Prescriptive analytics bring a higher level of efficiency to asset management decision making. By synthesizing condition-based and predictive maintenance decision processes with operational data modeling and mathematical algorithms, doing what needs to be done – and when – is simplified. Prescriptive maintenance puts the analytics into action.
Prescriptive analytics and maintenance are cognitive computing tools that incorporate modern technologies such as pattern recognition, machine learning, and artificial intelligence. They can be used to monitor and trend equipment performance in the field, predict performance expectations going forward, and compel the necessary actions to achieve the desired operations and maintenance outcomes.
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.
Using this data, the solution will model “what if” scenarios of possible options and outcomes down to the work execution level. Each option is systematically evaluated in order to identify the optimal course of action, and that recommendation is then pushed to the EAM and appropriate personnel for action.
Prescriptive analytics for asset management extend the value of the more seasoned descriptive and predictive counterparts:
- Descriptive analytics mine historical or current data to produce information that an individual must then interpret and act upon. The timeliness and effectiveness of the corrective action(s) depend on the knowledge and ability of the individual doing the interpretation.
Example: Descriptive analytics presented in a business intelligence dashboard illustrate a supplier’s on-time delivery rates for critical parts over a period of time. Users must decide if the on-time trend is consistent, improving or declining, and if it is time to switch vendors or renegotiate the purchasing agreement.
- Predictive analytics leverage historical and trending data to model and predict what may occur in the future. Maintenance timeliness is improved, but knowing which action(s) to take and when best to take it remains subject to the skills of the responsible individual.
Example: Predictive analytics indicate a motor’s temperature is rising above the normal range and the motor can be expected to fail within 30 days at this rate. Users must decide on the cause of the degradation and what action to take (for improper lubrication, do you correct the lubrication practices, replace the overheated bearing, or replace the motor?), and then schedule the work at the most opportune time before a failure occurs.
- Prescriptive analytics propose actions that are likely to trigger a particular outcome and suggest when to perform them. This approach saves time and avoids the risk of human error since the decision is optimized by a combination of predictive data modeling, statistical algorithms, business rules and machine learning.
Example 1: Prescriptive analytics help to determine what to do about an improperly functioning joy stick for a critical rubber tire gantry crane. Using diagnostic algorithms developed based on asset maintenance history, the solution determines that because the joy stick stutters before it takes off, either the electrical contact needs to be cleaned or the joy stick needs to be replaced. A work request is automatically generated to initiate the corrective action.
Example 2: Prescriptive analytics leveraging past performance and mathematical modeling conclude that deploying PdM for certain additional critical equipment will reduce maintenance costs, improve profitability, and increase overall equipment effectiveness (OEE) by X percent. The recommendation is automatically transmitted to the appropriate personnel.