Optimization of Inventory through an Integrated System Approach for Repairable Spare Parts

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Addis Ababa University


Repairable spare parts inventory optimization deals with special type of inventories that are repaired and returned to usable conditions rather than discarding. Surplus repairable spare parts lead to a high carrying cost, whereas inadequate spare parts can result into aircraft down time. This is always a problem and it is the trade- off between the size of spare parts inventory and carrying cost. There have been numerous studies performed concerning repairable spare parts optimization. However, literatures that consider no fault found (NFF) and scrap rate (S) as an input variable for repairable inventory systems is, to the best of the author’s knowledge, lacking. Because the existing literatures assume negligible no fault found and negligible scrap rate which actually deny the reality encountered by maintenance organizations in practice. This study deals with repairable spare parts optimization in Ethiopian Maintenance repair and Overhaul division. In order to reach that goal, multi stage purposive sampling technique including cluster sampling and sequential sampling to determine sample size of repairable spares. Poison distribution, diminishing Marginal Returns and mixed integer linear programming (MILP) were applied to four different scenarios to study the impact of maintenance turnaround time (TAT), no fault found (NFF) and scrap rate on repairable inventory size. Finally, mixed integer linear programming (MILP) model developed that minimizes overall inventory costs and improves service level. The quality assurance of all the data inputs were verified by using sensitivity analysis and overall comparison of model out puts. Sensitivity analysis is by transposition of mixed integer linear programming equations and by varying service level. Model outputs were validated by comparison of the model output with the actual existing system and the results of other research findings. The finding of the study shows better cost saving, compared to the current actual cost. Mixed integer linear programming model gives largest aggregate inventory cost saving result of 19.57% by considering Scrap Rate and No fault found. Sensitivity analysis indicates that total inventory cost saving of 4% can be achieved by reducing the shop repair cycle time by 20% without affecting no fault found impact. Combined total inventory saving of 37% can be achieved by reducing the repair cycle time by 20% and by reducing no fault found by 10% without affecting the service level. Financial Impact of No fault found (NFF) is about 5 % (4.936%) when compared to baseline scenario based on mixed integer linear programming (MILP). The Combined Financial Impact of No fault found (NFF) and Scrap Rate (SR) for marginal analysis and mixed integer programming models are 13.483% and 29.066% higher respectively when compared to baseline scenario. Based on the cost saving result and the financial impact of NFF and SR, it is concluded that mixed integer linear programming (MILP) results superior cost saving as compared to diminished marginal returns and poisson distribution under consideration of NFF and SR as input variable.



mixed integer linear programming, diminishing Marginal Returns, No fault found, Sensitivity analysis, maintenance turnaround time, scrap rate