Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

Application of SARIMAX Model to Forecast Daily Sales in Food Retail Industry

Nari Sivanandam Arunraj (Deggendorf Institute of Technology, Deggendorf, Germany), Diane Ahrens (Deggendorf Institute of Technology, Deggendorf, Germany) and Michael Fernandes (Deggendorf Institute of Technology, Deggendorf, Germany)
DOI: 10.4018/IJORIS.2016040101
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During retail stage of food supply chain (FSC), food waste and stock-outs occur mainly due to inaccurate sales forecasting which leads to inappropriate ordering of products. The daily demand for a fresh food product is affected by external factors, such as seasonality, price reductions and holidays. In order to overcome this complexity and inaccuracy, the sales forecasting should try to consider all the possible demand influencing factors. The objective of this study is to develop a Seasonal Autoregressive Integrated Moving Average with external variables (SARIMAX) model which tries to account all the effects due to the demand influencing factors, to forecast the daily sales of perishable foods in a retail store. With respect to performance measures, it is found that the proposed SARIMAX model improves the traditional Seasonal Autoregressive Integrated Moving Average (SARIMA) model.
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1. Introduction

Discount retail stores have been a noticeable feature of German retail market since the 1980s. In particular, the growth in number of discount retail stores have significantly increased after reunification of Germany. Recently, there is a growing trend of increasing varieties of fruits and vegetables with year-around availability across all the German discount retail outlets rather than just in their traditional growing season. In order to attract customers and remain competitive in the market, the fruits and vegetables are exported from foreign countries and stocked for longer periods. Particularly, increase in number of retail stores, availability of varieties of fruits and vegetables (in stock) with short shelf-lives, frequent price variations, and different storage conditions increase the complexity and results in huge amount of food waste. In Germany, the retail sector produces the food waste of around 0.5 million tons per year (Kranert et al., 2012). Although the retail sector contributes only 5% of the total food waste in food supply chain, mostly they are avoidable food waste (wasting food which is fit for consumption). The quantity of food waste that occurs in the home (61%) is partially due to the management decisions in the retail sector (e.g. frequent promotions) that stimulate the consumer’s eagerness to purchase, and distract them to equate their demand with the purchase (Arunraj et al., 2014; Gooch et al., 2010). Hence, the proper decision making in the retail sector can help the suppliers and consumers to avoid the food waste. The role of sales forecasting in reducing the food waste in retail stores is a significant topic of discussion in the recent food waste related studies (Mena et al., 2011; Mena et al., 2014). According to Mena et al. (2011) and Stenmarck et al. (2011), the improvement of forecast accuracy is one of the essential remedial measures to reduce the food waste in the retail sector of food supply chain.

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