تحلیل توانمندی ـ جذابیت فناوری‌های تحول‌آفرین در زنجیره تأمین بشردوستانه ایران

نوع مقاله : مقاله علمی پژوهشی

نویسندگان

1 دانشیار، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

2 کارشناس ارشد، گروه مدیریت صنعتی، دانشکده مدیریت، دانشگاه تهران، تهران، ایران.

3 کارشناس ارشد، گروه مدیریت صنعتی و فناوری، دانشکده مدیریت و حسابداری، دانشکدگان فارابی دانشگاه تهران، قم، ایران.

10.22059/imj.2022.343062.1007944

چکیده

هدف:  استفاده از فناوری‌های تحول‌آفرین، می‌تواند به بهبود زنجیره تأمین بشردوستانه برای کاهش آلام و آسیب‌های ناشی از حوادث کمک کند. این پژوهش به‌دنبال شناسایی و اولویت‌بندی فناوری‌های تحول‌آفرین در زنجیره تأمین بشردوستانه ایران است.
روش: با بررسی ادبیات، 12 فناوری تحول‌آفرین در حوزه زنجیره تأمین بشردوستانه شناسایی شد و پس از روایی محتوا، 8 فناوری برای بررسی بیشتر انتخاب شد. جامعه پژوهش، متخصصان زنجیره تأمین بشردوستانه و آشنا با فناوری‌های تحول‌آفرین بودند. برای سنجش دو عامل جذابیت و توانمندی، دو دسته پرسش‌نامه جداگانه توزیع شد. برای سنجش جذابیت از روش بهترین ـ بدترین و برای سنجش توانمندی از مقیاس لیکرت استفاده شد. در نهایت با روش ماتریس توانمندی ـ جذابیت فناوری‌ها اولویت‌بندی شدند. روش پژوهش کاربردی و از نظر ابزار گردآوری اطلاعات، توصیفی از نوع پیمایشی ـ تک‌مقطعی است.
یافته‌ها: بر اساس یافته‌ها، فناوری‌های «ربات هوشمند، پرینتر سه‌بُعدی و مشاور هوشمند» با توانمندی و جذابیت زیاد، در ناحیه حفظ جایگاه و توسعه قرار گرفتند. فناوری «هوش مصنوعی» با جذابیت زیاد و توانمندی به‌نسبت کم، در ناحیه «بهبود» و در رده دوم توجه قرار گرفت. فناوری‌های «اینترنت اشیا و بازی‌گونه‌سازی» با توانمندی زیاد و جذابیت نسبی کم و فناوری‌های «کلان‌داده» و «واقعیت افزوده» با توانمندی و جذابیت کمابیش کمتر در رده‌های بعدی قرار گرفتند.
نتیجه‌گیری: نتایج این پژوهش می‌تواند راهنمای خوبی برای بازیگران کلیدی زنجیره تأمین بشردوستانه، به‌منظور بهره‌گیری جهت کاهش خسارات و آلام ناشی از فجایع و بحران‌ها در کشور باشد.

کلیدواژه‌ها


عنوان مقاله [English]

Analyzing the Capability-attractiveness Matrix for Emerging Technologies in Iran’s Humanitarian Supply Chain

نویسندگان [English]

  • Mohammad Reza Sadeghi Moghadam 1
  • Reihane Noferesti 2
  • Amin Farahani 3
1 Associate Prof., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
2 MSc., Department of Industrial Management, Faculty of Management, University of Tehran, Tehran, Iran.
3 MSc., Faculty of Management and Accounting, College of Farabi, University of Tehran, Qom, Iran.
چکیده [English]

Objective: The influence of climate change on the frequency and severity of natural disasters is ever-increasing on a global scale. While natural disasters are unpredictable, emerging technologies can help with their prevention. Technologies allow responders to act sooner rather than later. The importance of the humanitarian supply chain in reducing the suffering and injuries caused by natural disasters is also undeniable. Developing emerging technologies offers an opportunity to improve the efficiency and effectiveness of responses of the humanitarian supply chain to disasters. Since Iran is one of the most natural catastrophe-prone countries in the world, this study aims to identify and prioritize emerging technologies suitable for its humanitarian supply chain. This study could provide solutions for responders and decision-makers to achieve an effective humanitarian supply chain.
Methods: This is descriptive survey research and its statistical population consists of five emerging technology experts in the Iranian humanitarian supply chain. To identify and prioritize applications of emerging technologies in the humanitarian supply chain, the Best-Worst method (BWM) was used.
Results: Reviewing the extant academic literature, 12 emerging technologies in the humanitarian supply chain were investigated. After assessing their content validity, experts selected eight relevant technologies for Iran’s humanitarian supply chain; including “Big Data”, “Internet of Things”, “Augmented Reality”, “Smart Robots”, “Artificial Intelligence”, “Gamification”, “3D Printing”, and “Smart Advisors”. In the next step, two sets of questionnaires were distributed among the experts. The questionnaires were collected and analyzed to measure the attractiveness and capabilities of emerging technologies in Iran’s humanitarian supply chain. The weights of technologies' attractiveness were calculated based on five filled-in questionnaires and by the Best-Worst Method and LINGO software. Next, to assess the technical capability of each emerging technology, eight Likert scale questionnaires were distributed. Finally, the gathered research data was analyzed using SPSS 26. Capability-Attractiveness Matrix was then formed. Analyzing areas of the Matrix helped the researchers determine specific actions suitable for each emerging technology. Based on the findings, “Smart Robots”, “3D Printing” and “Smart Advisors” technologies were placed in the “Position protection/Development” area with the highest capability and attractiveness. “Artificial Intelligence” was placed in the “improvement” area with high attractiveness and relatively low capability. “Internet of Things” and “Gamification” had a high capability and relatively low attractiveness. “Big Data” and “Augmented Reality” with relatively low capability and attractiveness had the least priority in Iran’s Humanitarian Supply Chain.
Conclusion: The critical role that emerging technologies play in disaster preparedness and recovery is increasingly becoming recognized. The findings of the BWM Method and Capability-Attractiveness Matrix can be a good help for decision-makers and key players in the humanitarian supply chain. Natural disaster preparedness and collaboration to reduce impact across the humanitarian supply chain have never been more important. Utilizing these emerging technologies can minimize fatalities and injuries caused by natural disasters. Emerging technologies need to be adapted to various situations that arise during disaster relief.

کلیدواژه‌ها [English]

  • Humanitarian supply chain
  • Emerging technologies
  • Best-worst method
  • Capability-attractiveness matrix
 
References
Ahir, S., Telavane, D., & Thomas, R. (2020, September). The impact of artificial intelligence, blockchain, big data and evolving technologies in coronavirus disease-2019 (covid-19) curtailment. In 2020 International Conference on Smart Electronics and Communication (ICOSEC) (pp. 113-120). IEEE.
Ansari, R., Soltanzadeh, J., Sharifian, A., Nateghian, M., & FarabiKhanghahi, S. (2015). Evaluation matrix of the attractiveness and capability of the technological strategy development tool (case study: iron ore recovery process technology). Journal of Improvement Management, 9 (3), 109-135. (in Persian)
Asadi, M,. (2019). Fourth industrial revolution and digital economy: drivers of sustainable economic growth. Journal of Applied Studies in Management and Development Sciences, 17, 1-26. (in Persian)
Asadzadeh, A., Samad-Soltani, T., & Rezaei-Hachesu, P. (2021). Applications of virtual and augmented reality in infectious disease epidemics with a focus on the COVID-19 outbreak. Informatics in medicine unlocked, 24, 100579.
Ashton, K. (2009). That ‘internet of things’ thing. RFiD Journal, 22(7), 97-114.
Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., &MacIntyre, B. (2001). Recent advances in augmented reality. IEEE computer graphics and applications, 21(6), 34-47.
Bag, S., Gupta, S., & Wood, L. (2020). Big data analytics in sustainable humanitarian supply chain: Barriers and their interactions. Annals of Operations Research, 1-40.
Baharmand, H., Maghsoudi, A., & Coppi, G. (2021). Exploring the application of blockchain to humanitarian supply chains: insights from Humanitarian Supply Blockchain pilot project. International Journal of Operations & Production Management, 41(9), 1522-1543.
Bedkowski, J., Piszczek, J., Kowalski, P., & Masłowski, A. (2009). Improvement of the robotic system for disaster and hazardous threat management. IFAC Proceedings Volumes, 42(13), 569-574.
Behl, A., & Dutta, P. (2020). Engaging donors on crowdfunding platform in Disaster Relief Operations (DRO) using gamification: A Civic Voluntary Model (CVM) approach. International Journal of Information Management, 54, 102140.
Beltagui, A., Nathan, K. & Gold, S. (2019). The Role of 3D Printing and Open Design on Adoption of Socially Sustainable Supply Chain Innovation. International Journal of Production Economics, doi:10.1016/j.ijpe.2019.07.035.
Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., &Ivkovic, M. (2011). Augmented reality technologies, systems and applications. Multimedia Tools and Applications, 51(1), 341-377.
Connell, R. B. (2010). The attractiveness-competitiveness matrix: a methodology used to assist policy makers select priorities for industrial development initiatives. International Journal of Business and Management, 5(7), 3.
Corsini, L., Aranda-Jan, C. B., & Moultrie, J. (2022). The impact of 3D printing on the humanitarian supply chain. Production Planning & Control, 33(6-7), 692-704.
Dalenogare, L. S., Benitez, G. B., Ayala, N. F., & Frank, A. G. (2018). The expected contribution of Industry 4.0 technologies for industrial performance. International Journal of Production Economics, 204, 383-394.
Dennehy, D., Oredo, J., Spanaki, K., Despoudi, S., & Fitzgibbon, M. (2021). Supply chain resilience in mindful humanitarian aid organizations: the role of big data analytics. International Journal of Operations & Production Management, 41(9), 1417-1441.
Deparday, V., Gevaert, C.M., Giuseppe, M.M., Soden, R.J. & Balog-Way, S. (2019). “Machine Learning for Disaster Risk Management.” In.: World Bank.
Dubey, R., Bryde, D. J., Foropon, C., Tiwari, M., Dwivedi, Y., & Schiffling, S. (2021). An investigation of information alignment and collaboration as complements to supply chain agility in humanitarian supply chain. International Journal of Production Research, 59(5), 1586-1605.
Dubey, R., Gunasekaran, A., Childe, S. J., Roubaud, D., Wamba, S. F., Giannakis, M., & Foropon, C. (2019). Big data analytics and organizational culture as complements to swift trust and collaborative performance in the humanitarian supply chain. International Journal of Production Economics, 210, 120-136.
Fernandez-Luque, L., & Imran, M. (2018). Humanitarian health computing using artificial intelligence and social media: A narrative literature review. International journal of medical informatics, 114, 136-142.
Fleming, K., Abad, J., Booth, L., Schueller, L., Baills, A., Scolobig, A., ... & Leone, M. F. (2020). The use of serious games in engaging stakeholders for disaster risk reduction, management and climate change adaption information elicitation. International Journal of Disaster Risk Reduction, 49, 101669.
Fox, G. C., Kamburugamuve, S., & Hartman, R. D. (2012, May). Architecture and measured characteristics of a cloud based internet of things. In 2012 international conference on Collaboration Technologies and Systems (CTS) (pp. 6-12). IEEE.
Frank, A.G., Dalenogare, L.S., & Ayala, N.F. (2019). Industry 4.0 technologies: Implementation patterns in manufacturing companies. International Journal of Production Economics, 210, 15-26.
Gartner (2018). Hype Cycle for Emerging Technologies 2018. Retrieved from: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
Gartner (2019). Hype Cycle for Emerging Technologies 2019. Retrieved from: https://www.gartner.com/en/research/methodologies/gartner-hype-cycle
Gartner (2021). Hype Cycle for Emerging Technologies 2021. Retrieved from: https://www.gartner.com/en/webinars/4004100/the-gartner-hype-cycle-for-emerging-technologies-2021#:~:text=The%20Gartner%202021%20Hype%20Cycle,blockchain%20 evolution%20and%20human%20augmentation%3F
Gevaert, C. M., Carman, M., Rosman, B., Georgiadou, Y., & Soden, R. (2021). Fairness and accountability of AI in disaster risk management: Opportunities and challenges. Patterns, 2(11), 100363.
Ghasemi, R., Alidoosti, A., Hosnavi, R., & Norouzian Reykandeh, J. (2018). Identifying and Prioritizing Humanitarian Supply Chain Practices to Supply Food before an Earthquake. Industrial Management Journal10(1), 1-16.
Gupta, S., Altay, N., & Luo, Z. (2019). Big data in humanitarian supply chain management: A review and further research directions. Annals of Operations Research, 283(1), 1153-1173.
Hax, A. C., & No, M. (1993). Linking technology and business strategies: a methodological approach and an illustration. In Perspectives In Operations Management (pp. 133-155). Springer, Boston, MA.
Heybatallahpour Z., Mehralizadeh Y., Barkat G., Nasiri M. (2021). Modeling of Human Resource Development Strategies in the Era of the Fourth Industrial Revolution in Chemical Companies Based in Ahwaz Industrial Towns. Journal of Educational Planning Studies, 8(16), 177-202. (in Persian)
Hoffman, S. G. (2021). A story of nimble knowledge production in an era of academic capitalism. Theory and Society, 50, 541-575.
Holmström, J. & Partanen, J. (2014). Digital Manufacturing-Driven Transformations of Service Supply Chains for Complex Products. Supply Chain Management: An International Journal, (4), 421. doi:10.1108/SCM-10-2013-0387.
Imran, M., Ofli, F., Caragea, D., & Torralba, A. (2020). Using ai and social media multimodal content for disaster response and management: Opportunities, challenges, and future directions. Information Processing & Management, 57(5), 102261.
Jolly, D. R. (2012). Development of a two-dimensional scale for evaluating technologies in high-tech companies: An empirical examination. Journal of Engineering and Technology Management, 29(2), 307-329.
Kang, H. J. (2018). Established smart disaster safety management response system based on the 4th industrial revolution. Journal of Digital Contents Society, 19(3), 561-567.
Kankanamge, N., Yigitcanlar, T., Goonetilleke, A., & Kamruzzaman, M. (2020). How can gamification be incorporated into disaster emergency planning? A systematic review of the literature. International Journal of Disaster Resilience in the Built Environment. Retrieved from: https://eprints.qut.edu.au/199587/1/QUT_e_prints.pdf
Kaplan, A. & Haenlein, M. (2019). Siri, Siri, in my Hand: Who’s the Fairest in the Land? On the Interpretations, Illustrations, and Implications of Artificial Intelligence. Business Horizons, 62 (1), 15–25. doi:10.1016/j.bushor.2018.08.004.
Karimian, H., Attarzadeh, A. (2012). The Role Of Industrial Revolution In Developments Of Persian’S Handicrafts. Journal of Historical Studies of Islam, 3(11), 99-120. (in Persian)
Khajavi, S. H., Partanen, J. & Holmström, J. (2014). Additive Manufacturing in the Spare Parts Supply Chain. Computers in Industry, 65 (1), 50–63. doi:10.1016/j.compind.2013.07.008.
Khan, M., Imtiaz, S., Parvaiz, G. S., Hussain, A., & Bae, J. (2021). Integration of internet-of-things with blockchain technology to enhance humanitarian logistics performance. IEEE Access, 9, 25422-25436.
Kim, I. S., Choi, Y., & Jeong, K. M. (2017). A new approach to quantify safety benefits of disaster robots. Nuclear Engineering and Technology, 49(7), 1414-1422.
Koizumi, S. (2019). The light and shadow of the fourth industrial revolution. Innovation Beyond Technology, 4: 63-86.
Kovacs, G., & Spens, K. M. (2009). Identifying challenges in humanitarian logistics. International Journal of Physical Distribution & Logistics Management, 39, 506–528.
LeHong, H., Fenn, J. & Toit, R. (2014). Hype Cycle for Emerging Technologies 2014, Gartner.
Liang, F., Brunelli, M., & Rezaei, J. (2020). Consistency issues in the best worst method: Measurements and thresholds.Omega, 96, 102175.
Liu, C. (2021). Seeing like a state, enacting like an algorithm:(Re) assembling contact tracing and risk assessment during the COVID-19 pandemic. Science, Technology, & Human Values, 01622439211021916.
Lovreglio, R., & Kinateder, M. (2020). Augmented reality for pedestrian evacuation research: promises and limitations. Safety science, 128, 104750.
Ma, D., Shi, Y., Zhang, G., & Zhang, J. (2021). Does theme game-based teaching promote better learning about disaster nursing than scenario simulation: A randomized controlled trial. Nurse education today, 103, 104923.
Marx, V. (2013). The big challenges of big data. Nature, 498(7453), 255-260.
McKinsey, I. (2016). 4.0. after the initial hype. Where manufacturers are finding value and how they can best capture it. McKinsey Digital.
Miorandi, D., Sicari, S., DePellegrini, F. & Chlamtac, I. (2012). Internet of things: Vision, applications and research challenges. Ad Hoc Networks, 10(7), 1497-1516.
Mitsuhara, H., Tanimura, C., Nemoto, J., & Shishibori, M. (2021). Expressing Disaster Situations for Evacuation Training Using Markerless Augmented Reality. Procedia Computer Science, 192, 2105-2114.
Mokhtarzadeh, N. G., Mahdiraji, H. A., Beheshti, M., & Zavadskas, E. K. (2018). A novel hybrid approach for technology selection in the information technology industry. Technologies, 6(1), 34.
Mokhtarzadeh, N. G., Mahdiraji, H. A., Jafari-Sadeghi, V., Soltani, A., & Kamardi, A. A. (2020). A product-technology portfolio alignment approach for food industry: a multi-criteria decision making with z-numbers. British Food Journal, 122(12), 3947-3967.
Murphy, R. R., Gandudi, V. B., Amin, T., Clendenin, A., & Moats, J. (2022). An analysis of international use of robots for COVID-19. Robotics and autonomous systems, 148, 103922.
Murphy, R. R., Tadokoro, S., & Kleiner, A. (2016). Disaster robotics. In Springer Handbook of Robotics (pp. 1577-1604). Springer, Cham.
Nadkarni, P. M., Ohno-Machado, L., & Chapman, W. W. (2011). Natural language processing: an introduction. Journal of the American Medical Informatics Association, 18(5), 544-551.
OCHA (2021) Emerging technologies in humanitarian action, Retrieved from https://www.unocha.org/sites/unocha/files/OCHA%20Technology%20Report.pdf .
Oe, H., & Kawakami, S. (2021). A disaster prevention programme using virtual schemes: Recommendation of tradition populaire integrated with tendenko as an approach to immersive training. International journal of disaster risk reduction, 57, 102135.
Philbeck, T., & Davis, N. (2018). The fourth industrial revolution. Journal of International Affairs, 72(1), 17-22.
Qadir, Z., Ullah, F., Munawar, H. S., & Al-Turjman, F. (2021). Addressing disasters in smart cities through UAVs path planning and 5G communications: A systematic review. Computer Communications, 168, 114-135.
Qalavand, K., Karimi Ghahroodi, M. & Hajimola, M. (2021). The Impact of Evolution Making Technologies on Regulating the National Virtual Space. Naja Strategic Studies Journal, 5(18), 113-146. (in Persian)
Radianti, J., Dokas, I., Boersma, K., Saad Noori, N., Belbachir, N. & Stieglitz, S. (2019). Enhancing Disaster Response for Hazardous Materials Using Emerging Technologies: The Role of AI and a Research Agenda. Paper presented at theEngineering Applications of Neural Networks, Cham, 2019//.
Reitmayr, G., & Drummond, T. (2006). Going out: robust model-based tracking for outdoor augmented reality. Paper presented at the Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality.
Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49-57.
Rodríguez-Espíndola, O., & Beltagui, A. (2018). Can 3D Printing address operations challenges in Disaster Management? Available in: https://publications.aston.ac.uk/id/eprint/ 33651/1/ Proceedings_Euroma_2018.pdf
Rodríguez-Espíndola, O., Chowdhury, S., Beltagui, A., & Albores, P. (2020). The potential of emergent disruptive technologies for humanitarian supply chains: the integration of blockchain, Artificial Intelligence and 3D printing. International Journal of Production Research, 58(15), 4610-4630.
Sadeghi Moghadam, M., & Ghasemian Sahebi, I. (2018). A Mathematical Model to Improve the Quality of Demand Responding in Emergency Medical Centers in a Humanitarian Supply chain. Modern Research in Decision Making3(1), 217-242.
Sahebi, I. G., Masoomi, B., & Ghorbani, S. (2020). Expert oriented approach for analyzing the blockchain adoption barriers in humanitarian supply chain. Technology in Society, 63, 101427.
Saripalle, S., Maker, H., Bush, A., & Lundman, N. (2016, October). 3D printing for disaster preparedness: Making life-saving supplies on-site, on-demand, on-time. In 2016 IEEE Global Humanitarian Technology Conference (GHTC) (pp. 205-208). IEEE.
Sengupta, T., Narayanamurthy, G., Moser, R., Pereira, V., & Bhattacharjee, D. (2021). Disruptive technologies for achieving supply chain resilience in COVID-19 era: An implementation case study of satellite imagery and blockchain technologies in fish supply chain. Information Systems Frontiers, 1-17.
Sharma, K., Anand, D., Sabharwal, M., Tiwari, P. K., Cheikhrouhou, O., & Frikha, T. (2021). A Disaster Management Framework Using Internet of Things-Based Interconnected Devices. Mathematical Problems in Engineering, 2021.
Shen, L., Zhou, J., Skitmore, M., & Xia, B. (2015). Application of a hybrid Entropy–McKinsey Matrix method in evaluating sustainable urbanization: A China case study. Cities, 42, 186-194.
Subramanya, K., & Kermanshachi, S. (2021). Exploring Utilization of the 3D Printed Housing as Post-Disaster Temporary Shelter for Displaced People. In Construction Research Congress 2022 (pp. 594-605).
The MarketWatch News (2021). Covid-19 Impact on Smart Advisors Market Share with Top Countries Data, Size, Growth, Product Type, Industry Trends and Forecast to 2026, India, Pune, May 7, 2021, Retrieved from https://www.marketwatch.com/press-release/covid-19-impact-on-smart-advisors-market-share-with-top-countries-data-size-growth-product-type-industry-trends-and-forecast-to-2026-2021-05-07.
Tönissen, D. D., & Schlicher, L. (2021). Using 3D-printing in disaster response: The two-stage stochastic 3D-printing knapsack problem. Computers & Operations Research, 133, 105356.
Van Krevelen, D., &Poelman, R. (2010). A survey of augmented reality technologies, applications and limitations. International Journal of Virtual Reality, 9(2), 1.
Yadav, D. K. & Barve, A. (2018). Segmenting critical success factors of humanitarian supply chains using fuzzy Dematel. Benchmarking an International Journal, 25(2), 400-425.
Yu, J., Shannon, H., Baumann, A., Schwartz, L., & Bhatt, M. (2016). Slum upgrading programs and disaster resilience: A case study of an Indian ‘Smart City’. Procedia Environmental Sciences, 36, 154-161.
Zhang, X. (2021). Prediction of fire risk based on cloud computing. Alexandria Engineering Journal, 60(1), 1537-1544.