Improving Inventory Performance with Clustering based Demand Forecasts

Subject Areas : Industrial Management

Yaser Taghinezhad ¹

1 - Graduate of Industrial Management, Faculty of Management and Accounting, college of Farabi, University of Tehran, Iran

Received: 2017-05-12 Accepted : 2017-09-23 Published : 2017-12-24

Keywords:

Abstract :

Proper management and better control of inventory of food items are one of the most important and important goals of food store managers. In this study, we try to provide knowledge of customer segmentation based on various characteristics as inputs in predicting retail demand. The purpose of this paper is to provide a prediction model for retailers based on customer clustering to improve inventory performance. Customer clustering with the genetic algorithm is performed in MATLAB R2016a software. The proposed model is used to predict the demand for five items of supermarket goods in Gorgan. In this paper, the predictions of ARIMA, ARIMA, MLP, and GMDH neural networks are used to predict. Modeling of these models has been done in MATLAB software. The results showed that the GMDH neural network with the clustering of customers had the least predictive error. The model predicted by the inventory control policy reduces the number of days that the shortage faces and increases the level of service to the customer. Retailers can use the proposed model to predict the demand for various items to improve inventory performance and profitability of operations.

References:

1. Aburto, L., & Weber, R. (2007). Improved supply chain management based on hybrid demand. Applied Soft Computing, 7 )1(, 136-44.


2. Agrawal, D., & Schorling, C. (1996). Market share forecasting: an empirical comparison of. Journal of Retailing, 72 )4(.


3. Anandarjan, M., & Anandarjan, A. (1999). A Comparison of Machine Learning Techniques with a Qualitative Response Model for Auditors Going Concer Responding. Experts Sestems with Application, 16.


4. Bala, P. (2009). A data mining model for investigating the impact of promotion in retailing. Proceedings of IEEE International Advance Computing Conference, Patiala, India, March 6-7, pp. 670-4.


5. Bala, P. (2012). Data mining for retail inventory management. in Ao, S.I. and Gelman, L. (Eds), Advances in Electrical Engineering and Computational Science, LNEE Series, 39, Springer, New York, NY, pp. 587-98.


6. Bala, P., Sural, S., & Banerjee, R. (2010). Association rule for purchase dependence in multi-item inventory. Production Planning & Control, 21 (3), 274-85.


7. Baluni, P., & Raiwani, Y. (2014). Vehicular accident analysis using neural network. International Journal of Emerging Technology and Advanced Engineering, 4(9),161–164.


8. Barksdale, H., & Hilliard, J. (1975). A cross-spectral analysis of retail inventories and sales. Journal of Business, 48 (3), 365-82.


9. Fakhraei, H. (2006). Comparison Water Demand forecast by using Structural patterns, time series and neural networksFaculty of Economics, . University of Tehran. (in persian).


10. Fildes, R., Goodwin, P., Lawrence, M., & Nikolopo, K. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25 (1), 3-23.


11. Ghasemi, A. R., Taghinezhad, Y., & Fani, R. (2017). Using multi-layer perceptron neural network approach In anticipation of the request Perishable commodity In retail stores. First Conference on Modern Management Studies, 214- 234.


12. Ho, S., Min, X., & Thong Ngee, G. (2002). A Comparative Study of Neural Network and Box-Jenkins ARIMA Modeling in Time Series Prodiction. Computers andIndustrial Engineering, 371- 375.


13. Hongyan, D., Fang, W., & Chang, Y. (2007). The Spatial Analysis of Clustering based on Genetic Algorithms. Institute of surveying and Mapping. China.: University Zhengzhou.


14. Huarng, K., & Yu, T. (2006). The application of neural networks to forecast fuzzy time series. Physica A: Statistical Mechanics and its Applications, Physica A: Statistical Mechanics and its Applications.


15. Huarng, K., Yu, T., & Kao, T. (2008). Analyzing structural changes using clustering techniques. International Journal of Innovative Computing Information and Control, 4 (5), 1195-201.


16. Huarng, K., Yu, T., & Sole Parellada, F. (2011). An innovative regime switching model to forecast Taiwan tourism demand. The Service Industries Journal (Special Issue on Tourism Services), 31 (10), 1603-12.


17. Makridakis, S., S. C, W., & R. J, H. (1998). Forecasting: Methods and Applications. 3rd ed: John Wiley, Hoboken, N.j.


18. Mohammadi. (2007). Compare the predictive power of ANN with other forecasting methods. Ecology of Crop Plants, 14 (13), 85-100. (in persian).


19. Moon, M. (2003). Conducting a sales forecasting audit. International Journal of Forecasting, 19 (1), 5-25.


20. Ngai, E., Xiu, L., & Chau, D. (2009). Application of data mining techniques in customer relationship management: a literature review and classification. Expert Systems with Applications, 36 (2), 2592-602 (Part-2).


21. Tarokh, & sharifiyan. (2006). Application of Data Mining At Customer relationship. Quarterly Industrial Management Studies, Sixth year. No. 17, pp: 153-181.(in persian).


22. Thall, N. (1992). Neural forecasts: a retail sales booster. Discount Merchandiser, 23 (10).


23. ZHOU, M., & Sun, Z. (1999). Genetic Algorithms Theory and Applications. China: Beijing.