Akıllı ve Sürdürülebilir Tedarik Zinciri Yönetiminin Hazırlık ve Olgunluk Düzeyinin Değerlendirilmesi için Geometrik Ortalamaya Dayalı Yeni Bir Model Önerisi

Year 2022, , 95 - 115, 31.05.2022

Sercan Demir , Mehmet Akif Gündüz , Turan Paksoy

https://doi.org/10.26650/JTL.2022.1023071

Abstract

Son yıllarda küreselleşme ve küresel rekabetteki artış, artan teknolojik büyüme hızı, müşteri taleplerindeki çeşitlilik ve tedarik zinciri süreçlerinin giderek karmaşıklaşması firmaların tedarik zinciri stratejilerine akıllı ve sürdürülebilir paradigmalar eklemelerine neden olmuştur. Tedarik zinciri oyuncuları arasındaki gerçek zamanlı bilgi paylaşımı ve zincirin her bir basamağının etkin koordinasyonu, tedarik zincirinin verimli şekilde yönetimi için önemli rol oynamaktadır. Bu da geleneksel tedarik zincirinden dijital tedarik zincirine dönüşüm ile mümkündür. Endüstri 4.0 olarak adlandırılan ve 2011 yılında Almanya’da doğan Dördüncü Sanayi Devrimi bilgi teknolojileri, nesnelerin interneti, yapay zeka, bulut bilişim teknolojisi, otonom araçlar, robotik sistemler, sensor ve otomasyon ağları, sanal ve arttırılmış gerçeklik gibi teknolojilerin üretim süreçlerine yoğun biçimde entegrasyonunu hedef alan yenilikçi bir paradigmadır. Ne var ki, Endüstri 4.0’a uyum ve uyum sonrası olgunluk dönemi birçok firma için beklenmedik problemlere yol açabilmektedir. Akıllı fabrikaların kurulmasında ve dijital dönüşümün uygulanmasında en büyük sorunlardan biri, Endüstri 4.0 yetkinliklerinin tüm operasyonlara eş zamanlı olarak etkin şekilde uygulanamamasıdır. Bu bağlamda, firmaların Endüstri 4.0’a hazırlık ve uyum sonrası olgunluk düzeylerinin niceliksel ölçümü ve değerlendirilmesi, üst yönetim için büyük önem arz etmektedir. Bu çalışmanın amacı firmaların Endüstri 4.0’a hazırlık ve olgunluk düzeylerinin daha iyi anlaşılıp ölçülebilmesi için, dijital tedarik zincirlerinin akıllı ve sürdürülebilir boyutta olgunluk düzeylerinin eş zamanlı ölçülebilmesine olanak sağlayan bir model önermektir. Modelin uygulandığı nümerik örnekte, her bir Endüstri 4.0 aracının sürdürülebilirlik boyutlarına ne derece uyum sağladığı belirlenmiştir. Örneğin, eklemeli imalat ve arttırılmış gerçeklik sürdürülebilirliğin ekonomik boyutunda yüksek olgunluk skoru alırken, çevresel ve sosyal boyutlara göre daha düşük skor almıştır. Benzer şekilde, yapay ve dikey sistem entegrasyonu her üç boyut için yüksek olgunluk seviyesinde iken, yapay zeka çok düşük olgunluk seviyesinde kalmıştır. 

Keywords

Endüstri 4.0, Akıllı ve Sürdürülebilir Tedarik Zincirleri, Hazırlık ve Olgunluk Düzeyi, Model Önerisi

A New Evaluation Model for the Readiness and Maturity Level of Intelligent and Sustainable Supply Chain Management Based on Geometric Mean

Abstract

Recently, companies have added smart and sustainable paradigms to their supply chain strategies as a result of globalization and increased global competition, increasing technological growth rate, diversity in customer demands, and increasing complexity in supply chain processes. Real-time information sharing among supply chain players and the effective coordination of each step of the chain are critical for efficient supply chain management. This is made possible by the transition from the traditional supply chain to the digital supply chain. The Fourth Industrial Revolution, also known as Industry 4.0, was coined for the first time in Germany in 2011. It is an innovative paradigm with the goal of intensely integrating technologies, such as information technologies, the Internet of Things, artificial intelligence, cloud computing technology, autonomous vehicles, robotic systems, sensor and automation networks, and virtual and augmented reality into production processes. However, for many companies, the adaptation of Industry 4.0 and the subsequent maturity period may present unexpected challenges. One of the most difficult challenges in establishing smart factories and implementing digital transformation is that Industry 4.0 competencies cannot be effectively applied to all operations simultaneously. In this context, quantitative measurement and evaluation of firms’ maturity levels following Industry 4.0 preparation and adaptation is critical for senior management. The goal of this study is to propose a model for measuring the maturity level of digital supply chains while considering smart and sustainable dimensions. We determined the extent to which each Industry 4.0 tool was compatible with the sustainability dimensions in the numerical example where the model was applied. For example, although additive manufacturing and augmented reality receive high scores in the economic dimension of sustainability, they receive lower scores in the environmental and social dimensions. Similarly, although horizontal and vertical systems integration has high levels of maturity in all three sustainability dimensions, artificial intelligence has an exceptionally low level of maturity.

Keywords

Industry 4.0, Smart and Sustainable Supply Chains, Level of Readiness and Maturity, Model Proposal

References

• Aguiar, T., Gomes, S. B., da Cunha, P. R., & da Silva, M. M. (2019, October). Digital transformation capability maturity model framework. In 2019 IEEE 23rd International Enterprise Distributed Object Computing Conference (EDOC) (pp. 51-57). IEEE. google scholar
• Ahi, P., & Searcy, C. (2013). A comparative literature analysis of definitions for green and sustainable supply chain management. Journal of cleaner production, 52, 329-341. google scholar
• Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. In Industry 4.0: Managing the digital transformation (pp. 61-94). Springer, Cham. google scholar
• Alcacer, V, & Cruz-Machado, V (2019). Scanning the industry 4.0: A literatüre review on technologies for manufacturing systems. Engineering Science and Technology, an International Journal. 22(3), 899-919. google scholar
• Barreto, L., Amaral, A., & Pereira, T. (2017). Industry 4.0 implications in logistics: an overview. Procedia Manufacturing, 13, 1245-1252. google scholar
• Basl, J. (2017). Pilot study of readiness of Czech companies to implement the principles of Industry 4.0. Management and Production Engineering Review, 8(2), 3-8. google scholar
• Basl, J. (2018, September). Analysis of industry 4.0 readiness indexes and maturity models and proposal of the dimension for enterprise information systems. In International Conference on Research and Practical Issues of Enterprise Information Systems (pp. 57-68). Springer, Cham. google scholar
• Basl, J., & Doucek, P. (2019). A metamodel for evaluating enterprise readiness in the context of Industry 4.0. Information, 10(3), 89. google scholar
• Basl, J., & Kopp, J. (2017). Study of the Readiness of Czech Companies to the Industry 4.0. Journal of Systems Integration, 8(3), 40-45. google scholar
• Beamon, B. M. (1999). Designing the green supply chain. Logistics information management. google scholar
• Bechtold, J., & Lauenstein, C. (2014). Digitizing Manufacturing: Ready Set Go. Capgemini. https://www. capgemini.com/consulting-de/wp- content/uploads/sites/32/2017/08/digitizing-manufacturing_0.pdf. google scholar
• Botha, A. P. (2018). Rapidly arriving futures: Future readiness for Industry 4.0. South African Journal of Industrial Engineering, 29(3), 148-160. google scholar
• Brunelli, J., Lukic, V., Milon, T., & Tantardini, M. (2017). Five lessons from the Frontlines of Industry 4.0. The Boston Consulting Group. google scholar
• Butner, K. (2010). The smarter supply chain of the future. Strategy & Leadership. google scholar
• Candanedo, I. S., Nieves, E. H., Gonzalez, S. R., Martin, M. T. S., & Briones, A. G. (2018, August). Machine learning predictive model for industry 4.0. In International Conference on Knowledge Management in Organizations (pp. 501-510). Springer, Cham. google scholar
• Carlozo, L. (2017). What is blockchain?. Journal of Accountancy, 224(1), 29. google scholar
• Carter, C. R., & Rogers, D. S. (2008). A framework of sustainable supply chain management: Moving toward new theory. International Journal of Physical Distribution & Logistics Management. google scholar
• De Carolis, A., Macchi, M., Negri, E., & Terzi, S. (2017a, June). Guiding manufacturing companies towards digitalization a methodology for supporting manufacturing companies in defining their digitalization roadmap. In 2017 International Conference on Engineering, Technology and Innovation (ICE/ITMC) (pp. 487-495). IEEE. google scholar
• De Carolis, A., Macchi, M., Negri, E., & Terzi, S. (2017b, September). A maturity model for assessing the digital readiness of manufacturing companies. In IFIP International Conference on Advances in Production Management Systems (pp. 13-20). Springer, Cham. google scholar
• Drath, R., & Horch, A. (2014). Industrie 4.0: Hit or Hype? [Industry Forum]. IEEE Industrial Electronics Magazine, 8(2), 56-58. google scholar
• El Kadiri, S., Grabot, B., Thoben, K. D., Hribernik, K., Emmanouilidis, C., Von Cieminski, G., & Kiritsis, D. (2016). Current trends on ICT technologies for enterprise information systems. Computers in Industry, 79, 14-33. google scholar
• Elibal, K., & Özceylan, E. (2021). A systematic literature review for industry 4.0 maturity modeling: state-of-the-art and future challenges. Kybernetes, 50(11), 2957-2994. google scholar
• Elibal, K., & Özceylan, E. (2022). Comparing industry 4.0 maturity models in the perspective of TQM principles using Fuzzy MCDM methods. Technological Forecasting and Social Change, 175, 121379. google scholar
• Ernst, F., & Frische, P. (2015). Industry 4.0/industrial internet of things-related technologies and requirements for a successful digital transformation: An investigation of manufacturing businesses worldwide. Available at SSRN 2698137. google scholar
• Faller, C., & Feldmüller, D. (2015). Industry 4.0 learning factory for regional SMEs. Procedia Cirp, 32, 88-91. google scholar
• Fleischmann, M., & Minner, S. (2004). Inventory management in closed loop supply chains. In Supply chain management and reverse logistics (pp. 115-138). Springer, Berlin, Heidelberg. google scholar
• Gausemeier, J., Schmidt, M., Anderl, R., Schmid, H. J., Leyens, C., Seliger, G., Winzer, P., Kohlhuber, M., Kage, M., & Karg, M. (2017). Additive Manufacturing. google scholar
• Geissbauer, R., Vedso, J., & Schrauf, S. (2016). Industry 4.0: Building the digital enterprise. Retrieved from PwC Website: https://www.pwc.com/gx/en/industries/industries-4.0/landing- page/industry-4.0-building-your-digital-enterprise-april-2016. Pdf. google scholar
• Gökalp, E., Şener, U., & Eren, P. E. (2017, October). Development of an assessment model for industry 4.0: industry 4.0-MM. In International Conference on Software Process Improvement and Capability Determination (pp. 128-142). Springer, Cham. google scholar
• Guide, V. D. R. Jr, & Van Wassenhove, L. N. (2002). Closed-Ioop supply chains. In A. Klose, M. Grazia Speranza, L.N. Van Wassenhove (ed.), Quantitative Approaches to Distribution Logistics and Supply Chain Management (pp. 47-60). Berlin: Springer google scholar
• Hart, S. L. (1997). Beyond greening: Strategies for a sustainable world. Harvard Business Review, 75(1), 66-77. google scholar
• Hayes, B. (2008). Cloud computing. Communications of the ACM, 51(7), 9-11. google scholar
• Hermann, M., Pentek, T., & Otto, B. (2016, January). Design principles for industrie 4.0 scenarios. In 2016 49th Hawaii international conference on system sciences (HICSS) (pp. 3928- 3937). IEEE. google scholar
• Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An international journal. google scholar
• Kenton, W. (2018). Augmented reality. Investopedia Fundamental Analysis: Sectors & Industries Analysis. Retrieved from Web Site: https://www.investopedia.com /terms/a/augmented-reality.asp. google scholar
• Kim, T., Glock, C. H., & Kwon, Y. (2014). A closed-loop supply chain for deteriorating products under stochastic container return times. Omega, 43, 30-40. google scholar
• Kohlegger, M., Maier, R., & Thalmann, S. (2009). Understanding maturity models: Results of a structured content analysis (pp. 51-61). google scholar
• Krikke, H., Bloemhof-Ruwaard, J. M., & Van Wassenhove, L. N. (2001). Design of closed loop supply chains: a production and return network for refrigerators. Rotterdam: Erasmus Research Institute of Management (ERIM). google scholar
• Krikke, H., Blanc, I. L., & van de Velde, S. (2004). Product modularity and the design of closed-loop supply chains. California management review, 46(2), 23-39. google scholar
• Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & information systems engineering, 6(4), 239-242. google scholar
• Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial Artificial Intelligence for industry 4.0-based manufacturing systems. Manufacturing Letters, 18, 20-23. google scholar
• Lee, E. A. (2008, May). Cyber physical systems: Design challenges. In 2008 11th IEEE International Symposium on Object and Component-Oriented Real-Time Distributed Computing (ISORC) (pp. 363369). IEEE. google scholar
• Lezzi, M., Lazoi, M., & Corallo, A. (2018). Cybersecurity for Industry 4.0 in the current literature: A reference framework. Computers in Industry, 103, 97-110. google scholar
• Lichtblau, K., Stich, V., Bertenrath, R., Blum, M., Bleider, M., Millack, A., ... & Schröter, M. (2015). IMPULS-industrie 4.0-readiness. Impuls-Stiftung des VDMA, Aachen-Köln. google scholar
• Lin, T. C., Wang, K. J., & Sheng, M. L. (2020). To assess smart manufacturing readiness by maturity model: a case study on Taiwan enterprises. International Journal of Computer Integrated Manufacturing, 33(1), 102-115. google scholar
• Lucato, W. C., Pacchini, A. P. T., Facchini, F., & Mummolo, G. (2019). Model to evaluate the Industry 4.0 readiness degree in Industrial Companies. IFAC-PapersOnLine, 52(13), 1808-1813. google scholar
• Machado, C. G., Winroth, M., Carlsson, D., Almström, P., Centerholt, V., & Hallin, M. (2019). Industry 4.0 readiness in manufacturing companies: challenges and enablers towards increased digitalization. way, 1(2), 3-4. google scholar
• Masciari, E. (2012). SMART: Stream monitoring enterprise activities by RFID tags. Information Sciences, 195, 25-44. google scholar
• Monostori, L. (2014). Cyber-physical production systems: Roots, expectations and R&D challenges. Procedia Cirp, 17, 9-13. google scholar
• Özlü, F. (2017). The advent of Turkey’s industry 4.0. Turkish Policy Quarterly, 16(2), 29-38. google scholar
• Prinz, C., Morlock, F., Freith, S., Kreggenfeld, N., Kreimeier, D., & Kuhlenkötter, B. (2016). Learning factory modules for smart factories in industrie 4.0. Procedia CiRp, 54, 113-118. google scholar
• Qi, Q., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585-3593. google scholar
• Rajnai, Z., & Kocsis, I. (2018, February). Assessing industry 4.0 readiness of enterprises. In 2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI) (pp. 000225-000230). IEEE. google scholar
• Rockwell Automation. (2016). The Connected Enterprise Maturity Model. Retrieved from Website: http:// literature.rockwellautomation.com/idc/groups/literature/documents/wp/ ciewp002_-en-p.pdf google scholar
• Roland Berger (2014) Think act: Coo insights - Industry 4.0. Available at: https://www.rolandberger.com/ de/Publications/COO-Insights-Industry-4.0.html (Accessed: 14 February 2020). google scholar
• RüBmann, M., Lorenz, M., Gerbert, P, Waldner, M., Justus, J., Engel, P, & Harnisch, M. (2015). Industry 4.0: The future of productivity and growth in manufacturing industries. Boston Consulting Group, 9(1), 54-89. google scholar
• Samaranayake, P., Ramanathan, K., & Laosirihongthong, T. (2017, December). Implementing industry 4.0—A technological readiness perspective. In 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 529-533). IEEE. google scholar
• Sanders, A., Elangeswaran, C., & Wulfsberg, J. P. (2016). Industry 4.0 implies lean manufacturing: Research activities in industry 4.0 function as enablers for lean manufacturing. Journal of Industrial Engineering and Management (JIEM), 9(3), 811-833. google scholar
• Santos, R. C., & Martinho, J. L. (2019). An Industry 4.0 maturity model proposal. Journal of Manufacturing Technology Management. google scholar
• Seitz, K. F., & Nyhuis, P. (2015). Cyber-physical production systems combined with logistic models-a learning factory concept for an improved production planning and control. Procedia CIRP 32 (2015), 32, 92-97. google scholar
• Sisco, C., Chorn, B., & Pruzan-Jorgensen, P. M. (2011). Supply chain sustainability: A practical guide for continuous improvement. United Nations Global Compact. google scholar
• Schlüter, F., & Henke, M. (2017). Smart supply chain risk management-a conceptual framework. In Digitalization in Supply Chain Management and Logistics: Smart and Digital Solutions for an Industry 4.0 Environment. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 23 (pp. 361-380). Berlin: epubli GmbH. google scholar
• Seuring, S., & Müller, M. (2008). From a literature review to a conceptual framework for sustainable supply chain management. Journal of cleaner production, 16(15), 1699-1710. google scholar
• Stentoft, J., Jensen, K. W., Philipsen, K., & Haug, A. (2019, January). Drivers and barriers for Industry 4.0 readiness and practice: A SME perspective with empirical evidence. In Proceedings of the 52nd Hawaii International Conference on System Sciences. google scholar
• Strandhagen, J. W., Alfnes, E., Strandhagen, J. O., & Swahn, N. (2016, November). Importance of production environments when applying Industry 4.0 to production Logistics-A multiple case study. In 6th International Workshop of Advanced Manufacturing and Automation. Atlantis Press. google scholar
• Tao, F., Zhang, M., Cheng, J., & Qi, Q. (2017). Digital twin workshop: A new paradigm for future workshop. Computer Integrated Manufacturing Systems, 23(1), 1-9. google scholar
• Tjahjono, B., Esplugues, C., Ares, E., & Pelaez, G. (2017). What does industry 4.0 mean to supply chain?. Procedia Manufacturing, 13, 1175-1182. google scholar
• Vaidya, S., Ambad, P., & Bhosle, S. (2018). Industry 4.0-a glimpse. Procedia Manufacturing, 20, 233-238. google scholar
• Valkokari, K., Kansola, M., & Valjakka, T. (2011). Towards collaborative smart supply chains-capabilities for business development. International Journal of Enterprise Network Management, 4(4), 380-399. google scholar
• Veza, I., Mladineo, M., & Peko, I. (2015, January). Analysis of the current state of Croatian manufacturing industry with regard to Industry 4.0. In 15th International Scientific Conference on Production Engineering-CIM’2015: Computer Integrated Manufacturing and High Speed Machining. google scholar
• Wells, P., & Seitz, M. (2005). Business models and closed-loop supply chains: A typology. Supply Chain Management: An International Journal. google scholar
• White, K. P., & Ingalls, R. G. (2015, December). Introduction to simulation. In 2015 Winter Simulation Conference (WSC) (pp. 1741-1755). IEEE. google scholar
• Wong, C., Yang, E., Yan, X. T., & Gu, D. (2017, September). An overview of robotics and autonomous systems for harsh environments. In 2017 23rd International Conference on Automation and Computing (ICAC) (pp. 1-6). IEEE. google scholar
• Wu, H. J., & Dunn, S. C. (1995). Environmentally responsible logistics systems. International journal of physical distribution & logistics management. google scholar
• Wu, L., Yue, X., Jin, A., & Yen, D. C. (2016). Smart supply chain management: a review and implications for future research. The International Journal of Logistics Management. google scholar
• Yuvaraj, S., & Sangeetha, M. (2016, March). Smart supply chain management using internet of things (IoT) and low power wireless communication systems. In 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET) (pp. 555-558). IEEE. google scholar
• Zelbst, P. J., Green, K. W., Sower, V. E., & Reyes, P. M. (2012). Impact of RFID on manufacturing effectiveness and efficiency. International Journal of Operations & Production Management. google scholar