PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Powiadomienia systemowe
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
  • Sesja wygasła!
Tytuł artykułu

Industry 4.0 technologies and managers’ decision-making across value chain. Evidence from the manufacturing industry

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper aims to identify how Industry 4.0 technologies affect the quality and speed of the managers’ decision-making process across the different stages of the value chain, based on the example of the manufacturing sector. The paper adopts qualitative research, based on nine in-depth interviews with key informants, to capture senior executives’ experiences with implementing Industry 4.0 technologies in their organisations. The research is focused on three manufacturing industries: the automotive, food and furniture industries. The research shows that depending on the stage of the value chain, different Industry 4.0 technologies are more suitable for the support of managers’ decisions. Various Industry 4.0 technologies support decisionmaking at different stages of the manufacturing value chain. In the Design stage, 3D printing and scanning technologies play a crucial role. In the case of Inbound Logistics, robotisation, automation, Big Data analysis, and Business Intelligence are most useful. During the Manufacturing stage, robotisation, automation, 3D printing, scanning, Business Intelligence, cloud computing, and machine-to-machine (M2M) integration enable quick decision-making and speed up production. Sensors and the Internet of Things (IoT) optimise distribution in the Outbound Logistics stage. And finally, Business Intelligence supports decisions within the Sales and Marketing stage. It is also the most versatile technology among all particular stages. The paper provides empirical evidence on the Industry 4.0 technology support in decision-making at different stages of the manufacturing value chain, which leads to more effective value chain management, ensuring faster and more accurate decisions at each value-chain stage. When using properly selected Industry 4.0 technologies, managers can optimise their production processes, reduce costs, avoid errors and improve customer satisfaction. Simultaneously, Industry 4.0 technologies facilitate predictive analytics to forecast and anticipate future demand, quality issues, and potential risks. This knowledge allows organisations to make better decisions and take proactive actions to prevent problems.
Rocznik
Strony
69--83
Opis fizyczny
Bibliogr. 50 poz., tab.
Twórcy
  • Poznań University of Economics and Business, Poland
  • Poznań University of Economics and Business, Poland
autor
  • Poznań University of Economics and Business, Poland
Bibliografia
  • Abdelmajied, F. Y. (2022). Industry 4.0 and Its Implications: Concept, Opportunities, and Future Dirctions. In T. Bányai, A. Bányai, & I. Kaczmar (Eds.), Supply Chain – Recent Advances and New Perspectives in the Industry 4.0 Era. London, UK: Intechopen.
  • Alvesson, M., & Ashcraft, L. K. (2012). Interviews. In G. Symon, & C. Cassell (Eds.), Qualitative Organizational Research. Core Methods and Current Challenges. Los Angeles: Sage.
  • Bartodziej, C. J. (2017). The concept Industry 4.0. In: The Concept Industry 4.0.Wiesbaden: BestMasters. Springer Gabler.
  • Bastug, S., Arabelen, G., Vural, C. A., & Deveci, D. A. (2020). A value chain analysis of a seaport from the perspective of Industry 4.0. International Journal of Shipping and Transport Logistics, 12(4), 367-397.
  • Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021). Implementing Industry 4.0 principles. Computers and Industrial Engineering, 158. doi: 10.1016/j.cie.2021.107379
  • Candi, M., & Beltagui, A. (2019). Effective use of 3D printing in the innovation process. Technovation, 80-81, 63-73.
  • Castelo-Branco, I., Oliveira, T., Simões-Coelho, P., Portugal, J., & Filipe, I. (2022). Measuring the fourth industrial revolution through the Industry 4.0 lens: The relevance of resources, capabilities and the value chain. Computers in Industry, 138.
  • Curasi, C. F. (2001). A Critical Exploration of Face-to Face Interviewing vs. Computer-Mediated Interviewing. International Journal of Market Research, 43(4), 1-13. doi: 10.1177/147078530104300402
  • 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.
  • Darioshi, R., & Lahav, E. (2021) The impact of technology on the human decision-making process. Human Behavior and Emerging Technologies, 3, 391-400.
  • Darwish, H., Saki, N., Sahraei, M., Zakrifar, F., & Talebi, S. M. (2014). Effects of Automated Office Systems (Automation) on Improve Decision- Making of Staff Managers (At the Airports Company of Country). Journal of Educational and Management Studies, 4(3), 554-564.
  • de Sousa Jabbour, A. B. L., Jabbour, C. J. C., Foropon, C., & Godinho Filho, M. (2018). When Titans Meet–Can Industry 4.0 Revolutionise the Environmentally-Sustainable Manufacturing Wave? The Role of Critical Success Factors. Technological Forecasting and Social Change, 132, 18-25.
  • Gomes, K., Guenther, E., Morris, J., Miggelbrink, J., & Caucci, S. (2022). Resource nexus oriented decision making along the textile value chain: The case of wastewater management. Current Research in Environmental Sustainability, 4. doi: 10.1016/j.crsust.2022.100153
  • Hermann, M., Pentek, T., & Otto, B. (2016), Design Principles for Industrie 4.0 Scenarios: A Literature Review. 49th Hawaii International Conference on System Sciences (HICSS), 3928-3937.
  • Hofmann, E., & Rüsch, M. (2017). Industry 4.0 and the current status as well as future prospects on logistics. Computers in Industry, 89, 23-34. doi: 10.1016/j.technovation.2018.05.002
  • Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338-345. doi: 10.1016/j.jbusres.2016.08.007
  • Kašparová, P. (2022). Intention to use business intelligence tools in decision making processes: applying a UTAUT 2 model. Central European Journal of Operations Research, 31, 991-1008. doi: 10.1007/s10100- 022-00827-z
  • Kaya, I., & Kahraman, C. (2010). Development of fuzzy process accuracy index for decision making problems. Information Sciences, 180(6), 861-872. doi: 10.1016/j.ins.2009.05.019
  • Kearney. (2021). A brave new world for manufacturing. Retrieved from https://www.kearney.com/service/operations-performance-transformation/
  • Koc, T., & Bozdag, E. (2017). Measuring the degree of novelty of innovation based on Porter’s value chain approach. European Journal of Operational Research, 257(2), 559-567. doi: 10.1016/j.ejor.2016.07.049.
  • Konur, S., Lan, Y., Thakker, D., Morkyani, G., Polovina, N., & Sharp, J. (2021). Towards design and implementation of Industry 4.0 for food manufacturing. Neural Computing and Applications. doi: 10.1007/s00521-021-05726-z
  • Liao, Y., Deschamps, F., Loures, E., de, F. R., & Ramos, L. F. P. (2017). Past, present and future of Industry 4.0 – a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609-3629.
  • Loderer, K., Pekrun, R., & Lester, J. C. (2020). Beyond cold technology: A systematic review and meta-analysis on emotions in technology-based learning environments. Learning and Instruction, 70.
  • Lucianetti, L., Chiappetta Jabbour, Ch. J., Gunasekaran, A., & H. Latan, H. (2018). Contingency Factors and Complementary Effects of Adopting Advanced Manufacturing Tools and Managerial Practices: Effects on Organizational Measurement Systems and Firms’ Performance. International Journal of Production Economics, 200, 318-328.
  • Lunenburg, F. (2010). The Decision-Making Procedure. National Forum of Educational Administration and Supervision Journal, 27(4), 179-258. doi: 10.1007/978- 3-030-69441-8_6
  • Marschan-Piekkari, R., & Welch, C. (2004). Qualitative research methods in international business: the state of the art”, In R. Marschan-Piekkari, & C. Welch (Eds.), Handbook of Qualitative Research Methods for International Business (pp. 5-24). Northhampton: Edward Elgar.
  • Mehta, P., Butkewitsch-Choze, S., & Seaman, C. (2018). Smart manufacturing analytics application for semi-continuous manufacturing process – A use case’. Procedia Manufacturing, 26, 1041-1052. doi: 10.1016/j.promfg.2018.07.138.
  • Müller, F., Jaeger, D., & Hanewinkel, M. (2019). Digitization in wood supply – A review on how Industry 4.0 will change the forest value chain. Computers and Electronics in Agriculture, 162, 206-218.
  • Nauhria, Y., Kulkarni, M. S., & Pandey, S. (2018). Development of Strategic Value Chain Framework for Indian Car Manufacturing Industry. Global Journal of Fle ible Systems Management, 19(1), 21-40. doi: 10.1007/s40171-017-0179-z
  • Neziraj, E. Q., & Shaqiri, A. B. (2018). The impact of information technology in decision making process of companies in Kosovo. Informatologia, 51(1–2), 13- 23. doi: 10.32914/i.51.1-2.2
  • Núñez-Merino, M., Maqueira-Marín, J. M., Moyano-Fuentes, J., & Martínez-Jurado, P. J. (2020). Information and digital technologies of Industry 4.0 and Lean supply chain management: a systematic literature review. International Journal of Production Research, 58(16), 5034-5061. doi: 10.1080/00207543.2020.1743896
  • Oláh, J., Aburumman, N., Popp, J., Khan, M. A., Haddad, H., & Kitukutha, N. (2020). Impact of industry 4.0 on environmental sustainability. Sustainability, 12, 4674.
  • Porter, M. E., & Heppelmann, J. E. (2014). How smart, connected products are transforming competition. Harvard Business Review, 92, 64-88.
  • Pozzi, R., Rossi, T., & Secchi, R. (2023). Industry 4.0 technologies: critical success factors for implementation and improvements in manufacturing companies. Production Planning & Control, 34(2), 139-158.
  • Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: A theoretical model and simulation analysis. Decision Support Systems, 26(4), 275-286. doi: 10.1016/S0167-9236(99)00060-3
  • Ribeiro, A., Amaral, A., & Barros, T. (2021). Project Manager Competencies in the context of the Industry 4.0. Procedia Computer Science, 181, 803-810.
  • Robert, M., Giuliani, P., & Gurau, C. (2020). Implementing Industry 4.0 real-time performance management systems: the case of Schneider Electric. Production Planning and Control, 33, 1-17.
  • Savastano, M., & Amendola, C. (2018). How Digital Transformation is Reshaping the Manufacturing Industry Value Chain: The New Digital Manufacturing Ecosystem Applied to a Case Study from the Food Industry. Network, Smart and Open, 24, 127-142. doi: 10.1007/978-3-319-62636-9
  • Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing industry 4.0 readiness and maturity of manufacturing enterprises, Procedia CIRP, 52, 161-166.
  • Shepherd, N. G., Mooi, E. A., Elbanna, S., & Rudd, J. M. (2021). Deciding Fast: Examining the Relationship between Strategic Decision Speed and Decision Quality across Multiple Environmental Contexts. European Management Review, 18(2), 119-140. doi: 10.1111/emre.12430
  • Simatupang, T., Ginardy, R., & Handayati, Y. (2018). New framework for value chain thinking. International Journal of Value Chain Management, 9(3), 289-309.
  • Stouthuysen, K. A. (2020). Perspective on “The building of online trust in e-business relationships”. Electronic Commerce Research and Applications, 40.
  • Sun, Z., Sun, L., & Strang, K. (2018). Big Data Analytics Services for Enhancing Business Intelligence. Journal of Computer Information Systems, 58(2), 162-169. doi: 10.1080/08874417.2016.1220239
  • The Smart Industry Readiness Index (SIRI). (2020). Manufacturing transformation. Insight report. EDB Singapore.
  • Toušek, Z., Hinke, J., Gregor, B., Prokop, M., & Streimikiene, D. (2022). Shareholder value creation within the supply chain – working capital perspective. Polish Journal of Management Studies, 26(1), 310-324. doi: 10.17512/pjms.2022.26.1.19
  • Unhelkar, B., Joshi, S., Sharma M., Prakash, S., Krishna Mani, A., & Prasad, M. (2022). Enhancing supply chain performance using RFID technology and decision support systems in the industry 4.0 – A systematic literature review. International Journal of Information Management Data Insights, 2(2), 100084. doi: 10.1016/j.jjimei.2022.100084
  • Villalobos, J. R., Soto-Silva, W. E., González-Araya, M. C., & González-Ramirez, R. G. (2019). Research directions in technology development to support real-time decisions of fresh produce logistics: A review and research agenda. Computers and Electronics in Agriculture, 167, 105092. doi: 10.1016/j.compag.2019.105092
  • Wieder, B., & Ossimitz, M. L. (2015). The Impact of Business Intelligence on the Quality of Decision Making – A Mediation Model. Procedia Computer Science, 64, 1163-1171. doi: 10.1016/j.procs.2015.08.599
  • Yasin, E. T., Hamadamen, N., Loganathan, G. B., & Ganesan, M. (2021). Recent Scope for AI in the Food Production Industry Leading to the Fourth Industrial Revolution. Webology, 18(2), 1066-1080. doi: 10.14704/web/v18i2/web18375
  • Zehir, C., & Özşahin, M. (2008). A field research on the relationship between strategic decision-making speed and innovation performance in the case of Turkish large-scale firms. Management Decision, 46(5), 709- 724. doi: 10.1108/00251740810873473
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-46391e0e-f807-442b-9338-250615714595
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.