Improving Spare Parts Inventory Management System to Enhance Customer Satisfaction in My Wish Enterprise PLC
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Date
2025-06
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Addis Ababa University
Abstract
The primary objective of this study was to assess and improve the spare parts inventory management system to enhance customer satisfaction at My Wish Enterprise Plc. The research addressed persistent challenges in aligning inventory performance with customer expectations, particularly in demand forecasting, stock level monitoring, supply chain coordination, and inventory accuracy. Inefficient inventory systems often result in service delays, stock-outs, and reduced customer satisfaction—issues observed within the company. A quantitative design employed a structured questionnaire, administered to 20 employees via purposive sampling for primary data collection. Data analysis utilized SPSS for descriptive statistics, correlation, and multiple linear regression techniques to examine relationships between variables. Complementary time series analysis of historical spare parts sales in STATA was also conducted to specifically assess the operational aspects and challenges of demand forecasting accuracy for these items. Findings from the employee survey revealed that all four inventory practices positively and statistically significantly affect customer satisfaction (p<0.05). The regression model demonstrated strong explanatory power, with an R² of 0.798, indicating that nearly 80% of the variation in customer satisfaction is explained by the four predictor variables. However, the ARIMA (0, 1, and 1) time series analysis on spare parts sales highlighted inherent challenges in forecasting intermittent demand, evidenced by a Mean Absolute Error (MAE) of 64,112.96 and a Root Mean Squared Error (RMSE) of 179,628.26. This indicated the model's struggle to accurately predict large, sporadic sales spikes, underscoring the operational complexity of achieving high forecasting accuracy for such items. The study concludes that continuous improvements in demand forecasting, supply chain coordination, and inventory accuracy are essential for achieving higher customer satisfaction. Recommendations include adopting more advanced and data-driven forecasting models specifically suited for intermittent demand (e.g., specialized time series and AI/ML approaches), enhancing real-time supply chain coordination, and investing in digital inventory tracking systems. These measures are expected to streamline inventory operations, boost service responsiveness, and meet customer expectations more effectively.
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Keywords
Inventory Management, Customer Satisfaction, Demand Forecasting, Supply Chain Coordination, Inventory Accuracy, My Wish Enterprise, and Intermittent Demand Forecasting