Strategy&, PwC’s strategy consulting team, for instance, in its Global Digital Operations Study 2018 finds that ‘globally, digitization will lead to higher productivity and wealth’. According to the study, “digitization and smart automation are expected to contribute as much as 14 per cent to global GDP gains by 2030, equivalent to about US$15 trillion in today’s value.”
Despite this, the same PwC report found that “just 10 percent of global manufacturing companies are Digital Champions, while almost two-thirds have barely or not yet begun on the digital journey.”
The survey also found that Europe and the Middle East are lagging behind the rest of the world in terms of digital maturity, with just five per cent of companies in the region earning the tag “digital champions”, compared to 11 per cent in the Americas and 19 per cent in the Asia-Pacific.
Beyond regional tailwinds, key areas of operational excellence driving digital transformation include improving asset reliability, optimising operations and supply chain, reducing capital expenditure while accelerating innovation, and continuous improvement in safety and sustainability. Key digital transformation technologies that will support these operational excellence goals include artificial intelligence and machine learning, automation of knowledge work, the Industrial Internet of Things (IIoT) and system-level thinking.
To keep up with the demands of the new digitised era, asset-intensive industries need to improve the flexibility and agility in repurposing their extremely high investment capital assets while at the same time eliminating production losses caused by unplanned downtime. The resulting increase in asset utilisation will result in a single, greatest financial improvement in production operations.
ARC Advisory Group calculates that the global process industry loses $20 billion annually from unplanned downtime. Companies spend millions of dollars on traditional maintenance approaches searching for specific wear and age-based failures using techniques to optimise inspection routines. However, the ability to detect the “apparently” random failures causing more than 80% unplanned downtime eludes them.
With this massive market opportunity now apparent to the industry, lead time becomes essential to detect all kinds of degradation early to enable the necessary decision-making to change the outcome.
This key function of asset optimisation is one important area where digital transformation has a great opportunity to make a real difference to an organisation. More widely, there is a huge opportunity here for organisations to reevaluate how business operates in the face of rapid evolution of digital technology. Indeed, digital transformation represents a real breakthrough opportunity for businesses to rework their existing business models, drive far-reaching efficiencies and emerge as industry leaders.
Finding a Solution
Turnkey solutions transform fundamental analytics and data science methods into easily adoptable solutions to business challenges. For example, advanced machine learning software, when packaged as prescriptive maintenance solutions, has demonstrated incredible success in the early identification of equipment failure and can identify behavioral patterns from streams of digital data produced by sensors on the relevant equipment. Autonomous in nature, this advanced technology constantly learns and adapts to new signal patterns when operating conditions change. Failure signatures learned on one machine inoculates that specific machine, to prevent the same condition from reoccurring. Learned signatures readily transfer to similar machines, preventing the same degradation conditions from affecting them. The net result is a disruptive technology that can predict failures 50-70 days in advance and prescribe operating actions to avoid the failures.
Beyond machine learning, mobility is a key driver of digital transformation, as mobile devices and applications can enable plant workers to make decisions on the go. With social networks, like-minded professionals can collaborate virtually and round the clock to solve problems via social networks. Powerful cloud containers can also streamline the deployment experience, reduce the cost of ownership and increase application scope.
The IIoT connects the plant with model-based sensors on all equipment. Advanced algorithms used in search and pattern recognition automatically detect data-based patterns to predict outcomes and guide optimal responses. Analytics, models and big data enable the exploration of data potential inside the plant fence and across the company.
High performance computing provides the necessary computational horsepower to address larger issues around asset optimisation and advance metadata sharing across industries for greater efficiency. What becomes most visible, though, is the easily adaptable mobile app interfaces that give access to important plant systems, such as the refinery plan, to key personas in simple views, such as a crude oil trader’s view of the refinery plan that he may use when making split second decisions.
Accelerate operational excellence
A major driver of digital transformation, asset optimisation is a continuous journey that addresses the entire lifecycle to achieve operational excellence. Customers can maximise uptime through actionable insights. For example, Saras, owner of the most complex refinery in the Mediterranean, has increased refining uptime by one – ten days in a year with Aspen Mtell software. Borealis, a leading provider of innovative solutions in the fields of polyolefins, base chemicals and fertilizers, has proven the ability to achieve warning of failures 27 – 28 days in advance and therefore projected improved profit margins with Aspen Mtell software. SABIC, a global leader in diversified chemicals, has minimised capital expenditure, while ensuring 99.9% uptime with Aspen Fidelis Reliability software enterprise risk modeling systems.
With asset optimisation, customers can push the boundaries of what is possible. For example, KUWAIT OIL COMPANY has saved $22M lifecycle profit per unit via economics knowledge automation. INEOS, one of the world’s largest manufacturers of chemicals and oil products, has saved $10M revenue per CDU/VDU per year with the deployment of Aspen HYSYS and integrated Exchanger Design and Rating models online.
Smart companies transform digitally
To be successful in digital transformation, companies need a clear roadmap that aligns with business objectives and measurable outcomes. Companies need to maximise value from existing technology and understand the level of maturity in their organisations. It is also necessary to define business drivers, challenges and key success metrics. Workforce skills development should also be encouraged. Finally, smart companies with the ability to successfully transform digitally and pursue operational excellence via asset optimisation will be tomorrow’s market leaders.