Driving value and competitive advantage with industrial AI

According to the late American futurist Alvin Toffler, “Change is not merely necessary to life – it is life.” Indeed, change is paramount, as the world works its way out of the current pandemic.

What does the new normal look like? No-one knows for sure, but it is clear that to secure a leading position tomorrow, companies need to accelerate digitalisation today and position themselves for unexpected twists tomorrow. It is necessary for businesses to achieve a high level of overall resilience and be more productive with increased agility. In the face of relentless global competition, process and capital-intensive companies need to be strategic and focused. Acceleration is good but not enough: it is mission-critical for businesses to concentrate on generating measurable value by deploying technology that improves the dual challenges of sustainability and profitability.

Industrial AI is a strategic business weapon, as it combines the power of analytics and AI machine learning with the crucial guard-rails of domain expertise, to extract value from industrial data. Industrial AI will help navigate increasingly complex supply chain options and decisions. Momentum from rapidly changing global carbon mitigation necessitates focus on energy transition, and it is also necessary to focus on plastic waste concerns driving the circular economy — for all of which, industrial AI is already generating value for users. Companies need to deploy embedded AI-driven industrial solutions with crucial in-built domain expertise, augmenting those AI tools and data to unlock true value.

Visibility into the game plan

With virtual or hybrid working arrangements still in place, companies need to demonstrate their ability to operate and manage assets remotely. These businesses also need to increase the integrity and safety standards of their assets concurrently — via the use of analytics.

In a survey conducted jointly with Crystol Energy, most respondents from energy and chemical companies indicated that they were less well prepared digitally, when this pandemic arose. The most important factor for companies in asset-intensive sectors is to ensure the safety of their workers and safe operation of assets. Providing a flexible working environment and accelerating digitalisation via increased stakeholder transparency were factors that respondents agree upon. Skills shortage was also highlighted as a long-term risk to business sustainability, as economic volatility has led to early exit of the most experienced workers — now replaced by a workforce that needs training and organisations that are short of domain expertise. Access to global capital is increasingly being tied to sustainability performance, which increases the importance of related goals.

Even before this pandemic-induced economic slowdown, employees and customers expected the energy industry to operate clean, efficient businesses. However, the latest generation of workers and customers are demanding even greater levels of accountability around sustainability. In a new global industry survey of sustainability patterns, by AspenTech and consultant Dr Robert Socolow, 48% of chemical industry respondents report that customers are key drivers of their sustainability initiatives, and 65% say that a broad societal obligation is a key driver. Organisations know that if they want to protect their brand reputation and attract these people to work for and engage with them, they must build cleaner, safer, more sustainable businesses that allow them to contribute to creating a world fit for tomorrow.

Many energy players are also diversifying their energy mix, all of which points to more projects in energy transition areas, such as the hydrogen economy, carbon capture, biofeedstock and renewable energy assets.

A new industry normal

The skills shortage will intensify beyond the next five years, as industry downsizing whittles down valuable domain expertise. With most data scientists still relatively new on the job, the use of digital tools and analytics looks to be on the rise — especially solutions that accelerate collaboration between the new wave of data scientists and technical domain experts. Cost and carbon footprint reduction will remain high on the agenda. A shift in refining production mix towards chemical feedstocks is expected, as growth in chemicals is expected to account for half of the near-term demand growth for oil.

As Asian economies and middle-class growth continue to work towards recovery, mega-integrated plant projects are on track to address changing market demands efficiently. There is a shift from oil to gas consumption, especially in areas of chemical feedstocks and power generation, and natural gas and renewables can also address the increasing demand for electricity, while the hydrogen economy is an emerging dimension.

Hybrid models combining rigorous AI-driven models are increasingly required to optimise complex operations, more accurately and autonomously, especially for energy transition technology options. To manage project risk efficiently, it is necessary to visualise, analyse, benchmark and share data to increase speed and certainty. The result is a more agile, collaborative and informed asset design — with a seamless and more predictable execution process.

Long-term trajectory on course

The long-term vision of a self-optimising plant is a fully digitally enabled asset that is self-learning, self-adapting and self-sustaining. Some aspects of operations will become autonomous in the relatively short term, while broader autonomy is a longer-term five- to ten-year goal. Typically, oil and chemical assets are too complex to run completely autonomously, at least within the next five to ten years. Instead, such businesses are driving towards enabling a self-sustaining plant with operators increasingly performing strategic oversight functions.

Alvin Toffler wrote: “Knowledge is the most democratic source of power” and “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” These quotes highlight the importance of digital technology and industrial AI in empowering workers to mobilise knowledge and learn from industrial data.

Indeed, the self-optimising plant will capitalise on data to generate knowledge and industrial AI will provide companies with the massive ability to learn, unlearn and relearn. Toffler is spot on when it comes to process and capital-intensive companies embracing change and driving value with the unique power of industrial AI right here, right now, in this new industry normal.

Image: ©stock.adobe.com/au/Gorodenkoff