Bütünleşik bulanık ÇKKV yaklaşımı ile dijital tedarik zinciri açısından en uygun sektörün belirlenmesi

Günümüzde işletmeler tedarik zincirlerindeki dijital ilerlemeler nedeniyle birçok zorlukla karşı karşıya kalmaktadır. Dijitalleşmenin hem şirketler hem de geleceğin tedarik zincirleri içn giderek daha önemli bir rol oynaması beklenmektedir. Buna paralel olarak işletmelerin gelecekte karşılaşacakları zorluklarla baş edebilmek için sektörlerin tedarik zincirlerinin dijitalleşmeye uygunluğunun belirlenmesi önem arz etmektedir. Bu çalışmada temel amaç orta ölçekli işletmelerin çalıştığı sektörlerin tedarik zincirlerinin dijitalleşmeye uygunluğunun belirlenmesidir. Bu amaç doğrultusunda dijital tedarik zincirinin oluşumunu etkileyen kriterler kapsamlı bir literatür araştırması ile belirlenmiştir. Söz konusu kriterlerin ağırlıklandırılmasında Bulanık Analitik Hiyerarşi Prosesi (BAHP) yönteminden yararlanılmıştır. Çalışmanın alternatiflerini ise değerlendirmeye alınan orta ölçekli işletmelerin çalıştığı sektörler oluşturmaktadır. Kriter ağırlıklarının belirlenmesinin ardından, sektörlerin dijital tedarik zinciri açısından uygunluğu ise öncelikle Bulanık TOPSIS (BTOPSIS) ardından Bulanık VIKOR (BVIKOR) yöntemiyle değerlendirilmiştir. Bu iki yöntemden elde edilen sonuçların bütünleştirilerek nihai kararın elde edilmesi için BORDA sayım tekniğinden yararlanılmıştır. Çalışmanın sonuçları; Küresel Bağlantı dijital tedarik zinciri açısından en önemli kriter, BTOPSIS’e göre İmalat sektörü, BVIKOR’a göre Sağlık sektörü dijitalleşmeye en uygun sektör olarak belirlenmiştir. BORDA Sayım tekniğinin sonuçları ise genel sıralamada İmalat sektörünün dijitalleşmeye en uygun sektör olduğunu göstermektedir.

Determining the most suitable sectors in terms of the digital supply chain by integrated fuzzy MCDM approach

In this day and age, businesses come up against several challenges due to digital progresses in their supply chain. Digitalization has been expected to play an increasingly important role for both companies and the supply chains of the future. Concordantly, it is important to determine the suitability of the supply chains of the sectors for digitalization in order to cope with the difficulties that the enterprises will face in the future. The main objective of this study is to determine the suitability of the supply chains of the sectors in which medium-sized enterprises operate for digitalization. For this purpose, the criteria which affecting the constitution of the digital supply chain are determined with a comprehensive literature search. Fuzzy Analytical Hierarchy Process (FAHP) is utilized to weighting of the relevant criteria. Sectors which medium-sized enterprises are constitute the alternatives of the study. After the weighting of the criteria, in terms of digital supply chain the suitability of the sectors was evaluated via Fuzzy TOPSIS (FTOPSIS) and Fuzzy VIKOR (FVIKOR). BORDA count technique is utilized to obtain the final decision by integrating the results from these two methods. According to the results of the study, the most important criterion in terms of Global Connectivity digital supply chain was determined as the Manufacturing sector according to FTOPSIS and the Health sector has been determined as the most suitable sector for digitalization according to FVIKOR. As to BORDA Count method, results show that the manufacturing sector is the most suitable sector for digitalization in the general ranking.

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  • [1] Pflaum A, Bodendorf F, Prockl, G, Chen, H. “The digital supply chain of the future: Technologies, applications and business models minitrack”. Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii, USA, 4-7 January 2017.
  • [2] Tavana, M, Shaabani, A, Caprio, DD, Amiri, M. “An integrated and comprehensive fuzzy multicriteria model for supplier selection in digital supply chains”. Sustainable Operations and Computers, 2, 149-169, 2021.
  • [3] Gezdur, A, Bhattacharjya, J. “Digitization in the oil and gas industry: Challenges and opportunities for supply chain partners”. 18th Working Conference on Virtual Enterprises (PROVE), Vicenza, Italy, 18-20 September 2017.
  • [4] Nasiri M, Ukko J, Saunila M, Rantala T. “Managing the digital supply chain: The role of smart Technologies”. Technovation, 96-97, 1-6, 2020.
  • [5] Korpela, K, Hallikas, J, Dahlberg, T. “Digital supply chain transformation toward blockchain integration”. Proceedings of the 50th Hawaii International Conference on System Sciences, Hawaii, USA, 4-7 January 2017.
  • [6] Khouja, M, Wang, Y. “The impact of digital channel distribution on the experience goods industry”. European Journal of Operational Research, 207, 481-491, 2010.
  • [7] Berman, SJ. “Digital transformation: Opportunities to create new business models”. Strategy & Leadership, 40(2), 16-24, 2012.
  • [8] Bhargava, B, Ranchal, R, Othmane, L B. “Secure information sharing in digital supply chains”. 3rd IEEE International Advance Computing Conference, Ghaziabad, India, 22-23 February 2013.
  • [9] Xue, L. “Governance-knowledge fit and strategic risk taking in supply chain digitization”. Decision Support Systems, 62, 54-65, 2014.
  • [10] European A.T. Kearney/WHU. “Digital supply Chains: Increasingly critical for competitive edge”. https://www.es.kearney.com/documents/291362523/2 91365048/Digital%2BSupply%2BChains.pdf/82bf637ebfa9-5922-ce03-866b7b17a492?t=1493922200000 (10.12.2021).
  • [11] Brinch M, Stentoft J. “Digital supply chains: Still more ‘wannabe’ than practice”. DILF Orientering, 54(2), 22-28, 2017.
  • [12] Farahani P, Meier C, Wilke J. Digital Supply Chain Management Agenda for The Automotive Supplier Industry. Editors: Oswald G, Kleinemeier, M. Shaping the Digital Enterprise, 157-172, Switzerland, Springer Cham, 2017.
  • [13] PWC. “Industry 4.0: How Digitization Makes the Supply Chain More Efficient, Agile, and Customer-Focused”. https://www.strategyand.pwc.com/gx/en/insights/201 6/industry-4-digitization/industry40.pdf (08.12.2021).
  • [14] Scuotto V, Caputo F, Villasalero M, Giudice MD. “A multiple buyer-supplier relationship in the context of smes’ digital supply chain management”. Production Planning & Control, 28(16), 1378-1388, 2017.
  • [15] Agrawal P, Narain R. “Digital supply chain management: An overview”. IOP Conf. Series: Materials Science and Engineering, 455, 1-6, 2018.
  • [16] Bechtsis D, Tsolakis N, Vlachos D, Srai JS. “Intelligent autonomous vehicles in digital supply chains: A framework for integrating innovations towards sustainable value networks”. Journal of Cleaner Production, 181, 60-71, 2018.
  • [17] Büyüközkan G, Göçer F. “An extension of ARAS methodology under interval valued intuitionistic fuzzy environment for digital supply chain”. Applied Soft Computing, 69, 634-654, 2018.
  • [18] Büyüközkan G, Göçer F. “Digital supply chain: literature review and a proposed framework for future research”. Computers in Industry, 97, 157-177, 2018.
  • [19] Iddris F. “Digital supply chain: Survey of the literature”. International Journal of Business Research and Management, 9(1), 47-61, 2018.
  • [20] Giovanni PD. “Digital supply chain through dynamic inventory and smart contracts”. Mathematics, 7(12), 1-25, 2019.
  • [21] Hartley JL, Sawaya WJ. “Tortoise, not the hare: Digital transformation of supply chain business processes”. Business Horizons, 62, 707-715, 2019.
  • [22] Holmström J, Holweg M, Lawson B, Pil FK, Wagner SM. “The digitalization of operations and supply chain management: Theoretical and methodological implications”. Journal of Operations Management, 65(8), 728-734, 2019.
  • [23] Liao H, Wen Z, Liu L. “Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section”. Technological and Economic Development of Economy, 25(6), 1188-1212, 2019.
  • [24] Ivanov D, Dolgui A, Sokolov B. “The impact of digital technology and industry 4.0 on the ripple effect and supply chain risk analytics”. International Journal of Production Research, 57(3), 829-846, 2019.
  • [25] Sahara CR, Paluluh JDE, Aamer, AM. “Exploring the key factor categories for the digital supply chain”. 9th International Conference on Operations and Supply Chain Management, Ho Chi Minh, Vietnam, 15-18 December 2019.
  • [26] Ageron B, Bentahar O, Gunasekaran A. “Digital supply chain: Challenges and future directions”. Supply Chain Forum: An International Journal, 21(3), 133-138, 2020.
  • [27] Gupta S, Modgil S, Gunasekaran A, Bag S. “Dynamic capabilities and institutional theories for industry 4.0 and digital supply chain”. Supply Chain Forum: An International Journal, 21(3), 139-157, 2020.
  • [28] Haddud A, Khare A. “Digitalizing supply chains potential benefits and impact on lean operations”. International Journal of Lean Six Sigma, 11(4), 731-765, 2020.
  • [29] Hennelly PA, Srai JS, Graham G, Wamba SF. “Rethinking supply chains in the age of digitalization”. Production Planning & Control, 31(2-3), 93-95, 2020.
  • [30] Kamble SS, Gunasekaran A, Gawankar SA. “Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications”. International Journal of Production Economics, 219, 179-194, 2020.
  • [31] Preindl R, Nikolopoulos K, Litsiou K. “Transformation strategies for the supply chain: The impact of industry 4.0 and digital transformation”. Supply Chain Forum: An International Journal, 21(1), 26-34, 2020.
  • [32] Seyedghorban Z, Tahernejad H, Meriton R, Graham G. “Supply chain digitalization: Past, present and future”. Production Planning & Control, 31(2-3), 96-114, 2020.
  • [33] Shah S, Menon S, Ojo OO, Ganji EN. “Digitalisation in sustainable manufacturing - A literature review”. IEEE International Conference on Technology Management, Operations and Decisions (ICTMOD), Marrakech, Morocco, 24-27 November 2020.
  • [34] Varona JMG, Poza D, Acebes F, Villafanez F, Pajares J, Paredes AL. “New business models for sustainable spare parts logistics: A case study”. Sustainability, 12, 1-16, 2020.
  • [35] Zhu Z, Zhao J, Bush AA. “The effects of e-business processes in supply chain operations: Process component and value creation mechanisms”. International Journal of Information Management, 50, 273-285, 2020.
  • [36] Annosi MC, Brunetta F, Bimbo F, Kostoula M. “Digitalization within food supply chains to prevent food waste. Drivers, barriers and collaboration practices”. Industrial Marketing Management, 93, 208-220, 2021.
  • [37] Beaulieu M, Bentahar O. “Digitalization of the healthcare supply chain: A roadmap to generate benefits and effectively support healthcare delivery”. Technological Forecasting & Social Change, 167, 1-10, 2021.
  • [38] Büyüközkan G, Göçer F. “A novel approach integrating AHP and COPRAS under pythagorean fuzzy sets for digital supply chain partner selection”. IEEE Transactions on Engineering Management, 68(5), 1486-1503, 2021.
  • [39] Cagliano AC, Mangano G, Rafele C. “Determinants of digital technology adoption in supply chain. An exploratory analysis”. Supply Chain Forum: An International Journal, 22(2), 100-114, 2021.
  • [40] Hockenberry M. “Redirected entanglements in the digital supply chain”. Cultural Studies, 35(4–5), 641-662, 2021.
  • [41] Khan SA, Naim I, Sarpong KS, Gupta H, Idrisi AR. “A knowledge-based experts’ system for evaluation of digital supply chain readiness”. Knowledge-Based Systems, 228, 1-19, 2021.
  • [42] Perez HD, Amaran S, Erisen, E, Wassick, JM, Grossmann IE. “Optimization of extended business processes in digital supply chains using mathematical programming”. Computers and Chemical Engineering, 152, 1-22, 2021.
  • [43] Pyun J, Rha JS. “Review of research on digital supply chain management using network text analysis”. Sustainability, 13, 1-24, 2021.
  • [44] Rasool F, Greco M, Grimaldi M. “Digital supply chain performance metrics: A literature review”. Measuring Business Excellence, 26(1), 23-38, 2022.
  • [45] Saryatmo MA, Sukhotu V. “The influence of the digital supply chain on operational performance: A study of the food and beverage industry in Indonesia”. Sustainability, 13(9), 1-18, 2021.
  • [46] Yang M, Fu M, Zhang Z. “The adoption of digital technologies in supply chains: drivers, process and impact”. Technological Forecasting & Social Change, 169, 1-13, 2021.
  • [47] Yevu SK, Yu ATW, Darko A. “Digitalization of construction supply chain and procurement in the built environment: Emerging technologies and opportunities for sustainable processes”. Journal of Cleaner Production, 322, 1-14, 2021.
  • [48] Kumar R, Dwivedi SB, Gaur S. “A comparative study of machine learning and fuzzy-AHP technique to groundwater potential mapping in the data-scarce region”. Computers & Geosciences, 155, 1-11, 2021.
  • [49] Tahri M, Kaspar J, Madsen AL, Modlinger R, Zabihi K, Marusak R, Vacik, H. “Comparative study of fuzzy-AHP and BBN for spatially-explicit prediction of bark beetle predisposition”. Environmental Modelling and Software, 147, 1-17, 2022.
  • [50] Mandic K, Delibasic B, Knezevic S, Benkovic S. “Analysis of the financial parameters of serbian banks through the application of the fuzzy AHP and TOPSIS methods”. Economic Modelling, 43, 30-37, 2014.
  • [51] Coffey L, Glaudio D. “In defense of group fuzzy AHP: A comparison of group fuzzy AHP and group AHP with confidence intervals”. Expert Systems with Applications, 178, 1-9, 2021.
  • [52] Khan AA, Shameem M, Nadeem M, Akbar, MA. “Agile trends in Chinese global software development industry: Fuzzy AHP based conceptual mapping”. Applied Soft Computing Journal, 102, 1-24, 2021.
  • [53] Göksu A, Güngör İ. “Bulanık analitik hiyerarşik proses ve üniversite tercih sıralamasında uygulanması”. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 13(3), 1-26, 2008.
  • [54] Calabrese A, Costa R, Menichini T. “Using fuzzy AHP to manage intellectual capital assets: An application to the ICT service industry”. Expert Systems with Applications, 40, 3747-3755, 2013.
  • [55] Kannan D, Jabbour ABLS, Jabbour CJC. “Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company”. European Journal of Operational Research, 233, 432-447, 2014.
  • [56] Baharin NH, Rashidi NF, Mahad NF. “Manager selection using fuzzy TOPSIS method”. Journal of Physics: Conference Series 1988, 2021. doi:10.1088/1742- 6596/1988/1/012057
  • [57] Kumar S, Kumar S, Barman AG. “Supplier selection using fuzzy TOPSIS multi criteria model for a small scale steel manufacturing unit”. Procedia Computer Science, 133, 905-912, 2018.
  • [58] Ayvaz B, Kuşakçı, AO. “A trapezoidal type-2 fuzzy multicriteria decision making method based on TOPSIS for supplier selection: An application in textile sector”. Pamukkale University Journal of Engineering Sciences, 23(1), 71-80, 2017.
  • [59] Kizielewicz B, Baczkiewicz A. “Comparison of fuzzy TOPSIS, fuzzy VIKOR, fuzzy WASPAS and fuzzy MMOORA methods in the housing selection problem”. Procedia Computer Science, 192, 4578-4591, 2021.
  • [60] Opricovic S, Tzeng GH. “Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS”. European Journal of Operational Research, 156, 445-455, 2014.
  • [61] Kısa ACG, Perçin S. “Bütünleşik bulanık DEMATEL-bulanık VIKOR yaklaşımının makine seçimi problemine uygulanması”. Journal of Yaşar University, 12(48), 249-256, 2017.
  • [62] Koppiahraj K, Bathrinath S, Saravanasankar S. “A fuzzy VIKOR approach for selection of ergonomic assessment method”. Materials Today: Proceedings, 45, 640-645, 2021.
  • [63] Parvez S. “Application of fuzzy VIKOR and cluster analysis for performance evaluation of original equipment manufacturers”. Materials Today: Proceedings, 27, 1411-1416, 2020.
  • [64] Lamboray C. “A comparison between the prudent order and the ranking obtained with borda's, copeland's, slater's and kemeny's rules”. Mathematical Social Sciences, 54, 1-16, 2007.
  • [65] Momeni M, Maleki MH, Afshari MA, Moradi JS, Mohammadi J. “A fuzzy MCDM approach for evaluating listed private banks in Tehran stock exchange based on balanced scorecard”. International Journal of Business Administration, 2(1), 80-97, 2011.
  • [66] Wu WW. “Beyond travel & tourism competitiveness ranking using DEA, GST, ANN and borda count”. Expert Systems with Applications, 38, 12974-12982, 2011.
  • [67] Dey B, Bairagi B, Sarkar B, Sanyal SK. “Multi objective performance analysis: A novel multi-criteria decision making approach for a supply chain”. Computers & Industrial Engineering, 94, 105-124, 2016.
  • [68] Büyüközkan G, Çifçi G. “A novel hybrid mcdm approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers”. Expert Systems with Applications, 39(3), 3000-3011, 2012.
  • [69] Santos LFOM, Osiro L, Lima RHP. “A model based on 2- tuple fuzzy linguistic representation and analytic hierarchy process for supplier segmentation using qualitative and quantitative criteria”. Expert Systems with Applications, 79, 53-64, 2017.
  • [70] Xu J, Shen F. “A new outranking choice method for group decision making under atanassov’s interval-valued intuitionistic fuzzy environment”. Knowledge-Based Systems, 70, 177-188, 2014.
  • [71] Shidpour H, Cunha CD, Bernard A. “Group multi-criteria design concept evaluation using combined rough set theory and fuzzy set theory”. Expert Systems with Applications, 64, 633-644, 2016.
  • [72] Lee J, Cho H, Kim YS. “Assessing business impacts of agility criterion and order allocation strategy in multi-criteria supplier selection”. Expert Systems with Applications, 42, 1136-1148, 2015.
  • [73] Schlaefer RC, Koch M. “Industry 4.0. Challenges and solutions for the digital transformation and use of exponential Technologies”. Deloitte, 1-30, 2015.
  • [74] DHL. “Logistics Trend Radar Delivering Insight Today. Creating Value Tomorrow!”. https://www.dhl.com/content/dam/dhl/global/core/do cuments/pdf/g0-core-trend-radar-widescreen-2019.pdf (15.12.2021).
  • [75] Roberts N, Grover V. “Leveraging information technology infrastructure to facilitate a firm’s customer agility and competitive activity: An empirical investigation”. Journal of Management Information Systems, 28(4), 231-270, 2012.
  • [76] Oh LB, Teo HH, Sambamurthy V. “The effects of retail channel integration through the use of information technologies on firm performance”. Journal of Operations Management, 30(5), 368-381, 2012.
  • [77] Chang HH, Tsai YC, Hsu CH. “E-Procurement and supply chain performance”. Supply Chain Management an International Journal, 18(1), 34-51, 2013.
  • [78] Rai A, Tang XL. “Leveraging IT capabilities and competitive process capabilities for the management of interorganizational relationships portfolios”. Information Systems Research, 21(3), 516-542, 2010.