A Demand Prediction Model of Repairable Components based on Grey Birth and Death Process

During the life cycle of equipment, the failure and repair rates of repairable components show uncertain characteristics. The birth and death process (BDP) based on the determined failure and repair rates may not meet the demand forecasting of spare parts. In order to resolve this problem, the grey state transition matrix is constructed by using interval grey numbers to appropriately represent the failure and repair rates of repairable components. In addition, the grey BDP model is built for the demand forecasting of spare parts. The memoryless and existence conditions of steady solution of the grey BDP are studied. To some extent, the spare parts demand law with the uncertain information of the failure and repair rates can easily be revealed. The practical case study is provided to verify the validity and practicability of the proposed model. Also, it provides a new perspective for the spare parts demand prediction problem under the condition of uncertain Markov Process. Accordingly, airlines can predict the maintenance resources demand more accurately and avoid two situations which are not allowed: (1) lower spare parts inventory will lead to the delay production; and (2) higher spare parts inventory will lead to the operating cost pressure.