Reducing carbon footprint and promoting resource sustainability in the retail industry through the prevention of dead stocks

Abstract

In the retail industry, majority of the traditional inventory management methods commonly focus on minimizing business costs but fail to consider implications on environmental sustainability. Existing inventory classification methods have failed to consider the interaction among the different performance indicators for a timely recognition of the potential dead stocks and dead stocks in the inventory. This study aims to identify and effectively classify potential dead stocks and dead stocks through an enhanced inventory classification method that integrates the remaining product shelf life as an indicator for timely recognition of dead stocks. The study also aims to provide guidelines in setting up decision rules needed in objectively classifying inventory based on each performance indicator through an automated algorithm with decision rules. Using data sets taken from open databases that provide real-world data, results show that the automated algorithm can effectively detect the presence of dead stocks and distinguish such inventory from potential dead stocks in the system which existing inventory classification methods are unable to do. One-Way ANOVA Tests performed showed that the proposed classification method can consistently classify inventory across different retail data sets while Monte Carlo simulation was used to simulate the amount of inventory that becomes dead stocks in a multi-period inventory management system. Results show that 17% of these potential dead stocks, if undetected, become dead stocks in the following period. Over a ton of carbon emissions is prevented with lesser dead stock waste disposal. The value of this study lies in the fact that classifying inventory to detect the presence of potential dead stocks is an essential first step in solving the dead stock problem not just for business profitability but also for sustainable resource consumption and a sustainable environment.