The Path from Data to Actionable Information as a Driver for the Industrial Ecosystem
equipment . For example , predictive vibration analytics can find a pattern for undesirable vibration when a machine is making a 20mm slot in steel with a 40mm endmill having three cutting items and rotating at 2500 RPM . The analytics will be continually refined as more data is collected and the model is improved . This update cycle allows the local systems to continue functioning and ensure safety even if they are not able to communicate with other parts of the ecosystem .
Prediction is often referred to as knowledge since one is building models that are capturing the causality relating to the objective truth . It has the potential of adding tremendous value to the manufacturing processes since it allows for the avoidance of loss and reduces unexpected process disruptions . With surrogate models and simulations , first principle models can be calibrated to the reality of the actual manufacturing execution and become more prescriptive .
PRESCRIPTIVE
Following from predictive analytics , focusing on avoidance of problems before they occur , prescriptive analytics allows the system to avoid problems by predicting future outcomes and working around situations that are highly likely to cause problems or determining best practices . Examples of prescriptive analytics are technologies that prescribe optimal process parameters when using certain tools to cut a feature in a certain material , in this case , the information will inform the Computer Aided Manufacturing ( CAM ) engineers to better specify how tooling is used . One can also prescribe optimized material flows at the enterprise scale to increase on-time delivery and machine utilization . Maintenance strategies can be significantly improved by prescribing when repairs should occur based on the machine capabilities and the required activities in the job queue .
Prescriptive analytics avoid losses before they occur . By combining the IIoT information streams with the intent-based models of the product geometry and inspection plans , the causality of decisions and impact on outcomes can be better understood . The eventual goal is to get to a level of prescription where the outcomes can be forecast to the extent that ondemand scheduling can adapt to rapid changes in product requirements and new orders , down to individual parts .
As with predictive models , the prescriptive models will be created using large amounts of historical data . The models will be updated as they are refined and better predictions become available . The standards that are currently applicable to the prescriptive analytics are SysML for system engineering , STEP , specifically AP-242ed2 19 for solid model geometry and GD & T as well as QIF for quality reporting and statistics . Models like AP-238 or STEP-NC can also be used to provide the expected execution stage models to compare the engineering intent .
19 http :// www . ap242 . org
IIC Journal of Innovation - 31 -