Joint Optimization Method of Spare Parts Stocks and Level of Repair Analysis Considering the Multiple Failure Modes

For the repair level and spare parts stocking problems, generally METRIC type methods and Level of repair analysis (LORA) are used separately. Since LORA does not consider the availability of capital goods, solving LORA and spare parts stocking problems sequentially may lead to suboptimal solutions. On these considerations, this study presents a joint optimization method to minimize the service logistics cost under the constraints of system availability. Maintenance capability factor and maintenance decisions are introduced into the joint optimization model to express the influence of multiple failure modes on repair level and spare parts stocking. Thus, we establish the bridge relationship between LORA and METRIC models. The joint optimization model is solved by an improved iterative algorithm, and a typical fleet system is taken as an example to verify the correctness and effectiveness of the model and the algorithm. Compared with the optimization of spare parts inventory and maintenance level independently, the joint optimization method could effectively reduce the service logistic system cost.

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.

Managing Spare Parts Inventory by Incorporating Holding Costs and Storage Constraints

A key factor for motivating intending buyers of raw materials is vendor responsiveness. Therefore, to meet demand, a pre-approved level of stocks is often maintained. In contrast, the decision to keep an uncontrolled amount of stock could be counter-productive with cost components associated with holding often ignored unintentionally. In this study, the objective is to develop a spare parts inventory model that incorporates ignored holding costs with a storage constraint for a motorcycle assembly plant (MAP). The inventory policy, structure of holding costs, and spare parts sales reports were consulted for relevant data. The spare parts were categorized and selected using ABC analysis. A spare parts inventory model, which considers ignored holding cost, was formulated. The model was executed using Lingo optimisation software release 18.0.56 to determine the pair of the order quantity (Ɋ) and reorder point (Ɍ). 177 spare part items were identified using ABC analysis. The parts categorisation revealed that 21, 31, 125 part items belong to categories A, B, and C with 81, 15 and 4% of annual sales value, respectively. From category A, nine items contributed significantly to overall sales. The demand pattern for these items was probabilistic based on their coefficient of variation. The pair (Ɋ, Ɍ) for items N, Z, AY, K, AM, J, P, AL and AZ are (174,688), (71,147), (78,150), (86,163), (18,15), (88,170), (128,118), (33,43) and (87,152), respectively. These pairs yielded a total inventory cost of ₦2,177,363 when compared to the current total inventory investment of ₦6,800,000 resulting in a 67.9% cost reduction. A model to manage spare parts inventory with relevant holding cost components was developed for MAP to ensure the availability of items, maximize usage of storage space, and minimize total inventory cost.