NOV-DEC 2017

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FACTORY AUTOMATION tool calibration and cage management, warehouse management, and intra- plant inventory operations) dispatch prioritized event messages and job or ders to stage all required documents (i.e., SOPs, checklist), tools/equipment, and parts and materials at the adverse event location. The critical smart man- ufacturing requirement is to stage all resources for the specialist at the loca- tion, so the specialist can quickly per- form the corrective action and mini- mize the capacity loss of production flows. Smart manufacturing requires the supporting departments' on-shift knowledge worker to also drive these staging activities by workflow messages and alerts. The point here is the knowledge stage is defined by this workflow engi- neering of manufacturing operations management (MOM) and process control applications, with the knowl- edge worker trained in each operations event response procedure and remedy. Otherwise, smart manufacturing is not so smart. It would be constrained to the information stage, where the plant and its workers drive in the review mirror based on use-interface alerts and out- of-shift periodic reports and analytics. In the past 30 years, return on invest- ment typically comes from how well the manufacturing system enables the plant worker to reduce or eliminate—in near real time—the cascading effects and cost of adverse events. Smart manufacturing is making this a science, not an art. Same information challenges Smart manufacturing devices, ma- chines, and systems must support the operations event message approach for IIoT to achieve the business case for on-shift, reliable decision making. Issues l What is the business case for IIoT and smart manufacturing for my plant type with its existing systems and infrastructure? l How does a manufacturer best apply IIoT interface methods to its existing plant systems (150+ per typical plant) and infrastructure to realize the real- time Industry 4.0 cyber-physical con- nection for the knowledge worker? smart plant is em- powered with the "standard work" from smart tools, proce- dures, and opera- tions processes. New product introduction, make-to-order, or ex- pedited processes are optimized to achieve time-to-volume more quickly to maximize margin. The lean method of standard work for response tasks must be applied across the manufacturing execution system (MES) and operations manage- ment systems for knowledge workers to be cross trained quickly through common definitions in their user inter- faces/faceplates for every work cell and machine in the plant. This a must for smart manufacturing to happen. A guiding principle is that the prima- ry system functionality gives operators and supervisors "same shift" feedback for all points of daily data collection. This drives the knowledge worker to care about continuous improvement. To characterize process and product quality, the resulting culture focuses daily on what to improve through per- formance feedback. Once the work process waste streams (lean value-stream analysis) and pro- cess variances (Six Sigma) are identi- fied, the specific metrics and standard event responses as workflows for each adverse event are engineered across in- teractive operations management sys- tems. The engineered event re sponse is electronically pushed to the account- able on-shift specialist. He or she must acknowledge and then commit to ad - dressing the adverse event or escalating it to the next specialist or supervisor in the workflow based on the situation's priority. These engineered workflows are enforced with on-shift responses of each step. When the specialist accepts the task, all the collaborating department work- flow applications (e.g., MES, opera- tions scheduling, inventory movement, quality operations, asset management, ing stage is engineered in the form of simulations and predictive models for more accurately planning and execut - ing available plant resources to meet customer and rescheduling demands. This is artificial intelligence. Realizing KPI value By characterizing and aligning the op- erations performance and financial metrics through continuous improve- ment methods (another day's arti- cle), the criteria for operations alerts, alarms, and resource test specifications are used to engineer the operations re- sponses, priorities, and controls to ef- fectively lower the unit cost and other financial metric dependencies. A significant amount of untapped value exists in every manufacturing operation and physical process (figure 2). The recoverable value is typically from compromised margin and profit from the increased cost of lost capac- ity and capabilities when resources are constrained (equipment, material, and personnel). This limits the scheduling and workflow alternatives of events like downed equipment, rework, or ineffec- tive storage. The cascading effects of abnormal events are minimized by en- gineering the manufacturing systems for on-shift corrective action to adverse conditions. The more time a system takes to convert data from the physi- cal process or operations workflow to event information, the less value the response action brings. The knowledge worker in today's 24 INTECH NOVEMBER/DECEMBER 2017 WWW.ISA.ORG Time Value Data stored in useful format Information available Action implementation Earlier accurate decisions deliver higher value Operations or physical event Lost margin and profit © Industrial Management Enhancement, 2011. Figure 2. On-shift accurate decisions have the highest value

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