AI-enabled data analysis unlocks hidden process optimization potential, effortlessly and without tying up your resources. Our free white paper reveals where to start.
Whether you’re responsible for operating a chemical plant, part of the engineering team that keeps it up and running, or you oversee the commercial aspects of its wider business, one thing is increasingly certain: you need to digitalize sooner or later.
Most chemical producers recognize digitalization is the key to realizing further efficiency gains, on top of what has already been achieved by adding to, replacing, or redesigning the mechanical aspects of their operation.
Today, all plants already gather data from across their process to one degree or another. Data that offers the ideal foundation for optimization, if only it could be used to its full potential rather than just for monitoring KPIs and troubleshooting.
What’s getting in the way? The day to day. The reality of operations presents various challenges that limit the use of data to looking at well-known metrics and trends. Anything more takes people, time and money that can be hard to find or justify.
New technologies, deployed in the right way, mean there’s no longer the need to.
Unlock the potential in your data.
You can achieve next-level optimization now, without taking up your team’s valuable time. In fact, with ongoing support to help them act fast and continuously optimize, you could even free more time for other valuable tasks. And there’s a good chance you’ve already got what’s needed to get started.
Machine learning, a type of artificial intelligence, deployed to harness your plant data offers a way to maximize reliability, optimize efficiency and so profitability, while maintaining your process safety. A partner that can learn your process, read and interpret historic and real-time data, and continually recommend ways to optimize, avoiding inefficiencies or disruptions.
Forecasts suggest such technologies could be worth USD 17.2 billion to manufacturing, including chemical production, by 2025.1 And they’re expected to boost gross value added (GVA)2 by almost USD 14 trillion by 2035.3
One producer used machine learning algorithms to increase phenol production by over 5,500 tons a year – or USD 5.5 million in value4. What could these technologies do for you?
Take the first step with our free white paper.
Navigance can help guide you through it, from scoping out the right approach to make better use of your data and deliver the value you need. So we’ve created a white paper to help you get started.
It covers the potential of technologies like machine learning for the industry as a whole and operations like yours. It also explains how you can realize the value of digitalization without using lots of in-house resource.
Discover how you can move from advanced analysis of your plant data to real-time recommendations you can act on fast to optimize with confidence. Plus, how to be up and running within weeks, backed by ongoing support from a partner with deep-rooted experience in your sector and these game-changing technologies.
Download it now. There’s a good chance the next step’s simpler than you think.
[Footnotes]
1 Artificial Intelligence in Manufacturing Market by Offering (Hardware, Software, and Services), Technology (Machine Learning, Computer Vision, Context-Aware Computing, and NLP), Application, Industry, and Geography – Global Forecast to 2025 by MarketsandMarkets.com
2 Gross Value Added: a measure of output value for goods and services produced in a certain sector. Think of it as that sector’s contribution to economic growth, similar to a country’s GDP.
3 How AI boosts industry profits and innovation by Mark Purdy and Paul Daugherty – Accenture research (2017)
https://www.accenture.com/fr-fr/_acnmedia/36dc7f76eab444cab6a7f44017cc3997.pdf
4 Calculation based on a market price of phenol of ~$1000 recorded in January 2020.
