Investigating the Demand for Short-shelf Life Food Products for SME Wholesalers

Accurate forecasting of fresh produce demand is one the challenges faced by Small Medium Enterprise (SME) wholesalers. This paper is an attempt to understand the cause for the high level of variability such as weather, holidays etc., in demand of SME wholesalers. Therefore, understanding the significance of unidentified factors may improve the forecasting accuracy. This paper presents the current literature on the factors used to predict demand and the existing forecasting techniques of short shelf life products. It then investigates a variety of internal and external possible factors, some of which is not used by other researchers in the demand prediction process. The results presented in this paper are further analysed using a number of techniques to minimize noise in the data. For the analysis past sales data (January 2009 to May 2014) from a UK based SME wholesaler is used and the results presented are limited to product ‘Milk’ focused on café’s in derby. The correlation analysis is done to check the dependencies of variability factor on the actual demand. Further PCA analysis is done to understand the significance of factors identified using correlation. The PCA results suggest that the cloud cover, weather summary and temperature are the most significant factors that can be used in forecasting the demand. The correlation of the above three factors increased relative to monthly and becomes more stable compared to the weekly and daily demand. Keywords—Demand Forecasting, Deteriorating Products, Food Wholesalers, Principal Component Analysis and Variability Factors.

[1]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[2]  Chris Chatfield,et al.  The Holt-Winters Forecasting Procedure , 1978 .

[3]  Zhang Hao,et al.  Forecasting demand of short life cycle products by SVM , 2008, 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings.

[4]  Richard Weber,et al.  Improved supply chain management based on hybrid demand forecasts , 2007, Appl. Soft Comput..

[5]  F. L. Chen,et al.  Gray relation analysis and multilayer functional link network sales forecasting model for perishable food in convenience store , 2009, Expert Syst. Appl..

[6]  Mykola Pechenizkiy,et al.  Food Sales Prediction: "If Only It Knew What We Know" , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[7]  Sanjay Jharkharia,et al.  Agri‐fresh produce supply chain management: a state‐of‐the‐art literature review , 2013 .

[8]  David P. Reilly,et al.  Forecasting Consumer Product Demand with Weather Information: A Case Study , 2004 .

[9]  G. Robertson.  Shelf Life of Packaged Foods, Its Measurement and Prediction , 1999 .

[10]  Jonathon Shlens,et al.  A Tutorial on Principal Component Analysis , 2014, ArXiv.

[11]  Na Han,et al.  Notice of RetractionCompetitiveness of Supply Chain Customer Satisfaction , 2010, 2010 International Conference on E-Business and E-Government.

[12]  Shelby H. McIntyre,et al.  A Two-Stage Sales Forecasting Procedure Using Discounted Least Squares , 1994 .

[13]  Αλέξανδρος Π. Αλεξανδρίδης,et al.  Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing , 2015 .

[14]  Feng-rong Zhang,et al.  Industrial land-use efficiency and planning in Shunyi, Beijing , 2008 .