More than 80% of executives in the chemical industry surveyed by IBM admit that artificial intelligence (AI) will have an immense impact on their business within the next three years. The areas where AI is most implemented in this sector are research and development (74%), manufacturing (61%), forecasting and planning (47%), and risk management (58%).
The latest technologies are allowing chemical companies to cut operational costs, increase profits, and improve the quality of products.
In this article, we will review the implementation of AI in the chemical industry and examine its benefits and potential in the sector.
When it comes to chemical manufacturers, machine learning (ML) and AI solutions help them automate data extraction, supply chain planning, and quality testing. Moreover, by implementing AI and automating tasks, chemical industry specialists can accelerate the process of discovering new vaccines, treatments, and ingredients.
Here are some of the areas of chemical manufacturing that AI improves:
The research and development (R&D) process is composed of activities that an organization does to create a new product. The goal is to take the best version of the new product to market. AI solutions can help companies speed up and increase the accuracy of the R&D process.
ML tools have been found helpful in discovering and recognizing molecules, creating formulas, and defining exact proportions and quantities of chemicals. AI helps analyze possible chemical combinations and predict whether they will be effective in their predefined purpose.
ML and AI-enabled models and advanced analytics help predict how much raw material for chemical production is left and how much more is needed. AI forecasting leaves place for alterations at every step of molecule development.
AI can also predict future prices for materials. This helps adjust the production process to the market faster and significantly reduces the company’s losses.
AI used in the chemical industry is capable of reducing forecasting error by 50% compared to human forecasting. Companies can streamline the supply chain and avoid overstocking by forecasting demands with AI.
Manufacturing companies have to fit certain regulatory standards. If a company exceeds the norms of carbon emissions, water consumption, or level of pollution in production premises, it’s going to pay hefty fees. Controlling these indicators manually would take ages. With AI-enabled data analysis, companies can easily track and adjust their production to the norms implemented by authorities.
Furthermore, together with sensor technology, smart data analytics identifies defects and alerts staff about inconsistencies. Knowing the root of the problem, experts can intervene in the manufacturing process and promptly resolve the issue.
In the chemical industry, the importance of timely quality assurance is paramount. If a wrong substance gets on a product line, it may take seconds for it to ruin the whole batch of products. AI is capable of detecting such instances and preventing accidents on the product line. Moreover, ML tools can learn from such occurences and use this knowledge in similar cases in the future — to be even more efficient in solving the issue.
Quality assurance in chemical production is mainly ensured by computer vision. With the help of deep learning algorithms, computers scan substances on production lines, evaluate and classify them based on their properties.
AI and ML technologies are not new to the chemical manufacturing industry. They’ve been used for product development, demand forecasting, and quality testing by chemical companies worldwide. Here are some of the most popular AI use cases in the chemical industry.
Chemical companies use robots for cleaning production premises to minimize human contact with toxic substances. Context-aware robots can also improve the efficiency of logistics and reduce the travel time of raw materials or finished products between parts of production lines.
Companies like Novartis use robots to distribute chemical compounds into multi-well plates. They help the company run testing on substances and products 24/7, which speeds up the process of discovering new drugs.
AI-enabled optical systems are used for defect detection like mechanical inclusions, color differences, and damaged packaging. AI platforms like SG Vision AI provide advanced monitoring tools that help companies increase the accuracy of data collection and accelerate the model validation process.
Multi-billion companies like Dow use AI monitoring to detect and eliminate safety risks of confined space entries.
Traditional systems for forecasting are overwhelmed by the amount of data available on the Internet. AI algorithms analyze vast data resources and forecast the demand for a certain product. Companies can adjust their production planning and increase cost-effectiveness based on the AI analysis results. Moreover, they can implement AI to gather point-of-sale data to predict customer demand and reduce waste associated with out-of-demand items.
Organizations like Blue Yonder promote AI and ML techniques to optimize forecasting and replenishment while being capable of simultaneously adjusting the pricing.
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One of the main reasons the world of manufacturing is working towards digitalization and automating is the profitability of AI solutions. ML tools help companies increase their sales and productivity by taking the possibility of human error out of the picture. Here are some of the main advantages of implementing AI in the chemical industry.
AI algorithms provide companies with higher yields through operational optimization. AI root cause analytics and testing procedures lead to the reduction of scrap rates and the improvement of product quality. They stabilize the flow and increase equipment efficiency.
Organizations can adjust to various production variations with the help of AI tools. It can also automatically control manifestation conditions like stirring rate, temperature, and process duration. Higher throughput and yield detraction by 30% is possible.
AI algorithms help analyze changing customer demand and optimize offerings. It’s beneficial for getting maximal profit and preventing overstocking. AI solutions are capable of reducing lost sales and inventory by up to 65% and 50% respectively.
AI helps to reduce waste. It’s not only beneficial from an economic standpoint, but it also has a positive impact on the environment. According to Nature Communications, AI solutions enable organizations to be 63% more environment-friendly.
AI technologies like advanced analytics, real-time data collection, and the Industrial Internet of things (IIoT) can help improve the safety of staff and physical assets. ML tools prevent potential hazards of manufacturing by eliminating the need for direct human involvement. By gathering onsite data, AI makes compliance with data collection and documentation requirements much easier.
AI can be used in product development to facilitate a productive idea-to-market process and speed up innovation. It accelerates the process of product discovery. For example, Pfizer aims to identify new and more precise treatment options by combining AI and data science with real-world data. The company leverages AI to redefine and speed up the completion time of chemical studies.
As the technology behind AI and ML is still under research and development, a range of new benefits and applications will appear in the following years.
Today, only 4 out of 10 chemical companies widely implement AI in their operations. The slow progress of AI integration in manufacturing is caused by challenges of their implementation:
To minimize the adverse effects of these challenges, companies should prepare for the incorporation of AI into their business processes. It is vital to have a clear understanding of the goals of AI implementation and the specific areas where it will be used.
With the advance in technology, the benefits of the use of artificial intelligence in chemical industry begin to overweight the challenges of its implementation.
AI and ML technologies have enormous potential for improving chemical manufacturing processes. From demand forecasting to quality control, AI-enabled technologies are completely redefining the concepts of chemical production.
Significantly reduced costs, increased production speed, and overall efficiency of business process are the new standards that AI tools instate. Chemical companies that have already implemented artificial intelligence demonstrate impressive ROIs, improved product quality, and streamlined supply chain processes.
If you are looking for a way to introduce AI into your business, Postindustria can assist you with a wide scope of services needed to create a fitting AI solution.
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