Predictive, prescriptive and detective analytics for smart manufacturing in the information age


The solution of today’s complex decision-making forsmart manufacturing are dependent onthe ability to: a) realistically modelthe manufacturing system, b) easily and timely integrate valid and consistent plant data, c) solve the problem efficiently withreasonable computational efforts,andd) incorporate feedback to continuously improve the decision-making process over time. Insuch a context, advanced analytics such asthepredictive, prescriptive and detective analytics are thefoundation ofsmartmanufacturing intheinformation age. Predictive analytics examinesraw data to be augmentedwith the purpose of concludingthe behaviourof the systems,by estimating and anticipating what is likely to happenwithin the forthcomingfuture.Prescriptive analytics automatesthe decision-making of any physical system concerningits design, planning, scheduling, control and operation using any combination of optimisation, heuristics, machine-learningandcyber-physical systems. Detective analytics makesdiagnostics ondata to improve both the predictive and prescriptive analytics. In the former, by identifying and eliminating gross-errors for better predictions.In the latter, byuncoveringand rectifyinginfeasibilities and inconsistencies for optimal prescriptions. We construct aplot of the connections of the advanced analyticsat their time-spacesconsidering thewell-established, the currentand the next generationof analyticstechniques. An example of an automated application of advanced analyticsconsideringa multi-unit real-time estimation and optimisation engine relying on data integration and integrityfor better decision-making is highlighted.

MENEZES, Breno C.; KELLY, Jeffrey D.; LEAL, Adriano Galindo; LE ROUX, Galo C. Predictive, prescriptive and detective analytics for smart manufacturing in the information age. In: IFAC SYMPOSIUM ON DYMANICS AND CONTROL OF PROCESS SYSTEMS, INCLUIND BIOSYSTEMS DYCIPS, 12., 2019, Florianópolis. Proceedings… IFAC Papers On Line, v.52, n.1, p.568-573, 2019.

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