IAMM: A maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities

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Date
2016
Authors
O'Donovan, Peter
Bruton, Ken
O'Sullivan, Dominic T. J.
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PHM Society
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Abstract
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 knowledgebased 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.
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Keywords
Engineering , Statistics , Computing , Industrial analytics , Multi-dimensional maturity model
Citation
O’Donovan, P., Bruton, K. and O’Sullivan, D. T. J. (2016) 'IAMM: a maturity model for measuring industrial analytics capabilities in large-scale manufacturing facilities', International Journal of Prognostics and Health Management, 7, 032, (11pp).
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