Integrated ABC–VED–AHP Framework for Prioritizing Returned Materials in Electricity Transmission Warehouse Management
DOI:
https://doi.org/10.55826/jtmit.v5i3.2097Keywords:
Material Return, Reverse Logistics, ABC Analysis, VED Analysis, Analytic Hierarchy ProcessAbstract
Returned materials in electricity transmission warehouses may still possess economic value and operational utility, but their accumulation reduces warehouse efficiency and delays asset recovery decisions. This study aims to develop an integrated decision framework for prioritizing returned materials. The proposed framework combines ABC analysis, VED analysis, physical condition assessment, reuse potential evaluation, and the Analytic Hierarchy Process (AHP), and was applied to 580 returned material items with a total replacement value of IDR 256,057,003,073. The results show that operational criticality is the most influential criterion (0.5342), followed by physical condition, economic value, and reuse potential. The consistency ratio (0.0455) indicates reliable expert judgment. The final prioritization classified 397 items as High Priority, 116 as Medium Priority, and 67 as Low Priority. The proposed framework provides an objective and data-driven approach for returned material management, supporting warehouse optimization, dead stock reduction, and improved asset recovery in electricity infrastructure systems.
References
[1] S. Chopra and P. Meindl, Supply Chain Management: Strategy, Planning, and Operation, 7 ed. Pear-son Education, 2021.
[2] P. Scarf, A. Syntetos, and R. Teunter, “Joint Maintenance and Spare-Parts Inventory Models: A Review and Discussion of Practical Stock-Keeping Rules,” IMA Journal of Management Mathematics, vol. 35, no. 1, 2024.
[3] P. T. P. L. N. (Persero), “Internal Warehouse Capacity Report of Transmission Implementation Units,” 2025, Unpublished internal document.
[4] P. T. P. L. N. (Persero), “Material Return and Warehouse Inventory (MRWI) Implementation Guideline,” 2024, Unpublished internal document.
[5] H. Tang and A. R. Thelkar, “A Fuzzy Mathematical Model for Hybrid Inventory and Purchase Optimi-zation in a Reverse Logistics System Considering Shortage and Warehouse Capacity,” Sci. Prog., vol. 106, no. 4, 2023.
[6] K. et al. Salas-Navarro, “Reverse Logistics and Sustainability: A Bibliometric Analysis,” Sustainability, vol. 16, no. 13, 2024.
[7] M. Alkahtani, A. Ziout, and M. Olayan, “An Insight into Reverse Logistics with a Focus on Collection Systems,” Sustainability, vol. 13, no. 2, 2021.
[8] M. Giacomelli, F. Pilati, and M. Brunelli, “Bi-Objective Inventory Policy with Comprehensive Envi-ronmental Factors Formulation and Service Level Constraints,” Sustainability, vol. 16, no. 17, 2024.
[9] N. Handa, S. R. Singh, and C. Katariya, “Sustainability in a Reverse Logistics Inventory Model with Carbon Emission, Reliability and Credit Financing Policy,” Process Integration and Optimization for Sustainability, vol. 8, no. 4, 2024.
[10] A. M. et al. Paredes Rodríguez, “Fuzzy AHP-TOPSIS Methodology for Multicriteria ABC Inventory Classification,” Journal of Engineering, 2023.
[11] Y. Khanorkar and P. V Kane, “Selective Inventory Classification Using ABC Classification, Multi-Criteria Decision Making Techniques, and Machine Learning Techniques,” Mater. Today Proc., vol. 72, 2023.
[12] S. Sarkar, “ABC Classification Using MCDA, DDF and TOPSIS Approach,” International Journal of Operational Research, vol. 50, no. 2, 2024.
[13] O. B. Herawan, G. Baskoro, and A. T. Pratama, “Optimizing Spare Parts by Using Criticality Level and Replenishment Policy in Oil and Gas Industry,” in Proceedings of the Conference on Management and Engineering in Industry, 2023.
[14] H. Y. Amer and M. K. Jawad, “Inventory Analysis Using the ABC–VED Matrix: Applied Research in Al-Zawraa State Company,” International Journal of Professional Business Review, vol. 8, no. 5, 2023.
[15] T. L. Saaty, The Analytic Hierarchy Process. McGraw-Hill, 1980.
[16] T. L. Saaty, “Decision Making with the Analytic Hierarchy Process,” International Journal of Services Sciences, vol. 1, no. 1, 2008.
[17] S. K. Jha, R. Agrawal, and S. K. Mangla, “A Grey Decision-Making Trial and Evaluation Laboratory Model for Digital Warehouse Management in Supply Chain Networks,” Decision Analytics Journal, vol. 8, 2023.
[18] X. et al. Li, “A Two-Level Decision-Support Framework for Reverse Logistics Network Design Consid-ering Technology Transformation in Industry 4.0,” International Journal of Advanced Manufacturing Technology, vol. 134, 2024.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Dias Rizal Adianto, Niniet Indah Arvitrida
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
