By Adam Cohen, CEO, SKUR
I recently spent two days at a conference where heavy industry discussed digitization and the benefits & pitfalls associated with adoption. As I attend these conferences I always wonder if I’m just sitting with the believers or the industry is truly committed to beginning the digital transformation. Let’s get one thing straight – this industry has a profit margin of 1.8% (CII), a sad and paltry statistic which penalizes the industry in the capital markets and draws laughs and groans from every industry except agriculture and hunting (McKinsey).
Transformation sounds great on paper but the reality is it’s an extremely difficult and painful process. Let’s just start with the various stakeholders – not only do we have organizational challenges – finance speaking with operations speaking with facility operators – but then the next layer is sub-contractors and fabricators; then the next layer is project managers, some of whom are resistant to change or have the mindset of why change it if it ain’t broke.
Digitization Without Interoperability Is Pointless
As we keep peeling back the onion we begin to think about interoperability between data sources. Interoperability is key to breaking down silos and unlocking value. Does my design data speak to my 4D data, and do those speak to my 5D data – say what? It’s clearly evident that proprietary, client-based applications become more of a hindrance rather than an asset. If we’re all on islands then how can we possibly work together? Building bridges of course! The software equivalent of a bridge is an API (Application Programming Interface), which creates interoperability across software platforms. Now the one stop shop for design, labor and supply chain goes from pipe dream to reality. Designers, constructors, operators and owners all benefit. Ok, now we’re talking.
The Value of Automation
Let’s go one layer deeper and begin to think about what digitization offers us. The simple answer is automation. Now automation is a deep, multilayered issue in and of itself. Where do we start? Naturally data entry is one place where dividends would be quickly paid – we have not only time savings but the elimination of human error. One example given was transforming paper-based data to a database – the company had allocated dollars to hire two people to spend 6000 hours manually retyping the data in an error-prone, mind numbing exercise. The hero steps in and introduced 20-year-old technology that allowed the process to be completed in 45 minutes, saving 5999.25 hours. And it was error free, that’s a win.
Once that data was accurate and ported to the design environment it provided immediate value. But there’s still a step that’s missing – the automation of the human judgement for certain tasks. Now let’s be clear, we’re a long way from computers being sophisticated enough to replace many human functions but there are functions that can be automated. Data can be curated for reporting, intelligence and interoperability can be utilized to focus users on likely problematic issues, saving the users time finding what they’re looking for. And that’s a great intermediate step – job threatening to some but creating efficiency for the enterprise.
Artificial Intelligence Enables Predictive Analytics
Now we’re in a place where iterative development and things like machine learning to feed AI (Artificial Intelligence) actually begin to make sense. We’re reaching the outskirts of full automation, data collected in the field, visually or through IoT begins to feed a process of consumption by smart & interoperable design suites. These design platforms coalesce with labor and supply chain platforms, with data available to all stakeholders – curated with AI to produce intelligence that matches the job function whether in the field or the executive suite. Suddenly the data becomes even more valuable in decision support for financing and modeling greenfield and brownfield projects. We’re arriving at predictive analytics, plugging additional environmental variables into visual models and further augmenting those models with real world data and delivering results within basis points of the modeled assumptions. We’ve digitized our business and created a digital twin.
The Bumpy Road
That’s the roadmap – but look out for roadblocks, collapsing infrastructure and human resistance. And it’s a decade to get there, there’s no quick fix to compensate for the courage that it takes to decide to drive down the road. But there needs to be a starting point – visual data needs to be integrated to develop accurate as-built models. This allows us to form a basis of what we really have, what is real and what is not. This visual data allows us to create interoperability and coordination on a global scale. Next steps, more interoperability to design platforms, 4D & 5D controls. This process will create massive amounts of data that can be consumed and cultivated to make our process more efficient and cost effective. Owners enjoy the benefits of portability of the design data to develop operational digital twins. All of this drives margins for the industry as a whole but especially the contractors where valuation parity begins to mirror other major, technologically savvy markets.