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Industrial Ethernet Book 104

Technology 32 SOURCE: EI3 CORPORATION Sample OEE Dashboard . industrial ethernet book 2.2018 Technology forth a “productivity crisis.” Dr. Skinner contended that competitive agility was yet another factor to consider. In the conventional manufacturing model of the time, factories produced myriad products for many different customers, propelled by the notion of the “economy of scale.” Factory within a factory The factory within a factory model is a way to address issues of supply chain management in a timely, cost-effective manner while preserving responsiveness to customer needs and requests. Instead of relying upon an independent manufacturer to provide a part or materials when ordered, the subordinate operation establishes a physical location within the primary manufacturing facility. There are multiple considerations that impact the feasibility of such an arrangement. As a positive, it streamlines process for procurement when time and distance is of the essence. Other complexities involving financial investment, term length, logistics, labor, protection of intellectual property and tax implications also present themselves for investigation. To truly leverage the potential benefits, all aspects must be implemented effectively. Compared with traditional manufacturing, a green patch environment introduces integrated devices, applications and tools which generate volumes of actionable data. Therefore, a green patch can unlock opportunities to explore and develop a specific strategy for integrated machine leaning, predictive insights, and applying analytics to show the impact of IoT optimization to drive revenue, cut costs and innovate your operations. Getting started with analytics Analytics are a fundamental element of IIoT. Determining the data to acquire for analysis is a good starting point for a green patch project. A 2015 technical report from McKinsey on analytics in semiconductor manufacturing offers broad advice, with the key piece of advice to make data actionable. For data to be actionable, it must be robust with all relevant details collected in a structured, targeted, consistent format. A vital aspect is the maintenance of data integrity. Ramifications of substituting lost data with averaged content include missing patterns and generating false positives. Data must be stored effectively with an eye towards easy retrieval. Actionable data is the foundation of advanced analytics. When supported at the enterprise level as a strategic priority and not merely a managerial tactical responsibility, advanced analytics drives predictive insights into where to invest, ways to optimize productivity, keys to reduce time to market and other essential metrics. Success measurement: OEE Like any capital project, the green patch needs success metrics. One of the best in manufacturing is the lean Six Sigma key performance indicator of Overall Equipment Effectiveness (OEE). For many years, manufacturing enterprises have implemented different varieties of OEE measurements to different degrees of success. For example, some OEE proponents consider all downtime, including scheduled maintenance and holidays, while others exclude all but unplanned downtime. The green patch can be used to redefine a consistent OEE metric moving forward, for how and what to measure, analyze and optimize in the green patch, and therefore serve as a framework for OEE future process improvements without impacting existing operations. The contrast between IIoT will be most dramatic where data are still manually gathered, demonstrating the value of automated data acquisition, more frequent sampling and more consistent data through the use of new standards such as ISA TR88.00.02 (PackML) and MTConnect, both OPC UA enabled. OEE data has many practical uses. One is to determine the optimum (as opposed to fastest) production rate for a line. Running a line at its fastest can cause the bottleneck machine (there is usually one machine that limits overall line speed) to result in stoppages and actually reduce throughput compared to operating at a lower speed. Process optimization Much has been discussed about big data and analytics. To make use of analytics, it’s necessary to close the loop and provide a mechanism to apply the data to process optimization. Total Productive Maintenance (TPM) is one such tool that can take OEE data and improve the operation of machines and lines. TPM is a methodology for acting on and resolving issues identified by OEE. Similar to OEE, TPM is a topic familiar to manufacturing professionals and upon which IIoT can improve. TPM is used here as an example of ‘what to do with analytics once you have the data and you’ve analyzed it.’ Without IIoT’s continuous, automated monitoring and feedback, TPM might be used to set limits on line speed, based on the bottleneck. Conclusion The more time you spend in a brownfield, the better a green patch looks. By focusing on a single new line or cell, it can be much easier to get started with an initial deployment, learning how to apply IIoT in a controlled environment and scale, with new technologies designed for the task. The ‘green patch’ is intended to propose a readily accessible approach for mid- market manufacturing enterprises to obtain the advantages of IIoT without becoming overwhelmed by a large-scale rollout. Spencer Cramer, CEO, Ei3 Corporation and John Kowal, Director of Business Development at B&R Industrial Automation..


Industrial Ethernet Book 104
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