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Cited by 1,965Digital Transformation in IndustryImpact of AI and Big Data on Business and SocietyService and Product Innovation

Service Innovation and Smart Analytics for Industry 4.0 and Big Data Environment

Open access
Lee, Jay, Kao, Hung-An, Yang, Shanhu·2014·Procedia CIRP
DOI 10.1016/j.procir.2014.02.001
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Abstract

Today, in an Industry 4.0 factory, machines are connected as a collaborative community. Such evolution requires the utilization of advance- prediction tools, so that data can be systematically processed into information to explain uncertainties, and thereby make more “informed” decisions. Cyber-Physical System-based manufacturing and service innovations are two inevitable trends and challenges for manufacturing industries. This paper addresses the trends of manufacturing service transformation in big data environment, as well as the readiness of smart predictive informatics tools to manage big data, thereby achieving transparency and productivity.

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Crossref · 10.1016/j.procir.2014.02.001

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1 of 1 references verified0 citation errors · 0 not checkable

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  • A 5-minute pipeline feeding an operations control room dashboard has different failure consequences than a daily pipeline feeding a monthly reliability report.

    “A 5-minute pipeline feeding an operations control room dashboard has different failure consequences than a daily pipeline feeding a monthly reliability report [10].”

    From Data Observability Framework for Mission-Critical Industrial ETL Pipelines: Anomaly Detection, SLA Modeling, and Proactive Failure Identification (2026) · cites this paper as [10]

    Section 4 describes a daily data transfer for remote engine monitoring, but not a monthly reliability report. Sections 3.2 and 4 discuss operational decisions and missing data; they do not compare the failure consequences of a five-minute dashboard pipeline with those of a daily reporting pipeline. Searches for the dashboard, reporting cadence, and failure-consequence comparison found no matching passage. (LLM, conf=0.76)

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Cites

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Research works printed but not linked: 9

  1. Vandermerwe, S., & Rada, J. (1989). Servitization of business: adding value by adding services. European Management Journal, 6(4), 314-324.
  2. Baines, T. S., Lightfoot, H. W., Benedettini, O., & Kay, J. M. (2009). The servitization of manufacturing: a review of literature and reflection on future challenges. Journal of Manufacturing Technology Management, 20(5), 547-567.
  3. McAfee, A., & Brynjolfsson, E. (2012). Big data: the management revolution. Harvard business review, 90(10), 60-68.
  4. Cohen, J., Dolan, B., Dunlap, M., Hellerstein, J. M., & Welton, C. (2009). MAD skills: new analysis practices for big data. Proceedings of the VLDB Endowment, 2(2), 1481-1492.
  5. Al-Noukari, M., & Al-Hussan, W. (2008, April). Using data mining techniques for predicting future car market demand; DCX case study. In Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on (pp. 1- 5). IEEE.
  6. Chon, S. H., Slaney, M., & Berger, J. (2006, October). Predicting success from music sales data: a statistical and adaptive approach. In Proceedings of the 1st ACM workshop on Audio and music computing multimedia (pp. 83-88). ACM.
  7. Provost, F., & Fawcett, T. (2013). Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data, 1(1), 51-59.
  8. Lee, B. E., Michaloski, J., Proctor, F., Venkatesh, S., & Bengtsson, N. (2010, January). MTConnect-based Kaizen for machine tool processes. In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 1183-1190). American Society of Mechanical Engineers.
  9. Lee, J., Lapira, E., Bagheri, B., & Kao, H. A. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41.

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Authors
Lee, J., Kao, H.-A., & Yang, S.
Affiliations
University of Cincinnati (US)
Corresponding author
Lee, Jay
ORCID
Lee, Jay
Venue
Procedia CIRP
Publisher
Elsevier BV
Published
2014
Volume
16
Pages
3-8
ISSN
2212-8271 (ISSN-L)
DOI
10.1016/j.procir.2014.02.001
OpenAlex
W2044049559
Cited by
1,965
References
1
Citations by year
2026: 112 · 2025: 126 · 2024: 147 · 2023: 156 · 2022: 159 · 2021: 231 · 2020: 263 · 2019: 296 · 2018: 235 · 2017: 141 · 2016: 64 · 2015: 31 · 2014: 4
FWCI
141.95
Type
journal article
Language
en
Open access
Licence
Locations
Procedia CIRP (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Social Sciences › Business, Management and Accounting › Marketing › Service and Product Innovation
Keywords
service innovation, Industry 4.0, big data, cyber-physical systems, manufacturing services, predictive maintenance, decision support

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