Optimizing Profit in Perishable Inventory Systems for SMEs: A Model with Price and Credit Link Demand with Preservation Investment and AI Integration

Abstract This paper presents an integrated inventory model for perishable items that considers both economic profitability and environmental sustainability. The model features price- and credit-period-sensitive demand, time-dependent partial backordering to reflect customer impatience, and preservation technology investment to reduce product deterioration. Holding costs are modeled as time-dependent to capture rising storage expenses. The framework incorporates carbon emission policies, such as cap-and-trade, and leverages advanced technologies like AI, machine learning, and barcode systems to enhance operational efficiency. Replenishment is assumed to be instantaneous with a finite lot size. The profit function is optimized with respect to selling price and preservation investment, with numerical analysis confirming its concavity. Sensitivity analysis is conducted to assess the influence of key parameters, offering valuable managerial insights. This model provides a comprehensive tool for SMEs to manage perishable inventories efficiently, balancing cost-effectiveness with environmental responsibility.