Skip to main content

Transforming big industry with new AI

(Image credit: Image Credit: Razum / Shutterstock)

The advent of technology is transforming big industry in dramatic ways and giving company leaders the insights they need to revamp traditional business models. It's an evolution that has seen organizations undergo major evolutions in style, scale, technique and efficiency in recent years.

That said, massive industrial operations also involve some very inefficient processes, which tend to require continual refinement, repair and adjustment. It means that, despite significant progress, much remains to be done to streamline operations, enhance production efficiency, reduce downtime and increase the bottom line.

The panacea for big industry

This is why Industry 4.0 - the collective noun for the vast array of advancements and innovations within big industrial and manufacturing operations - remains such a hot topic. And given the potential for growth of some of the transformational technologies like; AI, Industrial IoT and big data it’s not hard to see why.

Modern businesses are producing enormous volumes of data thanks in the main to a steep drop in sensor costs that has allowed data collection at every stage of production. This, coupled with a far greater awareness and understanding of the power of this data when used effectively, has turned disparate plants and machinery into intelligent and connected things, commonly known as Industrial IoT. Today, a typical industrial IoT deployment has thousands of sensors and data sources that span the supplier, manufacturer, logistics and warehouse participants. But due to the interdependency of data and participants’ actions along with the volume, velocity and variety of data, IoT’s benefit cannot be fully realized without AI to filter and understand it. Little surprise then that the market for AI in manufacturing alone is projected to grow by nearly 50 percent per year, to hit $17.2 billion by 2025.

When you add in other technologies like machine learning, connected worker capabilities and digital twins, it is possible to visualize the panacea for big industry - a highly interconnected, optimized, and autonomous operation that takes automation to the next level and requires little to no human interference.

Need for agility

Attainment of this vision is critical because organizations need to make a concerted and ongoing investment in the knowledge, capabilities, processes and cultures that foster one distinct quality - agility. The first six months of 2020 have highlighted the need for businesses to adroitly respond to change, exploit uncertainty deliberately and decisively, and fulfill the unprecedented promise of the technology that is in their grasp.

Even before the Covid-19 pandemic, most companies were facing volatile and unpredictable demand due to trends such as increased global competition, shorter business cycles, environmental pressures and broader product ranges but these pressures - and businesses' response to them - has been exponentially accelerated. In fact, working models of how we live have changed faster in the first six months of 2020 than in the past three years, while analysts estimate that two years’ worth of transformation has been compressed into about 10 weeks.

If the need for an organization to be agile was not a necessity before, then it certainly is now.

Blending the best of all worlds

There is no doubt that organizations must be able to combine technologies to realize synergies that will be the difference in a world of post-Covid commerce. This means using AI and machine learning technology to collect data from a vast range of different sources and having the agility to respond to areas of improvement, repair, expansion and even new business opportunities - all in an expedient manner, saving time and effort for company leaders and maintaining the safety for employees. Put simply, what is required is an application to blend the best of all worlds.

The creation of such an application is a significant development because it serves as a single interface into operations by bridging the information technology and operational technology divide for increased agility and situational awareness. In doing so it provides industrial businesses with an accelerated path toward implementing AI in the control room or on the plant floor, while presenting real-time anomaly detection in a context-aware visual display. It also provides more sophisticated intelligence and insight as multiple types of AI and advanced analytics are combined through this single interface.

It has the added benefit of simplifying processes, too. A clear interface enables operations, maintenance and production teams to quickly train AI engines to adapt to the enterprise’s specific implementation. An intuitive thumbs-up or thumbs-down confirmation ensures AI-driven notifications are relevant to the needs of the user and support overall enterprise objectives, with no programming or data science knowledge required. This closed-loop feedback improves the accuracy of the AI prediction engine over time and enables users to see what matters. As anomalous patterns are identified, they can be captured and presented, delivering insights directly where operators need it.

Identify. Investigate. Improve.

This is important because, for all of the benefits IIoT has brought (and there are many) it has also fueled a massive increase in the collection of real-time operations and manufacturing data. As a result, operators and employees face information overload and often cannot effectively react to or distinguish between process-critical situations and false positive alarm conditions, resulting in the loss of operational time and resources.

An AI-driven application - hosted in the cloud - that can collect, store, and visualize data will not only enable operators to take proactive action, before process and maintenance problems occur, but it ensures every sensor, machine and plant is aligned with information directed into one source and the applications are almost limitless; predictive maintenance, remote monitoring, self-optimizing production, automated inventory management and distributed generation and storage.

Success needs best-in-class applications

There is no doubt that success for major industrial businesses requires best-in-class IIoT applications. Ones that integrate with the enterprise processes to provide a collaborative, standards-based foundation to unify assets across all facilities for continuous operational improvement and real-time decision support. And ones that have the built-in intelligence to help course-correct and guide organizations to achieve maximum success.

It is perhaps the only way to ensure enterprise-wide standards of compliance across processes, teams, and sites - something that is required when it comes to managing critical operations and improving decision support for maximum profitability in these fast-changing times.

Jim Chappell, Head of AI & Advanced Analytics, AVEVA (opens in new tab)

Jim Chappell, Head of AI & Advanced Analytics, AVEVA.