A SPHERICAL FUZZY BASED DECISION MAKING FRAMEWORK WITH EINSTEIN AGGREGATION FOR COMPARING PREPAREDNESS OF SMEs IN QUALITY 4.0

Sanjib Biswas, Darko Božanić, Dragan Pamučar, Dragan Marinković

DOI Number
https://doi.org/10.22190/FUME230831037B
First page
453
Last page
478

Abstract


Researchers work hard to embrace technological changes and redefine the quality management as Quality 4.0 (Q 4.0). In this context, the purpose of the current work is twofold. First, it aims to compare the preparedness of the small and medium enterprises (SMEs) for sustaining in Q4. Second, it intends to propose a novel hybrid spherical fuzzy based multi-criteria group decision-making (MAGDM) framework with Einstein aggregation (EA). A real-life case study on six SMEs is carried out with the help of three experts. For aggregating the individual responses (using spherical fuzzy numbers or SFNs), EA is used. Then two very recent models such as Simple Ranking Process (SRP) and Symmetry Point of Criterion (SPC) are extended using SFN to rank the SMEs. Finally, the validation tests and sensitivity analysis are carried out. It is noted that the application of analytical tools, knowledge management and use of technology under the support and mentorship of visionary leadership are the key criteria for building up the capability to embrace Q 4.0. Interestingly, it is noted that medium scale firms are better prepared than small-scale enterprises. This work is apparently a first of its kind that focuses on SMEs for assessing their quality management practices in Industry 4.0 era.

Keywords

Quality 4.0, Spherical Fuzzy Sets (SFS), Einstein aggregation, Simple Ranking Process (SRP), Symmetry Point of Criterion (SPC)

Full Text:

PDF

References


Masood, T., Sonntag, P., 2020, Industry 4.0: Adoption challenges and benefits for SMEs, Computers in Industry, 121, 103261.

Kusiak, A., 2017, Smart manufacturing must embrace big data, Nature, 544(7648), pp. 23-25.

Chiang, Y., Lee, D., 2017, Smart manufacturing with the internet of makers, Journal of the Chinese Institute of Engineers, 40(7), pp. 585-592.

Zheng, P., Sang, Z., Zhong, R.Y., Liu, Y., Liu, C., Mubarok, K., Yu, S., Xu, X., 2018, Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives, Frontiers of Mechanical Engineering, 13(2), pp. 137-150.

Zhong, R.Y., 2018, Analysis of RFID datasets for smart manufacturing shop floors. Proc. 15th International Conference on Networking, Sensing and Control (ICNSC) (pp. 1-4). IEEE.

Syafrudin, M., Fitriyani, N.L., Alfian, G., Rhee, J., 2019, An affordable fast early warning system for edge computing in assembly line. Applied Sciences, 9(1), 84.

Marr, B., 2015, Big Data: Using SMART big data, analytics and metrics to make better decisions and improve performance, John Wiley & Sons Ltd, Chichester.

Tao, W., Lai, Z.H., Leu, M.C., Yin, Z., 2018, Worker activity recognition in smart manufacturing using IMU and sEMG signals with convolutional neural networks, Procedia Manufacturing, 26, pp. 1159-1166.

Menon, K., Kärkkäinen, H., Wuest, T., Gupta, J.P., 2019, Industrial internet platforms: A conceptual evaluation from a product lifecycle management perspective. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 233(5), pp. 1390-1401.

Deguchi, A., Hirai, C., Matsuoka, H., Nakano, T., Oshima, K., Tai, M., Tani, S., 2020, What is society 5.0, Society, 5, pp. 1-23.

Banerjee, A., Datta, D., Gupta, S.K., 2021, Application of IoT in Industry 4.0 for Predictive Analytics. In Integration of Cloud Computing with Internet of Things (eds M. Mangla, S. Satpathy, B. Nayak and S. N. Mohanty), https://doi.org/10.1002/9781119769323.ch10.

Hermann, M., Pentek, T., Otto, B., 2016, Design principles for industrie 4.0 scenarios. Proc. 49th Hawaii International Conference on System Sciences (HICSS) (pp. 3928-3937). IEEE.

Tupa, J., Simota, J., Steiner, F., 2017, Aspects of risk management implementation for Industry 4.0, Procedia manufacturing, 11, pp. 1223-1230.

Costa, J.M., 2018, Human Resources Portugal. https://hrportugal.pt/sociedade-5-0-amudanca-que-ai-vem (last access: 06.02.2022)

Serpa, S., Ferreira, C., 2018, Society 5.0 and social development: Contributions to a discussion, Sciedu Press, 5, pp. 26-31.

Broday, E.E., 2022, The evolution of quality: from inspection to quality 4.0, International Journal of Quality and Service Sciences, https://doi.org/10.1108/IJQSS-09-2021-0121.

Sreenivasan, S., 2021, Quality 4.0 – road to excellence in the digital world. A Report by KPMG. Available https://home.kpmg/in/en/home/insights/2021/02/quality-4-0-road-to-excellence-indigital-world (last access: 06.02.2023)

Carvalho, A.V., Enrique, D.V., Chouchene, A., Charrua-Santos, F., 2021, Quality 4.0: an overview, Procedia Computer Science, 181, pp. 341-346.

Zonnenshain, A., Kenett, R.S., 2020, Quality 4.0—the challenging future of quality engineering, Quality Engineering, 32(4), pp. 614-626.

de Souza, F.F., Corsi, A., Pagani, R.N., Balbinotti, G., Kovaleski, J.L., 2021, Total quality management 4.0: adapting quality management to Industry 4.0, The TQM Journal. DOI 10.1108/TQM-10-2020-0238.

Brandenburger, J., Schirm, C., Melcher, J., Hancke, E., Vannucci, M., Colla, V., Cateni, S., Sellami, R., Dupont, S., Majchrowski, A., Arteaga, A., 2020, Quality 4.0-transparent product quality supervision in the age of Industry 4.0, In Cybersecurity workshop by European Steel Technology Platform (pp. 54-66). Springer, Cham.

Ali, K., Johl, S.K., 2021, Impact of total quality management on SMEs sustainable performance in the context of industry 4.0. Proc. International Conference on Emerging Technologies and Intelligent Systems (pp. 608-620). Springer, Cham.

Dias, A.M., Carvalho, A.M., Sampaio, P., 2021, Quality 4.0: literature review analysis, definition and impacts of the digital transformation process on quality, International Journal of Quality & Reliability Management, doi: 10.1108/IJQRM-07-2021-0247.

Escobar, C.A., Chakraborty, D., McGovern, M., Macias, D., Morales-Menendez, R., 2021, Quality 4.0—Green, Black and Master Black Belt Curricula, Procedia Manufacturing, 53, pp. 748-759.

Sader, S., Husti, I., Daroczi, M., 2022, A review of quality 4.0: Definitions, features, technologies, applications, and challenges, Total Quality Management & Business Excellence, 33(9-10), pp. 1164-1182.

Shin, W.S., Dahlgaard, J.J., Dahlgaard-Park, S.M., Kim, M.G., 2018, A Quality Scorecard for the era of Industry 4.0, Total Quality Management & Business Excellence, 29(9-10), pp. 959-976.

Sony, M., Antony, J., Douglas, J.A., 2020, Essential ingredients for the implementation of Quality 4.0: a narrative review of literature and future directions for research, The TQM Journal, 32(4), pp. 779-793.

Sony, M., Antony, J., Douglas, J.A., McDermott, O., 2021, Motivations, barriers and readiness factors for Quality 4.0 implementation: an exploratory study, The TQM Journal, 33(6), pp. 1502-1515.

Rowlands, H., Milligan, S., 2021, Quality-driven industry 4.0. In Key Challenges and Opportunities for Quality, Sustainability and Innovation, In the Fourth Industrial Revolution: Quality and Service Management in the Fourth Industrial Revolution—Sustainability and Value Co-creation (pp. 3-30).

Javaid, M., Haleem, A., Singh, R.P., Suman, R., 2021, Significance of Quality 4.0 towards comprehensive enhancement in manufacturing sector, Sensors International, 2, 100109.

Chiarini, A., 2020, Industry 4.0, quality management and TQM world. A systematic literature review and a proposed agenda for further research, The TQM Journal, 32(4), pp. 603-616.

Emblemsvåg, J., 2020, On Quality 4.0 in project-based industries, The TQM Journal, 32(4), pp. 725-739.

Gunasekaran, A., Subramanian, N., Ngai, W.T.E., 2019, Quality management in the 21st century enterprises: Research pathway towards Industry 4.0, International journal of production economics, 207, pp. 125-129

Hernandez-de-Menendez, M., Morales-Menendez, R., Escobar, C.A., McGovern, M., 2020, Competencies for industry 4.0, International Journal on Interactive Design and Manufacturing, 14(4), pp. 1511-1524.

Kannan, K.S.P., Garad, A., 2020, Competencies of quality professionals in the era of industry 4.0: a case study of electronics manufacturer from Malaysia, International Journal of Quality & Reliability Management, 38(3), pp. 839-871.

Kupper, D., Knizek, C., Ryeson, D., Noecker, J., 2019, BCG Global, [WWW document]. https://www.bcg.com/publications/2019/quality-4.0-takes-more-than-technology (last access: 07.02.2023).

Santos, G., Sá, J.C., Félix, M.J., Barreto, L., Carvalho, F., Doiro, M., Zgodavová, K., Stefanović, M., 2021, New needed quality management skills for quality managers 4.0, Sustainability, 13(11), 6149.

Suarta, I.M., Suwintana, I.K., Sudhana, I.F.P., Hariyanti, N.K.D., 2020, Employability skills for sustainable development and supporting industrial revolution 4.0: a study for polytechnic curriculum development, Proc. First International Conference on Applied Science and Technology (iCAST 2018) (pp. 36-39). Atlantis Press.

Watson, G., 2019, The Ascent of Quality 4.0 j ASQ, [WWW Document]. https://asq.org/quality-progress/articles/the-ascent-of-quality-40?id58321f828c7c44634b996b2b1ba25a315 (last accessed: 10.02.2023).

Arsovski, S., 2019, Social oriented quality: from Quality 4.0 towards Quality 5.0. Proc. 13th International Quality Conference (Vol. 13, pp. 397-404).

Sohal, A., Nand, A.A., Goyal, P., Bhattacharya, A., 2022, Developing a circular economy: An examination of SME’s role in India, Journal of Business Research, 142, pp. 435-447.

IBEF Report, 2021, MSME Industry in India. https://www.ibef.org/industry/msme.aspx (last access: 07.02.2023).

Stentoft, J., Adsbøll Wickstrøm, K., Philipsen, K., Haug, A., 2021, Drivers and barriers for Industry 4.0 readiness and practice: empirical evidence from small and medium-sized manufacturers, Production Planning & Control, 32(10), pp. 811-828.

Bravi, L., Murmura, F., 2021, Evidences about ISO 9001: 2015 and ISO 9004: 2018 implementation in different-size organisations, Total Quality Management & Business Excellence, 33(11-12), pp. 1366-1386.

Shuaib, K.M., He, Z., 2021, Impact of organizational culture on quality management and innovation practices among manufacturing SMEs in Nigeria, Quality Management Journal, 28(2), pp. 98-114.

Zakeri, S., Chatterjee, P., Konstantas, D., Ecer, F., 2023, A decision analysis model for material selection using simple ranking process, Scientific Reports, 13(1), 8631.

Gligorić, Z., Gligorić, M., Miljanović, I., Lutovac, S., Milutinović, A., 2023, Assessing Criteria Weights by the Symmetry Point of Criterion (Novel SPC Method)--Application in the Efficiency Evaluation of the Mineral Deposit Multi-Criteria Partitioning Algorithm, Computer Modeling in Engineering & Sciences, 136(1), pp. 955-979.

Zadeh, L.A., 1965, Fuzzy sets. Information and control, 8(3), pp. 338-353.

Atanassov, KT., 1986, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, 20, pp. 87–96.

Atanassov, K., 1989, Geometrical interpretation of the elements of the intuitionistic fuzzy objects, Preprint IM-MFAIS-1-89, Sofia

Atanassov, K.T., Gargov, G., 1989, Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems, 31, pp. 343–349.

Smarandache, F., 1999, A unifying field in logics: Neutrosophic logic. In: Philosophy, American Research Press, pp. 1–141.

Cuong, B.C., Kreinovich, V., 2013, Picture fuzzy sets-a new concept for computational intelligence problems. Proc. 2013 third world congress on information and communication technologies (WICT 2013) (pp. 1-6). IEEE.

Cuong, B.C., Kreinovich, V., 2014, Picture fuzzy sets, Journal of Computer Science and Cybernetics, 30(4), pp. 409-420.

Yager, R.R., 2013, Pythagorean fuzzy subsets. Proc. 2013 joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61). IEEE.

Yager, R.R., 2016, Generalized orthopair fuzzy sets, IEEE Transactions on Fuzzy Systems, 25(5), pp. 1222-1230.

Kutlu Gündoğdu, F., Kahraman, C., 2019, Spherical fuzzy sets and spherical fuzzy TOPSIS method, Journal of intelligent & fuzzy systems, 36(1), pp. 337-352.

Kutlu Gündoğdu, F., Kahraman, C., 2019, Spherical fuzzy analytic hierarchy process (AHP) and its application to industrial robot selection. Proc. International Conference on Intelligent and Fuzzy Systems (pp 988-996), Springer.

Ashraf, S., Abdullah, S., Mahmood, T., Ghani, F., Mahmood, T., 2019, Spherical fuzzy sets and their applications in multi-attribute decision making problems, Journal of Intelligent & Fuzzy Systems, 36(3), pp. 2829-2844.

Senapati, T., Yager, R.R., 2020, Fermatean fuzzy sets, Journal of Ambient Intelligence and Humanized Computing, 11(2), pp. 663-674.

Ayan, B., Abacıoğlu, S., Basilio, M.P., 2023, A Comprehensive Review of the Novel Weighting Methods for Multi-Criteria Decision-Making, Information, 14(5), 285.

Hezam, I.M., Basua, D., Mishra, A.R., Rani, P., Cavallaro, F., 2023, Intuitionistic fuzzy gained and lost dominance score based on symmetric point criterion to prioritize zero-carbon measures for sustainable urban transportation, Kybernetes, https://doi.org/10.1108/K-03-2023-0380.

Hezam, I.M., Vedala, N.R.D., Kumar, B.R., Mishra, A.R., Cavallaro, F., 2023, Assessment of Biofuel Industry Sustainability Factors Based on the Intuitionistic Fuzzy Symmetry Point of Criterion and Rank-Sum-Based MAIRCA Method, Sustainability, 15(8), 6749.

Ali, J., 2021, A novel score function based CRITIC-MARCOS method with spherical fuzzy information, Computational and Applied Mathematics, 40(8), 280.

Ashraf, S., Abdullah, S., Abdullah, L., 2019, Child development influence environmental factors determined using spherical fuzzy distance measures, Mathematics, 7(8), 661.

Kovač, M., Tadić, S., Krstić, M., Bouraima, M.B., 2021, Novel Spherical Fuzzy MARCOS Method for Assessment of Drone-Based City Logistics Concepts, Complexity, 2021, 2374955.

Menekşe, A., Camgöz Akdağ, H., 2022, Distance education tool selection using novel spherical fuzzy AHP EDAS, Soft Computing, 26(4), pp. 1617-1635.

Mahmood, T., Ilyas, M., Ali, Z., Gumaei, A., 2021, Spherical fuzzy sets-based cosine similarity and information measures for pattern recognition and medical diagnosis, IEEE Access, 9, pp. 25835-25842.

Unver, M., Olgun, M., Türkarslan, E., 2022, Cosine and cotangent similarity measures based on Choquet integral for Spherical fuzzy sets and applications to pattern recognition, Journal of Computational and Cognitive Engineering, 1(1), pp. 21-31.

Aydoğdu, A., Gül, S., 2022, New entropy propositions for interval‐valued spherical fuzzy sets and their usage in an extension of ARAS (ARAS‐IVSFS), Expert Systems, 39(4), e12898.

Oztaysi B., Kahraman C., Onar S.C., 2022, Spherical Fuzzy REGIME Method Waste Disposal Location Selection. In: Kahraman C., Cebi S., Cevik Onar S., Oztaysi B., Tolga A.C., Sari I.U. (eds) Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation. INFUS 2021. Lecture Notes in Networks and Systems, vol 308. Springer, Cham.

Seyfi-Shishavan, S.A., Kutlu Gündoğdu, F., Donyatalab, Y., Farrokhizadeh, E., Kahraman, C., 2021, A novel spherical fuzzy bi-objective linear assignment method and its application to insurance options selection, International Journal of Information Technology & Decision Making, 20(2), pp. 521-551.

Sharaf, I.M., 2021, Global Supplier Selection with Spherical Fuzzy Analytic Hierarchy Process. In: Kahraman C., Kutlu Gündoğdu F. (eds) Decision Making with Spherical Fuzzy Sets. Studies in Fuzziness and Soft Computing, vol 392. Springer, Cham.

Gul, M., & Ak, M.F., 2021, A modified failure modes and effects analysis using interval-valued spherical fuzzy extension of TOPSIS method: case study in a marble manufacturing facility, Soft Computing, 25(8), pp. 6157-6178.

Dogan, O., 2021, Process mining technology selection with spherical fuzzy AHP and sensitivity analysis, Expert Systems with Applications, 178, 114999.

Hashmi, M.R., Tehrim, S.T., Riaz, M., Pamucar, D., Cirovic, G., 2021, Spherical Linear Diophantine Fuzzy Soft Rough Sets with Multi-Criteria Decision Making, Axioms, 10(3), 185.

Farrokhizadeh, E., Seyfi-Shishavan, S.A., Gündoğdu, F.K., Donyatalab, Y., Kahraman, C., Seifi, S.H., 2021, A spherical fuzzy methodology integrating maximizing deviation and TOPSIS methods, Engineering Applications of Artificial Intelligence, 101, 104212.

Erdoğan, M., 2022, Assessing farmers' perception to Agriculture 4.0 technologies: A new interval‐valued spherical fuzzy sets based approach, International Journal of Intelligent Systems, 37(2), pp. 1751-1801.

Biswas, S., Chatterjee, S., Majumder, S., 2022. A spherical fuzzy framework for sales personnel selection, Journal of Computational and Cognitive Engineering. https://doi.org/10.47852/bonviewJCCE2202357.

Ashraf, S., Abdullah, S., Chinram, R., 2022, Emergency decision support modeling under generalized spherical fuzzy Einstein aggregation information, Journal of Ambient Intelligence and Humanized Computing, 13, pp. 2091-2117.

Nezhad, M.Z., Nazarian-Jashnabadi, J., Rezazadeh, J., Mehraeen, M., Bagheri, R. 2023, Assessing Dimensions Influencing IoT Implementation Readiness in Industries: A Fuzzy DEMATEL and Fuzzy AHP Analysis, Journal of Soft Computing and Decision Analytics, 1(1), pp. 102-123.

Bavandi, S., Bigdeli, H., 2023, A Maximum Flow Network Interdiction Model in Fuzzy Stochastic Hybrid Uncertainty Environments, Yugoslav Journal of Operations Research, 33(3), pp. 409-424.

Biswas, S., Majumder, S., Pamucar, D., Dawn, S.K., 2021, An Extended LBWA Framework in Picture Fuzzy Environment Using Actual Score Measures Application in Social Enterprise Systems, International Journal of Enterprise Information Systems, 17(4), pp. 37-68.

Biswas, S., Pamučar, D.S., 2021, Combinative distance based assessment (CODAS) framework using logarithmic normalization for multi-criteria decision making, Serbian Journal of Management, 16(2), pp. 321-340.

Pamucar, D., Torkayesh, A.E., Biswas, S., 2023, Supplier selection in healthcare supply chain management during the COVID-19 pandemic: a novel fuzzy rough decision-making approach, Annals of Operations Research, 328, pp. 977–1019.

Puška, A., Kozarević, S., Okičić, J., 2020, Investigating and analyzing the supply chain practices and performance in agro-food industry, International Journal of Management Science and Engineering Management, 15(1), pp. 9-16.

Stević, Ž., Tanackov, I., Puška, A., Jovanov, G., Vasiljević, J., Lojaničić, D., 2021, Development of Modified SERVQUAL–MCDM Model for Quality Determination in Reverse Logistics, Sustainability, 13(10), 5734.

Puška, A., Kozarević, S., Stević, Ž., Stovrag, J., 2018, A new way of applying interval fuzzy logic in group decision making for supplier selection, Economic Computation and Economic Cybernetics Studies and Research, 52(2), pp. 217-234.

Biswas, S., Majumder, S., Dawn, S.K., 2022, Comparing the socioeconomic development of G7 and BRICS countries and resilience to COVID-19: An entropy–MARCOS framework, Business Perspectives and Research, 10(2), pp. 286-303.

Badi, I., Abdulshahed, A., Alghazel, E. 2023, Using Grey-TOPSIS approach for solar farm location selection in Libya, Reports in Mechanical Engineering, 4(1), pp. 80–89.

Pramanik, P.K.D., Biswas, S., Pal, S., Marinković, D., Choudhury, P., 2021, A Comparative Analysis of Multi-Criteria Decision-Making Methods for Resource Selection in Mobile Crowd Computing, Symmetry, 13(9), 1713.

Verma, V., Bisht, P., Joshi, S., 2022, Sustainable Supply chain Systems of Food and Beverages SMEs: Analyzing sustainable performance using Structured Equation Modeling, Journal of Decision Analytics and Intelligent Computing, 2(1), pp. 53–68.

Keshavarz Ghorabaee, M., Zavadskas, E.K., Amiri, M., Antucheviciene, J., 2016, Evaluation by an Area-based Method of Ranking Interval Type-2 Fuzzy Sets (EAMRIT-2F) for Multi-criteria Group Decision making, Transformations in Business & Economics, 15(3(39)), pp. 76-95.

Zavadskas, E.K., Kaklauskas, A., Peldschus, F., Turskis, Z., 2007, Multi-attribute assessment of road design solutions by using the COPRAS method, The Baltic journal of Road and Bridge engineering, 2(4), pp. 195-203.

Puška, A., Stević, Ž., Pamučar, D., 2022, Evaluation and selection of healthcare waste incinerators using extended sustainability criteria and multi-criteria analysis methods, Environment, Development and Sustainability, 24, pp. 11195–11225.

Biswas, S., Anand, O.P., 2020, Logistics Competitiveness Index-Based Comparison of BRICS and G7 Countries: An Integrated PSI-PIV Approach, The IUP Journal of Supply Chain Management, 17(2), pp. 32-57.

Biswas, S., Pamucar, D., Chowdhury, P., Kar, S., 2021, A New Decision Support Framework with Picture Fuzzy Information: Comparison of Video Conferencing Platforms for Higher Education in India, Discrete Dynamics in Nature and Society, 2021, 2046097.

Puška, A., Nedeljković, M., Hashemkhani Zolfani, S., Pamučar, D., 2021, Application of Interval Fuzzy Logic in Selecting a Sustainable Supplier on the Example of Agricultural Production, Symmetry, 13(5), 774.

Ali, A., Ullah, K., Hussain, A. 2023, An approach to multi-attribute decision-making based on intuitionistic fuzzy soft information and Aczel-Alsina operational laws, Journal of Decision Analytics and Intelligent Computing, 3(1), pp. 80-89.

Dağıstanlı, H.A., 2023, An Integrated Fuzzy MCDM and Trend Analysis Approach for Financial Performance Evaluation of Energy Companies in Borsa Istanbul Sustainability Index, Journal of Soft Computing and Decision Analytics, 1(1), pp. 39-49.

Görçün, Ö.F., Pamucar, D., Biswas, S., 2023, The blockchain technology selection in the logistics industry using a novel MCDM framework based on Fermatean fuzzy sets and Dombi aggregation, Information Sciences, 635, pp. 345-374.

Gupta, H., Prakash, C., Vishwakarma, V., Barua, M.K., 2017, Evaluating TQM adoption success factors to improve Indian MSMEs performance using fuzzy DEMATEL approach, International Journal of Productivity and Quality Management, 21(2), pp. 187-202.

Liu, H.C., Liu, R., Gu, X., Yang, M., 2023, From total quality management to Quality 4.0: A systematic literature review and future research agenda, Frontiers of Engineering Management, 10(2), pp. 191-205.

Deng, N., Shi, Y., Wang, J., Gaur, J., 2022, Testing the adoption of blockchain technology in supply chain management among MSMEs in China, Annals of Operations Research, pp. 1-20. https://doi.org/10.1007/s10479-022-04856-4.

Singh, R., Ojha, M.K., Sindhwani, R., 2023, Identification of Critical Success Factors (CSFs) for Implementation of Industry 4.0 in MSME Sector. In: Phanden, R.K., Kumar, R., Pandey, P.M., Chakraborty, A. (Eds), Advances in Industrial and Production Engineering. FLAME 2022. Lecture Notes in Mechanical Engineering. Springer, Singapore, pp. 103-113.




DOI: https://doi.org/10.22190/FUME230831037B

Refbacks

  • There are currently no refbacks.


ISSN: 0354-2025 (Print)

ISSN: 2335-0164 (Online)

COBISS.SR-ID 98732551

ZDB-ID: 2766459-4