In the evolving landscape of perishable goods management, where the balance between minimizing waste and maximizing profitability is paramount, this work introduces an innovative approach to pricing and inventory decisions for products with limited shelf lives, focusing on vegetables in supermarkets. The contribution lies in its integration of an automated pricing and restocking decision model that leverages autoregressive integrated moving average (ARIMA) forecasting techniques alongside dynamic pricing strategies tailored to the goods’ freshness and remaining shelf life. The study uses a comprehensive sales, spoilage rates, and customer demand dataset to apply ARIMA forecasting for optimal restocking and adjusts prices dynamically based on product freshness, promoting competitive pricing and waste reduction. The results demonstrate the model’s effectiveness, reducing spoilage rates by up to 30% and increasing profitability margins by about 15%, highlighting its practical utility in real-world scenarios. The research highlights the potential for supermarkets to improve perishable goods inventory management, leading to significant economic benefits and reduced food waste. This study contributes to sustainable retail practices aligning with global responsible consumption and production initiatives, offering a scalable economic efficiency and environmental stewardship solution.

In order to address the structural limitations inherent in the current grey prediction model, this paper proposes the time power conformable fractional derivative non-linear grey Bernoulli model (CFNGBM(p,1) model) based on the traditional grey Bernoulli model, leveraging the theory of fractional calculus. Firstly, by utilizing the computational advantages of conformable fractional derivatives, the grey derivative of the traditional whitening equation is extended from first-order to fractional-order, making the model structure more flexible. Secondly, a time power term is introduced into the model structure to fully capture the non-linear relationship presented in real systems. Additionally, to address the overfitting phenomenon in grey models, an extrapolation optimization mechanism is incorporated into the traditional parameter optimization process by simulating future prediction scenarios. Finally, a case study of monthly production data of photovoltaic glass in the crystalline silicon industry chain demonstrates that the proposed CFNGBM(p,1) model outperforms three other grey Bernoulli models in terms of accuracy, and the use of extrapolation optimization mechanism significantly reduces overfitting.