Digital transformation has already started to change the way manufacturing works, paving the way for demand for a wide range of modern engineering skills to fill new roles for automation engineers, control engineers, process engineers and continuous improvement engineers. So how are manufacturers managing the transition and what criteria are they using to determine the pace of change?
Data driven predictive technology can show when a machine needs repairing or servicing. The technology is already widely in use and return on investment can be easily measured, making it fairly straightforward to prove a business case for adoption.
Best practice recommends determining the pace of change by focusing on problems rather than on the emerging technologies and the solutions they might offer. Bigger problems motivate a faster pace of change. A perfect example of this is the Covid pandemic, which looks likely to become one of the largest ever drivers of global digital transformation.
A testbed is a vehicle for trying out scientific theories, computational tools and new innovations. Siemens is currently collaborating on a new process industry testbed in Chicago to support the transition to smart manufacturing, which will look at how software, IoT and hardware can be used to speed up digitisation in process industries.
Process engineering is all about optimising manufacturing processes. You can read more about the growth of this area of engineering in our article how automation is driving demand for a new era of engineering skills.
Digitisation in general has benefits in terms of health and safety, productivity and cost efficiency and engineers are now looking closely at how different processes and workflows can allow engineers to improve smart factory operations and integrate them in manufacturing facilities where traditional practices are still dominant.
The hope is that this will result in increased facility productivity and greater resilience should problems such as the Covid-19 pandemic arise again in the future.
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