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.
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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).
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Aktepe, A., Yanık, E. & Ersöz, S. Demand forecasting application with regression and artificial intelligence methods in a construction machinery company. J Intell Manuf (2021). https://doi.org/10.1007/s10845-021-01737-8
- Construction machinery sector
- Demand forecasting
- Support vector regression
- Artificial neural networks
- Multiple linear regression
- Multiple nonlinear regression