طراحی مدل ساختاری عوامل کلیدی زنجیره‌تامین انعطاف‌پذیردر صنعت قطعات یدکی خودرو

نوع مقاله : ترویجی

نویسندگان

1 استادیار گروه مدیریت، واحد یزد، دانشگاه آزاد اسلامی، یزد، ایران

2 دانشیار گروه مدیریت، دانشگاه میبد، میبد، ایران

چکیده

در پاسخ به تغییرات روزافزون محیط‌های کسب و کار، صنایع به­منظور بقا و رشد، توسعه سامانه‌های انعطاف‌پذیر در سراسر زنجیره‌تامین خود را به­عنوان یکی از راهبرد‌های حیاتی پذیرفته‌اند. این پژوهش با هدف شناسایی عوامل کلیدی انعطاف‌پذیری در زنجیره‌تامین، بازنگری روش مدلسازی ساختاری تفسیری و ارائه یک قاعده جدید سطح‌بندی ارائه شده است. قاعده پیشنهادی علاوه بر در نظرگیری تاثیرگذاری و تاثیرپذیری عناصر، وزن‌ عوامل را نیز در سطح‌بندی لحاظ می‌کند، که این امر دقت خروجی و امکان تفسیر دقیق‌تر را افزایش می‌دهد. در این پژوهش با مطالعه ادبیات موضوعی و رویکرد تحلیل محتوای متنی، تعداد دوازده عامل انعطاف‌پذیری زنجیره‌تامین شناسایی شد، که برای بومی‌سازی آن در حوزه صنعت ساخت قطعات یدکی خودرو از روش دلفی در سه دور استفاده شد. داده‌ها  با استفاده از پرسشنامه جمع‌آوری گردید که جامعه آماری آن را متخصصان، مدیران و کارشناسان فعال در مرکز تحت بررسی تشکیل دادند. به منظور طراحی مدل ساختاری از روش مدلسازی ساختاری تفسیری بهره گرفته شد، که در آن عوامل کلیدی ابتدا توسط قاعده سطح‌بندی تکرار و سپس توسط قاعده پیشنهادی، سطح‌بندی گردید. در نهایت با استفاده از نمودار وابستگی-نفوذ، عوامل کلیدی در چهار دسته طبقه بندی شدند. نتایج نشان داد که مدیریت تقاضا و انعطاف‌پذیری در محصول تولیدی به عنوان تاثیرگذارترین عامل در حوزه انعطاف‌پذیری زنجیره‌تامین قطعات یدکی خودرو شناخته شدند. بنابراین، به مدیران صنعت مربوطه پیشنهاد می‌گردد که به این دسته از عوامل توجه بیشتری داشته باشند. همچنین اجرای دو قاعده سطح‌بندی حاکی از آن است که خروجی هر دو قاعده سطح‌بندی یکسان است، با این تفاوت که قاعده پیشنهادی تصمیم‌گیرنده را از بسیاری از وظایف ناخوشایند، وقت گیر و مستعد خطا مرتبط با سیستم‌های پیچیده ساختاردهی رها می‌کند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Designing a Structural Model for Key Factors of Supply Chain Flexibility in the Automotive Spare Parts Industry

نویسندگان [English]

  • Hoda Moradi 1
  • hamid babaei meybodi 2
1 Department of Management, Yazd Branch, Islamic Azad University, Yazd, Iran,
2 Department of Management, Meybod University, Meybod, Iran
چکیده [English]

In response to the ever-evolving business environment, industries have adopted flexible systems throughout their supply chains as a critical strategy for survival and growth. This study aims to identify the key factors of supply chain flexibility, review the interpretive structural modeling (ISM) method, and propose a new leveling rule. The proposed rule considers not only the influence and dependence of elements but also the weight of factors, enhancing the accuracy of results and allowing for more precise interpretations. Through a literature review and content analysis, twelve supply chain flexibility factors were identified. The Delphi method, conducted over three rounds, was used to localize these factors in the automotive spare parts   manufacturing industry. Data were collected via a questionnaire, with the statistical population consisting of specialists, managers, and experts in the studied system. The ISM method was employed to design the structural model, where key factors were initially leveled using the existing rule, followed by the proposed rule. Ultimately, the key factors were categorized into four groups using the dependence-influence diagram. The results revealed that demand management and product flexibility were the most influential factors in the automotive spare parts supply chain. It is recommended that industry managers focus on these factors. Additionally, while both leveling rules produced identical results, the proposed rule frees decision-makers from many of the time-consuming, error-prone tasks involved in structuring complex systems.
 

کلیدواژه‌ها [English]

  • Flexibility
  • Supply Chain
  • Leveling
  • Interpretive Structural Modeling

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[1] S. Bag and M. S. Rahman, “The role of capabilities in shaping sustainable supply chain flexibility and enhancing circular economy-target performance: an empirical study,” Supply Chain Manag An Int J, vol. 28, no. 1, pp. 162–178, 2023,  DOI:10.1108/SCM-05-2021-0246.
[2] E. Purwaningsih, M. Muslikh, S. Suhaeri, and B. Basrowi, “Utilizing blockchain technology in enhancing supply chain efficiency and export performance, and its implications on the financial performance of SMEs,” Uncertain Supply Chain Manag, vol. 12, no. 1, pp. 449–460, 2024, DOI: 10.5267/j.uscm.2023.9.007.
[3] D. Ivanov, A. Das, and T.-M. Choi, “New flexibility drivers for manufacturing, supply chain and service operations,” International Journal of Production Research, vol. 56, no. 10. Taylor & Francis, pp. 3359–3368, 2018, DOI: 10.1080/00207543.2018.1457813.
[4]E. Ramos, A. S. Patrucco, and M. Chavez, “Dynamic capabilities in the ‘new normal’: a study of organizational flexibility, integration and agility in the Peruvian coffee supply chain,” Supply Chain Manag An Int J, vol. 28, no. 1, pp. 55–73, 2023, DOI: 10.1108/SCM-12-2020-0620.
[5]B. Huo, M. Z. U. Haq, and M. Gu, “The impact of information sharing on supply chain learning and flexibility performance,” Int J Prod Res, vol. 59, no. 5, pp. 1411–1434, 2021, DOI: 10.1080/00207543.2020.1824082.
[6]D. V. Enrique, L. V. Lerman, P. R. de Sousa, G. B. Benitez, F. M. B. C. Santos, and A. G. Frank, “Being digital and flexible to navigate the storm: How digital transformation enhances supply chain flexibility in turbulent environments,” Int J Prod Econ, vol. 250, p. 108668, 2022, DOI:10.1016/j.ijpe.2022.108668.
[7]J. H. Han, Y. Wang, and M. Naim, “Reconceptualization of information technology flexibility for supply chain management: An empirical study,” Int J Prod Econ, vol. 187, pp. 196–215, 2017.DOI: 10.1016/j.ijpe.2017.02.018.
[8]J. N. Warfield and S. Member, “Developing Interconnection Matrices in Structural Modeling,” no. 1, pp. 81–87, 1974, DOI: 10.1109/TSMC.1974.5408524.
[9]J. N. Warfield, “Societal systems planning, policy and complexity,” Cybern Syst, vol. 8, no. 1, pp. 113–115, 1978.
[10]J. N. Warfield, An assault on complexity, no. 3. Battelle, Office of Corporate Communications, 1973.
[11]S. Rajput and S. P. Singh, “Identifying Industry 4.0 IoT enablers by integrated PCA-ISM-DEMATEL approach,” Manag Decis, vol. 57, no. 8, pp. 1784–1817, 2019, DOI: 10.1108/MD-04-2018-0378.
[12]R. Attri, N. Dev, and V. Sharma, “Interpretive Structural Modelling (ISM) approach: An Overview,” Res J Manag Sci, vol. 2, no. 2, pp. 1–8, 2013.
[13]F. R. Janes, “Interpretive structural modelling: a methodology for structuring complex issues,” trans inst MC, vol. 10, no. 3, pp. 145–154, 2016, DOI: 10.35940/ijitee.D1607.029420.
[14]Z. Yanga, Y. Lin, Z. Yang, Y. Lin, Z. Yanga, and Y. Lin, “The effects of supply chain collaboration on green innovation performance: An interpretive structural modeling analysis,” Sustain Prod Consum, vol. 23, pp. 1–10, 2020, DOI: 10.1016/j.spc.2020.03.010.
[15]N. Adabavazaeh and M. Nikbakht, “Interpretive Structural Modeling Analysis of Reverse Supply Chain Critical Success Factors in Air Industry,” in 15th iran international industrial engineering conference, 2019, pp. 1-11, DOI: 10.1109/IIIEC.2019.8720737.
[16]A. Jamwal, R. Agrawal, S. Gupta, G. S. Dangayach, M. Sharma, and M. A. Z. Sohag, “Modelling of Sustainable Manufacturing Barriers in Pharmaceutical Industries of Himachal Pradesh: An ISM-Fuzzy Approach,” Smart Innov Syst Technol, vol. 174, pp. 157–167, 2020, DOI: 10.1007/978-981-15-2647-3_15.
[17]F. J. C. de Melo and D. D. de Medeiros, “Applying interpretive structural modeling to analyze the fundamental concepts of the management excellence model guided by the risk-based thinking of ISO 9001: 2015,” Human and Ecological Risk Assessment, vol. 27, no. 3. pp. 742–772, 2021, DOI: 10.1080/10807039.2020.1752144.
[18]M. Rashidpour, A. Pirhayati, J. Niknafs, and M. Aidi, “Designing an Interpretative Structural Model (ISM) of Fear Appeal Based Advertising in Selected Insurance Companies,” Int J Innov Manag Organ Behav, vol. 4, pp. 1–11, Apr. 2024, DOI: 10.61838/kman.ijimob.4.2.1.
[19]M. S. Nikabadi, F. Bahrami, and  abbas ali Rastegar, “Designing a green supply chain performance evaluation model with the ISM approach in the steel industry,” in 3rd international conferences on system thinking in practice, 1402, pp. 1–18. DOI: 10.22105/riej.2022.298634.1327.
[20]F. Naebi, R. Tehrani, and R. Radfsr, “Interpretive structural modeling of choosing a financing strategy for the supply chain of petrochemical industries,” Advert Sales Manag, vol. 3, no. 3, pp. 230–245, 1401, DOI: 10.52547/JABM.3.1.437.
[21]H. shahbandar zadeh and M. Maddai, “Developing a supply chain strategy using the ISM technique,” in 7th International Conference on Management, Accounting and Economic Development, 1400, pp. 1-22 (in persian).
[22] A. A. Jamaluddin, I. A. Alkathiri, W. A. Alghmadi, H. A. Khadawardi, and A. Y. Alqahtani, “Overcoming IoT Implementation Challenges in the Saudi Healthcare Supply Chain: An Integrated ISM-MICMAC Analysis,” in 14th International Conference on Industrial Engineering and Operations Management, 2024, pp. 1283–1295, DOI: 10.46254/AN14.20240317.
[23]P. Agrawal and R. Narain, “Analysis of enablers for the digitalization of supply chain using an interpretive structural modelling approach,” Int J Product Perform Manag, vol. 72, no. 2, pp. 410–439, 2023,DOI: 10.1108/IJPPM-09-2020-0481.
[24]S. Elhidaoui, K. Benhida, S. El Fezazi, S. Kota, and A. Lamalem, “Critical success factors of blockchain adoption in green supply chain management: contribution through an interpretive structural model,” Prod Manuf Res, vol. 10, no. 1, pp. 1–23, 2022, DOI: 10.1080/21693277.2021.1990155.
[25]S. Piya, A. Shamsuzzoha, and M. Khadem, “An approach for analysing supply chain complexity drivers through interpretive structural modelling,” Int J Logist Res Appl, vol. 23, no. 4, pp. 311–336, 2020,DOI: 10.1080/13675567.2019.1691514.
[26]J. N. Warfield, Science of generic design: managing complexity through systems design. Iowa State Press, 1994.
[27] H. Moradi, H. Babaei Meybodi, M. Rabbani, H. B. Meybodi, and M. Rabbani, “Novel Approaches for Determining Exogenous Weights in Dynamic Networks DEA,” Interdiscip J Manag Stud (Formerly known as Iran J Manag Stud, vol. 17, no. 1, pp. 259–275, 2024, DOI: 10.22059/ijms.2023.341472.675056.
[28]S. Menon and M. Suresh, “Total interpretive structural modelling: Evolution and applications,” in International Conference on Innovative Data Communication Technologies and Application, 2019, pp. 257–265.
[29]U. Awan, A. Kraslawski, and J. Huiskonen, “Understanding influential factors on implementing social sustainability practices in Manufacturing Firms: An interpretive structural modelling (ISM) analysis,” in 28th International Conference on Flexible Automation and Intelligent Manufacturing, 2018, pp. 1039–1048.
[30]S. Yadav and A. Sharma, “Modelling of Enablers for Maintenance Management by ISM Method,” Ind Eng Manag, vol. 06, no. 01, 2017, DOI: 10.4172/2169-0316.1000203.
[31]M. S. Amini, K. Kenarkoohi, and T. Mostafa, “Identifying and assessing the risk of Internet startup companies using the Interpretive Structural Modelling (ISM) technique,” Adv Stand Appl Sci, vol. 2, no. 2, pp. 50–57, 2024, DOI: 10.22034/asas.2024.447292.1053.
[32]M. R. H. Ardakani, | Mohammad Hossein Sarai, M. M. Karimnejad, S. A. Modarressi, and S. Moayedfar, “Interpretive structural modeling of factors affecting the internal development of cities in arid regions (Case study: Ardakan city),” J Appl Res Geogr Sci, vol. 24, no. 75, pp. 465–478, 1403.
[33]M. Bijan, S. Hasani moghadam, and  mohammad mehdi Mohtadi, “Interpretive structural modeling of optimal strategic control in institutional universities based on fuzzy technique,” Q J Strateg Knowl Interdiscip Stud, vol. 12, no. 48, pp. 141–167, 1401. DOI: 20.1001.1.24234621.1401.12.48.6.0. (in Persian).
[34]B. Singh, “Barriers of supply chain for Industries in Indian scenario: Pandemic Covid-19 impact using ISM approach,” J Futur Sustain, vol. 4, no. 4, pp. 179–188, 2024.
[35]M. Attiany, S. Al-Kharabsheh, M. Abed-Qader, S. Al-Hawary, A. Mohammad, and A. Rahamneh, “Barriers to adopt industry 4.0 in supply chains using interpretive structural modeling,” Uncertain Supply Chain Manag, vol. 11, no. 1, pp. 299–306, 2023, DOI: 10.5267/j.uscm.2022.9.013.
[36]R. R. Menon and V. Ravi, “Analysis of barriers of sustainable supply chain management in electronics industry: An interpretive structural modelling approach,” Clean Responsible Consum, vol. 3, p. 100026, 2021, DOI: 10.1016/j.clrc.2021.100026.
[37]Z. Yanga and Y. Lin, “The effects of supply chain collaboration on green innovation performance:An interpretive structural modeling analysis,” Sustain Prod Consum, vol. 23, pp. 1–10, 2020, DOI: 10.1016/j.spc.2020.03.010.
[38]P. Santaguida et al., “Protocol for a Delphi consensus exercise to identify a core set of criteria for selecting health related outcome measures (HROM) to be used in primary health care,” BMC Fam Pract, vol. 19, pp. 1–14, 2018, DOI: 10.1186/s12875-018-0831-5.
[39]C. L. Paul, “A modified delphi approach to a new card sorting methodology,” J Usability Stud, vol. 4, no. 1, pp. 7–30, 2008.
[40] mohammad ali Sarlak,  seyed mehdi Veiseh, Y. Pour ashraf, and H. Mahdizadeh, “Designing the model of Spirituality Based Organization in Higher Education System of Iran,” J Public Manag Res, vol. 5, no. 18, pp. 5–24, 2012, DOI:  10.22111/jmr.2013.998 (in Persian).
[41]H. N. Nateri, A. Mehrara, and M. Matani, “Interpretive Structural Modeling of Factors Affecting on the Implementation of Job Rotation Based on the Organization, Methods and Value Chain of National Gas Company of Iran,” Iran J Supply Chain Manag, vol. 25, no. 78, pp. 37–46, 2023. DOR: 20.1001.1.20089198.1402.25.78.4.6, (in Persian).
[42]H. Moradi, M. Rabbani, H. Babaei Meybodi, and M. T. Honari, “Sustainable Supplier Selection : A New Integrated Approach of Fuzzy Interpretive Structural Modeling and Dynamic Network Data Envelopment Analysis,” Iran J Oper Res, vol. 12, no. 2, pp. 14–36, 2021, https://rb.gy/yhb3yi.
[43]S. Mandal, “Supply and demand effects on supply chain flexibility: An empirical exploration,” Knowl Process Manag, vol. 22, no. 3, pp. 206–219, 2015, DOI: 10.1002/kpm.1475.
[44]R. K. Singh, S. Modgil, and P. Acharya, “Identification and causal assessment of supply chain flexibility,” Benchmarking An Int J, vol. 27, no. 2, pp. 517–549, 2020, DOI: 10.1108/BIJ-01-2019-0003.
[45]C. M. Karuppan, “Strategies to foster labor flexibility,” Int J Product Perform Manag, vol. 53, no. 6, pp. 532–547, 2004, DOI: 
[46]R. Sreedevi and H. Saranga, “Uncertainty and supply chain  risk: The moderating role of supply chain flexibility in risk mitigation,” Int J Prod Econ, vol. 193, pp. 332–342, 2017, https://doi.org/10.1016/j.ijpe.2017.07.024..
[47]R. K. Singh, S. Modgil, and P. Acharya, “Assessment of Supply Chain Flexibility Using System Dynamics Modeling,” Glob J Flex Syst Manag, vol. 20, no. 1, pp. 39–63, 2019, DOI:10.1007/s40171-019-00224-7.
[48]A. M. Sánchez and M. P. Pérez, “Supply chain flexibility and firm performance: a conceptual model and empirical study in the automotive industry,” Int J Oper Prod Manag, vol. 25, no. 7, pp. 681–700, 2005,DOI: 10.1108/01443570510605090.
[49]K. A. Fantazy and M. Salem, “The value of strategy and flexibility in new product development: The impact on performance,” J Enterp Inf Manag, vol. 29, no. 4, pp. 525–548, 2016,DOI: 10.1108/01443570510605090.
[50]A. T. L. Chan, E. W. T. Ngai, and K. K. L. Moon, “The effects of strategic and manufacturing flexibilities and supply chain agility on firm performance in the fashion industry,” Eur J Oper Res, vol. 259, no. 2, pp. 486–499, 2017, DOI: 10.1016/j.ejor.2016.11.006.
[51]C. Y. Yi, E. W. T. Ngai, and K. Moon, “Supply chain flexibility in an uncertain environment: exploratory findings from five case studies,” Supply Chain Manag An Int J, vol. 16, no. 4, pp. 271–283, 2011.DOI: 10.1108/13598541111139080.