IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

Peter O'Donovan, Ken Bruton, and Dominic T.J. O'Sullivan
Publication Target: 
IJPHM
Publication Issue: 
Special Issue Big Data and Analytics
Submission Type: 
Full Paper
Supporting Agencies (optional): 
Irish Research Council and DePuy Johnson & Johnson
AttachmentSizeTimestamp
ijphm_16_032.pdf1.34 MBNovember 22, 2016 - 1:07am

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledge-based competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

Publication Year: 
2016
Publication Volume: 
7
Publication Control Number: 
032
Page Count: 
11
Submission Keywords: 
industrial big data; big data analytics; industrial analytics; smart manufacturing; maturity model; assessment
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Industrial applications
Submitted by: 
  
 
 
 

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