The Research on Optimal Replenishment Decision-making strategies for Vegetable Markets in the Context of Big Data Analysis

Abstract

In consideration of the limited shelf life of vegetable commodities, this study analysed the correlation between sales data for said commodities. The analysis was then used to propose pricing and replenishment decisions for vegetable commodities. Firstly, correlations between sales volume and each medium vegetable category were analysed using bubble charts. the results showed that leafy vegetables and cauliflower, edibles and aquatic rootstocks exhibited significant positive correlations (Pearson r > 0), while aubergines exhibited negative correlations with edibles and chillies. Secondly, a seasonal index model was developed and seasonal indices and other influential parameters were calculated, the analysis also enabled the prediction of replenishment and pricing for the following week. Finally, the restocking volume of the six categories in the coming week was predicted and the pricing strategy was formulated and this followed the dynamic pricing pattern of “high early-week→mid-week discounts→weekend rebound”. The present study puts forward a series of recommendations for vegetable markets regarding their replenishment decision-making strategies, which has broad practical value and promotional significance in guiding intelligent decision-making and optimizing procurement and replenishment strategies.