Data driven Comprehensive Replenishment and Pricing Model

Abstract

Pricing and replenishment of vegetable commodities is a key step in solving the practical problem of short shelf life in vegetable markets. Our research introduces a nuanced, data-driven approach to optimize replenishment and pricing decisions. At the core of our methodology is the development of a comprehensive model that leverages an enhanced ARIMA, meticulously tailored for distinct vegetable categories. This model is designed to capture the intricate dynamics of market demand and supply fluctuations, enabling precise predictions that guide effective pricing and replenishment strategies. To further refine our model's predictive capabilities, we integrate advanced machine learning techniques, including an optimized XGBoost algorithm, facilitated by a genetic algorithm. This integration not only augments the model's accuracy but also provides a robust framework for analyzing and interpreting complex market data. Through a detailed example analysis, we demonstrate the model's efficacy in forecasting demand and optimizing inventory levels, thereby ensuring that pricing and replenishment actions are both strategic and data-informed.