
Beyond the hype
It's time to get real with digitalisation.
Mikael Maasalo
Digitalisation has long been a buzzword within energy and process industries, but the results of digital initiatives have been varied and not always successful. This is changing rapidly as the overall understanding of how to implement digital strategies has become clearer. The era of digital experimentation is over; it’s time to achieve concrete results by operating industrial plants more profitably and efficiently while progressing towards autonomous operations
Digital use case purgatory
In recent years, numerous new digital tools and systems have been connected to industrial processes, both to running and greenfield plants, and the trend is expected to continue. As the number of different solutions grow, so does the complexity of the digital landscape. Quite a few companies have reported that their situation has started to become almost chaotic; there are way too many different separate tools, systems, and platforms that it’s almost impossible to manage all of them effectively. Also, the license and service costs have started to run out of control.
When the asset owner hasn’t got a solid digital architecture together with proper standards and governance for their data and digital systems, external vendors tend to reap the benefits of the digital development through inflated fees. In the worst case, the asset owner can even lose the control of their own data, which the vendors are more than happy to sell back - for an additional fee.
These issues can indeed be handled: it is possible to design digital architecture in such a way that the asset owner will remain in full control of all their digital development. However, this is not part of conventional engineering and is easily omitted.
It appears this is about to change: the leading companies have got more educated about digitalisation, and started to request including it as an integral part in their engineering projects.
Ultimate vision for digitalisation: autonomous plants with optimised operations
Leading process and energy industry companies want their new plants to be:
– Remotely and centrally supervised;
– Operated and maintained effectively by utilising best-in-class digital use cases; and
– On a path towards becoming autonomously operated.
These objectives are likely to become key requirements for any site, whether it be a greenfield or a running plant. However, achieving these goals calls for a fundamental change in how industrial plants are being engineered today.
Business first!
In the digital context, it is obvious that early experimentation with new technologies has not always been successful, and quite a few initiatives have failed financially. However, wasting money in digital development shouldn’t be tolerated anymore.
Digitalisation work should always begin with the definition of the business KPIs. All digital use cases have to be linked to at least one of the business KPIs so that it is always clear how the financial impact is achieved.
Industrial digitalisation doesn’t succeed without a digital foundation
It is fairly straightforward to come up with a long list of digital use cases. What tends to be missing from engineering project scopes and brownfield strategies alike is a “digital foundation”. In order to do agile digital development, there are a lot of things that are not part of conventional engineering: additional field instruments, data storages, integrations, data models, platform software, specifications for data governance, and so on. All these must be designed, specified, and implemented to be able to achieve proper results and, ultimately, to be able start the journey towards an autonomous and fully digitally mature plant. In the engineering context, it’s a fairly minor additional cost in a project, but extremely expensive to do when the plant is running.
Navigating the hype:
realising AI’s potential in industrial efficiency
Artificial intelligence applications are likely to have a significant impact on the operational efficiency of industrial plants. The hype is, however, yet to materialise in significant breakthroughs. While many AI applications are rather quick to develop, they tend to rely on data availability and quality as much as any other digital use cases. AI is only one of the tools in the digital toolbox, and all the same pitfalls should be avoided as in other digital work: remember to link all AI cases to the business performance, and don’t let the vendors reap the benefits of all the good work.
Conclusions
Industry leaders have already included digitalisation as one of the key disciplines in engineering projects. The objective is to design and specify the necessary use cases and their governance to avoid inflated licence fees and loss of control of data and to achieve the intended savings. The goal is a digital enterprise where the operations are centrally and remotely supervised, operational efficiency is optimised with advanced data analytics and AI, and which is on a steady path towards autonomous operations.
Did you know?
The goal of industrial digitalisation is a “digital enterprise” where:
1. All digital applications positively impact the overall profitability.
2. All engineering (ET), operational (OT), and IT data are integrated and made available for agile digital development using unified tools and platforms.
3. A structured approach to managing the development, implementation, and maintenance of digital tools and applications is in place.
4. Operations are typically supervised and optimised from a centralised control room.
5. Digital use cases do not limit only to visualisations and advanced analytics, but operators’ decision making is also replaced with Advanced Process Control (APC) applications.