How AI-driven digitalisation is transforming the engineering sector

We live in a progressively more digital world. As digitalisation ramps up in various industrial sectors, so does the need for companies to rethink how they implement products, services, and engagement strategies. For over two decades, industry 4.0 has been on the horizon, and then in its infancy. This transition had previously been gradual. Engineering firms were discussing their future digital transformation initiatives. If they already had roadmaps, they were mapped out across anywhere from a year to over a decade.

Accelerating the evolutionary process
Over time, evolution has accelerated significantly with the recent rapid change in engineering practices, a key example. For many years, the different engineering disciplines operated alone, focusing only on their specific fields. And that worked! But recently, market dynamics have forced process engineers to look at the “bigger picture”, to optimise their process by integrating different engineering disciplines and improving collaboration.

Across the globe, firms have identified millions of dollars in lost opportunities that can be attributed to a lack of collaboration. Enhanced workflows that enable better communication and sharing of information between process engineers, project engineers, estimators, and safety and energy specialists deliver the potential to capture those opportunities. Local initiatives such as the UAE’s Fourth Industrial Revolution Strategy aims to improve the UAE’s position as a global hub and to develop its contribution to the economy, by advancing modernisation and future technologies.

In terms of digital transformation itself though, most engineering firms were either initiating or contemplating initiatives to improve engineering productivity and reduce project and financial risk. These initiatives were being undertaken, in large measure, to consolidate and connect their portfolios of engineering software and technology in support of new, streamlined, digitalised workflows spanning departments, disciplines, and offices. Anticipated business benefits include shorter cycle times, lower cost, and higher-quality designs, as well as fewer errors and problems encountered during design, construction, and handover.

The pandemic has acted as a further catalyst for digital transformation in the engineering sector. Supply chains globally were exposed to cracks that led to empty shelves and pandemic-induced panic. Engineering firms had to embrace industrial digitalisation for new ways to maintain customer engagement and recover from supply chain disruptions. Roadmaps from companies that already had a digital strategy underway were shortened drastically. What was once mapped across a year, or more was narrowed down to weeks and days.

Delivering new opportunities through AI
Today, a growing number of engineering firms are extending their digital initiatives into new areas to take advantage of escalating business opportunities. Artificial intelligence (AI) often lies at the heart of these opportunities. Indeed, senior executives are now prioritising AI to reduce setup and programming overheads that are commonly required for individual tasks. If reports are to be believed will contribute $96bn in 2030 (13.6 per cent GDP) to the UAE economy. In line with this, we have seen several examples of AI-driven digitalisation delivering benefits across the industry.

The use of AI-driven digital twin technology, in particular, can bring high value to these companies’ clients, as it captures real-time data of the asset once it is in operation. Additionally, being in a position to leverage digital design and engineering data during handover provides greater potential for offering value-added services during operations and maintenance, making the firm less reliant on capital spending alone.

AI can also play an important role in supporting and outlining an engineering approach to a project, the constructibility of a design, and the planning of how materials, equipment, and labour are organised. This early planning approach has been proven to reduce costs and speed up schedules.

Risk and best-case scenarios evaluation
The use of first-principle models for defining and predicting a project’s performance and its outcomes is standard in process industries. However, there are some processes that are more difficult to predict. Often these are managed through less-precise techniques such as operator experience or rules of thumb, but this can lead to less than expected performance levels.

However, AI can simulate thousands of design options which very quickly narrow down the options that not only best meet the owner’s requirements, but are the safest, most environmentally friendly, and most cost-effective. A new capability, known as multi-case analysis, offers engineering firms the opportunity to transform the way these early decisions are made. Previously, engineers would define these critical parameters using limited data from a limited number of operating cases and conditions. Imagine designing an iPad with such a limited set of data, never mind a bespoke $10bn process plant. Yet that has been standard practice in the past.

Multi-case analysis helps to optimise early design decisions based on the consideration of hundreds or even thousands of operating conditions and cases. Leveraging AI and high-performance computing, either in the cloud or on a desktop, allows designers to rely on a significantly broader set of data to adjust and fine-tune their designs.

From the many different grades of crude oil to varying ambient weather conditions, this improvement in understanding how a potential design would perform in real conditions can result in improvements across the board, from construction materials, and the size of equipment to the type of utilities and even the location of the plant. These decisions will often have a major impact on the plant’s capital and operating costs, its risk analysis, as well as the overall fit for its intended purpose.

A multi-case analysis is undoubtedly a key area of focus for engineering firms using AI-driven digitalisation today. However, in speaking with customers across all regions, the highest priority area to be addressed under digitalisation is the consolidation of engineering software and technology portfolios, followed by the digitalisation of remaining applications and business processes. Critical to this effort is the ability to find and re-use data across the organisation, and eventually across their ecosystem of vendors, sub-contractors, and consultants.

There is so much to be gained (some companies estimate there is an opportunity for double-digit improvement in engineering and estimating productivity alone) that those who do not move forward risk being less competitive in the future.

Positive prospects
Looking ahead to the future of the engineering sector, digital transformation will inevitably accelerate and bring about significant improvements. While the path of change was already firmly established prior to the arrival of Covid-19, the pandemic has acted as a catalyst to speed this up. For engineering firms to succeed it is imperative that they adopt the advanced technology that is now available to them which includes AI tools. This will ensure they achieve operational efficiencies across the end-to-end value chain, giving them that all-important competitive edge. That, in turn, will position them well as they look to migrate beyond Industry 4.0 to the rapidly emerging fifth industrial revolution, or Industry 5.0, which takes the existing paradigm one step further by highlighting operational excellence and innovation as key drivers for change.

Sonali Singh is the VP – Product Management at AspenTech