3 minute read
INDUSTRY 4.0
AI for plant monitoring: platform or purpose-built?
Andrew Normand, UptimeAI partnership lead at Encora Energy, discusses which approach could be the right fit for your business.
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Industry 4.0 is the watchword for the fourth industrial revolution that’s taking place right now. Nearly every global chemical manufacturer is investing in disruptive technology with advanced analytics and artificial intelligence (AI). When applied to monitoring plant equipment these can give significant advantages in plant uptime, increased efficiency and cost reduction. However, one of the critical choices that companies face is deciding between building apps on AI-based platforms or purpose-built AI software for plant monitoring. While both have their place in the world of digital transformation, which approach is the right one for your organisation? There are significant differences between both solutions, and choosing an approach requires careful consideration of their pros and cons. AI platforms help software, data science and IT engineers bring together disparate technologies to build an analytics application. To address this, AI platforms offer an integrated environment with pre-built data connectors, transformations, machine learning models, user interface (UI) development, and DevOps tools. Essentially, platforms are tools to build your analytics. On the other hand, purpose-built plant monitoring solutions are developed for plant engineers to increase the availability and efficiency of plant operations. They offer pre-built connectors, data transformations, models, dashboards and deployment options that are designed specifically for the challenges in plant monitoring.
CORE FUNCTIONALITY
Platforms offer software and data science functionality but require the user’s knowledge of workflows and plant operations. While some of the platforms provide prebuilt apps for predictive maintenance, they can lack an understanding of equipment and processes and are often too data science-driven.
In contrast, AI-based purpose-built solutions bring the software, data science, domain knowledge and workflows into a single application. However, these solutions are limited to algorithms and technology that’s specific to solving plant issues. For example, a purpose-built solution won’t have a connector for stock market data, whereas a platform is likely to have this feature.
COST AND RETURN ON INVESTMENT
Building a plant monitoring solution on an AI platform can be a costly endeavour that can run into tens of millions of pounds on top of the platform costs. Users have to bear the cost of developing a solution. As a result, the return on investment (ROI) is often lower and takes longer with AI platforms. Often users start out with a pilot and then get stuck there. AI-based purpose-built solutions are typically much cheaper to get started, typically a few hundred thousand pounds per year. A vendor offering a purpose-built solution distributes the cost of building the solution over multiple customers; hence the price of the software to the end customer is much lower. As a result, ROI is generally much faster and higher – typically in six months or less. However, significant customisation on purpose-built solutions can be costly and may not even be possible.
SUMMARY
AI platforms and purpose-built AI solutions for plant monitoring both have merits and limitations. With AI platforms, it’s a do-it-yourself job, whereas purpose-built solutions offer a ready-to-use solution. Unlike more straightforward use cases such as sales or demand forecasting, plant monitoring applications are highly complex due to the integration of IT, OT, operational workflows, domain knowledge, changing process conditions and data science.
As an analogy, developing your own plant monitoring application on an AI platform is like building your car from scratch. Is it easy? Are the results guaranteed? Do you have the budget, experience and skillset? Perhaps not. But if you get it right, you may be able to build your own Ferrari! Despite the odds, AI platforms offer the promise of building something unique and custom. Organisations prepared to justify such investment to gain a competitive edge should consider this approach. For the rest, dedicated AI-based plant monitoring solutions offer the quickest and highest ROI. However, one needs to carefully evaluate the true AI capabilities as these solutions are pre-built. Alternatively, organisations may choose to go with a hybrid approach. A purpose-built AI solution could be chosen for plant monitoring, and custom applications on AI platforms for less complicated uses in sales, marketing, HR and finance.
Andrew Normand is UptimeAI partnership lead for Encora Energy. With more than 15 years’ professional experience and an extensive global background in technical/management consulting and operations-based engineering, Andrew is currently driving forward Encora Energy’s roll-out of UptimeAI’s pioneering technology in the UK and Europe. Andrew and the Encora Energy team offer in-depth knowledge of the European process industries – particularly the energy, oil, gas, chemicals and power generation sectors – as well as the regulatory environment in the UK and Europe.
