Implementation of Internet of Things in the Manufacturing Value Chain: A Comprehensive Study in Industry of Piping

Rahimah  Kassim ¹ (Universiti Kuala Lumpur, Malaysian Ins. of Ind. Tech)
Hasnah  Mustapa ² (Universiti Kuala Lumpur, Malaysian Ins. of Ind. Tech)
Azizah  Rahmat ³ (Universiti Kuala Lumpur, Kuala Lumpur, Malaysian Ins. of Info. Tech )
Hamad Raza ⁴ (GC University Faisalabad)

Submited date : 2024-04-28 accepted date : 2024-06-06

Keywords: Internet of Thing, Valur Chain, Manufacturing,

Abstract :

The value chain process has significant importance in the manufacturing industry for achieving operational success. In order to mitigate errors, it is essential for manufacturing firms to optimize their operational processes with utmost efficiency. The Internet of Things (IoT) is a technology that effectively enhances operational excellence inside enterprises. In order to actualize the Internet of Things (IoT), it is essential for the industrial sector to actively participate in and effectively incorporate this technology into its routine operations. Numerous studies have been undertaken to explore the possibilities of the Internet of Things (IoT) for various enterprises. Nevertheless, the use of IoT remains limited in several sectors, such as the Manufacturing industry in poorer nations. The objective of this article is to identify and assess the elements that have an impact on the adoption of Internet of Things (IoT) in the manufacturing sector of Malaysia. Additionally, a proposed model for the implementation of IoT in this industry will be presented. The identification of drivers was conducted by means of a comprehensive analysis of prior examinations. Additionally, a paradigm known as the technology-organization-environment (TOE) framework is suggested, drawing on the idea of information system adoption. The Delphi approach was used to conduct a survey among users of the Internet of Things (IoT), and the findings revealed that the selected parameters examined in this research had a noteworthy influence on the deployment of IoT within the manufacturing sector of Malaysia. This study aims to facilitate the comprehension of Manufacturing organizations about the many facets of Internet of Things (IoT) implementation. It seeks to enhance their business structure and investment in IoT, while also serving as a source of inspiration for researchers to delve into further research on novel factors related to IoT adoption or implementation.

References:

Ashton, J., Barrow, F., & Bh, D. (1998). Cost efficiency , economies of scale and economies of scope in the British retail banking sector. In Finance (Issue 13).
Bondi, A. B. (2000). Characteristics of scalability and their impact on performance. Proceedings Second International Workshop on Software and Performance WOSP 2000, January 2000, 195–203. https://doi.org/10.1145/350391.350432
Boone, J. (2000). Competitive Pressure: The Effects on Investments in Product and Process Innovation. The RAND Journal of Economics, 31(3), 549. https://doi.org/10.2307/2601000
Eraut, M. (2005). Expert and expertise: meanings and perspectives. Learning in Health and Social Care, 4(4), 173–179. https://doi.org/10.1111/j.1473-6861.2005.00102.x
GE Intelligent Platforms. (2012). The Rise of Industrial Big Data. Whitepaper, 6.
Gupta, A. (2018). Flexible and Scalable Systems for Network Management. September, 1–165.
Hartman, B. D. (2016). Aspects of the Nature of Engineering for K-12 science education: A Delphi study. 204.
Hobday, M., Davies, A., & Prencipe, A. (2005). Systems integration: A core capability of the modern corporation. Industrial and Corporate Change, 14(6), 1109–1143. https://doi.org/10.1093/icc/dth080
Hsu, C. C., & Sandford, B. A. (2019). The Delphi Technique: Making Sense of onsensus. Practical assessment, research, and evaluation, 12(1), 10.
Jitpaibon, T., & Kalaian, S. A. (2005). Analyzing The Effect Of Top Management Support On Information System (IS) Performance Across Organizations And Industries Using Hierarchical. Journal of International Technology Information Management, 14(1), 131–144.
Karlsson, J. (2021). Firm size and growth barriers: a data-driven approach. Small Business Economics, 57(3), 1319–1338. https://doi.org/10.1007/s11187-020-00350-y
Kassim, Rahimah, A. H. A. (2022). Understanding Critical Success Factors of Cloud Computing Implementation in Higher Education Institutions: Consensus Evaluation in Delphi. Advanced Transdisciplinary Engineering and Technology, Springer, 174(January), 321. https://doi.org/10.1007/978-3-031-01488-8
Martins, R., Oliveira, T., & Thomas, M. A. (2016). An empirical analysis to assess the determinants of SaaS diffusion in firms. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2016.03.049
Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2021). Smart manufacturing and tactile internet based on 5G in industry 4.0: Challenges, applications and new trends. Electronics, 10(24), 3175.
Nyrhinen, M. (2006). IT Infrastructure: structure, properties and processes.
Olsson, J. G., & Yuanjing, X. (2018). Industry 4.0 Adoption in the Manufacturing Process. Multiple case study of electronic manufacturers and machine manufacturers. Linneuniversitetet Kalmar Vaxjo.
Raharjo, S. T., & Perdhana, M. S. (2012). ANALYSIS OF FACTORS AFFECTING SOCIAL MEDIA USAGE TO BUSINESS PERFORMANCE. 1–17.
Sam Bourgi, D. D. C. (2015). Big Data & the Intelligence Economy. Information and Communication Technology Council, 30.
Stosic, L., & Velickovic, D. (2013). Computer security and security technologies. Journal of Process Management. New Technologies, 1(1), 14–19. https://doi.org/10.5937/jpmnt1301014s