In-season retail sales forecasting using survival models

Abstract

A large South African retailer (hereafter referred to as the Retailer) faces the problem of selling out inventory within a specified finite time horizon by dynamically adjusting product prices, and simultaneously maximising revenue. Consumer demand for the Retailer's fashion merchandise is uncertain and the identification of products eligible for markdown is therefore problematic. In order to identify products that should be marked down, the Retailer forecasts future sales of new products. With the aim of improving on the Retailer's current sales forecasting method, this study investigates statistical techniques, viz. classical time series analysis (Holt's smoothing method) and survival analysis. Forecasts are made early in the product life cycle and results are compared to the Retailer's existing forecasting method. Based on the mean squared errors of predictions resulting from each method, the most accurate of the methods investigated is survival analysis.

Digital twins enable the quantification of the trade-offs in maintaining citrus quality and marketability in the refrigerated supply chain

“…Several studies have modeled the purchase probability of a product in retail stores using a sigmoidal utility function for quality 73 , or as logistic regression of a dichotomous variable 74 , or survival analysis for the failure of an event 75,76 . In this study, we considered the commercial marketability of citrus fruit as a binary or dichotomous variable for retailers, implying that there are only two possible outcomes for a fruit (1=marketable and 0=unmarketable).…”