Concluding Remarks

DIKW - the construct of data - then turning that into information. Then turning information into knowledge, then knowledge into wisdom. On this journey, first came the reactive application of analytics to manufacturing data. Then it was analytics in real time. Rather than looking at the incident in the past, anticipating it and engaging in the present. The next progression was operationalizing the analytics by connecting the signals to engineers and operators for consistent and immediate action. The next frontier of manufacturing analytics (and digital transformation) is now upon us – capturing the information from those actions and making that knowledge available across the enterprise, and using this information for future generations, tied to the data that forced the connection, or brought the opportunity to light.

The systematic retention of process knowledge associated with analytics signals addresses some of the industry’s top challenges including: alleviating the loss of institutional knowledge, improving time-to-productivity for new hires, shrinking skills gaps. Ultimately, this codification and amplification of knowledge accelerates time to problem discovery, reduces time to issue resolution and increases daily operating performance. This topic will be discussed and examples provided to demonstrate the latest initiative to integrate knowledge with the data.