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JUL-AUG 2019

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22 INTECH JULY/AUGUST 2019 WWW.ISA.ORG "What if" analytics run on actual plant data is key to more effective decision making By Joseph Ting and Duncan Micklem D ecision cycles across industrial environ- ments are becoming increasingly dis- rupted by proliferation of data, new data sources, and compute speeds within an increas- ingly volatile business environment. The digital twin is the key to effective decision making in this new world. Making better decisions, faster, that can be executed perfectly every time is vital for deliver - ing superior results, sustained. However, this is easier said than done. Every individual perspec - tive is underpinned by a series of unique cogni- tive biases that drive swift action in adversity but make accurately weighing evidence, assessing probabilities, and deciding logically a challenge. Look no further than the constant discrepancy between strategic planning/ambition and results realization (figure 1). A single view of the truth and analytics is there- fore key to situational awareness and effective organizational decision making. But many play- ers in the industry are stuck on determining what type of analytics they need. The solution to this question should be driven by the problem, not by how much analytics can be thrown at data in the hope it will both find the problem and solve it. The desired outcome should influence the type of analytics being sought and the available analytics technology that is fit for purpose (figure 2). Basic analytics technology can move data around and display key performance indicators (KPIs) to the right people at the right time to en - able decision making. They work well for under- standing what happened in hindsight. However, increasing plant complexity requires more sophis - ticated ways of approaching KPIs and targets. In some cases, a rudimentary approach to KPI set - ting and monitoring can even become ineffec- tive and counterproductive. In this case, deeper analytics technology, using digital twins, is nec -

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