From Automated To Autonomous Systems: The Next Evolution In Digital Transformation Is On The Doorstep

Morgan Palmer is CTO of ETQ, helping customers achieve success by attaining new levels of excellence through quality for over 25 years.

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Since its introduction close to a decade ago, Industry 4.0—the Fourth Industrial Revolution—has ushered in new ways of manufacturing smarter and more efficiently in order to boost manufacturing competitiveness through greater productivity, flexibility and data-driven insights. Advances in technology, automation and interconnectivity are making this possible, and fully automated manufacturing processes and interconnected systems today operate with much less human intervention. The full realization of Industry 4.0, however, is in the transition from automated systems to autonomous ones.

What exactly is the difference? Today’s automated systems leverage digital tools to make routine and repetitive processes easier with minimal human intervention. Consider something such as robotic process automation (RPA), which may count how many widgets move across the production line and determine if any have defects based on predefined rules. Decisions made or actions taken by an automated system are always based on predefined processes or rules.

Autonomous systems, on the other hand, are trained, can adapt to changing environments and can learn to make decisions on their own based on an assortment of integrated, enterprise-wide data. Using the example above, an autonomous system will not only count the widgets but identify those with new and emerging issues, determine the source of the problem and, in some cases, prescribe a course of action.

Data Doing Its Best Work

The digital tools driving this evolution of interconnected autonomous systems in industrial settings include things like IoT-based sensors, drones, and machine learning and deep learning systems. It also includes automated quality management systems (QMS), enterprise resource planning (ERP) systems, customer relationship management (CRM) systems and quality control (QC) systems. Yet the common thread between all of them is their ability to collect and store data. The future of autonomous systems is the integration of data between these systems and the analytical tools that help to derive actionable insights. At ETQ, we like to say that autonomy is data doing its best work.

Autonomous operations will empower manufacturers with what is most needed—greater productivity and efficiency, improved product quality, lower costs and less waste since scrap from out-of-spec (OOS) products can be reduced. In fact, according to a 2019 Deloitte study, “more than 86% of survey respondents believe smart factory initiatives will be the main driver of manufacturing competitiveness in the next five years.” In reality, by fusing the physical world with the digital one, autonomous systems can enable companies to make the major leap from automation.

Yet that’s where it gets tricky. While autonomous systems will enable all of these great benefits, when it comes to quality, humans always will need to be in the loop to some degree.

Humans In The Loop

Consider the current state of quality automation in the manufacturing organization. Today, automated QC systems on the shop floor may identify a product that is OOS, which is fed into a QMS where a nonconformance is generated. This may sound like an autonomous system, yet this intelligent process doesn’t tell you what to do about the problem. A human must evaluate the root cause and determine whether corrective actions should be initiated, such as freezing a supplier’s contract, removing that supplier or taking other actions such as issuing new work procedures. This human judgment call will always be the missing link in full autonomy. While machine learning can trigger some suggested actions, such as removing a supplier from the program after three strikes, humans need to still make the ultimate decision. Workflows can be autonomous, but decisions are not quite there yet.

Workflow autonomy can provide key benefits to companies today. We worked with rail technology company Wabtec as it turned to integrated QC and QMS to automate the process of identifying defects in freight train parts, their root cause and how to take corrective action. Today, the company can identify parts that are trending out of specification, automatically generate nonconforming materials reports, determine what action to take with the supplier and automatically prevent staff from issuing new POs to that supplier. As a result of this program and many other QMS efficiency benefits, Wabtec has seen a $30 million improvement in cost of quality, a 25% reduction in defects and $12 million in productivity improvements.

How To Make The Autonomous Future A Reality Today

What can companies do today to make the autonomous future a reality? Consider the following:

• Get your data house in order. Since data is the most essential tool for effective autonomy, it’s important to audit all sources of data across your company since data may be coming from siloed departments as well as captured in IoT systems, quality and other systems. Once systems are interconnected, data analytics should show you what the data is telling you and make it actionable for AI and decision-making.

• Decide the rules of the road for decision-making. If you’re going to let systems make decisions on their own as part of an autonomous workflow, it’s critical to set the ground rules in advance. For example, how many times must a supplier send faulty parts before it is cut off?

• Create the tech talent. Autonomous systems require the expertise of data scientists, systems engineers and programmers. It’s important to either have that talent in-house or outsource it with a strong partner, but autonomous systems contain a complexity far beyond that which is required of automated operations.

We’re moving in the right direction from automation toward autonomous manufacturing, driven by interconnected systems and data. While humans will play a less hands-on role when it comes to day-to-day operations in this next stage of Industry 4.0, the strategic insights and time they gain by letting autonomous systems do the heavy lifting should result in improved quality and better business value.

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