Purpose The purpose of this study is to develop an optimal production control strategy for manufacturing systems producing perishable goods under stochastic demand and machine unreliability. It aims to determine the best production rates and inventory levels that minimize total operational costs – including production, holding, shortage and disposal – while ensuring customer demand satisfaction. Additionally, the study investigates the impact of shelf-life variability and system uncertainties on operational performance, providing practical guidelines for cost-effective and resilient production planning in industries where perishability and equipment disruptions are critical concerns. Design/methodology/approach A stochastic production control model is developed for perishable goods in unreliable manufacturing systems under random demand, machine failures and limited shelf life. The model integrates production, inventory, shortage and disposal costs into a unified framework. A numerical optimization procedure is employed to determine the optimal production rates and inventory levels that minimize total operational costs while maintaining a desired service level. Sensitivity analyses are conducted to evaluate the impact of key parameters, such as shelf-life variability, demand fluctuations and shortage costs, on system performance and cost-efficiency. Findings The study demonstrates that the proposed stochastic production control model effectively minimizes total operational costs while maintaining a desired service level. Numerical results show that dynamically adjusting production based on machine status and inventory aging reduces shortages and product waste. Sensitivity analyses reveal that shelf-life duration, demand variability and shortage costs significantly influence optimal production and inventory policies. The model provides actionable insights for managing perishable goods in unreliable manufacturing systems, highlighting the importance of integrating demand uncertainty, machine failures and product perishability into production planning for cost-effective and resilient operations. Originality/value This study presents a novel stochastic production control framework that simultaneously addresses perishability, machine unreliability and stochastic demand – factors often treated separately in existing literature. By integrating production, inventory, shortage and disposal considerations into a unified model, it provides a practical tool for cost-effective and resilient production planning. The numerical optimization approach enables parameterized control policies adaptable to real-world variability. The findings offer both theoretical insights and actionable strategies for industries such as food, pharmaceuticals and biotechnology, where perishable products and system uncertainties critically impact operational efficiency and service levels.
Hajej et al. (Tue,) studied this question.