Digital Transformation in the Process Industries: 5 Stages
The Digital Transformation – everyone and everything is a part of it in some way. In the 20th century, breakthroughs in technology allowed for the ever-evolving computing machines that we now depend upon so totally, we rarely give them a second thought. Even before the advent of microprocessors and supercomputers, there were certain notable scientists and inventors who helped lay the groundwork for the technology that has since drastically reshaped every facet of modern life.
For many of us, It started with the very first personal computers like the Commodore 64, as well as e-mail, then our first mobile phones, and has worked up to Amazon’s Alexa and IPhone’s Siri. We use our smartphones, which are essentially our own portable mini-computers, to navigate so many areas of our lives. The digital transformation has undoubtedly also affected operations in our work lives as well as how data and communications are managed and how issues are resolved within processes.
In the past few years, our customers in the process industries have become more aware of digital technology and we are hearing about their digital journeys more now than ever. In this awareness, there has been confusion with terms like Industry 4.0 and IIOT. These two concepts in particular can create confusion.
The first is a perception that these technologies are new, when in fact many of the technologies have been around for decades and the fundamental problem of data accessibility is not new. There are new enablers or catalysts, like reduced cost and wireless technology, but the fundamental concept of collecting data to make better decisions has been around for decades. The second issue is the association of technology (i.e. products) to a broad theme like IIOT or Industry 4.0. This broad theme to the classic trap of solutions looking for problems to solve and customers starting with technology instead of starting with their challenges and problems.
The reality is that there are huge opportunities and we are all trying to figure out the right solutions. Engaging terminology is one way to convey messages in a way customers will notice and remember. IIOT and Industry 4.0 are examples of this terminology.
One term we hear often is Digital Transformation. Digital Transformation is a term that resonates. The term implies that the journey of becoming digital is not binary. There are not digital or non-digital companies; we are all on a digital spectrum.
Stages of Digital Transformation in the Process Industries
At dataPARC, we like to take this one-step further and identify 5 stages of digital transformation in the process industries. Each customer falls into one of these categories. Assigning transformation categories provides a framework to define the broad challenges for each customer and present the best solution for a customer. It also helps give customers a big picture view of what is possible for our customers. Perhaps you can see yourself and your plant in one of the stages.
Stage 1: Stage one is an early stage. If you are in stage one you likely lots of manual operations and limited or no PLC or DCS. On the early end of the scale are the Stage 1 customers, still looking to modernize their control systems from analog to digital. There are opportunities at this end, but the primary focus should be identifying the right solutions (PLC upgrades, additional sensors/instruments, etc.) and topics like AR (Augmented Reality) and ML (Machine Learning) should be a longer-term vision.
Stage 2: If you are in stage two, you are in a toddler stage. Your plant has a PLC or DCS, but does not have a historian. You also likely have many functions that require manual data.
Stage 3: In stage three things are starting to move and you are starting to walk. Your plant may be ready from a cultural standpoint but lacks the software tools. You may have a small historian that is not heavily used or other homegrown tools, but you are able to see the vision of what is needed to go forward.
Stage 4: Beginning to run describes stage four. At stage four, your plant is fairly mature in its use of data. You have a historian with most of the data coming in. You likely has a variety of systems for LIMS, manual data, etc. The culture is there to embrace data for decision-making, but the site lacks the right software to bring the data together.
Stage 5: Marathon training. If you are in stage five, you are in a very sophisticated plant that has a well-developed strategy for data and decision support. You have settled on one major historian and have spent a lot of time and effort to transfer all data from other sources (LIMS, MES, ERP, etc.) into the historian and have a number of business systems pulling from the historian. Customers at Stage 5 are ready, from both an infrastructure and cultural perspective, for advanced topics like AR & ML.
At all stages there are opportunities, one just needs to match the right solution to the right situation. The other thing to keep in mind is that these stages are not like mile markers on a highway, where once you pass the marker it is in your review mirror and no longer relevant. A plant is like paddling a boat upstream, there are always forces trying to pull you back. End-users need to keep an eye on the fundamentals and not lose sight that even improvements to fundamentals can deliver a lot of value.
Delivering value to the plant does not have to be the next big thing; it could be an improvement to something you are already doing. What are some of the small changes you could make to improve your plant’s operations?