A decision aid for Aggregate Production Planning (APP) strategies

Soaring complexity in markets and fast changes of products, technologies and supply chains as well as increasing uncertainty make it nearly impossible to fully integrate all relevant factors and information into decision-making processes in manufacturing. As a result, selection of decision making strategies and their complexity is a pressing issue in production. Aggregate Production Planning attempts to reconcile expected mid-term and short-term demand with equipment, workforce and orders and presents such a key problem. Basic, heuristic solution strategies often reach a very good performance and stand out for their ease of adaptability and integration, rapid response times, computational efficiency, and broad applicability. However, strategy selection is challenging because a comprehensive overview of their efficiency across varying demand and cost scenarios is still lacking. Various demand scenarios are considered in this paper, and the performance of Aggregate Production Planning strategies is evaluated considering factors such as employees, subcontractors, overtime, inventory, and backlog. A decision aid for Aggregate Production Planning (APP) problems is proposed.