It seems that Charles Darwin’s theory of “survival of the fittest” applies to industrial plant management (especially throughout 2020 and beyond). Only the smartest enterprises can survive today’s aggressive competition and volatile economic conditions. Fortunately, modern next-generation analytics are more accessible than ever, giving plant maintenance managers new tools they can leverage to work smarter, not harder.

             Phil Lewis, Infor VP Solution Consulting EMEA.

Driven by Artificial Intelligence (AI) and Machine Learning (ML), augmented analytics provide advanced, prescriptive insights for extending the life of critical assets, analysing overall equipment effectiveness (OEE), and preventing unexpected downtime.

Previous generations of analytics focused on using data to produce aesthetically pleasing charts and dashboards. To dive deeper, managers often had to call in experts. Skilled data scientists and business consultants had to work behind-the-scenes magic to extract meaningful conclusions from mountains of data. All too often, the data lost contextual relevance and urgency by the time it passed through the data analyst’s filters. This old-school process meant traditional asset maintenance programmes were knee-jerk reactionary and seldom preventive or prescriptive. However, this process is no longer sufficient. Plant managers must upgrade their performance if they want to remain competitive.

Today, advanced analytics can perform “heavy lifting” in the back end and work to connect, prepare and relate data from a variety of disparate sources across the enterprise. According to Phil Lewis, Infor VP Solution Consulting EMEA, “This removes the barriers to entry, giving maintenance teams easy-to-use tools that help to define goals, select algorithms, train the module and test outcomes. AI’s mystery has been replaced by user-empowering interfaces. The result is trusted insights for better business decisions. For industrial plant maintenance managers, this means prescriptive asset management and understanding best steps for extending the value of equipment and preventing unplanned downtime.”

The use of voice-activated personal digital assistants has also become a reality. Imagine business users being able to speak into their phones and ask, “How many replacement valves should I order?” or “What is the OEE score for this piece of machinery?”. This represents a new wave of disruption, which some analyst firms, like Gartner, call “Augmented Analytics.”

So, what does augmented analytics mean for plant maintenance? Solution providers, who specialise in analytics, are setting out to transform the Business Intelligence (BI) experience from descriptive (what is happening) to diagnostic (why is it happening) and predictive (what will happen). They are focusing on capabilities which tap into the power of data science to instantly understand the variables driving their Key Performance Indicators (KPIs).

“Requiring no specialised expertise, such tools help business users automatically find meaningful relationships between a given KPI and countless business variables, and then automatically generate visualisations and dashboards that explain the KPI’s behaviour,” says Lewis. “For asset maintenance teams, the KPIs could track cost of asset down-time, investment in replacement parts, overall equipment efficiency, and energy consumption. The goal is to monitor and identify early warning signs of a potential asset failure. By spotting the warning signs early, action can be taken to prevent the failure. More than that, modern analytics will help prescribe the best response. In complex manufacturing, there are often several possible solutions to any asset performance issue.”

Lewis advises that there are some critical components that must be present when considering a modern augmented analytics solution. These include; visualisation tools, natural language capabilities, personal digital assistants, contextual relationships, built-in machine learning, and contributing factors (like the ability to add data about geographic location, environment, weather, suppliers, and product specifications).

“Industrial plant maintenance faces many challenges today, such as pressures to speed response time, meet customer orders, reduce waste and boost productivity. For many organisations, the key is boosting performance of assets. Improving the lifespan of equipment and making smart decisions about efficiency and repair can make or break an organisation. With advanced augmented analytics, the maintenance team has another valuable resource on its side. Now is the time to take the steps to become one of the tech-savvy plants, which leverage smart analytics,” concludes Lewis.

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Munesu Shoko
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