Sustainable Inventory Model for Temperature-Dependent Deteriorating Products under Condition Monitoring

Cold-chain inventory processes that handle temperature-sensitive items are still facing issues because of deterioration losses, which have a direct impact on business profitability as well as sustainability. In contemporary logistics network, real-time condition monitoring systems are becoming more prevalent in operational practice; yet, their consequences for stock decision-making are often overlooked in optimization models. This study formulates an inventory model for temperature-dependent deteriorating products under price-sensitive demand, integrating the benefit of continuous monitoring by lowering actual deterioration rate. Optimum price and replenishing actions are derived by solving nonlinear optimization problem. Numerical analysis is performed under various temperature conditions to investigate the financial implications of monitoring-based deterioration reduction. The model is developed and evaluated in PYTHON, showing the reliability of the numerical findings. The results show that continuous monitoring drastically lowers deterioration-induced losses, leading to greater optimum replenishment periods and increased total profit across every temperature levels. This sustainable strategy highlights the importance of data-driven managerial oversight that enhances both resource utilization and cost effectiveness in cold- chain inventory operations.