“Start now. What we are seeing across many industries is with very few sensors, which are typically already installed, information can be discovered leading to dramatic cost savings.”
Stuart Gillen, a senior director at SparkCognition
“Let’s say a piece of equipment is showing increasing bearing temperature. Predictive analytics looks at the temperature profile and tells you it is likely to fail in X amount of time. On the other hand, prescriptive analytics tells you that if you slow the equipment down by Y%, the time to failure can be doubled, putting you within the already scheduled maintenance window and revealing whether you can still meet planned production requirements.”
Dan Miklovic, principal analyst at LNS Research
“While there are positive indications for efficiency with the adoption of prescriptive analytics, there are some major hurdles to overcome. MROs would need to make significant investments in the development of sophisticated software, plan for integration with the MRP, and have an analytics group to produce viable interpretation of the information. Widespread adoption of the methodology within aviation may also require extensive regulatory approval.”
Vikram Bhatt, COO of Kapco Global
“Intel made the journey to PdM decades ago. There are many examples of solutions we’ve developed with our partners being deployed in both our facilities and in our partners’ customer sites. Evolving to prescriptive maintenance, where probable cause and automated maintenance are implemented, is a necessary next step in the Industry 4.0 journey in order to keep up with the demands of fast-paced change in our market.”
Mary Bunzel, general manager, manufacturing and industrial solutions at Intel
"Any individual or entity responsible for asset management can benefit from a prescriptive approach, whether it’s an original equipment manufacturer (OEM), an end user maintenance organization, or a third-party service provider. Well-developed prescriptive analytics solutions will draw from business intelligence (BI), operational intelligence (OI), enterprise asset management (EAM), enterprise resource planning (ERP), material requirements planning (MRP), and other business information systems and prognostic tools. Countless data points can be factored into the algorithms, such as asset criticality, work priority, fault data, ambient temperatures, and the average lifespan of a given part."
Sheila Kennedy, CMRP
