مدل‌سازی عوامل مؤثر بر بهینگی زنجیره تأمین در صنعت برق با رویکرد ترکیبی تحلیل اکتشافی و ساختاری تفسیری
محورهای موضوعی : مدیریت صنعتی گرایش زنجیره تأمینمجتبی امیدیان 1 , یونس وکیل الرعایا 2 , سید عبدالله حیدریه 3
1 - دانشجوی دکتری گروه مدیریت بازرگانی، واحد سمنان، دانشگاه آزاد اسلامی، سمنان، ایران
2 - دانشیار گروه مدیریت بازرگانی، واحد سمنان، دانشگاه آزاداسلامی، سمنان، ایران.
3 - استادیار گروه مدیریت صنعتی، واحد سمنان، دانشگاه آزاداسلامی، سمنان، ایران
کلید واژه: بهینگی, صنعت برق, زنجیره, تحلیل MICMAC, is, تأمین,
چکیده مقاله :
هدف از این مطالعه مدل‌سازی ساختاری تفسیری عوامل مؤثر بر بهینگی زنجیره تأمین در صنعت برق است. مطالعه حاضر با استفاده از روش ترکیبی، یک رهیافت کیفی-کمی را برای تدوین و اعتباریابی مدل عوامل مؤثر بر بهینگی زنجیره تأمین از دیدگاه نخبگان صنعت برق ارائه می‌کند. در راستای تدوین و شناسایی عوامل بهینگی، از تحلیل مضمون و شبکه مضمونها با استفاده از نرم‌افزار نویو برای تشکیل شبکه مضامین، الگوی کیفی عوامل مؤثر بر بهینگی زنجیره تأمین طراحی شد. برای تدوین الگوی اولیه عوامل مؤثر بر بهینگی زنجیره تأمین، تعداد 23 نفر از خبرگان و متخصصان صنعت برق با روش نمونه‌گیری هدفمند انتخاب شدند. در بخش کمی برای تجزیه‌وتحلیل داده‌ها و اعتبارسنجی عوامل مؤثر بر بهینگی زنجیره تأمین از روش کیفی، از روش مدل‌سازی ساختاری تفسیری ISMو تحلیل MICMAC استفاده شد. نتایج نشان داد که چارچوب اولیه الگوی کیفی بهینگی زنجیره تأمین در صنعت برق دارای شش بعد است که عبارتند از: عملکرد زنجیره تأمین، مدیریت زنجیره تأمین، مدیریت ریسک زنجیره تأمین، مدیریت کیفیت زنجیره تأمین، تولید و عملیات و پایداری زنجیره تأمین. در گراف ISM، متغیرها در چهارده سطح مختلف طبقه‌بندی شدند که در بالاترین سطح مدل عوامل "سازمان"و "راهبردها" و در پایین‌ترین سطح "رضایتمندی مشتری" قرار گرفت. پس از تحلیل MICMAC متغیرها در سه گروه متغیرهای مستقل با یازده متغیر، متصل با چهار متغیر و وابسته با ده متغیر قرار گرفتند و هیچ متغیری در گروه متغیرهای خودگردان قرار نگرفت.
The purpose of this study was interpretive structural modeling of the factors affecting supply chain optimization in the electricity industry. In this study, using a mixed method, a qualitative-quantitative approach was employed to model and verify the factors affecting supply chain optimization. To this end, through content analysis and thematic analysis using Novo software to form thematic networks, a qualitative model of factors influencing supply chain optimality was designed. In order to formulate a basic pattern of the factors affecting supply chain optimality, 23 experts in the electricity industry were selected through purposeful sampling method. In the quantitative section of the study, interpretive structural modeling (ISM) approach and MICMAC analysis were used. The results showed that the initial framework of supply chain optimality model has six dimensions including supply chain performance, supply chain management, supply chain risk management, supply chain quality management, production and operation, and supply chain stability. In the ISM graph, the variables were categorized at different levels, in which the "agency" and "strategy" factors were the highest and "customer satisfaction" was the lowest factor. After MICMAC analysis, the variables were categorized in three groups of independent variables with 11 variables, with four variables and dependent variables with 10 variables. No variables were included in the PA.
Abidi, H., Klumpp, M., & de Leeuw, S. (2015). Modelling Impact of Key Success Factors in Humanitarian Logistics. In Logistics Management (pp. 427-443). Springer, Cham.
Amiri, M., Mansouri Mohammad Abadi, S., Shaabani, A., &Mohammadi, K. (2016),An Analysis of Factors Affecting Supply Chain Performance Using a Integrated Approach of Confirmatory Factor Analysis and Fuzzy Topsis in Food Industry Companies of Shiraz Industrial City,Supply Chain Science Quarterly, Vol. 18, No. 54, page 15-4. [In Persian]
Anand, N., & Grover, N. (2015). Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs). Benchmarking: An International Journal, 22(1), 135-166.
Aydın, Serra Demiral, Selin Hanife Eryuruk, and F. Kalaoğlu. (2014), Evaluation of the performance attributes of retailers using the scor model and AHP: a case study in the Turkish clothing industry. Fibres & Textiles in Eastern Europe.
Azadeh, S., & Yavarzadeh, M.R. (2015), Factors Affecting Supply Chain Management in Industries,2nd International Conference on Modern Research in Industrial Management and Engineering. [In Persian]
Bawer Sad, B., Nili Ahmad Abadi, M., & Biranvand, T. (2018),Offering Sustainable Supply Chain Management in the Marine Industry (Case Study: Marine Industry Organization,Journal of Marine Science Education, No. 12, pp. 37-48. [In Persian]
Bolaños, R., Fontela, E., Nenclares, A., & Pastor, P. (2005). Using interpretive structural modelling in strategic decision-making groups. Management Decision, 43(6), 877-895.
Carvalho, M. S., Nóvoa, H., & Silva, S. D., Machado, M. C., Fernandes, A. C., Sampaio, P. (2016). Supply Chain Quality Management: a theoretical framework for integration measurement. InILS 2016-6th International Conference on Information Systems, Logistics and Supply Chain. International Conference on Information Systems, Logistics and Supply Chain.
Colin, M., Galindo, R. & Hernández, O.(2015). Information and communication technology as a key strategy for efficient supply chain management in manufacturing SMEs. Procedia Computer Science, 55, 833 - 842.
Dow Jones Sustaiablity Iindices.(DJSI)(2014).http://www. sustainabilityindices. com.
Emami Namivandi, S., Moradnejadi, H., & Sayi Mohammadi, S. (2019), Evaluation of Dairy Product Supply Chain Performance in Rural Areas of Kermanshah, Rural Research Quarterly, Drouh 10, No. 3, pp. 437-427. [In Persian]
Fakkorsahiyeh, A.M. (201), Measuringg Supply Chain Flexibility Using Gray Systems Theory, Management Research in Iran, Volume 19, Number 4, pp.177-117. [In Persian]
Fekri, R., & Mirzadzare, S.H. (2016), Supply Chain Evaluation Framework Based on Supply Chain Management Model (SCOR Model) Reference Model, MSc Thesis, Payam Noor University, Rey Branch. [In Persian]
Govindaraju, V. G. R. C., Kaliani Sundram,. V. P., Muhammad, A. B., Gunasekaran, A. (2016), "Supply chain practices and performance: the indirect effects of supply chain integration", Benchmarking International Journal, 23(6).
Ghasemieh, R., Jamali, G., & KarimiAsl, E. (2015), Analysis of Dimensions of Larch Supply Chain Management Approach in Integrating Multi-criteria Decision Making Techniques, Journal of Industrial Management, Volume 7, Number 4, pp. 836-813. [In Persian]
Ghosh, M. (2013). Lean manufacturing performance in Indian manufacturing plants. Journal of Manufacturing Technology Management.
Industrial and Commercial Bank of China (ICBC Bank). (2013). Corporate Social Responsibility Report.
JafarnejadCheghoushi; A,. Kazemi, A., & Arab, A. (2016), Identification and Prioritization of Indicators of Evaluation of Performance Evaluators Based on the Best and Worst Method, the Perspective of Industrial Management, No. 23, pp. 186-159. [In Persian]
Jafarnejd, A., Mohseni,M. (2015).Providing a framework for improving supply chain performance, Journal of supply chain mamagment,17(8). [In Persian]
Kabra, G., Ramesh, A., &Arshinder, K. (2015). Identification and prioritization of coordination barriers in humanitarian supply chain management. International Journal of Disaster Risk Reduction, 13, 128-138.
Kanda, A., &Deshmukh, S. G. (2008). Supply chain coordination: perspectives, empirical studies and research directions. International journal of production Economics, 115(2), 316-335.
Katiyar, R., Barua, M. K., &Meena, P. L. (2015). Modelling the measures of supply chain performance in the Indian automotive industry. Benchmarking: An International Journal, 22(4), 665-696.
Kundu, G. K., &MuraliManohar, B. (2012). A unified model for implementing lean and CMMI for Services (CMMI-SVC v1. 3) best practices. Asian Journal on Quality, 13(2), 138-162.
Machado, M. C, Fernandes, A. C., Sampaio, P., Sameiro, M. C, Nóvoa, H, Silva, S. D. (2016).Supply Chain Quality Management: a theoretical framework for integration measurement.
Mahmoudzadeh,M.,& Laleh,A. (2014). Evaluating Supply Network Efficiency by Using Social Networks Analysis (Case Study: Tractor Motor Manufacturing Company), Productivity Management, autumn8(3(30)),135-152. [In Persian]
Mohammadzadeh Larijani, F., Darban Astaneh, A., Razvani, M.,& Motiei Langroodi, S. H. (2019), Identification and Prioritization of Effective Components and Processes in Evaluating Performance Management of Mountain-Forest Tourism Supply Chain Management, Urban Tourism Quarterly, 6(31), pp. 106–87. [In Persian]
Mirghafouri, S., MarvatiSharifabadi, A.,& KarimiTakav, S. (2017),Application of Cognitive Mapping Method in Designing Sustainable Supply Chain Model of Hospitals in Type 2 Fuzzy Environment, Health and Treatment Management, 8 (3), pp64-51. [In Persian]
Nazeri, A., Nosratpour, M., & Asakereh, S. (2017),Investigating the Impact of Supply Chain Quality Management Measures on Performance in the Iranian Automotive Industry Considering the Mediating Role of Innovation, Business Research Journal, No. 85, pp.103-59. [In Persian]
Panizzolo, R., Garengo, P., Sharma, M. K., & Gore, A. (2012). Lean manufacturing in developing countries: evidence from Indian SMEs. Production Planning & Control, 23(10-11), 769-788.
RezaeiPendari, A.,& Azar, A. (2018),Designing a Supply Chain Management Model with a Data Theory Approach, Public Management Research, Eleventh Year, No. 39, pp. 5–32. [In Persian]
RezaeiPenderi, A., Azar, A., Taghavi, A., & MoghbelBarz, A. (2014), Presentation of Service Chain Performance Evaluation Model with Fuzzy Cognitive Mapping Approach (Case Study: Insurance Industry), Journal of Industrial Vision Management, No. 16, pp. 93-75. [In Persian]
SadeghiMoghaddam, M.R., Safari, H.,& AhmadiNozari, M. (2016),Supply Chain Stability Measurement Using Multi-Step / Multi-Fuzzy Inference System (Case Study: Parsian Bank), Industrial Management, 7 (3), pp562-533. [In Persian]
SalehiTadadi, E., & ShahzadKhani, N. (2017), Identifying and Prioritizing the Factors Influencing the Success of the Humanitarian Supply Chain, Journal of Rescue and Relief, Eighth Year, No. 3. [In Persian]
SeifiShojaei, H. (2016), Evaluating Factors Affecting Supply Chain Management Performance Improvement Using Hierarchical Analysis in Food Industry, Value Chain Management, Volume 1, Number 2. [In Persian]
Shafi'i, M.,&Tarmest, P. (2014),The Impact of Supply Chain Management Processes on Competitive Advantage and Organizational Performance (Sappco Company Case Study), Quarterly Journal of Management Studies, Volume 5, Number 2, pp. 104-105. [In Persian]
Shah, R &,.Ward, P. (2013).Toyota production system and kanban system.Journal of Operations Management,129-149.
Sharifi, M., & Akram, A. (2017), Investigation and Selection of the Most Important Parameters Affecting the Agility of the Co-operative Distribution Chain of Fars Province, Iranian Biosystems Engineering, Volume 48, Number 2, pp. 209-201. [In Persian]
Sharma, V. K., Chandna, P., & Bhardwaj, A. (2017). Green supply chain management related performance indicators in agro industry: A review. Journal of Cleaner Production, 141, 1194-1208.
Shirazi, H. K., Modiri, M., Pourhabibi, Z., &Gilevaee, A. R. (2017). Improving the Quality of Clinical Dental Services using the Importance-Performance Analysis (IPA) Approach and Interpretive-Structural Modeling (ISM).
Supeekit, T., Somboonwiwat, T., &Kritchanchai, D. (2016). DEMATEL-modified ANP to evaluate internal hospital supply chain performance. Computers & Industrial Engineering, 102, 318-330.
Vinodh, S., &Aravindraj, S. (2012). Axiomatic modeling of lean manufacturing system. Journal of Engineering, Design and Technology, 10(2), 199-216.
_||_Abidi, H., Klumpp, M., & de Leeuw, S. (2015). Modelling Impact of Key Success Factors in Humanitarian Logistics. In Logistics Management (pp. 427-443). Springer, Cham.
Amiri, M., Mansouri Mohammad Abadi, S., Shaabani, A., &Mohammadi, K. (2016),An Analysis of Factors Affecting Supply Chain Performance Using a Integrated Approach of Confirmatory Factor Analysis and Fuzzy Topsis in Food Industry Companies of Shiraz Industrial City,Supply Chain Science Quarterly, Vol. 18, No. 54, page 15-4. [In Persian]
Anand, N., & Grover, N. (2015). Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs). Benchmarking: An International Journal, 22(1), 135-166.
Aydın, Serra Demiral, Selin Hanife Eryuruk, and F. Kalaoğlu. (2014), Evaluation of the performance attributes of retailers using the scor model and AHP: a case study in the Turkish clothing industry. Fibres & Textiles in Eastern Europe.
Azadeh, S., & Yavarzadeh, M.R. (2015), Factors Affecting Supply Chain Management in Industries,2nd International Conference on Modern Research in Industrial Management and Engineering. [In Persian]
Bawer Sad, B., Nili Ahmad Abadi, M., & Biranvand, T. (2018),Offering Sustainable Supply Chain Management in the Marine Industry (Case Study: Marine Industry Organization,Journal of Marine Science Education, No. 12, pp. 37-48. [In Persian]
Bolaños, R., Fontela, E., Nenclares, A., & Pastor, P. (2005). Using interpretive structural modelling in strategic decision-making groups. Management Decision, 43(6), 877-895.
Carvalho, M. S., Nóvoa, H., & Silva, S. D., Machado, M. C., Fernandes, A. C., Sampaio, P. (2016). Supply Chain Quality Management: a theoretical framework for integration measurement. InILS 2016-6th International Conference on Information Systems, Logistics and Supply Chain. International Conference on Information Systems, Logistics and Supply Chain.
Colin, M., Galindo, R. & Hernández, O.(2015). Information and communication technology as a key strategy for efficient supply chain management in manufacturing SMEs. Procedia Computer Science, 55, 833 - 842.
Dow Jones Sustaiablity Iindices.(DJSI)(2014).http://www. sustainabilityindices. com.
Emami Namivandi, S., Moradnejadi, H., & Sayi Mohammadi, S. (2019), Evaluation of Dairy Product Supply Chain Performance in Rural Areas of Kermanshah, Rural Research Quarterly, Drouh 10, No. 3, pp. 437-427. [In Persian]
Fakkorsahiyeh, A.M. (201), Measuringg Supply Chain Flexibility Using Gray Systems Theory, Management Research in Iran, Volume 19, Number 4, pp.177-117. [In Persian]
Fekri, R., & Mirzadzare, S.H. (2016), Supply Chain Evaluation Framework Based on Supply Chain Management Model (SCOR Model) Reference Model, MSc Thesis, Payam Noor University, Rey Branch. [In Persian]
Govindaraju, V. G. R. C., Kaliani Sundram,. V. P., Muhammad, A. B., Gunasekaran, A. (2016), "Supply chain practices and performance: the indirect effects of supply chain integration", Benchmarking International Journal, 23(6).
Ghasemieh, R., Jamali, G., & KarimiAsl, E. (2015), Analysis of Dimensions of Larch Supply Chain Management Approach in Integrating Multi-criteria Decision Making Techniques, Journal of Industrial Management, Volume 7, Number 4, pp. 836-813. [In Persian]
Ghosh, M. (2013). Lean manufacturing performance in Indian manufacturing plants. Journal of Manufacturing Technology Management.
Industrial and Commercial Bank of China (ICBC Bank). (2013). Corporate Social Responsibility Report.
JafarnejadCheghoushi; A,. Kazemi, A., & Arab, A. (2016), Identification and Prioritization of Indicators of Evaluation of Performance Evaluators Based on the Best and Worst Method, the Perspective of Industrial Management, No. 23, pp. 186-159. [In Persian]
Jafarnejd, A., Mohseni,M. (2015).Providing a framework for improving supply chain performance, Journal of supply chain mamagment,17(8). [In Persian]
Kabra, G., Ramesh, A., &Arshinder, K. (2015). Identification and prioritization of coordination barriers in humanitarian supply chain management. International Journal of Disaster Risk Reduction, 13, 128-138.
Kanda, A., &Deshmukh, S. G. (2008). Supply chain coordination: perspectives, empirical studies and research directions. International journal of production Economics, 115(2), 316-335.
Katiyar, R., Barua, M. K., &Meena, P. L. (2015). Modelling the measures of supply chain performance in the Indian automotive industry. Benchmarking: An International Journal, 22(4), 665-696.
Kundu, G. K., &MuraliManohar, B. (2012). A unified model for implementing lean and CMMI for Services (CMMI-SVC v1. 3) best practices. Asian Journal on Quality, 13(2), 138-162.
Machado, M. C, Fernandes, A. C., Sampaio, P., Sameiro, M. C, Nóvoa, H, Silva, S. D. (2016).Supply Chain Quality Management: a theoretical framework for integration measurement.
Mahmoudzadeh,M.,& Laleh,A. (2014). Evaluating Supply Network Efficiency by Using Social Networks Analysis (Case Study: Tractor Motor Manufacturing Company), Productivity Management, autumn8(3(30)),135-152. [In Persian]
Mohammadzadeh Larijani, F., Darban Astaneh, A., Razvani, M.,& Motiei Langroodi, S. H. (2019), Identification and Prioritization of Effective Components and Processes in Evaluating Performance Management of Mountain-Forest Tourism Supply Chain Management, Urban Tourism Quarterly, 6(31), pp. 106–87. [In Persian]
Mirghafouri, S., MarvatiSharifabadi, A.,& KarimiTakav, S. (2017),Application of Cognitive Mapping Method in Designing Sustainable Supply Chain Model of Hospitals in Type 2 Fuzzy Environment, Health and Treatment Management, 8 (3), pp64-51. [In Persian]
Nazeri, A., Nosratpour, M., & Asakereh, S. (2017),Investigating the Impact of Supply Chain Quality Management Measures on Performance in the Iranian Automotive Industry Considering the Mediating Role of Innovation, Business Research Journal, No. 85, pp.103-59. [In Persian]
Panizzolo, R., Garengo, P., Sharma, M. K., & Gore, A. (2012). Lean manufacturing in developing countries: evidence from Indian SMEs. Production Planning & Control, 23(10-11), 769-788.
RezaeiPendari, A.,& Azar, A. (2018),Designing a Supply Chain Management Model with a Data Theory Approach, Public Management Research, Eleventh Year, No. 39, pp. 5–32. [In Persian]
RezaeiPenderi, A., Azar, A., Taghavi, A., & MoghbelBarz, A. (2014), Presentation of Service Chain Performance Evaluation Model with Fuzzy Cognitive Mapping Approach (Case Study: Insurance Industry), Journal of Industrial Vision Management, No. 16, pp. 93-75. [In Persian]
SadeghiMoghaddam, M.R., Safari, H.,& AhmadiNozari, M. (2016),Supply Chain Stability Measurement Using Multi-Step / Multi-Fuzzy Inference System (Case Study: Parsian Bank), Industrial Management, 7 (3), pp562-533. [In Persian]
SalehiTadadi, E., & ShahzadKhani, N. (2017), Identifying and Prioritizing the Factors Influencing the Success of the Humanitarian Supply Chain, Journal of Rescue and Relief, Eighth Year, No. 3. [In Persian]
SeifiShojaei, H. (2016), Evaluating Factors Affecting Supply Chain Management Performance Improvement Using Hierarchical Analysis in Food Industry, Value Chain Management, Volume 1, Number 2. [In Persian]
Shafi'i, M.,&Tarmest, P. (2014),The Impact of Supply Chain Management Processes on Competitive Advantage and Organizational Performance (Sappco Company Case Study), Quarterly Journal of Management Studies, Volume 5, Number 2, pp. 104-105. [In Persian]
Shah, R &,.Ward, P. (2013).Toyota production system and kanban system.Journal of Operations Management,129-149.
Sharifi, M., & Akram, A. (2017), Investigation and Selection of the Most Important Parameters Affecting the Agility of the Co-operative Distribution Chain of Fars Province, Iranian Biosystems Engineering, Volume 48, Number 2, pp. 209-201. [In Persian]
Sharma, V. K., Chandna, P., & Bhardwaj, A. (2017). Green supply chain management related performance indicators in agro industry: A review. Journal of Cleaner Production, 141, 1194-1208.
Shirazi, H. K., Modiri, M., Pourhabibi, Z., &Gilevaee, A. R. (2017). Improving the Quality of Clinical Dental Services using the Importance-Performance Analysis (IPA) Approach and Interpretive-Structural Modeling (ISM).
Supeekit, T., Somboonwiwat, T., &Kritchanchai, D. (2016). DEMATEL-modified ANP to evaluate internal hospital supply chain performance. Computers & Industrial Engineering, 102, 318-330.
Vinodh, S., &Aravindraj, S. (2012). Axiomatic modeling of lean manufacturing system. Journal of Engineering, Design and Technology, 10(2), 199-216.
