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. Couple this with a smaller, less experienced workforce than just 5 years ago, and the challenge is even greater. So how can manufacturing companies adapt?
Optimizing manufacturing operations through digital twins
For many, increasingly, 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 can 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, or they can be created for overall performance, to optimize overall manufacturing operations and help drive project decisions. 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 PI AF 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 empowerment of the right people with 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 work forces continues to widen, with the population of 60 and older workers increasing dramatically. This means not only new jobs to fill, but skills to replace, which can be difficult. Of the estimated 4.6M manufacturing jobs projected to need filling from 2018-2028, it's estimated that only 2.2M will likely be filled, due 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 in that regard. The right tools will need to be employed to capture and transfer knowledge, better organize and manage data flow, and model business outcomes. Techniques such as machine learning and other analytics should not be viewed as a silver bullet, though. While helpful, ML and multi-variate analytics are most useful for “eliminating ground” for SME's and allowing them to do their job 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 re-shaping the way we do business.