Optimising inventory, procurement and production with excess demand and random parameters

Abstract

Background: Manufacturing and service industries in many sectors face extraordinary situations, such as excessive demands and uncertain prices during the post-pandemic period. In this situation, ordinary decision-making support is no longer suitable. Objectives: This study aims to propose new mathematical programming in the form of probabilistic dynamical optimisation that can be used for optimising integrated inventory, procurement and production planning. The problem contains multiperiod, multisupplier, multiraw material and multiproduct. Furthermore, several parameters, including prices and costs were assumed to be probabilistic with some known probability distributions. Method: The expectation of the profit was maximised in the model and the uncertain programming algorithm was used to calculate the optimal decision. The laboratory scaled computational experiments were also conducted with some randomly generated data. Results: The results showed the proposed model successfully provided the optimal decision. This included the optimal amount of each observation period and raw material parts to be sold to each supplier and stored in the inventory. It also included the optimal amount of each product brand to be produced and stored with the maximal expectation of the profit earned for the whole optimisation horizon time. Conclusion: The proposed decision-making support can be used by the decision-makers and managers in industries. Contribution: A novel decision-making support is provided, which can be used to solve integrated inventory, procurement and production with excess demand and random parameters.

Keywords

• after pandemic
• recovery time
• decision-making
• probabilistic programming
• production planning
• raw part procurement
• supply chain