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Challenges and Use Cases for the Adoption of AI in Chemical Manufacturing

AI Adoption in Chemical Industry can be more aggressive


Chemical engineer using AI tools to facilitate daily operations

A recent report from IBM revealed that while the vast majority of executives in the chemical industry recognize the need for investment  in AI (80% responded that AI will be important to the success of their business in the next three years), only 4 in 10 chemicals executives said their company has already implemented an enterprise-wide AI strategy. Clearly, everyone understands that AI will be critical for their survival, but at present there are obstructions to mass adoption. We have observed that the main obstacles can be narrowed down to three areas:


  1. Lack of Expertise

  2. Lack of Organizational Buy-in

  3. Perception that AI Initiatives are Time Consuming and Costly


While these issues are pertinent, if CEOs really get behind AI and bring in appropriate training, companies are often quite surprised to find that they in fact can quickly see results in terms of streamlining operations, improving product quality, reducing costs, and driving new product development. We have outlined some of the major use cases where these benefits are commonly identified.


Use Cases of AI in Chemical Manufacturing

Procurement


Raw materials costs account for 50-70% of chemical companies' sales revenue, meaning that it's critical for a company's bottom line for procurement managers to be able to secure the best possible pricing. This is challenging in a complex chemical supply chain that involves manufacturers, distributors, and retailers and pricing that is always in flux. Procurement managers typically have holistic visibility to guide pricing decisions.


In Taiwan, we find that many manufacturers tend to make procurement decisions based on the personal judgment of key staff or if they do utilize data, then it tends to be centered on historical trend averages for material price. The weakness of this method is that it fails to consider wider macroeconomic conditions and changes.


Predictive analytics can make the difference and gift procurement with a much clearer picture through models that utilize various factors such as crude oil futures data, plastic futures data, exchange rates, market volatility, behavioral science, and geopolitical trends. This enables managers to be proactive rather than reactive in their decision-making process.


Research and Development


Chemical industries have begun to use generative AI models in their R&D process to identify new molecules, recipes, or compounds.


In a recent webinar, McKinsey & Company demonstrated the pace in which an AI model trained on an extensive database of chemical compounds, could identify new compounds. AI can expedite the discovery process by two or three times and find molecules that are much better in specific properties that companies are interested in.


Quality Management


​​In the chemical industry, it is crucial to take immediate action in the event of a defect in the production line, as it can lead to the contamination and spoilage of an entire batch. Traditional manufacturers often rely on personnel experience and trial-and-error experiments to address quality issues, which is not ideal in terms of effectiveness and means they still struggle to find the root of problems.


The combination of AI-based tools, sensors, and computer vision technologies is perfect for helping companies not only quickly identify and resolve issues, but also learn from them and prevent similar problems in the future.


Profet AI works with one client specializing in cellulose nanofiber coatings and uses Roll-to-Roll processing, in which films films or soft boards are unrolled from cylindrical rolls.This is a complex process with a relatively high defect rate of 12%. AI-powered quality factor analysis allowed them to quickly resolve defect issues, reducing the defect rate to only 5%.


Meeting ESG Targets

Demands from both governments and consumers have made sustainability a critical concern for chemical manufacturers, especially in the light of recent research that shows the chemical industry accounts for approximately 10% of global total final energy consumption and 7% of greenhouse gas emissions. Accordingly, 82% of chemical executives now prioritize environmental, social, and governance (ESG) and sustainability as much as revenue growth.


Artificial intelligence (AI) has proven to be an effective tool in optimizing operations for chemical factories to meet sustainability targets, particularly in reducing energy consumption, including electricity usage. This is done through using data such as historical electricity consumption, environmental conditions (temperature and humidity), production information, and equipment operation data. Commonly we have observed that AI adoption can help chemical companies in Taiwan reduce their electricity consumption by around 3%.

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