Navigating The Growing World Of Analytics


Navigating The Growing World Of Analytics

In the manufacturing world, analytics is a hot topic right now — and for good reason. Manufacturing generates more data than any other sector, and according to McKinsey, analytics has the potential to deliver more than $4 trillion of growth in industrial manufacturing alone. At the same time, in a proprietary survey my company conducted last year, digital leaders in the industrial sector cited new technology such as artificial intelligence (AI) as the top driver for digital transformation.

But with growing buzz can come confusion. It’s estimated there are hundreds of companies supplying products and services, such as analytics, that are driving the Fourth Industrial Revolution — promising performance improvements utilizing everything from machine learning and computer vision to video content recognition and smart robots.

Recent press and attention surrounding analytics has come from its use in customer buying preferences and marketing. While there are many analytics solutions that can help functions like customer management, human resources or finance, the highest-value opportunity for industrial manufacturers is the analytics surrounding the manufacturing production itself.

This is what we call operational analytics — analytics with the potential to impact and improve the performance of simple equipment, complex assets and process units and entire plants. When effectively applied, operational analytics can improve key performance areas such as reliability, safety, production optimization and energy management. These represent some of the biggest profit levers for a manufacturer’s operations.

Still, it can be difficult for digital leaders looking at analytics across a business to determine how best to deploy analytics to achieve real and measurable results in their operations. The first step to successfully navigate this landscape is to review and prioritize manufacturing pain points and identify those problems that can be tackled with analytics — and what type of analytics are best suited for those situations.

Using models (digital twins) or mathematical algorithms to diagnose problems in manufacturing is not a new concept. In many cases in the industrial world, the physical laws governing behavior, or the relationships between failure cause and effect, are known. Principles-driven analytics are based on known rules, physical laws or principles — and most manufacturing plants and equipment were originally designed using this knowledge.

An example of this in the manufacturing space is a heat exchanger. We know the flow equations, heat transfer rules, and other physical laws and principles around heat exchangers, so we can easily model heat exchanger online performance based on these laws. With the right sensors, we can determine if it’s underperforming due to fouling, for example. If we are trying to diagnose a failure condition, knowing how and why equipment fails can quickly diagnose a problem and determine how to repair it.

Coming up with such a failure model is known as failure mode and effects analysis (FMEA). With an FMEA model — and the right sensors — equipment can be continuously monitored, and impending signs of failure can be detected before they occur. And just as importantly, you know the root cause of the failure and how to fix it. For common equipment in an oil refinery, we can predict 80% of failures with FMEA models and the right sensors, according to our company research. This principle has been increasingly applied to vehicles, with a host of sensors that are able to “self-diagnose” a problem.


Navigating The Growing World Of Analytics