Vegetable Automatic Pricing and Replenishment Decision-Making Problem Based on Cost-pricing Model

Automatic pricing and replenishment decision based on vegetable items is a key prediction and decision-making problem in fresh food superstores. Solving this problem is of great practical significance for the retail industry, which can not only improve the sales efficiency and customer satisfaction, but also reduce the operation cost, optimise the management of the superstore, and promote the digital transformation and intelligent development of the retail industry. Firstly, we derived the interrelationships between categories as well as individual items by calculating the Pearson's correlation coefficient, and the results were: the correlation between eggplant & aquatic rhizomes, eggplant & edible mushrooms was extremely weak or no correlation; the correlation between foliar & eggplant, chilli & eggplant, cauliflower & eggplant was weak; the correlation between cauliflower & edible mushrooms, cauliflower & aquatic rhizomes, cauliflower & chilli, cauliflower & aquatic rhizomes were moderately correlated; chilli & aquatic rhizomes, cauliflower & cauliflower, cauliflower & edible mushrooms, cauliflower & chilli, edible mushrooms & aquatic rhizomes, chilli & the correlation for aquatic rhizomes is strong. Finally, we calculated the selling price and cost by category, and obtained the relationship between cost-plus pricing and sales volume by fitting the "price-sales volume" curve. In order to maximise the revenue of the superstore, we make the results close to the ideal value, and predict the daily replenishment volume and pricing decision in the coming week by fitting the curve, which shows that the daily replenishment volume of cauliflower, foliage, chilli, eggplant, edible fungus, and aquatic rootstalks are 41.33, 195.96, 28.89, 76.15, 48.86, and 29.11 respectively, and the price are 0.53119, 0.71435, 0.59513, 0.62312, 0.61153, 0.51531.