Digital transformation in manufacturing

Posted: April 14, 2022

How digital transformation is shaping the manufacturing industry

With IT and OT convergence on the rise, the way in which many manufacturing firms do business is changing. Manufacturing processes are becoming more complex, the workforce is rapidly changing, and many firms don't know where to start when it comes to digital transformation. Coupled with a smaller, less experienced workforce, the challenge is even greater. So how can manufacturing companies adapt?

Optimizing manufacturing operations through digital twins

For many, one answer lies in gaining greater visibility into operations, or cultivating operational intelligence, to better spot optimization opportunities. Consider digital twins, for example. A digital twin is a digital replica of a physical entity. Using industrial software, manufacturing firms can build digital replicas of their entire manufacturing operation, which in turn allows them to improve asset health, reduce maintenance costs, and improve overall performance at their facilities. Using near real-time digital images, firms can see their entire operation from end to end and run crucial simulations before actual devices are built and deployed.

As a generation of digital twin-enabling technologies emerge, manufacturing firms have new ways to model captured data and run analytics, allowing for stronger analysis that can be democratized across an enterprise. Digital twins can be created for specific projects to accelerate program timing, increase product fitness, and close the engineering loop. They can also be created to optimize overall manufacturing operations and help drive project decisions, improving overall performance. These optimizations, including the use of digital twins, are just a few examples of smart manufacturing techniques.

What smart manufacturing looks like

Phillips 66, a multinational energy company, used digital twins to improve data modeling by creating a single simulation model to help optimize refining output and improve situational awareness. By leveraging the power of AVEVA™ PI System™ asset framework templates, they were able to model non-physical assets (product streams, yields) in addition to physical ones in one dynamic, centrally managed design. This led to improved performance, data transparency, and workforce empowerment through the self-serve access to models they needed. As manufacturing processes continue to get smarter, the benefits they yield are expected to become larger.  

Closing the age and skills gap

The age gap between manufacturing workforces continues to widen, with the population of 60 and older workers increasing dramatically. This means not only new jobs to fill, but also skills to replace, which can be difficult. It’s estimated that of the millions of manufacturing jobs to be filled in the coming decade, many will go unfilled, largely in part to a skills shortage in the US manufacturing industry. So how can firms adapt?

In today's climate, resiliency is key. Passing institutional knowledge must be transferred effectively for business continuity, and digital technologies will prove crucial to accomplish it—capturing and transferring knowledge, better organizing and manage data flow, and modeling business outcomes. Techniques such as machine learning (ML) and other analytics won’t be silver bullets, though. While helpful, ML and multivariate analytics are most useful for “eliminating ground” for SMEs and allowing them to do their jobs faster, but they're not a perfect replacement.  Firms must optimize human capital, processes, and performance through the use of operational data.  

By optimizing projects, processes, and people, firms can better navigate changes associated with the digital transformation in manufacturing and fully embrace new technologies that are reshaping the way we do business.


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