ENHANCING QUALITY IN KNOWLEDGE-BASED ORGANIZATIONS THROUGH THE IMPLEMENTATION OF COLLABORATIVE ROBOTS INTO NONCONVENTIONAL TECHNOLOGIES IN AUTOMOTIVE
Quality is crucial for modern organizations and sustainable quality management is key to this. Collaborative robots can help improve the quality of production flows. Currently, mass production flows may involve the use of equipment with nonconventional technologies implemented. The integration of collaborative robots and nonconventional technologies into production processes must be done in a qualitative and knowledge-based way. In this context, knowledge-based management plays an extremely important role in ensuring the success of organizations in the face of competition. It facilitates the learning, transfer, improvement and maintenance of knowledge within the organization, contributing to the provision of high-quality products and services. Collaborative robots have also represented a significant shift in the traditional paradigm of human-robot collaboration, transforming into an nonconventional technology that has helped break down the barrier between these two entities. The scientific paper explores the correlation between the four concepts: quality, collaborative robots, nonconventional technologies and knowledge-based management, to ensure the success of organizations. A parallel can also be found in the scientific work between the knowledge-based organization and the learning organization.
2. Crosby, P. B., Quality Is Free, New York: McGraw-Hill, (1979).
3. Deming, W. E., Out of the Crisis, MA: MIT Center for Advanced Engineering Study, (1986).
4. Juran, J. M., Quality Control Handbook, New York: McGraw-Hill, (1988).
5. Oprean, C., Vanu, A., & Stan, S., Dicționar explicativ pentru știință și tehnologie: Managementul integrat al calității, București: AGIR, (2021).
6. International Organization for Standardization. Quality management systems - Requirements. ISO (ISO 9001:2015). Geneva, Switzerland: ISO, (2015).
7. Oprean, C., Țîțu, M. A., & Bucur, V., Managementul global al organizației bazată pe cunoștințe, București: AGIR, (2011).
8. Nanu, A., Nanu, D., Prelucrare prin eroziune electrica, Editura Universitati “Lucian Blaga” din Sibiu, 2004.
9. Gusan, V., Țîțu, M. A., Oprean, C., Industrial robots versus collaborative robots-The place and role in nonconvetional technologies, ACTA Technica Napocensis-Series: Applied Mathematics, Mechanics, and Engineering, 65(1S), (2022).
10. Retrieved from Dicționar explicativ al limbii române | dexonline: https://dexonline.ro
11. Klimecki, R., & Lassleben, H., What causes organizations to learn?, Konstanz: University of Konstanz, (1998).
12. Marsick, V. J., & Neaman, P. G., Individuals Who Learn Create Organizations that Learn, New Directions for Adult and Continuing Education, 72, 97-104, (1996).
13. Derouin, R. E., Fritzsche, B. A., & Salas, E, E-Learning in Organizations, Journal of management, 31(6), 920-940, (2005).
14. Juran, J. M., & Godfrey , A. B., Juran’s quality handbook 5th edition, New York, United States of America: McGraw-Hill, (1999).
15. Dhillon, B. S., Robot Reliability and Safety. New York: Springer, (1991).
Copyright (c) 2023 Vasile Gusan, Carmen Purcar, Mircea Badescu, Mihail Aurel Titu
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.