There are many definitions of Digital Transformation and although there may be variances and differing opinions, there does seem to be a consensus on the base premise.

“Digital Transformation is the use of digital technology to accelerate business strategy. It is about the careful selection and effective application of digital technologies to empower people, optimise processes, and automate systems of the organisation to achieve a step-change in business performance.”

Where issues of understanding and interpretation arise is usually around the advice, selection, suitability, and application of technologies. Often confused by the plethora of new entrants to industry, where process understanding and experience can be a significant issue, technologies which are designed for non-industrial applications find their way into the mix with unpredictable results. The greater damage is a loss of confidence and a growing sense of digital fatigue.

The collection and analysis of copious amounts of process data is the crux of a drive towards Industrial Autonomy. Data capture and mining has been around for decades, but until recently we lacked a cost-effective means of doing so at the scale we are now contemplating. Yet in the excitement of being presented with this new opportunity, many have forgotten a core tenet of the process and manufacturing gospel; is my data reliable?

Conservatism is not a dirty word

As engineers it is only natural to be enthusiastic about new and potentially transformative technologies. This despite the instinctive conservatism which has been a natural, and more importantly balancing element, keeping our industry reliable, and more importantly safe.

Contributing factors also emanate from other sources within the business, and externally. Cost, productivity, environment, staffing and safety all dragged more centrally into the spotlight, by the advent of Covid-19. Business Continuity Plans (BCP’s) challenged in unimaginable ways due Covid-19, pressure testing even the most robust contingency management practices. This has led to many producers reviewing their degree of preparedness for such ‘Black Swan’ events, while also re-evaluating their business shape for the future of the business.

This is where digital transformation has increased in prominence. Although looking to build increased levels of resilience due to Covid-19, businesses are also looking to construct a platform upon which they can create an evolutionary future through industrial autonomy. This in itself has created new market opportunities for those outside of industry who would covet the significant business opportunities it creates.

Industry has for many years been underserved in the data analytics space. As commercial sectors became flooded with service providers, attention suddenly locked onto the enormous potential of industry, once IIoT became a ‘thing’.

Blame for this lag resides with industry, due in principle to conservatism and process availability pressures. For industrial customers, reliability is critical, stability is crucial, safety without compromise. Yet as the whisper around digital transformation became a roar, so guards began to slip. The once impenetrable drawbridge of expectation around supplier capability, process understanding and fit for purpose products, slipping a few gears resulting in a flood of new entrants.

Innovative and alternative technologies should always be under consideration to help industry growth. The real issue has been the overwhelming and often confusing array of products, services, and uninformed "guidance", much of which has drawn attention away from base principles and expectations. The resultant clamour and promises of 'quick wins’ often delivering questionable outcomes causing leaders to retract their support for further initiatives. In cases where engagement continues, responsibility is often delegated to Information Technology (IT) teams while lacking a cohesive integration plan with Operational Technology (OT) teams. This leaves many gaps, from data reliability, or simply lack of understanding of the systems already in place and their capabilities.

When it’s not Digital Transformation

A recent case in the mining industry highlights this issue perfectly. A time consuming and expensive digital transformation solution had been sold to the miner to resolve process challenges encountered with one of their crushing circuits. Heralded as a unique Artificial Intelligence (AI) solution, it was designed to predict the level of crushed ore in the bins to keep them at optimal levels. On completion, further analysis by the OT team proved it was nothing more than a simple mass balance calculation, easily achieved within their existing Process Control System (PCS) at a fraction of the cost. Essentially, no AI required, just process understanding and capitalising on existing investment.

The end user can be absolved of much of the blame because many of the promises seemed compelling. Subjected to the sales and marketing might of a global software house, while hampered by limited communication between IT and OT departments it serves as a warning of potential frailties in understanding and guidance within industry.

There is certainly room for improvement in communication between IT and OT departments, especially when it comes to digital transformation. More so in light of the array of confusing advice from vendors. Even field device selection needs greater consideration to ensure they are fit for the environment in which they will operate. Resilience matters here or the data which underpins your improvement plans can easily be compromised by factors such as seasonal weather and operational cycles.

Lessons learned fuel best practice

NASA are a prime example of an organisation who have used decades of experience to underpin improvement methodologies through the implementation of a ‘Lessons Learned’ system. This publicly accessible database holds contributions from NASA and other organisations of various programs and projects across an array of disciplines. The collective learnings from decades of activity used to improve future performance in a safe and sustainable way, while actively encouraging technological advancement.

To be clear, there are numerous examples of successful digital transformation initiatives industry. Such cases are where the service provider not only possess deep understanding of the industry being addressed but has worked closely with both IT and OT departments. The goal from the start to bring that experience to bear while aligning the most suitable technology with clearly defined goals. Selection of devices designed for the environment they will operate, and surety on reliability and safety objectives.

With decades of experience, industry has learned many lessons the hard way. Valuable experience, often shared with service providers which can positively contribute to future growth in a sustainable manner. With an active push to working across the IT/OT divide, further resilience is being built into systems aiding normalisation and data extraction efforts to turn valuable insights into actionable outcomes. The latest technology applied in the right way, to achieve sought after outcomes.

Author

Sean Cahill has been working in the manufacturing and process industries for over thirty years, both in Europe and Australasia. With a background in software engineering and expert systems, he has been actively engaged with Energy, Resources, Power Generation, Water and Food industry segments from a control, safety and process instrumentation perspective. Now, as Marketing Manager for Yokogawa Australia & New Zealand, he applies himself to effectively communicating best practice engineering principles and leading technological solutions to the process industries supporting their journey to an autonomous future.