SEP-OCT 2018

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18 INTECH SEPTEMBER/OCTOBER 2018 WWW.ISA.ORG PROCESS AUTOMATION passing most digital transformation initiatives across manufacturing orga - nizations—beyond just implementing a manufacturing execution system (MES). Leading initiatives include asset perfor - mance management, environmental health and safety, quality management, new product introduction, manufac - turing operations management, and industrial energy management, just to name a few. In the digital age, the lines blur between these initiatives, because the same data can be used in different ways to improve processes and achieve operational excellence. New digital framework New operational software architectures are required to better enable and ac - celerate implementation of these ini- tiatives, including hybrid deployments (edge + cloud), mobile access, social collaboration, and access to big data and machine learning to better predict actions. The text at the bottom right of figure 3 shows the value proposition for imple- menting digital transformation to drive operational excellence, encompassing the delivery of a host of capabilities. As companies begin their digital transfor- mations, this type of operational archi- tecture is critical for long-term success. It is tempting to simply skip over this step and start applying point solutions to achieve specific results. This is a mis- take, as it results in a fractured land- scape of disjointed systems that do not interoperate, with data often replicated, inaccessible, or siloed. But just how does one design this type of operational architecture? Model-driven approach Using a model-driven approach to cre- ate an operational architecture means building upon an industrial software platform to: l define requirements via process map- ping l implement using "low code" technology l separate "content" from technology l reuse engineering l avoid custom coding Following these steps separates pro- cesses from the underlying software technologies, allowing deployment across a wide range of activities and tasks. The first step is to define require - ments via process mapping, using a methodology similar to business pro - cess management mapping, long prac- ticed with great success in commercial environments. Process mapping requires manufac- turers to digitize, standardize, and im- prove. Digitizing means assessing and documenting current process es—along with touch points to other systems, such as automation and lab. Digitiz- ing a task simply means to take what is in the minds of plant personnel when they perform an activity and to docu- ment it by putting it in writing, and then to enter this information into the appropriate software system (figure 4, see page 16). It also requires the cre- ation of needed workflows and user interfaces—along with integration to MES and other systems. Although standardizing a task or activity without digitizing it is theo- retically possible, it is very difficult and expensive, and it is almost impossible to integrate with all of the other tasks required to produce products. Once a task or activity has been digitally trans- formed, it can easily be standardized, because all plant personnel follow the same steps. Standardization allows manufacturers to: l account for needed variations among sites l create libraries of practices that are centrally governed l define and measure performance metrics l build deployable packages and push to the sites Figure 2. The five pillars of digital transformation link the market environment and its impact on manufacturers with technology trends. Figure 3. Digital strategies and technology enablers drive operational excellence initia- tives via new operational architectures.

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