شناسایی و اولویت‌بندی قابلیت‌های تکنولوژیکی به‌منظور افزایش تاب آوری زنجیره تأمین

حسینی دهشیری, سید جلال‌الدین; آقایی, مجتبی

doi:10.22105/imos.2021.288228.1101

نوع مقاله : مقاله پژوهشی

نویسندگان

¹ گروه مدیریت تولید و عملیات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران.

² گروه تحقیق در عملیات، دانشکده مدیریت و حسابداری، دانشگاه علامه طباطبایی، تهران، ایران.

https://doi.org/10.22105/imos.2021.288228.1101

چکیده

هدف: امروزه به دلیل پیچیدگی‌های محیط کسب‌وکار که با سطح بالایی از عدم اطمینان و ریسک همراه است، شرکت‌ها درصدد مدیریت و کاهش ریسک زنجیره تأمین خود می‌باشند. یکی از راهکارهای مقابله با ریسک‌های زنجیره تأمین، افزایش تاب‌آوری زنجیره تأمین است. برای افزایش تاب‌آوری زنجیره تأمین، لازم است قابلیت‌های مختلف آن موردتوجه قرار گیرد. یکی از مهم‌ترین قابلیت‌ها، قابلیت‌های تکنولوژیکی است. شرکت‌هایی که در قابلیت‌های تکنولوژیکی از بلوغ کافی برخوردار نیستند، نمی‌توانند چندین شیوه مدیریت ریسک را هم‌زمان اجرایی نمایند. از طرفی بسیاری از قابلیت‌های تکنولوژیکی به یکدیگر مرتبط بوده و قادر به ایجاد تأثیر متقابل بر سایر توانایی‌ها هستند. ازاین‌رو در این مقاله برای کاهش ریسک‌ها و به‌تبع آن افزایش تاب‌آوری زنجیره تأمین، پس از شناسایی شاخص‌های مربوط به قابلیت‌های تکنولوژیکی، این شاخص‌ها اولویت‌بندی شدند.
روش‌شناسی پژوهش: در ابتدا با بررسی تحقیقات انجام‌شده در حوزه تاب‌آوری زنجیره تأمین، شاخص‌های مربوط به قابلیت‌های تکنولوژیک برای افزایش تاب‌آوری زنجیره تأمین بررسی شد و فهرستی از معیارهای شناسایی‌شده در اختیار خبرگان شرکت قرار گرفت، سپس معیارهای موردنظر پس از بررسی موردمطالعه و بر اساس نظر خبرگان با روش دلفی فازی تعدیل، تائید و نهایی شد. در گام بعد بر اساس روش سو آرا شاخص‌های موردنظر وزن دهی شدند.
یافته‌ها: شاخص‌های قابلیت تکنولوژیکی همکاری، چابکی زنجیره تأمین، انعطاف‌پذیری عرضه به ترتیب به‌عنوان مهم‌ترین شاخص‌ها شناسایی گردیدند.
اصالت/ارزش افزوده علمی: با توسعه فناوری اطلاعات و لزوم تاب‌آوری زنجیره‌های تأمین، شناسایی قابلیت‌های تکنولوژیکی و تأثیر آن بر تاب‌آوری برای کاهش ریسک زنجیره تأمین ضروری است. در این مقاله ضمن شناسایی قابلیت‌های تکنولوژیکی برای افزایش تاب‌آوری زنجیره تأمین، این قابلیت‌ها اولویت‌بندی می‌گردند. همچنین استفاده تلفیقی از روش‌های دلفی فازی برای تائید شاخص‌ها و روش سوآرا منجر به استواری نتایج شده است.

کلیدواژه‌ها

20.1001.1.27831345.1400.2.3.2.2

عنوان مقاله [English]

Identifying and Ranking Technological Capabilities to Enhance Resilience of the Supply Chain

نویسندگان [English]

Seyyed Jalaladdin Hosseini Dehshiri ¹
Mojtaba Aghaei ²

¹ Department of Operation and Production Management, Faculty of Management and Accounting, Allameh Tabataba'i University, Tehran, Iran.

² Department of Operations Research, Faculty of Management and Accounting, Allameh Tabataba’i University, Tehran, Iran.

چکیده [English]

Purpose: A company seeks to manage and reduce risk in its supply chain because of the complexity of today's business environment associated with high levels of uncertainty and risk. One of the strategies to deal with supply chain risks is increasing the supply chain resiliency. Various capabilities must be considered to improve the supply chain's resilience. One of the most critical capabilities is technological capabilities. Companies that do not have sufficient maturity in technological capabilities cannot simultaneously implement several risk management methods. On the other hand, many technological capabilities are related and can interact with different abilities. Therefore, in this paper, to reduce risk and consequently increase the resilience of the supply chain, these indicators were prioritized after identifying indicators of technological capabilities. Therefore, in this paper, to reduce risk and consequently increase the resilience of the supply chain, these indicators were prioritized after identifying indicators of technological capabilities.
Methodology: At first, with the review conducted research in the field of supply chain resiliency, Indicators related to technological capabilities to increase supply chain resiliency were investigated, and a list of identified criteria was placed at the disposal of company experts. Then, these criteria, after being examined in the case study and according to the experts using the Fuzzy Delphi method, were modified and finalized—the next step weighted relevant indicators based on the SWARA method.
Findings: Criteria of technological collaboration, supply chain agility, and flexibility of supply, respectively, were recognized as the most critical indicators.
Originality/Value: With the development of information technology and the need for supply chain resilience, identifying technological capabilities and their impact on resilience is essential to reduce supply chain risk. In this paper, while identifying technological capabilities to increase supply chain resilience, these capabilities are prioritized. Also, the combined use of fuzzy Delphi methods to confirm the indicators and the SWARA method has led to the stability of the results.

کلیدواژه‌ها [English]

Resilience supply chain
Technological capabilities
Fuzzy Delphi
SWARA

مراجع

Aghaei, M., & Salehi Sadaghiani, J. (2019). Solutions for Removing Knowledge Management Implementation Barriers in the Supply Chain of Fast Moving Consumer Goods. IT management studies, 8(30), 79-114. (In Persian). DOI: 10.22054/IMS.2019.10619

Almutairi, K., Dehshiri, S. S. H., Dehshiri, S. J. H., Mostafaeipour, A., Issakhov, A., & Techato, K. (2021). A thorough investigation for development of hydrogen projects from wind energy: a case study. International journal of hydrogen energy, 46(36), 18795-18815. https://doi.org/10.1016/j.ijhydene.2021.03.061

Amiri, M., Hosseini Dehshiri, S. J., & Yousefi Hanoomarvar, A. (2018). Determining the optimal combination of larg supply chain strategies using swot analysis, multi-criteria decision-making techniques and game theory. Industrial management journal, 10(2), 221-246. (In Persian). DOI: 10.22059/imj.2018.257030.1007420

Bell, M., & Pavitt, K. (1995). The development of technological capabilities. Trade, technology and international competitiveness, 22(4831), 69-101.

Blackhurst, J., Dunn, K. S., & Craighead, C. W. (2011). An empirically derived framework of global supply resiliency. Journal of business logistics, 32(4), 374-391. https://doi.org/10.1111/j.0000-0000.2011.01032.x

Bouzon, M., Govindan, K., Rodriguez, C. M. T., & Campos, L. M. (2016). Identification and analysis of reverse logistics barriers using fuzzy Delphi method and AHP. Resources, conservation and recycling, 108, 182-197. https://doi.org/10.1016/j.resconrec.2015.05.021

Brusset, X., & Teller, C. (2017). Supply chain capabilities, risks, and resilience. International journal of production economics, 184, 59-68. https://doi.org/10.1016/j.ijpe.2016.09.008

Business Continuity Institute. (2011). Supply chain resilience. 3rd Annual Survey, Published November 2011.

Carvalho, H., Azevedo, S. G., & Cruz-Machado, V. (2012). Agile and resilient approaches to supply chain management: influence on performance and competitiveness. Logistics research, 4(1), 49-62. https://doi.org/10.1007/s12159-012-0064-2

Carvalho, H., Barroso, A. P., Machado, V. H., Azevedo, S., & Cruz-Machado, V. (2012). Supply chain redesign for resilience using simulation. Computers & industrial engineering, 62(1), 329-341. https://doi.org/10.1016/j.cie.2011.10.003

Cheng, C. H., & Lin, Y. (2002). Evaluating the best main battle tank using fuzzy decision theory with linguistic criteria evaluation. European journal of operational research, 142(1), 174-186. https://doi.org/10.1016/S0377-2217(01)00280-6

Croson, R., Donohue, K., Katok, E., & Sterman, J. (2014). Order stability in supply chains: coordination risk and the role of coordination stock. Production and operations management, 23(2), 176-196. https://doi.org/10.1111/j.1937-5956.2012.01422.x

Dahooie, J. H., Dehshiri, S. J. H., Banaitis, A., & Binkytė-Vėlienė, A. (2020). Identifying and prioritizing cost reduction solutions in the supply chain by integrating value engineering and gray multi-criteria decision-making. Technological and economic development of economy, 26(6), 1311-1338. DOI: https://doi.org/10.3846/tede.2020.13534

Dalkey, N., & Helmer, O. (1963). An experimental application of the Delphi method to the use of experts. Management science, 9(3), 458-467.

Dehnavi, A., Aghdam, I. N., Pradhan, B., & Varzandeh, M. H. M. (2015). A new hybrid model using step-wise weight assessment ratio analysis (SWARA) technique and adaptive neuro-fuzzy inference system (ANFIS) for regional landslide hazard assessment in Iran. Catena, 135, 122-148. https://doi.org/10.1016/j.catena.2015.07.020

Georgiadis, P., & Athanasiou, E. (2013). Flexible long-term capacity planning in closed-loop supply chains with remanufacturing. European journal of operational research, 225(1), 44-58. https://doi.org/10.1016/j.ejor.2012.09.021

Gulati, R., Nohria, N., & Zaheer, A. (2000). Strategic networks. Strategic management journal, 21(3), 203-215. https://doi.org/10.1002/(SICI)1097-0266(200003)21:3<203::AID-SMJ102>3.0.CO;2-K

Heidary Dahooie, j., & Hosseini Dehshiri, S. J. (2019). Identify and prioritize strategies to reduce plant power equipments supply chain costs through value engineering. Journal of industrial management studies , 17(52), 125-152. (In Persian). DOI: https://www.sid.ir/en/journal/ViewPaper.aspx?id=663301

Hohenstein, N. O., Feisel, E., Hartmann, E., & Giunipero, L. (2015). Research on the phenomenon of supply chain resilience: a systematic review and paths for further investigation. International journal of physical distribution & logistics management, 45(1/2), 90-117.

Hoseini Dehshiri, S. J., & Aghaei, M. (2020). Identifying and prioritizing human resources risks through the combination of swara and gray ARAS. Journal of human resource management, 10(1), 53-78. (In Persian). DOI: 10.22034/JHRS.2020.105969

Hosseini Dehshiri, S. J. (2019). Using gray numbers theory in multi-attribute decision making methods for the evaluation the risk of outsourcing of information technology projects. IT management studies, 7(28), 167-198. (In Persian). DOI: 10.22054/IMS.2019.10243

Hosseini Dehshiri, S. J., Aghaei, M., & Taghavifard, M. T. (2018). Schematic design of hotel recommendation systems by user precedence on twitter. Journal of business intelligence management studies, 7(25), 119-146. (In Persian), DOI: 10.22054/IMS.2018.9742

Huang, E., & Goetschalckx, M. (2014). Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk. European journal of operational research, 237(2), 508-518. https://doi.org/10.1016/j.ejor.2014.02.038

Hudnurkar, M., Jakhar, S., & Rathod, U. (2014). Factors affecting collaboration in supply chain: a literature review. Procedia-social and behavioral sciences, 133, 189-202. https://doi.org/10.1016/j.sbspro.2014.04.184

Iammarino, S., Piva, M., Vivarelli, M., & Von Tunzelmann, N. (2009). Technological capabilities and patterns of cooperation of UK firms: a regional investigation. IZA discussion paper, 4129. DOI: http://nbn-resolving.de/urn:nbn:de:101:1-20090513272

Ishikawa, A., Amagasa, M., Shiga, T., Tomizawa, G., Tatsuta, R., & Mieno, H. (1993). The max-min Delphi method and fuzzy Delphi method via fuzzy integration. Fuzzy sets and systems, 55(3), 241-253.

Jafarian, M., & Bashiri, M. (2014). Supply chain dynamic configuration as a result of new product development. Applied mathematical modelling, 38(3), 1133-1146. https://doi.org/10.1016/j.apm.2013.08.025

Jayant, A., & Ghagra, H. S. (2013). Supply chain flexibility configurations: perspectives, empirical studies and research directions. International journal of supply chain management, 2(1), 21-29.

Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study. Supply chain management: an international journal. 16(4), 246-259. https://doi.org/10.1108/13598541111139062

Kalbasi, R., Jahangiri, M., Mosavi, A., Dehshiri, S. J. H., Dehshiri, S. S. H., Ebrahimi, S., ... & Karimipour, A. (2021). Finding the best station in Belgium to use residential-scale solar heating, one-year dynamic simulation with considering all system losses: economic analysis of using ETSW. Sustainable energy technologies and assessments, 45, 101097. https://doi.org/10.1016/j.seta.2021.101097

Kannan, D., de Sousa Jabbour, A. B. L., & Jabbour, C. J. C. (2014). Selecting green suppliers based on GSCM practices: using fuzzy TOPSIS applied to a Brazilian electronics company. European journal of operational research, 233(2), 432-447. https://doi.org/10.1016/j.ejor.2013.07.023

Keršuliene, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new step‐wise weight assessment ratio analysis (SWARA). Journal of business economics and management, 11(2), 243-258.

Khalili-Damghani, K., & Tavana, M. (2013). A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains. The international journal of advanced manufacturing technology, 69(1-4), 291-318. https://doi.org/10.1007/s00170-013-5021-y

Lall, S. (1992). Technological capabilities and industrialization. World development, 20(2), 165-186. https://doi.org/10.1016/0305-750X(92)90097-F

Lin, H. F. (2014). Understanding the determinants of electronic supply chain management system adoption: Using the technology–organization–environment framework. Technological forecasting and social change, 86, 80-92. https://doi.org/10.1016/j.techfore.2013.09.001

Matook, S., Lasch, R., & Tamaschke, R. (2009). Supplier development with benchmarking as part of a comprehensive supplier risk management framework. International journal of operations & production management, 29(3), 241-267. https://doi.org/10.1108/01443570910938989

Meyr, H., Wagner, M., & Rohde, J. (2015). Structure of advanced planning systems. In Supply chain management and advanced planning (pp. 99-106). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55309-7_5

Mostafaeipour, A., Dehshiri, S. J. H., Dehshiri, S. S. H., & Jahangiri, M. (2020). Prioritization of potential locations for harnessing wind energy to produce hydrogen in Afghanistan. International journal of hydrogen energy, 45(58), 33169-33184. https://doi.org/10.1016/j.ijhydene.2020.09.135

Pereira, C. R., Christopher, M., & Da Silva, A. L. (2014). Achieving supply chain resilience: the role of procurement. Supply chain management: an international journal, 19(5/6), 626-642. DOI: https://doi.org/10.1108/SCM-09-2013-0346

Pettit, T. J., Croxton, K. L., & Fiksel, J. (2013). Ensuring supply chain resilience: development and implementation of an assessment tool. Journal of business logistics, 34(1), 46-76. https://doi.org/10.1111/jbl.12009

Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework. Journal of business logistics, 31(1), 1-21. https://doi.org/10.1002/j.2158-1592.2010.tb00125.x

Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain resilience. The international journal of logistics management. 31(1), 1-21. https://doi.org/10.1002/j.2158-1592.2010.tb00125.x

Qrunfleh, S., & Tarafdar, M. (2013). Lean and agile supply chain strategies and supply chain responsiveness: the role of strategic supplier partnership and postponement. Supply chain management: an international journal, 18(6), 571-582. https://doi.org/10.1108/SCM-01-2013-0015

Rajesh, R. (2016). Forecasting supply chain resilience performance using grey prediction. Electronic commerce research and applications, 20, 42-58. https://doi.org/10.1016/j.elerap.2016.09.006

Rajesh, R. (2017). Technological capabilities and supply chain resilience of firms: A relational analysis using Total Interpretive Structural Modeling (TISM). Technological forecasting and social change, 118, 161-169. https://doi.org/10.1016/j.techfore.2017.02.017

Rajesh, R., & Ravi, V. (2015). Modeling enablers of supply chain risk mitigation in electronic supply chains: A Grey–DEMATEL approach. Computers & Industrial Engineering, 87, 126-139. https://doi.org/10.1016/j.cie.2015.04.028

Sáenz, M. J., & Revilla, E. (2014). Creating more resilient supply chains. MIT Sloan management review, 55(4), 22-24.

Sheffi, Y. (2005). Building a resilient supply chain. Harvard Business Review, 1(8), 1-12.

Taghavi, A., & Chinnam, R. B. (2014). Assortment planning of automotive products with considerations for economic and environmental impacts of technology selection. Journal of cleaner production, 70, 132-144. https://doi.org/10.1016/j.jclepro.2014.02.004

Tang, C. S. (2006). Robust strategies for mitigating supply chain disruptions. International journal of logistics: research and applications, 9(1), 33-45. https://doi.org/10.1080/13675560500405584

Wieland, A., & Wallenburg, C. M. (2013). The influence of relational competencies on supply chain resilience: a relational view. International journal of physical distribution & logistics management. International journal of physical distribution & logistics management, 43(4), 300-320. https://doi.org/10.1108/IJPDLM-08-2012-0243

Yang, Y., & Xu, X. (2015). Post-disaster grain supply chain resilience with government aid. Transportation research part E: logistics and transportation review, 76, 139-159. https://doi.org/10.1016/j.tre.2015.02.007

Zhang, J., Gou, Q., Liang, L., & Huang, Z. (2013). Supply chain coordination through cooperative advertising with reference price effect. Omega, 41(2), 345-353. https://doi.org/10.1016/j.omega.2012.03.009

مدیریت نوآوری و راهبردهای عملیاتی

شناسایی و اولویت‌بندی قابلیت‌های تکنولوژیکی به‌منظور افزایش تاب آوری زنجیره تأمین

مراجع

مراجع

دوره 2، شماره 3
آذر 1400
صفحه 229-243

شناسایی و اولویت‌بندی قابلیت‌های تکنولوژیکی به‌منظور افزایش تاب آوری زنجیره تأمین

مراجع

مراجع

دوره 2، شماره 3آذر 1400صفحه 229-243

دوره 2، شماره 3
آذر 1400
صفحه 229-243