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The Compass To Navigate Industry 4.0: Industrial Analytics

As disruption escalates in 2017, industry faces increasing pressure to transform in order to remain competitive.

As disruption escalates in 2017, the chemical processing industry faces increasing pressure to transform in order to remain competitive. They need to leverage new technologies to optimize performance. One of the most promising ways is to apply advanced analytics to the data generated by revenue-generating assets and processes.

As challenges associated with achieving this goal are overcome, many companies are now enjoying the benefits of industrial analytics.

What Are Industrial Analytics?

This covers a wide range of data captured from all kinds of sources and devices, whether it’s an asset or a production process. Anything with a sensor creates data, and industrial analytics looks at all of it.

Naturally this means “big data” analytics, but industrial analytics differ from generic big data analytics systems in that they are designed to meet the exacting standards of the industry in which they work. This includes the ability to process vast quantities of time series data from various sources and turn it into actionable insights. Industrial analytics can be relevant to any company manufacturing and/or selling physical products.

Solving The Industrial Analytics Stalemate

Luckily, as disruptive technology creates new challenges, it also creates new solutions. In the last few years, there has been a growing trend toward self-service applications. This next generation of software uses advanced search algorithms, machine learning and pattern recognition technologies to make querying industrial data as easy as using Google.

With self-service industrial analytics, there is no need to model data. Companies do not need a data scientist to use the software, and there is no long project timeline or high cost. Instead, the subject matter experts directly query their process data at any time in a self-service application.

Using pattern recognition and machine learning algorithms permits users to search process trends for specific events or to detect process anomalies. By combining search capabilities on both the structured time series process data and the data captured by operators and other subject matter experts, users can predict more precisely what is occurring or what likely will occur within their continuous and batch industrial processes.

For example, an operator can compare multiple data layers or time periods to discover which sensors are deviating from the baseline more or less, and then make adjustments to improve production efficiency.

Empower The Subject Matter Experts

Self-service analytics tools are also designed with end users in mind.

Working with time-series data is best done by the subject matter experts (such as process engineers and control room staff) who know what to look for in case of anomalies in process behavior and finding root causes. They can also identify best performance regimes that can be used to define ideal production and identify conditions for live process monitoring and performance prediction.

These subject matter experts are, in fact, the key to improving the company’s profitability. All they need is the tool. By democratizing access to analytics insights, actionable information becomes available at all levels of the plant. This translates into the ability to achieve incremental improvements at all stages of the production process.

Enabling A Modern Engineering Analytics Organization

Just as technology has evolved to create connected plants, the role of engineers must also change so that they can manage these facilities. This is a critical shift in business culture, since the entire organization must understand the potential of analytics as it applies to their role.

Instead of relying solely on a central analytics team that owns all the analytics expertise, subject matter experts such as process engineers are empowered to answer their own day-to-day questions. Not only will this spread the benefits to the engineers involved in process management, it will also free the data scientists to focus on other critical business issues.

Enabling engineers does not mean asking them to become data scientists. It means providing them with access to the benefits of process data analytics. Process engineers will not become data scientists because the education background is different.

However, they can become analytics aware and enabled. This process is sometimes referred to as the “rise of the citizen data scientist,” a growing trend in which experts in their own disciplines (such as engineering) add analytics capabilities to their core competencies rather than splitting the analysis from the data.

Involving engineers in analytics allows them to solve more day-to-day questions independently and increase their own effectiveness. They will in turn provide their organizations with new insights based on their specific expertise in engineering. This delivers value to the owner-operator at all levels of the organization and leverages (human) resources more efficiently.

Edwin van Dijk is VP of Marketing at TrendMiner