Aggregate production planning considering organizational learning with case based analysis

Responding rapidly to customer needs is one of the main targets of industrial organizations that want to survive in the current market competition. This objective can be attained through robust planning. Workforce productivity is considered one of the important entities in production planning. However, it has a dynamic nature, i.e. the productivity growths thanks to on-job training or learning phenomenon. Considering this fact in manufacturing planning enhances the robustness of the developed plans. The present paper presents a mathematical model for medium-range production planning that is used to find the optimal aggregate production plan. The model aims to optimize the total production costs while respecting most of the operational constraints and considering the process of organizational learning. The presented model is constructed relying on the real industrial practices; the outcome is a mixed-integer linear program. The model was validated and checked using real data collected from an Egyptian factory that produces electric motors for home appliances. The proposed mathematical model was optimally solved using “ILOG-CPLEX 12.6”. By comparing the results obtained versus that of the method adopted in the factory, a cost reduction of 6.3% is achieved for the presented data set. A set of managerial aspects are concluded after the model analysis. Moreover, the impact of using detailed learning rates on the production cost is discussed.