In a previous post on enterprise operational analytics, we explained that process industry companies have started to recognize that the data created within their plants is underutilized. Quite often that data doesn’t extend past manufacturing operations. As a result, many companies are now taking steps to provide greater visibility and integrate plant data into wide-ranging business and operational analytics. The expectation is that by combining manufacturing, supply chain, financial, and even data external to the company can drive higher profitability. Companies are doing it by uncovering “hidden” opportunities ―the previously unrecognized synergies for improvement.
But what exactly does “provide greater visibility” mean? At LNS Research, we believe visibility consists of three critical capabilities:
Together these three capabilities allow people in a company to make better decisions and impact the key performance indicators (KPIs) most relevant to plant and corporate business outcomes.
Access to timely data is critical, but it’s both an operational technology (OT) and IT architectural challenge. According to a recent research report, industrial organizations are working to evolve their traditional OT architectures to incorporate pervasive sensors, wireless and wired Edge devices, and Cloud-based resources. While the plant historian is the primary source of production data, it is not the sole source of all the necessary data for enterprise operational analytics.
Enter the data engineer ― a role that is starting to emerge to address a complex problem. The data engineer’s role is to ascertain what data is needed and by whom, along with when, where, why, granularity, timeliness, format, and source. This individual is generally an OT professional, although even that is often a role missing in companies; OT data sources are usually managed by the people using the data or system. Whether it’s an official or de facto post, the data engineer is a fundamental role to drive the right operational architecture to support visibility.
The market makes vast data and analytics capabilities attainable for nearly every industrial organization today. Despite these resources, benchmarking and diagnosing performance within and among plants is something most companies do only periodically, rather than continuously. Of course, not all KPIs are comparative across different plants with different products, but many are, especially downtime, quality, energy intensity, and environment, health, and safety (EHS). Bona fide value chain optimization, let alone individual plant optimization, isn’t possible with only periodic snapshots like quarterly or monthly.
The ability to look ahead, forecast future performance, and perform what-if analysis on various scenarios is essential for optimizing current operations and the supply chain. It’s also the foundation for capital planning and allocation to debottleneck capacities and add new products to existing or new facilities. The industrial organization now has the power to establish Digital Twins of the production process, supply chain, and financial performance, and combine them to support superior decision-making using advanced analytics like machine learning and other artificial intelligence (AI) techniques. Twins can identify and unravel previously unseen, complex, and difficult to understand relationships … as former U.S. Secretary of Defense Donald Rumsfeld has often said, “The known unknowns and unknown unknowns.”
Every industrial organization should ―at a minimum ― be using the manufacturing, supply chain, and financial data it already has to drive ever-increasing profitability. If your company wants to uncover the hidden opportunities, it must take action to provide greater visibility of data created within the production environment. Early critical steps include:
Learn how industrial companies create visibility in the webcast with LNS Research, “Driving Profitability with Plant and Process Data.” To learn how GE can support your Industrial Transformation visit click here.
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