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Time Series Model Forecasting of Boot using Holt, Winter and Decomposition Method

Amrit Pal Singh, Manoj Kumar Gaur, Sharad Agrawal, Dinesh Kumar Kasdekar

Abstract


In business scenario, sales forecasting is very essential due to volatile demand of products. Because of the uncertainty with demand and supply, forecasting of shoe industry is required. Normally retail sales series contains trend and seasonal patterns, presenting challenges in developing effective forecasting models. This study compares the application of three forecasting methods like Holt’s, winters and Decomposition method to forecast the sale of boot. Best method is selected on the basis of least value of mean square deviation (MSD). For this, store outlet sales data from a shoe industry are collected and data are analyzed by statistics technique using Minitab 17 software. It is found that the lowest error of MSD is obtained by winter’s method which is the measurement of this study.


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References


Mentzer JT, Bienstock CC. Sales Forecasting Management. Sage, Thousand Oaks. 1998; CA.

Alon I, Min Q, Sadowski RJ. Forecasting Aggregate Retail Sales: A Comparison of Artificial Neural Networks and Traditional Method. Journal of Retailing and Consumer Services. 2001; 8(3): 147–156p.

Alon I. Forecasting Aggregate Retail Sales: The Winter’s Model Revisited. In: Goodale JC. (Ed.). The 1997 Annual Proceedings, Midwest Decision Science Institute. 1997; 234–236p.

Chu CW, Zhang PGQ. A Comparative Study of Linear and Non Linear Models for Aggregate Retail Sales Forecasting. Int. J. Prod. Econ. 2003; 86: 217–231p.

Peter W, Anders S. Evaluation of Forecasting Error Measurements and Techniques for Intermittent Demand. Int. J. Prod. Econ. 2010; 128: 625–636p.

Doganis P, et al. Time Series Sales Forecasting for Short Shelf-Life Food Products based on Artificial Neural Network and Evolutionary Computing. J. Food Eng. 2006; 75: 196–204p.

Sanwlani M, Vijayalakshmi M. Forecasting Sales through Time Series Clustering. International Journal of Data Mining & Knowledge Management Process (IJDKP). 2013; 3(1): 39–56p.

Chen RJ, Bloomfield P, et al. An Evaluation of Alternative Forecasting Methods to Recreation Visitation. J. Leis. Res. 2003; 35(4): 441–454p.

Ezeliora CD, et al. Moving Average Analysis of Plastic Production Yield in a Manufacturing Industry. International Journal of Multidisciplinary Sciences and Engineering (IJMSE). 2013; 4(2): 65–73p.

Ryu K, Sanchez A. The Evaluation of Forecasting Methods at an Institutional Food Service Dining Facility. The Journal of Hospitality Financial Management. 2003; 11(1): 27–45p.

Leve'n E, Segerstedt A. Inventory Control with a Modified Croston Procedure and Erlang Distribution. Int. J. Prod. Econ. 2004; 90: 361–367p.

Strasheim JJ. Demand Forecasting for Motor Vehicle Spare Parts. A Journal of Industrial Engineering. 1992; 6(2): 18–19p.




DOI: https://doi.org/10.3759/joise.v2i2.3503

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