INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

Impact Factor* - 6.2311


**Need Help in Content editing, Data Analysis.

Research Gateway

Adv For Editing Content

   No of Download : 151    Submit Your Rating     Cite This   Download        Certificate

SUPPORT VECTOR MACHINE MODEL FOR DEMAND FORECASTING IN AN AUTOMOBILE PARTS INDUSTRY: A CASE STUDY

    2 Author(s):  ANSHUL AGARWAL, ARVIND JAYANT

Vol -  9, Issue- 2 ,         Page(s) : 33 - 49  (2019 ) DOI : https://doi.org/10.32804/RJSET

Abstract

Demand Forecasting is very crucial for any business organization. Many times people involved in business activities have to take decisions on which business prospects depends. The decision-making should be accurate and precise as company’s revenue depends on this. For this forecasting models are developed which aids in making decisions. The objective of this work is to study the basics of Support Vector Machine (SVM) and its application in supply chain management and develop an SVM model, which will predict the future demand with high accuracy as compared to the conventional forecasting methods. To demonstrate the effectiveness of the present study, demand forecasting issue was investigated in a piston manufacturing industry as a real world case study. In this proposed research, a SVM model is developed using radial basis kernel function and sigmoid function. Various factors that affect the product demand such as produced units, inventory, sales cost, and number of competitors have been taken into consideration in the development of model. A comparative analysis of SVM model and various traditional forecasting methods like exponential smoothing, moving average and autoregressive model has been done based on the results obtained from forecasting models.

1. Chouksey, P., Deshpande, A., Agarwal, P. and Gupta, R.C., 2017. Sales Forecasting Study in an Automobile Company-A Case Study. Industrial engineering journal.
2. Kochak, A. and Sharma, S., 2015. Demand forecasting using neural network for supply chain management. International journal of mechanical engineering and robotics research, 4(1), pp.96-104.
3. Villegas, M.A., Pedregal, D.J. and Trapero, J.R., 2018. A support vector machine for model selection in demand forecasting applications. Computers & Industrial Engineering, 121, pp.1-7.
4. Zhu, Z., Chen, T. and Shen, T., 2014. A New Algorithm about Market Demand Prediction of Automobile. International Journal of Marketing Studies, 6(4), p.100.
5. Yolcu, U., Egrioglu, E. and Aladag, C.H., 2013. A new linear & nonlinear artificial neural network model for time series forecasting. Decision support systems, 54(3), pp.1340-1347.
6. Jaipuria, S. and Mahapatra, S.S., 2014. An improved demand forecasting method to reduce bullwhip effect in supply chains. Expert Systems with Applications, 41(5), pp.2395-2408.
7. Carbonneau, R., Laframboise, K. and Vahidov, R., 2008. Application of machine learning techniques for supply chain demand forecasting. European Journal of Operational Research, 184(3), pp.1140-1154.
8. Yue, L., Yafeng, Y., Junjun, G. and Chongli, T., 2007, August. Demand forecasting by using support vector machine. In Third International Conference on Natural Computation (ICNC 2007)(Vol. 3, pp. 272-276). IEEE.
9. Amirkolaii, K.N., Baboli, A., Shahzad, M.K. and Tonadre, R., 2017. Demand Forecasting for Irregular Demands in Business Aircraft Spare Parts Supply Chains by using Artificial Intelligence (AI). IFAC-PapersOnLine, 50(1), pp.15221-15226.
10. Fan, Z.P., Che, Y.J. and Chen, Z.Y., 2017. Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis. Journal of Business Research, 74, pp.90-100.
11. Arvind Jayant, M.S.Dhillon (2014).Use of Analytic Hierarchy Process (AHP) to Select Welding Process in High Pressure Vessel Manufacturing Environment” International Journal of Applied Engineering Research, Vol.10 (8), Pp 586-595.
12. Jayant Arvind, P.Gupta and S.K.Garg (2011).Design and Simulation of Reverse Logistics Network: A Case Study” in the proceedings of World Congress on Engineering (WCE-2011), London, U.K., during July 6-8, 2011.
13. Arvind Jayant, P Gupta, S K Garg. Reverse Logistics Network Design for Spent Batteries: a Simulation Study. International Journal of Logistics System and Management.2014. 18(3): 343–365.
14. A. Jayant, V. Paul, U. Kumar. Application of Analytic Network Process (ANP) in Business Environment: A Comprehensive Literature Review. International Journal of Research in Mechanical Engineering & Technology. 4(3): 29-43.
15. Arvind Jayant, A. Singh, and V. Patel. An AHP Based Approach for Supplier Evaluation and Selection in Supply Chain Management. International Journal of Advanced Manufacturing Systems.2011, 2(1):1-6
16. Arvind Jayant. Evaluation of 3PL Service Provider in Supply Chain Management: An Analytic Network Process Approach. International Journal of Business Insights and Transformation' (IJBIT). 2013, 6(2): 78-82.
17. Shabani, S., Yousefi, P. and Naser, G., 2017. Support vector machines in urban water demand forecasting using phase space reconstruction. Procedia Engineering, 186, pp.537-543.
18. Sheremetov, L.B., González-Sánchez, A., López-Yáñez, I. and Ponomarev, A.V., 2013. Time series forecasting: applications to the upstream oil and gas supply chain. IFAC Proceedings Volumes, 46(9), pp.957-962.
19. Kecman, V., 2005. Support vector machines–an introduction. In Support vector machines: theory and applications (pp. 1-47). Springer, Berlin, Heidelberg.
20. Mentzer, J.T., DeWitt, W., Keebler, J.S., Min, S., Nix, N.W., Smith, C.D. and Zacharia, Z.G., 2001. Defining supply chain management. Journal of Business logistics, 22(2), pp.1-25.
21. Deng, S. and Yeh, T.H., 2011. Using least squares support vector machines for the airframe structures manufacturing cost estimation. International Journal of Production Economics, 131(2), pp.701-708.
22. Carbonneau, R., Vahidov, R. and Laframboise, K., 2007. Machine learning-Based Demand forecasting in supply chains. International Journal of Intelligent Information Technologies (IJIIT), 3(4), pp.40-57.
23. García, F.T., Villalba, L.J.G. and Portela, J., 2012. Intelligent system for time series classification using support vector machines applied to supply-chain. Expert Systems with Applications, 39(12), pp.10590-10599.
24. Huang, L., Xie, G., Li, D. and Zou, C., 2018. Predicting and Analyzing E-Logistics Demand in Urban and Rural Areas: An Empirical Approach on Historical Data of China. International Journal of Performability Engineering, 14(7).
25. Sivak, M. and Tsimhoni, O., 2008. Future demand for new cars in developing countries: going beyond GDP and population size.
26. Lee, J. and Cho, Y., 2009. Demand forecasting of diesel passenger car considering consumer preference and government regulation in South Korea. Transportation Research Part A: Policy and Practice, 43(4), pp.420-429.
27. Aliyeva, K., 2017. Demand forecasting for manufacturing under Z-Information. Procedia computer science, 120, pp.509-514.
28. Zulkepli, J., Fong, C.H. and Abidin, N.Z., 2015, December. Demand forecasting for automotive sector in Malaysia by system dynamics approach. In AIP Conference Proceedings (Vol. 1691, No. 1, p. 030031). AIP Publishing.

*Contents are provided by Authors of articles. Please contact us if you having any query.






Bank Details