Developing a Decision Support System for Spare Parts Inventory Management to Reduce Repair Cost at Sunshine Construction.
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Date
2024
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Addis Ababa University
Abstract
The research aims to develop a decision support system (DSS) for spare parts inventory management in order to reduce maintenance costs at Sunshine Construction Company. The study employs a well-designed approach, incorporating data analysis techniques and leveraging DSS systems to gain insights for optimizing inventory management and reducing repair costs.
The literature review examines inventory management in the construction industry, focusing on areas like optimizing stock levels, lead-time, decision support systems, maintenance, and repair cost control. It explores strategies such as (s, S) and (q, r) policies, modern inventory management technologies, and the importance of collaboration among stakeholders. A critical issue identified in the review is the need to address the challenge of inaccurate demand forecasting, which can significantly impact inventory optimization efforts.
The data collection and analysis used both quantitative data from a case company concerned department recording data like inventory levels, repair costs, maintenance costs) and qualitative data (through interviews). Tools like Excel, FMEA, and the EOQ model are used to analyze and optimize factors like stock levels, lead times, reorder quantities, and safety stock.
The expected findings and recommendations aim to improve inventory performance, reduce costs, and promote business success in spare parts management at Sunshine Construction Company. The study aims to contribute innovative solutions related to demand forecasting, inventory policies, maintenance cost control, and technology integration
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Keywords
Spare Parts Inventory, Decision Support, Sunshine Construction