Worley: improved data decisions via a lifecycle approach
The way many industries collect, store, and use data for capital projects is inefficient. A lifecycle approach to data can improve decision-making both now and well into the future.
It is no longer a cliché to say that data is the new oil. After all, it’s what underpins the future of asset-intensive industries like energy, chemicals and resources. However, many companies operating in these sectors and other industrial domains struggle to implement digital solutions like big data, artificial intelligence, robotics, and augmented and virtual reality. Why? Because we’re bad at sharing data.
How current data sharing creates inefficiencies
Consider how data from a new capital project moves through the stages of a project. It’s handed from stakeholder to stakeholder and converted and adapted by each operating system into which it’s inputted. Inevitably, some of the detail is lost in translation or is passed across in so many different data sets that it’s not useful unless there is an inordinate spend on a common data model.
The more data created over the life of an asset, the greater the inefficiencies a project is likely to be exposed to. But by changing the way stakeholders interact with each other when it comes to sharing data, this underlying issue can often be addressed – and much more value can be created.
The benefits of adopting a lifecycle approach to data
One of the benefits of a data lifecycle approach for capital projects is the potential to reduce total cost of ownership by running assets as data models.
Each stakeholder’s input contributes to the completion of their aspect of the project and is tracked over time to understand the effect of decisions taken. This builds a picture of how decisions made earlier in the project’s life can affect operation and maintenance costs later.
What’s more, data analysis generated from the concept of a project right through operation, maintenance and decommissioning can create better visibility and ensure a highly reliable, safe, and sustainable asset.
Imagine if you collated all the data on project reworks, then brought together the project team and data analysts to identify the root cause. This could have a substantial impact, not only on the project at hand, but for upcoming projects too. The team could use this data to better model asset integrity and reliability and the change over an asset’s lifetime. The data could also be used to model how the asset is influenced by factors such as temperature, corrosion and operating hours, and help operators understand the causes of failures and unexpected events.
Physical data can be digitized too
Lifecycle data does not need to be numeric. Data from images and videos can be increasingly collated and analyzed as a source of intelligence. For example, video data can be used to track the movement of staff onsite and match workorders to workers and confirm whether all the right paperwork is in place for the work to proceed. Optical gas imaging can be used to detect gas leaks, helping to identify and prioritize repairs and reduce emissions. Video feeds of flares, coke drum operations and internal inspections can, when married with process data, lead to greater reliability over time.
Next steps to implementation
Using data as a central theme to lowering energy intensity and decarbonization will go hand in hand with new and upgraded digital plants. This will lead the industry to move away from a transactional approach to capital projects, to work more collaboratively across the project’s lifecycle.
To get the most out of the opportunity, the industry must focus on building ‘sensored’ assets which feed into a common data model, this allows us to collect data from the sensor for the lifecycle of the asset. Moving forward, we’ll not need pure-play data analysts, process data analysts and reliability data analysts, but hybrids – engineers ( who can confidently work with data and blend it with their own experience, to produce the most compelling insights.
Other industries such as finance, automotive, and fast-moving consumer goods have already reaped the benefits of a lifecycle approach to data, it’s time we did so too.
Beyond Limits: Cognitive AI in APAC
Courtesy of current estimates, it looks like Asia-Pacific AI will be worth US$136bn by 2025. Its governments and corporations invest more money than the rest of the world in AI tech, the data of its citizens is considered fair game, and its pilots are small-scale and, as a result, ruthlessly effective. This is why, according to Jeff Olson, Cognizant’s Associate Vice President for Projects, AI and Analytics, Digital Business and Technology, the APAC region ‘is right on the edge of an AI explosion’.
Now, startup Beyond Limits is pushing the boundaries of what AI can do, mirroring humans in its ability to find solutions with even limited information. As of this July, it’s partnered up with Mitsui, a global trading and investment company, to expand its impact in APAC.
How Does Beyond Limits Work?
Most AI companies claim that they can help businesses make better decisions. But many need astoundingly large stores of data to feed their information-hungry algorithms. Beyond Limits, in contrast, takes a different tack. Perfect data, after all, is largely a pipe dream kept alive by PhD students. In reality, systems must often make decisions from small, incomplete sets of intel.
But Beyond Limits’ AI is no black box. ‘When little to no data is available, Beyond Limits symbolic technologies rely on deductive, inductive, and abductive reasoning capabilities’, explained Clare Walker, Industry Analyst at Frost & Sullivan. While making these leaps in logic, however, the system also keeps track, ensuring that humans can review the AI’s ‘thought process’.
Why Partner With Mitsui?
Beyond Limits is built for specific applications such as energy, utilities, and healthcare—but lacks the extensive industry network of Mitsui. Partnering allows Beyond Limits to access a portfolio of firms specialising in minerals and metals, energy, infrastructure, and chemicals. ‘We’ve been working on this deal for several years’, said Mitsui’s Deputy General Manager Hiroki Tanabe. ‘Mitsui’s global portfolio and Beyond Limits’ AI technology will...deliver impact’.
In the first test of that dramatic statement, Liquified Natural Gas (LNG) will soon deploy Beyond Limits’ new system. If everything goes according to plan, LNG will optimise how it extracts and refines energy, making money for both itself and investors—including Mitsui. This, in fact, is Mitsui’s strategy: go digital and don’t look back.
Why Does This Matter?
Forty-five percent of Asia-Pacific companies surveyed in Cognizant’s thought leadership ebook consider themselves AI leaders. Positivity bias, that oh-so-common tendency of humans to position themselves as above average as compared to others, strikes again. (Most small companies fail to launch successful AI projects on their own.) And partly, this is because firms fail to integrate AI with industry expertise.
‘A large part of the focus on talent for AI today has been getting the people who are strong in mathematics, AI, and technologies’, said Olson. ‘But where you make your money out of AI projects is when you apply them to your business’. In short: APAC nations looking for ways to bridge the gap might follow Beyond Limits and Mitsui’s playbook—coupling startup AI with a corporate network.