Towards Eco-Efficient Production Planning: Stochastic Models Incorporating Quality Uncertainty and Environmental Impact

In industrial production, balancing demand volatility and customer lead times remains a critical challenge. Traditional production planning often assumes all manufactured products meet quality standards; however, defective parts detected during production can disrupt delivery schedules, leading to increased shipments to meet customer expectations. This not only affects customer satisfaction but also results in higher CO₂ emissions due to urgent premium transportation. This study addresses these challenges by integrating quality defect rates into daily production planning and transitioning from deterministic to stochastic approaches. The objective is to improve planning accuracy, mitigate the environmental impact of expedited shipments, and maintain high customer satisfaction. The methodology starts with a literature review of existing solutions, followed by a real-world automotive industry case study. Results highlight the effectiveness of the proposed model, providing decision-makers with multiple scenarios evaluated via a multi-criteria function to select the optimal plan, thus reducing premium shipments and supporting sustainable, customer focused production.