Did you say Predictive Analytics?
What is prescriptive analytics?
- quality improvements;
- service enhancements;
- cost reductions; and
- productivity increases
“The bottomline: Enterprises must stop wasting time and money on unactionable analytics. These efforts don’t matter if the resulting analytics don’t lead to better insights and decisions that are specifically linked to measurable business outcomes.”
What Exactly The Heck Are Prescriptive Analytics? by Mike Gualtieri, Vice President, Principal Analyst, Forrester
Don’t Stop With Knowing
Predictive maintenance (PdM) techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted.
Don’t Get Confused By Other Terms
- Condition-Based Maintenance (Usage-Based Maintenance)
- Asset Performance Maintenance (APM)
- Reliability Centered Maintenance (RCM)
Enter Prescriptive Analytics
The hierarchy of business analytics looks something like this:
- Descriptive Analytics = asset, operation, environmental and diagnostic information
- Diagnostic Analytics = identifies patterns of behavior (importance and urgency)
- Predictive Analytics = suggests a timeframe for an action
- Prescriptive Analytics = recommends specific actions
Below is a simple graphic of analytic type’s relative value:
Others have called this prescriptive approach Cased-Based Reasoning.
Case-based reasoning (CBR), broadly construed, is the process of solving new problems based on the solutions of similar past problems. An auto mechanic who fixes an engine by recalling another car that exhibited similar symptoms is using case-based reasoning.
Both prescriptive analytics and CBR have the same goal. They provide specific recommendations based on prior experiences and outcomes.
A Great Step Forward
Prescriptive analytics is a critical advancement in analytics. It can improve decision making and processes effectiveness. It helps us get closer to tying outcomes to specific situations.
While it’s not Nirvana for IIoT, it is clearly a step in the right direction.