In the manufacturing sector, machine failures frequently disrupt the supply chain, leading to financial losses, particularly when downtime is prolonged. Various methods have been proposed to assess the impact of machine failures on profitability. However, the literature lacks a comprehensive approach that addresses machine failures in the context of maintaining a continuous supply chain through the implementation of an alternative production source during shortage. To bridge this gap, this study proposes an Economic Production Quantity (EPQ) model that incorporates machine failure and compares the manufacturer's profit under two scenarios: one with shortages and another with an alternative production unit compensating for the shortfall. The model aims to maximize profit by optimizing the decision variables. The proposed model is validated through numerical examples, with a detailed sensitivity analysis and managerial insights provided to support decision-making from a business perspective.

Machine failures during production period may impose thousands to millions of dollars to a manufacturing system. In this paper, the impact of machine failures on production lines in a closed-loop supply chain systems is examined. For this purpose, a new method is proposed for scheduling manufacturing workshops in a supply chain systems. The aim is to determine the best production plans in a manufacturing system by considering alternative preventive maintenance programs while machine failures can affect system performance. To solve the model, a hybrid Ant Colony and Simulated Annealing algorithms is developed and the results are compared with branch and bound method. Our findings show that the condition of emerging machine failure affects machines’ capacity which yields to lost sale. The impacts of using appropriate preventive maintenance on reducing lost sale is also examined. The results indicate that the proposed method can significantly reduce the level of sale variation in supply chain systems.