Achieving the Promise of Autonomous Manufacturing

By ETQ on September 30, 2022

As manufacturers around the globe look for new ways to boost productivity and manufacture products more efficiently, they’re increasingly looking to automation and interconnectivity to operate with less human intervention. The next step in this journey is the transition from automation to autonomy.

In a recent Forbes article, Morgan Palmer, Co-founder and former ETQ CTO, explains the differences between automated systems that leverage digital tools designed to make processes run more efficiently and with fewer errors, and autonomous systems which are trained to adapt to changing environments and make independent decisions from integrated systems and enterprise-wide data. He also shares why human judgement will always be the missing link in fully autonomous manufacturing systems and what manufacturers should be doing today to prepare for a more autonomous future.

The evolution of autonomous systems in industrial settings represents the full realization of Industry 4.0, the fourth industrial revolution. Advances in technology, automation and interconnectivity are making this possible, and automated quality management systems (QMS), enterprise resource planning (ERP) systems, customer relationship management (CRM) systems and quality control (QC) systems are playing an integral role. The common thread between them is their ability to collect and store data, and the future of autonomous systems is the integration of that data between these systems and the analytical tools that help to derive actionable insights.

Achieving the promise of Autonomous Systems Today
In the Forbes article, Morgan shares key steps companies can take today to make the autonomous future a reality, including:

  • Audit your data. 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. Once systems are interconnected, data analytics should show you what the data is telling you and make it actionable for AI and decision-making.
  • Determine the rules. If your systems are going to be making the decisions as part of an autonomous workflow, it’s critical to develop the workflow, process plans and set the ground rules in advance. For example, how many times must a supplier send faulty parts before it is cut off?
  • Secure the 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.

Autonomous operations empower manufacturers with what is most needed – greater productivity and efficiency, improved product quality, lower costs and less waste. And, there is little doubt that humans will play an increasingly less hands-on role when it comes to manufacturing operations. The strategic insights and time they gain, however, by letting autonomous systems do the heavy lifting are resulting in improved quality and better business value. It’s a win-win scenario.