Prioritization of Warehouse Efficiency Key Indicator Application with The Analytic Hierarchy Process (AHP): Case Study Third Party Logistics Service Provider in Pathumthani Province.
DOI:
https://doi.org/10.53848/jlscc.v10i1.264756Keywords:
Analytic hierarchy process, Warehouse efficiency, Warehouse service provider, Third-Party logistics providerAbstract
This research aims to prioritize warehouse management efficiency measures. The research is based on a mixed methodology of applied research and qualitative research, using the Analytic Hierarchy Process (AHP) methodology to prioritize the importance of warehousing performance in the third-party logistics industry. The data were gathered on purpose from a case study of 32 participants, all of whom were warehouse managers, project managers, or warehouse managers. The researcher considers from the Consistency ratio of the answers obtained from the research did not exceed 10% of all criteria. According to the study, the most important warehouse efficiency measure for logistics providers is 40.7% reliability, followed by 21.4% lead time and 14.4% cost. The five sub-criteria, in descending order, were on-time delivery (27.4%), followed by inventory record accuracy (13.3%), customer response time (12.6%), customer relationship (6.2%), and transit time (6.1%), respectively. The findings of the study will assist warehouse operators and associated logistics service providers in evaluating the importance of warehouse efficiency indicators and developing a model for measuring their effectiveness.
References
กัลยา วานิชย์บัญชา. (2559). สถิติสำหรับงานวิจัย. พิมพ์ครั้งที่ 16, กรุงเทพฯ: สำนักงานพิมพ์จุฬาลงกรณ์มหาวิทยาลัย.
ศราวุธ ไชยธงรัตน์ และณัฐพัชร์ อารีรัชกุลกานต์. (2564). ประยุกต์ใช้กระบวนการวิเคราะห์เชิงลำดับชั้นในการคัดเลือกผู้ขายอย่างยั่งยืน กรณีศึกษา บริษัทเอบีซี. วารสารวิทยาลัยโลจิสติกส์และซัพพลายเชน, 7(2), 5- 17.
ศูนย์วิจัยกรุงศรี. (2565). แนวโน้มธุรกิจ/อุตสาหกรรมปี 2565-2567: ธุรกิจคลังสินค้า. ค้นเมื่อ 12 ธันวาคม 2565, จาก: https://www.krungsri.com/th/research/industry/industry-outlook/logistics/warehouse-space/io/io-warehouse-space-2022.
ศูนย์วิจัยกสิกรไทย. (2565). B2C E-Commerce ปี’ 65-66. เติบโตชะลอลงจาก ปัจจัยกดดันด้าน กำลังซื้อ. ค้นเมื่อ 11 ธันวาคม 2565, จาก: https://www.kasikornresearch.com/th/analysis/k-econ/business/Pages/B2C-E-Commerce-z3361.
Alzoubi, H. (2018). The role of intelligent information system in e-supply chain management performance. International Journal of Multidisciplinary Thought, 7(2), 363-370.
Brazhkin, V. (2018). Outside the Box Warehousing: When Thinking of Inputs as Outputs Makes Sense. Transportation Journal, 57(2), 219-232.
Eaidgah, Y., Abdekhodaee, A., Najmi, M., & Arab Maki, A. (2018). Holistic performance management of virtual teams in third-party logistics environments. Team Performance Management, 24(3/4), 186-202.
Esmaeili, M., Naghavi, M. S., & Ghahghaei, A. (2018). Optimal (R, Q) policy and pricing for two-echelon supply chain with lead time and retailer’s service-level incomplete information. Journal of Industrial Engineering International, 14(1), 43-53.
Faber, N., De Koster, R. B., & Smidts, A. (2018). Survival of the fittest: the impact of fit between warehouse management structure and warehouse context on warehouse performance. International Journal of Production Research, 56(1-2), 120-139.
Fattahi, M., Govindan, K., & Keyvanshokooh, E. (2017). Responsive and resilient supply chain network design under operational and disruption risks with delivery lead-time sensitive customers. Transportation Research Part E: Logistics and Transportation Review, 101, 176-200.
Frazelle, E. H. (2016). World-class warehousing and material handling. New York:McGraw-Hill Education.
Guthrie, B., Parikh, P. J., & Kong, N. (2017). Evaluating warehouse strategies for two-product class distribution planning. International Journal of Production Research, 55(21), 6470-6484.
Hilmola, O. P., & Tolli, A. (2018). Evaluation of Chinese E-commerce cost and Lead time performance to Estonia. Quality Innovation Prosperity, 22(1), 14-26.
Hoseini Shekarabi, S. A., Gharaei, A., & Karimi, M. (2019). Modelling and optimal lot-sizing of integrated multi-level multi-wholesaler supply chains under the shortage and limited warehouse space: generalised outer approximation. International Journal of Systems Science: Operations and Logistics, 6(3), 237-257.
Johnson, A.L., & McGinnis, L.F. (2010). Performance measurement in the warehousing industry. IIE Transactions, 43, 220 - 230.
Karim, N.H., Abdul Rahman, N.S.F., Md Hanafiah, R., Abdul Hamid, S., Ismail, A., Abd Kader, A.S., & Muda, M.S. (2021). Revising the warehouse productivity measurement indicators: ratio-based benchmark. Maritime Business Review, 6(1), 49-71.
Khan, S. A., Dweiri, F., & Chaabane, A. (2016). Fuzzy-AHP approach for warehouse performance measurement. IEEE International conference on industrial engineering and engineering management (IEEM), 4-7 December 2016 at Bali, 871-875.
Kłodawski, M., Jacyna, M., Lewczuk, K., & Wasiak, M. (2017). The issues of selection warehouse process strategies. Procedia Engineering, 187, 451-457.
Laosirihongthong, T., Adebanjo, D., Samaranayake, P., Subramanian, N., & Boon-itt, S. (2018). Prioritizing warehouse performance measures in contemporary supply chains. International Journal of Productivity and Performance Management, 67(9), 1703-1726.
Lu, W., McFarlane, D., Giannikas, V., & Zhang, Q. (2016). An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research, 248(1), 107-122.
Luo, H., Yang, X., & Kong, X. T. (2019). A synchronized production-warehouse management solution for reengineering the online-offline integrated order fulfillment. Transportation Research Part E: Logistics and Transportation Review, 122, 211-230.
MacKenzie, S. B., & Lutz, R. J. (1989). An empirical examination of the structural antecedents of attitude toward the ad in an advertising pretesting context. The Journal of Marketing, 52(2), 48-65.
Mourtzis, D., Samothrakis, V., Zogopoulos, V., & Vlachou, E. (2019). Warehouse design and operation using augmented reality technology: A papermaking industry case study. Procedia Cirp, 79, 574-579.
Peng, D. X., & Lu, G. (2017). Exploring the impact of delivery performance on customer transaction volume and unit price: evidence from an assembly manufacturing supply chain. Production and Operations Management, 26(5), 880-902.
Rahdar, M., Wang, L., & Hu, G. (2018). A tri-level optimization model for inventory control with uncertain demand and lead time. International Journal of Production Economics, 195, 96-105.
Ramli, A., Bakar, M. S., Pulka, B. M., & Ibrahim, N. A. (2017). Linking human capital, information technology and material handling equipment to warehouse operations performance. International Journal of Supply Chain Management, 6(4), 254-259.
Rao, S., Ellis, S. C., Goldsby, T. J., & Raju, D. (2019). On the “Invisible Inventory Conundrum” in RFID‐Equipped Supply Chains: A Data Science Approach to Assessing Tag Performance. Journal of Business Logistics, 40(4), 339-358.
Saaty, T. L. (2004). Decision making—the analytic hierarchy and network processes (AHP/ANP). Journal of systems science and systems engineering, 13(1), 1-35.
Student. (1992). The probable error of a mean. Breakthroughs in Statistics: Methodology and Distribution, 33-57
Tannady, H., Gunawan, F. E., & Tus, M. L. V. (2019). Redesigning Warehouse Layout Based on Warehouse Management System Policy to Minimize Material Handling Cost. International Journal of Mechanical Engineering and Technology, 10(5), 61-76.
Torabizadeh, M., Yusof, N. M., Ma’aram, A., & Shaharoun, A. M. (2020). Identifying sustainable warehouse management system indicators and proposing new weighting method. Journal of Cleaner Production, 248, 119-190.
Tuo, G., Feng, Y., & Sarpong, S. (2019). A configurational model of reward-based crowdfunding project characteristics and operational approaches to delivery performance. Decision Support Systems, 120, 60-71.
Wang, Y., & Sun, S. (2010). Assessing beliefs, attitudes, and behavioral responses toward online advertising in three countries. International Business Review, 19(4), 333–344.
Xia, D. F., Xu, S. L., & Qi, F. (1999). A proof of the arithmetic mean-geometric mean-harmonic mean inequalities. RGMIA research report collection, 2(1), 84-87.
Yadav, A. S., & Swami, A. (2019). A volume flexible two-warehouse model with fluctuating demand and holding cost under inflation. International Journal of Procurement Management, 12(4), 441-456.
