In industrial enterprises, data acquisition is an essential procedure, basically in the industry 4.0 context. It entails taking signals and converting them into digital values that a computer can manipulate. In order to transform analog waveforms into modern values for further processing, information gathering systems are essential. This article focuses on the process of acquiring data in industrial enterprises throughout the age of Industry 4.0 reviewing the constituents of data gathering systems and the significance of accurate and dependable data in portraying industrial processes. In addition, the study examines the classification of production systems according to criteria that influence data accessibility, as well as the various techniques and approaches used for data acquisition. The limitations of human data collection are highlighted, along with the benefits of automated and semi-automated data capturing technologies. Management support systems may get data from industrial automation systems, which are also investigated in the research. Using dedicated servers and communications protocols to consolidate data, it investigates the issues with industry-wide fragmentation in automation systems. The research goes deeper into how machine vision, barcodes, and RFID devices are used to gather data. Finally, the paper emphasizes the need of analyzing the company's organizational and technical environment and proposes a strategy for building a Manufacturing Information Acquisition System (MIAS).
Keywords
Manufacturing Execution System, Enterprise Resource Planning, Manufacturing Information Acquisition System, Supervisory Control and Data Acquisition.
E. J. Umble, R. R. Haft, and M. M. Umble, “Enterprise resource planning: Implementation procedures and critical success factors,” European Journal of Operational Research, vol. 146, no. 2, pp. 241–257, Apr. 2003, doi: 10.1016/s0377-2217(02)00547-7.
F. Almada-Lobo, “The Industry 4.0 revolution and the future of Manufacturing Execution Systems (MES),” Journal of Innovation Management, vol. 3, no. 4, pp. 16–21, Jan. 2016, doi: 10.24840/2183-0606_003.004_0003.
S. A. Boyer, SCADA:”Supervisory Control and Data Acquisition. 1993 [Online]”, Available: http://ci.nii.ac.jp/ncid/BA64840435.
H. Zhou and G. Alici, “Non-Invasive Human-Machine Interface (HMI) Systems with Hybrid On-Body Sensors for Controlling Upper-Limb Prosthesis: A Review,” IEEE Sensors Journal, vol. 22, no. 11, pp. 10292–10307, Jun. 2022, doi: 10.1109/jsen.2022.3169492.
A. A. Akinola, A. Kuye, and A. A. Ayodeji, “Cyber-Security Evaluation for a Hypothetical Nuclear Power Plant using the Attack Tree Method,” Computer Security and Reliability, vol. 4, no. 3, pp. 1–14, Dec. 2014, [Online]”, Available: https://stmjournals.com/index.php?journal=JoNET&page=article&op=view&path%5B%5D=5247.
A. Hassanzadeh et al., “A Review of Cybersecurity Incidents in the Water Sector,” Journal of Environmental Engineering, vol. 146, no. 5, May 2020, doi: 10.1061/(asce)ee.1943-7870.0001686.
E. Halmetoja, “The conditions data model supporting building information models in facility management,” Facilities, vol. 37, no. 7/8, pp. 484–501, May 2019, doi: 10.1108/f-11-2017-0112.
D. W. M. Chan, A. B. Baghbaderani, and H. Sarvari, “An Empirical Study of the Human Error-Related Factors Leading to Site Accidents in the Iranian Urban Construction Industry,” Buildings, vol. 12, no. 11, p. 1858, Nov. 2022, doi: 10.3390/buildings12111858.
I. Treadwell, “The usability of personal digital assistants (PDAs) for assessment of practical performance,” Medical Education, vol. 40, no. 9, pp. 855–861, Sep. 2006, doi: 10.1111/j.1365-2929.2006.02543. x.
A. Serra, J. O. Estima, and A. R. Da Silva, “Evaluation of Maestro, an extensible general-purpose data gathering and data classification platform,” Information Processing and Management”, vol. 60, no. 5, p. 103458, Sep. 2023, doi: 10.1016/j.ipm.2023.103458.
H. T. Sørensen, S. Sabroe, And J. Olsen, “A Framework for Evaluation of Secondary Data Sources for Epidemiological Research,” International Journal of Epidemiology, vol. 25, no. 2, pp. 435–442, 1996, doi: 10.1093/ije/25.2.435.
B. Edrington, B. Zhao, A. Hansel, M. Mori, and M. Fujishima, “Machine Monitoring System Based on MTConnect Technology,” Procedia CIRP, vol. 22, pp. 92–97, 2014, doi: 10.1016/j.procir.2014.07.148.
V. M. Igure, S. A. Laughter, and R. D. Williams, “Security issues in SCADA networks,” Computers & Security, vol. 25, no. 7, pp. 498–506, Oct. 2006, doi: 10.1016/j.cose.2006.03.001.
R. R. Panko, “Corporate computer and network security. 2003. [Online]”, Available: https://opac.library.uib.ac.id/index.php?p=show_ detail&id=2283&keywords=.
T. M. McPhillips et al., “Blu-Iceand theDistributed Control System: software for data acquisition and instrument control at macromolecular crystallography beamlines,” Journal of Synchrotron Radiation, vol. 9, no. 6, pp. 401–406, Nov. 2002, doi: 10.1107/s0909049502015170.
S. Munzert, C. Rubba, P. Meißner, and D. Nyhuis, “Automated Data Collection with R,” Jul. 2014, doi: 10.1002/9781118834732.
CRediT Author Statement
The authors confirm contribution to the paper as follows:
Conceptualization, Methodology, Writing- Original Draft Preparation, Visualization, Writing- Reviewing and Editing: Xiaofeng Li. All authors reviewed the results and approved the final version of the manuscript. The author reviewed the results and approved the final version of the manuscript.
Acknowledgements
Authors thanks to National Natural Science Foundation of China for this research support.
Funding
This study was funded by the National Natural Science Foundation of China (grant number 71677023).
Ethics declarations
Conflict of interest
The authors have no conflicts of interest to declare that are relevant to the content of this article.
Availability of data and materials
No data available for above study.
Author information
Contributions
All authors have equal contribution in the paper and all authors have read and agreed to the published version of the manuscript.
Corresponding author
Xiaofeng Li
School of Business Administration, Cheung Kong Graduate School of Business, Dongcheng, Beijing, China, 100006.
Open Access This article is licensed under a Creative Commons Attribution NoDerivs is a more restrictive license. It allows you to redistribute the material commercially or non-commercially but the user cannot make any changes whatsoever to the original, i.e. no derivatives of the original work. To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
Cite this article
Xiaofeng Li, “Data Acquisition and Management in Industrial Businesses Fundamentals and Methodologies”, Journal of Enterprise and Business Intelligence, vol.5, no.1, pp. 030-039, January 2025. doi: 10.53759/5181/JEBI202505004.
