Document Type : Original Research Manuscripts
Authors
1 Department of Managment, Najafabad Branch, Islamic Azad University, Najafabad, Iran
2 Department of Management, Faculty of Human Resource, Azad University, Dehaghan , Iran
Abstract
This research aimed to create new knowledge on supply chain performance management in private banks., It was performed in two phases using a thematic analysis under the qualitative methodology and the correlational analysis technique using structural equation modeling in the quantitative part. The data was obtained from in-depth and semi-structured interviews with 12 managers of five private banks in Iran, based on purposeful sampling and continued until reaching theoretical saturation. The statistical population in the quantitative part included all 235 managers of Mellat Bank's branches, from which 144 were selected by simple random sampling. A researcher-made questionnaire was used to collect the data for the first part. The validity was assessed by factor analysis of the extracted components. A reliability value of 0.7 was obtained using Cronbach's alpha. The factor load of causal factors on the main category was 0.53, contextual factors on strategies were 0.54, intervening factors on strategies were 0.37, the main category on strategies was 0.47, and finally, the factor load of strategies on consequences was 0.71. A performance measurement system is a framework for measuring supply chain performance. To create knowledge in the supply chain based on research findings, one must first know what knowledge is important to maintain and how it can be maintained effectively.
Keywords
Main Subjects
- Ahbabi, A., Ali, S., Singh, S. K., Balasubramanian, S., & Gaur, S. S. (2019). Employee perception of impact of knowledge management processes on public sector performance. Journal of Knowledge Management, 23(2), 351– 373 23p. https://doi.org/10.1108/JKM-08-2017-0348
- Alicke, K., Barriball, E. D., & Trautwein, V. (2021). How COVID-19 is reshaping supply chains. McKinsey and Company, 23.
- Asamoah, D., Agyei-Owusu, B., Andoh-Baidoo, F. K., & Ayaburi, E. (2021). Inter-organizational systems use and supply chain performance: Mediating role of supply chain management capabilities. International journal of information management, 58, 102195. https://doi.org/10.1016/j.ijinfomgt.2020.102195
- Bergstrom, JG., & Patrick, Stewart, Ian. (2020). Looking beyond the horizon - Preparing today’s supply chains to thrive in uncertainty. Deloitte: Deloitte.
- Bratianu, C., & Bejinaru, R. (2021). COVID‐19 induced emergent knowledge strategies. Knowledge and Process Management, 28(1), 11-17. https://doi.org/10.1002/kpm.1656
- Chae, B., & Olson, D. L. (2013). Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making, 12(01), 9-26. https://doi.org/10.1142/S0219622013500016
- Chams-Anturi, O., Moreno-Luzon, M. D., & Escorcia-Caballero, J. P. (2020). Linking organizational trust and performance through ambidexterity. Personnel Review, 49(4), 956– 973 18p. https://doi.org/10.1108/PR-07-2018-0239
- Hofmann, E. (2013). Supply Chain Management: Strategy, Planning and Operation, S. Chopra, P. Meindl.
- Croxton, K. L., Garcia-Dastugue, S. J., Lambert, D. M., & Rogers, D. S. (2001). The supply chain management processes. The international journal of logistics management, 12(2), 13-36. https://doi.org/10.1108/09574090110806271
- Destino, M. F. (2022). Julian; Müllerklein, Daniel; Trautwein, Vera to improve your supply chain, modernize your supply-chain IT. Mckinsey and Company.
- Franciosa, P., Sokolov, M., Sinha, S., Sun, T., & Ceglarek, D. (2020). Deep learning enhanced digital twin for Closed-Loop In-Process quality improvement. CIRP annals, 69(1), 369-372. https://doi.org/10.1016/j.cirp.2020.04.110
- Goli, A., Bakhshi, M., & Tirkolaee, E. (2017). A review on main challenges of disaster relief supply chain to reduce casualties in case of natural disasters. Journal of Applied Research on Industrial Engineering, 4(2), 77–88.
- Granados, M. L., Mohamed, S., & Hlupic, V. (2017). Knowledge management activities in social enterprises: Lessons for small and non-profit firms. Journal of Knowledge Management, 21(2), 376-396. https://doi.org/10.1108/JKM-01-2016-0026
- Gunasekaran, A., Patel, C., & McGaughey, R. E. (2004). A framework for supply chain performance measurement. International journal of production economics, 87(3), 333-347. https://doi.org/10.1016/j.ijpe.2003.08.003
- Husna, A., Amin, S. H., & Shah, B. (2021). Demand forecasting in supply chain management using different deep learning methods. In Demand forecasting and order planning in supply chains and humanitarian logistics (pp. 140-170). IGI Global. https://doi.org/10.4018/978-1-7998-3805-0.ch005
- Jerome, J. J. J., Saxena, D., Sonwaney, V., & Foropon, C. (2021). Procurement 4.0 to the rescue: catalysing its adoption by modelling the challenges. Benchmarking: An International Journal, 29(1), 217-254.
- Kamble, S. S., Gunasekaran, A., Subramanian, N., Ghadge, A., Belhadi, A., & Venkatesh, M. (2021). Blockchain technology’s impact on supply chain integration and sustainable supply chain performance: Evidence from the automotive industry. Annals of Operations Research, 1-26. https://doi.org/10.1007/s10479-021-04129-6
- Kazmi, S. W., & Ahmed, W. (2022). Understanding dynamic distribution capabilities to enhance supply chain performance: a dynamic capability view. Benchmarking: An International Journal, 29(9), 2822-2841. https://doi.org/10.1108/BIJ-03-2021-0135
- Lee, K., Romzi, P., Hanaysha, J., Alzoubi, H., & Alshurideh, M. (2022). Investigating the impact of benefits and challenges of IOT adoption on supply chain performance and organizational performance: An empirical study in Malaysia. Uncertain Supply Chain Management, 10(2), 537-550. https://doi.org/10.5267/j.uscm.2021.11.009
- Li, W. D., & Wang, S. (2018). Sustainable manufacturing and remanufacturing management. Coventry: Springer, 13-16. https://doi.org/10.1007/978-3-319-73488-0
- Lima-Junior, F. R., & Carpinetti, L. C. R. (2020). An adaptive network-based fuzzy inference system to supply chain performance evaluation based on SCOR® metrics. Computers & Industrial Engineering, 139, 106191. https://doi.org/10.1016/j.cie.2019.106191
- McDonald, K. (2002). Mastering the SAP business information warehouse. John Wiley & Sons.
- Milisavljevic-Syed, J., Thames, J. L., & Schaefer, D. (2020). The digitization of design and manufacturing: A state-of-the-art report on the transition from strategic vision to implementation in industry. Procedia CIRP, 93, 575-580. https://doi.org/10.1016/j.procir.2020.03.088
- Mokhr, A., Abbasi, H. (2018). Designing and explaining the sustainability model of banking service supply chain (case study of Bank Mellat), Management Researches in Iran, 23(3), 55-74. [in Persian]
- Mondol, E. P. (2021). The Impact of Block Chain and Smart Inventory System on Supply Chain Performance at Retail Industry. International Journal of Computations, Information and Manufacturing (IJCIM), 1(1). https://doi.org/10.54489/ijcim.v1i1.30
- Ni, D., Xiao, Z., & Lim, M. K. (2020). A systematic review of the research trends of machine learning in supply chain management. International Journal of Machine Learning and Cybernetics, 11, 1463-1482. https://doi.org/10.1007/s13042-019-01050-0
- Nsikan, J., Affiah, E. A., Briggs, I., & Koko, N. (2022). Sustainable supplier selection factors and supply chain performance in the Nigerian healthcare industry. Journal of Transport and Supply Chain Management, 16, 8. https://doi.org/10.4102/jtscm.v16i0.633
- Rejeb, A., Rejeb, K., Simske, S. J., & Treiblmaier, H. (2023). Drones for supply chain management and logistics: a review and research agenda. International Journal of Logistics Research and Applications, 26(6), 708-731. https://doi.org/10.1016/j.iot.2021.100434
- Shahbandarzadeh, H., & Behrouzi, Z. (2021). Presenting a model to evaluate the supply chain performance of National Bank branches under a two-stage process with the help of data overlay analysis, Supply Chain Management, 23(71), 47-59. [in Persian]
- Sharma, S. K., Srivastava, P. R., Kumar, A., Jindal, A., & Gupta, S. (2021). Supply chain vulnerability assessment for manufacturing industry. Annals of Operations Research, 1–31. https://doi.org/10.1007/s10479-021-04155-4
- Shepherd, C., & Günter, H. (2010). Measuring supply chain performance: current research and future directions. Behavioral operations in planning and scheduling, 105-121. https://doi.org/10.1007/978-3-642-13382-4_6
- Talukder, B., & Tripathi, S. (2021). Impact of Supply Chain Performance on Firms’ Export Capability: Development of a Statistical Model. Global Business Review, 09721509211044303. https://doi.org/10.1177/09721509211044303
- Tomé, E. G., Mello, A., Hatch, P., & Mauricio, F. (2020). KM and the post-COVID-19 new normal - theoretical analysis with application to universities – The future of universities edited by Dan Remenyi. Academic Conferences Publishing International, 133– 140.
)