Industry 4.0 Analysis as Differentiating Factor in Innovation: The Case of an Automotive Industry Using Technology with Optical Sensors to Optimize the Visibility in Cars

Roberto Carlos Valdez Hernandez; José Luis Arcos Vega; Juan Gabriel  López Hernández

doi:10.4067/S0718-27242024000100006

Authors

Roberto Carlos Valdez Hernandez Faculty of Administrative Sciences, Autonomous University of Baja California, Baja California, México https://orcid.org/0000-0001-9612-5526
José Luis Arcos Vega Engineering Institute, Autonomos University of Baja California, Baja California, México
Juan Gabriel López Hernández Engineering Institute, Autonomos University of Baja California, Baja California, México

DOI:

https://doi.org/10.4067/S0718-27242024000100006

Keywords:

Industry 4.0, Innovation, Technology, Internet of Things, Automotive Industry

Abstract

An investigation was made in an automotive industry considered as industry 4.0 in the city of Tijuana, Baja California, which is the northwest zone of the Mexican Republic, evaluating optical sensors to optimize the visibility in cars, as the internet of things (IOT) process, to improve the productivity and quality yielding in the manufacturing areas of this industry evaluated. The main objective of this scientific study was the analysis of the operational yielding of electronic sensors used in the industrial operations to count, control and obtain and safeguarding numerical data of parameters of the sensors evaluated as numerical in a data base in a cloud and in a computer system, as an innovation technology. This investigation shows also a correlation analysis of the implementation of innovation system in industrial process of the industry where was made the scientific study, with the evaluation of the operative yielding as innovation with the productivity and quality indices. The correlation analysis was made with the Spearmen method. This was made to determine the levels of impact on the functionality of these electronic sensors on the productivity and quality indices that led to the economic profit factor, as an aspect of innovation, technology and economy in this evaluated industry. The evaluations were with the MatLab software, using statistical methods as the Cronbach’s Alpha coefficient and the Spearmen used to evaluate the reliability of the instrument as a questionnaire, with five managers of the industry evaluated about the use of these electronic sensors. The principal objective of this investigation was to determine the principal reason to use the industry 4.0 tools as IOT to improve industrial process in an automotive industry that is a relevant industry in the worldwide. Results shows the importance of the use of this type of industry 4.0 tool.

Downloads

Download data is not yet available.

References

Albers, A., Gladysz, T., Pinner, V., Butnko, T. (2016). Procedure for Defining the System of Objectives in the Initial Phase of an Industry 4.0 Project Focusing on Intelligent Quality Control Systems, Procedia CIRP 52, pp. 262-267.

Alexopoulos, K., Makris, S., Xanthakis, V., Sipsas, K., Chryssolouris, G. (2016). A Concept for Context-aware Computing in Manufacturing: The White Goods Case, International Journal of Computer Integrated Manufacturing 29 (8), pp. 839-849.

Arnold, C., Kiel, D., Voigt, K. (2016). How the Industrial Internet of Things Changes Business Models in Different Manufacturing Industries, International Journal of Innovation Management 20 (8, pp. 1-25.

Arsénio, A., Serra, H., Francisco, R., (2014). Internet of Intelligent Things: Bringing Artificial Intelligence into Things and Communication Networks.” Studies in Computational Intelligence 495: 1-37.

Bag, G., Pang, Z., Johansson, M. (2016). Engineering Friendly Tool to Estimate Battery Life of a Wireless Sensor Node, Journal of Industrial Information Integration 4, pp.8-14.

Bandyopadhyay, D., Sen, J. (2011). Internet of Things: Applications and Challenges in Technology and Standardization, Wireless Personal Communications 58 (1), pp. 49-69.

Barrios, F. (2022). Effects of innovative effort on different components of productivity: Evidence for the Colombian manufacturing industry, Social Sciences & Humanities Open, 2022, 100330.

Bi, Z., Xu, L., Wang, C. (2014). Internet of Things for Enterprise of Modern Manufacturing, IEEE Transactions on Industrial Informatics 10 (2), pp.1537-1546.

Branger, J., Pang, Z. (2015). From Automated Home to Sustainable, Healthy and Manufacturing Home: A New Story Enabled by the Internet-of-Things and Industry 4.0, Journal of Management Analytics 2 (4): 314-332.

Cai, H., Xu, L., Xu, B., Xie, C. (2014). IoT-based Configurable Information Service Platform for Product Lifecycle Management, IEEE Transactions on Industrial Informatics 10 (2), pp.1558-1567.

Campos, J. (2010). Guest Editorial Special Section on Formal Methods in Manufacturing, IEEE Transactions on Industrial Informatics 6 (2), pp. 125-126.

Cheng, G., Liu, L., Qiang, X. (2016). Industry 4.0 Development and Application of Intelligent Manufacturing.” In Proceedings of 2016 International Conference on Information Systems and Artificial Intelligence, 407-410. Hong Kong: IEEE.

Colombo, A., Schleuter, D., Kircher, M. (2015). An Approach to Qualify Human Resources Supporting the Migration of SMEs into an Industry 4.0-compliant Company Infrastructure, In Proceedings of IECON 2015 Yokohama, 003761-003766. Yokohama: IEEE, pp. 34-48.

De Silva, P. (2016). Ipanera: An Industry 4.0 Based Architecture for Distributed Soil-less Food Production Systems, Proceedings of the 1st Manufacturing and Industrial Engineering Symposium, Colombo, Sri Lanka, pp. 23-40.

Deloitte, K. (2015). Industry 4.0 Challenges and Solutions for the Digital Transformation and Use of Exponential Technologies. Zurich: Deloitte.

Hagel, J., Brown, J., Kulasooriya, D., Giff, C., Chen, M. (2015). The future of Manufacturing - Making things in a changing world. Deloitte University Press, pp. 98.

Hermann, M., Pentek, T., Otto, B. (2016). Design Principles for Industrie 4.0 Scenarios, Proceedings of 2016 49th Hawaii International Conference on Systems Science, January 5-8, Maui, Hawaii.

IMAQ (2022) Reporte de la Industria Maquiladora de Tijuana.

Jia, X., Feng, O., Fan, T., Lei, Q. (2012). RFID Technology and its Applications in Internet of Things (IoT), In Proceedings of the 2nd IEEE International Conference on Consumer Electronics, Communications and Networks (CECNet), April 21-23, Yichang, China, 1282-1285.

Kagermann, H., Wahlster, W., Helbig, J. (2013). Recommendations for Implementing the Strategic Initiative Industrie 4.0: Final Report of the Industrie 4.0 Working Group, Acatech-National Academy of Science and Engineering, Germany, pp. 67-80.

Kusiak, A. (2017). Smart Manufacturing, International Journal of Production Research. Published online 14 July 2017, pp. 56-71.

Li, S., Xu, L., Wang, X. (2013). Compressed Sensing Signal and Data Acquisition in Wireless Sensor Networks and Internet of Things, IEEE Transactions on Industrial Informatics, 9 (4), pp. 2177-2186.

Liao, Y., Deschamps, F., Loures, E., Ramos, L. (2017). Past, Present and Future of Industry 4.0 - A Systematic Literature Review and Research Agenda Proposal, International Journal of Production Research 55 (12), pp.3609-3629.

Liu, Y., Han, W., Zhang, Y. (2016). An Internet-of-Things Solution for Food Safety and Quality Control: A Pilot Project in China, Journal of Industrial Information Integration 3, pp.1-7.

Lu, Y. (2017). Industry 4.0: A Survey on Technologies, Applications and Open Research Issues, Journal of Industrial Information Integration 6, pp.1-10.

Miorandi, D., Sicari, S., De Pellegrini, F. (2012). Internet of Things: Vision, Applications and Research Challenges, Ad Hoc Networks 10 (7), pp.1497-1516.

Moeuf, A., Pellerin, R., Lamouri, S. (2017). The Industrial Management of SMEs in the Era of Industry 4.0, International Journal of Production Research Published online 8 September 2017, pp. 212-230.

Oliveira, L., Álvares, A. (2016). Axiomatic Design Applied to the Development of a System for Monitoring and Teleoperation of a CNC Machine through the Internet, Procedia CIRP 53, pp.198-205.

Roland, B. (2014). Industry 4.0 - The new industrial revolution - How Europe will succeed. Roland Berger Strategy Consultants, pp. 78.

Saldivar, A., Li, Y., Chen, W., Zhan, Z. (2015). Industry 4.0 with Cyber-physical Integration: A Design and Manufacturing Perspective, Proceedings of 2015 21st International Conference on Automation and Computing (ICAC), Glasgow, UK, pp 69-90.

Secinaro, S., Brescia, V., Calandra, D., Biancone, P. (2020). Employing bibliometric analysis to identify suitable business models for electric cars, Journal of Cleaner Production, 264, 10, August 2020, pp. 121-134.

Shadmani, M., Marofi, S., Rokian, M. (2011). Trend Analysis in Reference Evapotranspiration Using Mann-Kendall and Spearman’s Rho Tests in Arid Regions of Iran, Water Resources Management, volume 26, pages 211-224.

Shaheen, S., Chan, N., Micheaux, C. (2015). One-way carsharing’s evolution and operator perspectives from the Americas Transportation, 42 (3) (2015), pp. 519-536.

Swat, M., Brünnet, H., Bähre, D. (2014). Selecting manufacturing process chains in the early stage of the product engineering process with focus on energy consumption. In: Technology and Manufacturing Process Selection. The Product Life Cycle Perspective, Springer, pp. 26-40.

Taber, K. (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education, Research in Science Education, 48, pp. 1273-1296.

Wang, C. (2016). “A Multidisciplinary Design and Analysis Environment and Its Application to Aircraft Flight Dynamics Analysis.” Journal of Industrial Information Integration 1, pp. 14-19.

Weber, A. (2016). Industry 4.0: Myths Vs. Reality, Assembly November, pp. 28-37.

Yan, H., Xu, L., Bi, Z. (2015). An Emerging Technology - Wearable Wireless Sensor Networks with Applications in Human Health Condition Monitoring, Journal of Management Analytics 2 (2), pp.121-137.

Zhou, H., Shou, Y., Zhai, X. (2014). Supply Chain Practice and Information Quality: A Supply Chain Strategy Study, International Journal of Production Economics 147 (Part C), pp. 624-633.

Zhang, P., Yan, F., Du, C. (2015). A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics Renew. Sustain. Energy Rev., 48 (2015), pp. 88-104.

Zhao, X., Wang, S., Wang, X. (2018). Characteristics and trends of research on new energy vehicle reliability based on the web of science Sustainability, 10 (10) (2018), p. 3560.

Zuluaga, J.C., Sánchez, I. D., Barrios, F. (2012). Regional environment and innovative performance of firms. A multilevel analysis proposal. Studies Managerial, 28 (spe), 169-189. Retrieved April 26, 2023, from Retrieved April 26, 2023, from http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0123-59232012000500010&lng=en&tlng=