias, J. (2016). Oral-care goods sales forecasting using artificial neural network model. Procedia Computer Science, 79, 238–243.

    Article  Google Scholar 

  60. Yanık, E. (2019). Application of demand forecasting with artificial neural networks in construction machinery sector, (Master’s thesis), Kırıkkale University, Kırıkkale, Turkey.

  61. Yücesoy, M. (2011). Sales forecasting with artificial neural networks in tissue paper sector, (Master’s thesis). Istanbul Technical University, Istanbul, Turkey.

  62. WEKA, (2019). Date of access: 05.01.2020.

  63. Xu, S., Chan, H. K., Ch’ng, E., & Tan, K. H. (2020). A comparison of forecasting methods for medical device demand using trend-based clustering scheme. Journal of Data, Information and Management, 2, 85–94.

    Article  Google Scholar 

  64. Xu, Z., Song, W., Zhang, Q., Ming, X. G., He, L., & Liu, W. (2017). Product service demand forecasting in hierarchical service structure. Procedia, 64, 145–150.

    Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Emre Yanık.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This study is a selected paper by paper assessment committee of IMSS’19 to special issue of Journal of Intelligent Manufacturing and was presented in IMSS’19 symposium in 2019 (Paper ID: 17, IMSS’19, pages:78–87).

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aktepe, A., Yanık, E. & Ersöz, S. Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. J Intell Manuf (2021).

Download citation


  • Construction machinery sector
  • Demand forecasting
  • Support vector regression
  • Artificial neural networks
  • Multiple linear regression
  • Multiple nonlinear regression