New era of intelligent manufacturing

Manufacturing must deal with a global manufacturing environment that’s rapidly changing due to technological advances.

The World Manufacturing Forum brings together industry, academic and policy leaders from around the world to discuss the future of manufacturing. Its annual report (to be released in November) shines a light on how common the challenges and opportunities are for manufacturers, regardless the country they hail from.

This year’s report emphasizes the importance of innovation and focuses specifically on how trends in artificial intelligence (AI) are defining the future of manufacturing. As a member of WMF’s Advisory Board, I can speak to a few of the key themes.

The analytical power of AI is growing exponentially. So too are its applications in every sector of the global economy. The WMF believes AI will reshape every aspect of manufacturing – not so much because of the nature of the technology itself, but because of its potential to increase industry growth. For that reason, manufacturing is expected to lead the way in AI applications.

It’s important to see AI primarily from a business rather than a technological view. Functions under the AI umbrella – natural language processing, knowledge representation and reasoning, automated planning, intelligent robots, machine perception, and machine learning – are all basically systems of prediction.

The question for manufacturers is: Where in the products, processes or general functions would a better system of prediction create greater value for customers in a more efficient, higher quality, more timely and more profitable way? The selection of AI tools and techniques should reflect the most appropriate way of achieving business outcomes.

Yet that’s not the way many manufacturers have approached AI or broader automation and digital transformation initiatives. All too often, it’s technology first without clear definition of the business objectives, understanding of critical processes or appreciation of business requirements for successful implementation. Maybe that’s why Deloitte reports 90% of all large-scale digitization projects fail, why more than half of Canadian manufacturers report their technology investments have not delivered expected business results, and why so many companies still struggle to get their ERP systems under control.

If AI is viewed as a tool, the issue becomes selecting the most appropriate tool or set of tools for the job, then ensuring they are managed in a productive way.

Approaching AI in this way demystifies the technology and focuses on how it and other technologies are deployed to grow a business instead of displacing jobs. In fact, it emphasizes that AI applications are likely to fail unless manufacturers have the right information systems, cybersecurity safeguards and skill sets in place to manage the technology effectively. It also underlines the importance of finding good technology partners that support AI deployment in a manageable, timely and cost effective way.

It’s how the business model, systems and people work together that counts. AI and automation help, but they must have a purpose with a clear understanding of how processes and external business relationships contribute to value creation.

Today materials, products and processes are rapidly becoming data platforms. Now the name of the game is to learn from it, rapidly predict outcomes from a more in-depth understanding of current conditions, and create new forms of value that offer unique competitive advantages.

Intelligent manufacturing is about the value of products, machines, production systems and supplier-customer interactions increasing rather than depreciating over time. In this fundamental respect, manufacturers are becoming more like technology companies. To the extent their purpose is defined increasingly by customer or social outcomes, they’re also becoming more like personal services companies, and there’s profit in that!

However, the game has changed. Risks and rewards aren’t as clear-cut. Decisions need to be taken, results tested and actions recalibrated quickly. Here too AI is providing some powerful tools when it comes to planning, scheduling, monitoring and maintaining operations. One caveat: it all depends on the quality of the data fed into the algorithms.

But what’s important? Start with data that contribute to business objectives, and ensure it’s good data. Low quality data leads to low quality outcomes. And gather it from all parts of the enterprise, including customers, suppliers and other business partners. Data silos lead to unintended consequences and ultimately to unmanageable deployments and unsustainable business results.

There’s a lot to be said for basic lean thinking here. Focus on value. Eliminate non-value adding activities. AI is radically expanding the boundaries of both these concepts. That doesn’t mean the principles no longer apply.

Watch for the WMF’s report (www.worldmanufacturingforum.org). It’s worth the read.

Jayson Myers, the CEO of Next Generation Manufacturing Canada, is an award-winning business economist and advisor to private and public sector leaders. E-mail jayson.myers@ngen.ca. Visit www.ngen.ca.

This feature originally appeared in the October 2020 print issue of PLANT Magazine.