AN OPTIMAL INVENTORY POLICY FOR REPAIRABLE SUPPLIES USING A NON-LINEAR MODEL

This paper aims to propose a method for developing an optimal inventory management policy for repairable supplies, optimizing logistical costs. A non-linear programming model is constructed, considering four associated logistics costs: ordering preparation cost, holding cost, acquisition cost, and shortage cost. The methodology employed combines a Multi-Criteria Decision Method (MCDM), a statistical treatment to determine the best sampling distribution that fits the data, and non-linear optimization. Solving the model and applying the proposed method, we obtained the ideal inventory turnover and answered the four questions to elaborate an optimal inventory policy. Finally, it is concluded that the proposed method is easily applied in the corporate environment, requiring relatively simple data. Future research on the subject indicates the possibility of using heuristics and metaheuristics, non-linear programming techniques such as Sequential Quadratic Programming (SQP), and also the use of Machine Learning, including Neural Networks for data processing.