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Operational excellence through asset optimisation

(Image credit: Image Credit: Chombosan / Shutterstock)

Digital transformation has the potential to help organisations to fundamentally change their traditional business models and drive significant operational efficiencies, higher profitability and dynamic business growth.

Strategy&, PwC’s strategy consulting team, for example, in its Global Digital Operations Study 2018 finds that ‘globally, digitisation will lead to higher productivity and wealth’. According to the study,

“digitisation 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.”

Nevertheless, the same PwC report also found that “just 10 per cent of global manufacturing companies are Digital Champions, while almost two-thirds have barely or not yet begun on the digital journey.”

The survey found that Europe and the Middle East are trailing 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 region.

Asset optimisation

Looking beyond specific local conditions, the main areas of operational excellence that are currently driving digital transformation include enhancing asset reliability, maximising the value of operations and supply chain, reducing capital expenditure while fast-tracking innovation, and continuous improvement in safety and sustainability.  Important digital transformation technologies that will support these operational excellence targets include machine learning and artificial intelligence (AI), knowledge work automation, the Industrial Internet of Things (IIoT) and system-level thinking or approaches.

To keep pace with the challenging demands of the new digitised age, asset-intensive industries need to improve the agility and flexibility they deliver in repurposing their extremely high investment capital assets while simultaneously eliminating production losses caused by unplanned downtime. The consequent increase in asset utilisation will result in significant 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 maximise the benefit from inspection routines. However, the ability to detect the ostensibly random failures causing more than 80 per cent unplanned downtime escapes them.

With this massive market opportunity now apparent across the whole sector, lead time becomes essential to detect all kinds of degradation quickly enough to enable the necessary decision-making to change the outcome.

This key function of asset optimisation is one important area where digital transformation has the chance to make a real difference to a business. More broadly, there is also a major opportunity here for organisations to re-evaluate how business operates in the face of rapid evolution of digital technology. Indeed, digital transformation represents a breakthrough opportunity for businesses to rework their existing models, deliver extensive efficiencies and emerge from the whole process as industry leaders.   

Finding a way forward

Turnkey solutions transform fundamental analytics and data science methodologies into easily adoptable solutions to business challenges. For example, advanced machine learning software, when packaged as prescriptive maintenance solutions, has already shown significant success in the early identification of equipment failure and can identify behavioural patterns from streams of digital data produced by sensors on the relevant equipment. Autonomous in nature, this advanced technology continuously learns and adapts to new signal patterns when operating conditions alter. 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 impacting them. The end result is a disruptive technology that can predict failures 50-70 days in advance and prescribe operating actions to eliminate the failures.

Beyond machine learning, mobility is an important driver of digital transformation, as mobile devices and applications allow plant workers to make decisions on the go. With social networks, like-minded professionals can collaborate virtually and round the clock to find answers to problems via social networks. Powerful cloud containers can also streamline the deployment experience, cut 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 an optimal response. Analytics, models and big data allow 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, like 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.

Fast-track operational excellence

A major driver of digital transformation, asset optimisation is a continuous journey that addresses the whole 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 fertilisers, 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 per cent uptime with Aspen Fidelis Reliability software enterprise risk modelling 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.

A digital transformation roadmap

To succeed 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. They also need to define business drivers, challenges and key success metrics. Workforce skills development should be actively encouraged. Ultimately, the key ‘take out’ is that smart companies with the ability to successfully transform digitally and pursue operational excellence via asset optimisation will be tomorrow’s market leaders.

Ron Beck, Energy Industry Marketing Director, AspenTech
Image Credit: Chombosan / Shutterstock

Ron Beck
Ron Beck is Director of Industry Marketing at Aspen Tech. During six years at Aspen, he has been responsible Engineering Product Marketing, Aspen Economic Evaluation, and Aspen Basic Engineering. He has over 20 years experience in providing software solutions to the process industries and 10 years experience in chemical engineering technology commercialization.