The MCDM-based Assessment of Solutions for Transition to Sustainable Industry 4.0: The Case of Serbia
Main Article Content
Abstract
Industry 4.0 implies the transformation of organizations into digital entities (Sony & Naik, 2020). It represents a new level of industrial development that has changed demands, competition, industry structure, and sustainability awareness (Dalenogare et al., 2018). The primary objective of this paper is to use Multiple-Criteria Decision Making (MCDM) to identify the principal obstacles and solutions for successfully adopting the technologies that will facilitate a transition of the Serbian industry to sustainable Industry 4.0. The barriers' significance was defined using the Preference Selection Index – PSI (Maniya & Bhatt, 2010). The assessment of the solutions was performed by three decision-makers using the following MCDM methods: PSI, Compromise Ranking of Alternatives from Distance to Ideal Solution – CRADIS (Puška et al., 2022), and Integrated Simple Weighted Sum-Product Method—WISP (Stanujkic et al., 2021). The results revealed that logistics, reverse logistics management, and technology integration are the most significant barriers. The significance of logistics and warehousing management lies in their role as crucial facilitators for the sustainable development of industries, ensuring efficient and responsible movement, storage, and distribution of goods. Also, the application and development of new technologies can improve efficiency and reduce the environmental impact of the Serbian industry. Based on the MCDM methods, the framework enabled the assessment of the barriers and solutions for technology adoption in light of the current business conditions in the Republic of Serbia. The managers and policymakers could easily perceive the main obstacles and optimal actions to fulfill the requirements of Industry 4.0 and promote sustainable operation.
Article Details
Section
Once the manuscript is accepted for publication, authors shall transfer the copyright to the publisher. If the submitted manuscript is not accepted for printing by the journal, the authors shall retain all their rights. The following rights on the manuscript are transferred to the publisher, including any supplementary materials and any parts, extracts or elements of the manuscript:
- the right to reproduce and distribute the manuscript in printed form, including print-on-demand;
- the right to print prepublications, reprints and special editions of the manuscript;
- the right to translate the manuscript into other languages;
- the right to reproduce the manuscript using photomechanical or similar means including, but not limited to photocopy, and the right to distribute these copies;
- the right to reproduce and distribute the manuscript electronically or optically using and all data carriers or storage media, and especially in machine readable/digitalized form on data carriers such as hard drive, CD-ROM, DVD, Blu-ray Disc (BD), Mini Disc, data tapes, and the right to reproduce and distribute the article via these data carriers;
- the right to store the manuscript in databases, including online databases, as well as the right to transmit the manuscript in all technical systems and modes;
- the right to make the manuscript available to the public or to closed user groups on individual demand, for use on monitors or other readers (including e-books), and in printable form for the user, either via the Internet, online service, or via internal or external networks.
References
Bai, C., & Sarkis, J. (2020a). A supply chain transparency and sustainability technology appraisal model for blockchain technology. International Journal of Production Research, 58(7), 2142-2162.
Bai, C., Dallasega, P., Orzes, G., & Sarkis, J. (2020b). Industry 4.0 technologies assessment: A sustainability perspective. International journal of production economics, 229, 107776.
Bajic, B., Rikalovic, A., Suzic, N., & Piuri, V. (2020). Industry 4.0 implementation challenges and opportunities: A managerial perspective. IEEE Systems Journal, 15(1), 546-559.
Chakraborty, S., Chatterjee, P., & Das, P. P. (2024). Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) Method. In Multi-Criteria Decision-Making Methods in Manufacturing Environments, pp. 343-347. Apple Academic Press.
Chang, S. C., Chang, H. H., & Lu, M. T. (2021). Evaluating industry 4.0 technology application in SMES: Using a Hybrid MCDM Approach. Mathematics, 9(4), 414.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.
Eldrandaly, K. A., El Saber, N., Mohamed, M., & Abdel-Basset, M. (2022). Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies. Sustainability, 14(12), 7376.
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.
Erdogan, M., Ozkan, B., Karasan, A., & Kaya, I. (2018). Selecting the best strategy for industry 4.0 applications with a case study. In Industrial Engineering in the Industry 4.0 Era: Selected papers from the Global Joint Conference on Industrial Engineering and Its Application Areas, GJCIE 2017, pp. 109-119. Vienna, Austria: Springer International Publishing.
Ford, S., & Despeisse, M. (2016). Additive manufacturing and sustainability: an exploratory study of the advantages and challenges. Journal of Cleaner Production, 137, 1573-1587.
Gadekar, R., Sarkar, B., & Gadekar, A. (2023). Analysis and Evaluation of Roadblocks Hindering Lean-Green and Industry 4.0 Practices in Indian Manufacturing Industries. International Journal of Decision Support System Technology (IJDSST), 15(1), 1-36.
Gallab, M., Bouloiz, H., Kebe, S. A., & Tkiouat, M. (2021). Opportunities and challenges of the industry 4.0 in industrial companies: a survey on Moroccan firms. Journal of Industrial and Business Economics, 48(3), 413-439.
Ghobakhloo, M. (2020). Industry 4.0, digitization, and opportunities for sustainability. Journal of Cleaner Production, 252, 119869.
Hezam, I. M., Rani, P., Mishra, A. R., & Alshamrani, A. M. (2023). A combined intuitionistic fuzzy closeness coefficient and a double normalization-based WISP method to solve the gerontechnology selection problem for aging persons and people with disability. AIMS Mathematics, 8(6), 13680-13705.
Hsu, C. H., He, X., Zhang, T. Y., Chang, A. Y., Liu, W. L., & Lin, Z. Q. (2022). Enhancing Supply Chain Agility with Industry 4.0 Enablers to Mitigate Ripple Effects Based on Integrated QFD-MCDM: An Empirical Study of New Energy Materials Manufacturers. Mathematics, 10(10), 1635.
Ibarra, D., Ganzarain, J., & Igartua, J. I. (2018). Business model innovation through Industry 4.0: A review. Procedia Manufacturing, 22, 4-10.
Javaid, M., Khan, S., Haleem, A., & Rab, S. (2022). Adoption of modern technologies for implementing industry 4.0: an integrated MCDM approach. Benchmarking: An International Journal, 30(10), 3753-3790.
Kumar, V., Vrat, P., & Shankar, R. (2021a). Prioritization of strategies to overcome the barriers in Industry 4.0: a hybrid MCDM approach. Opsearch, 1-40.Kumar, S., Suhaib, M., & Asjad, M. (2021b). Narrowing the barriers to Industry 4.0 practices through PCA-Fuzzy AHP-K means. Journal of Advances in Management Research, 18(2), 200-226.
Kumar, V., Vrat, P., & Shankar, R. (2023). MCDM model to rank the performance outcomes in the implementation of Industry 4.0. Benchmarking: An International Journal, Ahead-of-print.
Madić, M., Antucheviciene, J., Radovanović, M., & Petković, D. (2017). Determination of laser cutting process conditions using the preference selection index method. Optics & Laser Technology, 89, 214-220.
Maniya, K., & Bhatt, M. G. (2010). A selection of material using a novel type decision-making method: Preference selection index method. Materials & Design, 31(4), 1785-1789.
Müller, J. M., Kiel, D., & Voigt, K. I. (2018). What drives the implementation of Industry 4.0? The role of opportunities and challenges in the context of sustainability. Sustainability, 10(1), 247.
Nimawat, D., & Das Gidwani, B. (2022). Challenges facing by manufacturing industries towards implementation of industry 4.0: an empirical research. International Journal on Interactive Design and Manufacturing (IJIDeM), 16(4), 1371-1383.
Pathak, V. K., Singh, R., & Gangwar, S. (2019). Optimization of three-dimensional scanning process conditions using preference selection index and metaheuristic method. Measurement, 146, 653-667.
Patnaik, P. K., Mishra, S. K., & Ashish, A. T. (2020, March). Ranking of fiber-reinforced composite materials using PSI and PROMETHEE method. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), pp. 1-5. IEEE.
Puška, A., Božanić, D., Mastilo, Z., & Pamučar, D. (2023a). Extension of MEREC-CRADIS methods with double normalization-case study selection of electric cars. Soft Computing, 27(11), 7097-7113.
Puška, A., Nedeljković, M., Stojanović, I., & Božanić, D. (2023b). Application of fuzzy TRUST CRADIS method for selection of sustainable suppliers in agribusiness. Sustainability, 15(3), 2578.
Puška, A., Štilić, A., & Stević, Ž. (2023c). A Comprehensive Decision Framework for Selecting Distribution Center Locations: A Hybrid Improved Fuzzy SWARA and Fuzzy CRADIS Approach. Computation, 11(4), 73.
Puška, A., Stević, Ž., & Pamučar, D. (2022a). Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods. Environment, Development and Sustainability, 1-31.
Puška, A., Nedeljković, M., Prodanović, R., Vladisavljević, R., & Suzić, R. (2022b). Market assessment of pear varieties in Serbia using fuzzy CRADIS and CRITIC methods. Agriculture, 12(2), 139.Puška, A., Božanić, D., Nedeljković, M., & Janošević, M. (2022c). Green supplier selection in an uncertain environment in agriculture using a hybrid MCDM model: Z-Numbers–Fuzzy LMAW–Fuzzy CRADIS model. Axioms, 11(9), 427.
Raj, A., Dwivedi, G., Sharma, A., de Sousa Jabbour, A. B. L., & Rajak, S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 107546.
Rani, P., Pamucar, D., Mishra, A. R., Hezam, I. M., Ali, J., & Ahammad, S. K. (2023). An integrated interval-valued Pythagorean fuzzy WISP approach for industry 4.0 technology assessment and digital transformation. Annals of Operations Research, 1-40.
Rikalovic, A., Suzic, N., Bajic, B., & Piuri, V. (2021). Industry 4.0 implementation challenges and opportunities: A technological perspective. IEEE Systems Journal, 16(2), 2797-2810.
Sony, M., & Naik, S. (2020). Key ingredients for evaluating Industry 4.0 readiness for organizations: a literature review. Benchmarking: An International Journal, 27(7), 2213-2232.
Stanujkic, D., Popovic, G., Karabasevic, D., Meidute-Kavaliauskiene, I., & Ulutaş, A. (2021). An integrated simple weighted sum product method—WISP. IEEE Transactions on Engineering Management, 70(5), 1933-1944.
Starčević, V., Petrović, V., Mirović, I., Tanasić, L. Ž., Stević, Ž., & Đurović Todorović, J. (2022). A novel integrated PCA-DEA-IMF SWARA-CRADIS model for evaluating the impact of FDI on the sustainability of the economic system. Sustainability, 14(20), 13587.
Sutrisno, A., & Kumar, V. (2023). Supply chain sustainability risk assessment model using integration of the preference selection index (PSI) and the Shannon entropy. International Journal of Quality & Reliability Management, 40(3), 674-708.
Sutrisno, A., & Kumar, V. (2022). Supply chain sustainability risk decision support model using integrated Preference Selection Index (PSI) method and prospect theory. Journal of Advances in Management Research, 19(2), 316-346.
Torbacki, W. (2021). A hybrid MCDM model combining DANP and PROMETHEE II methods for the assessment of cybersecurity in industry 4.0. Sustainability, 13(16), 8833.
TürkeÈ™, M. C., Oncioiu, I., Aslam, H. D., Marin-Pantelescu, A., Topor, D. I., & CăpuÈ™neanu, S. (2019). Drivers and barriers in using industry 4.0: a perspective of SMEs in Romania. Processes, 7(3), 153.
Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., & Novaković, S. (2022a). Pallet truck selection with MEREC and WISP-S methods. Strategic Management-International Journal of Strategic Management and Decision Support Systems in Strategic Management, 27(4), 23-29.Ulutaş, A., Topal, A., Pamučar, D., Stević, Ž., Karabašević, D., & Popović, G. (2022b). A new integrated multi-criteria decision-making model for sustainable supplier selection based on a novel grey WISP and grey BWM methods. Sustainability, 14(24), 16921.
Wang, D., & Zhang, X. (2023). Application of the preference selection index method in multi-objective lightweight design of heavy commercial vehicle frames. Engineering Optimization, 55(6), 1020-1039.
Yüksel, H. (2020). An empirical evaluation of industry 4.0 applications of companies in Turkey: The case of a developing country. Technology in Society, 63, 101364.