Paints and coatings companies use AI to can gain competitive advantages, save costs, reduce production time, and improve product quality. It helps with color matching, formulation optimization, quality control, and predictive maintenance. Here, we’ll discuss the role of artificial intelligence in the paints and coatings industry.
The paints and coatings industry is a dynamic market and includes various sectors like buildings and construction, automobile finishes, furniture (wood and non-wood), medical products, and other equipment. From antimicrobial coatings to self-cleaning paints and solar panel paintings, the products are used in a plethora of industries.
The Covid-19 pandemic affected the industrial coatings sector in 2020, resulting in a 4.2% decline in the global market size. Fortunately, the industry is regaining its position and returning to pre-pandemic levels. According to Statista, the global paints and coatings industry is expected to touch $235.06 billion by 2029.
Digitalization and sustainability are two key factors driving the paints and coatings industry in today’s world. Manufacturers and retailers are adopting artificial intelligence and machine learning tools to streamline production, quality control, distribution, and sales.
AI can forecast coating performance, increase production efficiency, optimize formulas, improve customer satisfaction, and save time, effort, and cost for the paints and coatings industry. In this blog, we’ll read about the role of AI in the paints and coatings industry and the need for partnering with an artificial intelligence consulting company to increase ROI and profits.
Artificial intelligence has varied roles in the paints and coatings industry. From developing corrosion and wear-resistant paints to increasing their adhesiveness and enhancing the self-lubricating nature of paints, AI can help in conducting the necessary research into material properties and creating new chemical compositions. AI and ML algorithms are being used for data modeling about the properties of different types of paints (based on their chemical composition).
Factors like the toxicity of the paint and coating, price fluctuations based on market demand, environmental impact, alternative materials, etc., can also be determined using AI and ML technologies. In fact, computer vision and AI are used to analyze images in bulk to develop smart coatings. By understanding the functioning of the microstructures in the materials, a manufacturer can not only create more durable and better-quality paints but can also optimize production to reduce input costs.
An article by American Coatings Association shows how AI has been instrumental in using advanced algorithms to provide recommendations around resin formulations and recipes.
Furthermore, Eric Sapper, the assistant professor in the Department of Chemistry & Biochemistry and the Western Coatings Technology Center said “Centuries of scientific learning and discovery do not need to be tossed out the window when you’re building an AI workflow. Instead, new AI workflows can be built around the institutional and domain-specific knowledge that already exists within the organization.”
This means if you’re looking to implement AI in your product development and coatings experimentation, you don’t need to start everything from scratch. And the transition from manual to AI workflows and innovation around paintings & coatings would be smooth and friction-free.
Now, let’s understand how AI will help save time, effort, and money in the broad parameters of the paintings and coatings industry.
The role of AI and ML in the paints and coatings industry is not limited to research and development. It can also be used to match colors and create new shades, help customers choose the right colors based on their requirements, develop the right marketing strategy for the target audiences, streamline the supply chain, and enhance customer experience.
KCC Corporation, a South Korean paint company, developed an AI-based color matching and design system called K-Smart that can complete color mixing from any combination design in just five minutes. The company used fifty years’ worth of data to develop this AI model.
Similarly, there are other AI apps available, such as Sherwin-Williams ColorSnap Match, X-Rite Color-Eye, Datacolor MATCH TEXTILE, Nix Pro 2, etc. that help choose the best colors and combinations to paint the walls (interiors and exteriors).
Using artificial intelligence for matching and mixing colors delivers the following advantages:
AI-based color-matching tools can match colors within minutes, which is significantly faster than traditional color-matching methods that can take hours or even days. Traditionally, paint companies rely on an expert technician or a spectrophotometer (or both). However, investing in an AI model results in greater efficiency.
These tools also eliminate the potential for human error and subjectivity, resulting in more accurate and consistent color matches. This leads to fewer color mismatches and less rework. Color mismatches can be due to changes in the paint formula, weather conditions, paint quality and age, and the texture and type of surface on which it will be applied. The changes in the pigmentation levels also lead to color mismatch.
By enabling manufacturers to use only the optimum quantity of paint, these tools reduce waste and improve sustainability.
AI-based color-matching tools enhance the overall customer experience by satisfying their requirements. This leads to greater customer satisfaction and loyalty.
AI companies that provide these color-matching tools help manufacturers develop new and innovative color options that were previously difficult to achieve with traditional methods. As a result, painting companies are able to stand out in the crowded market.
The significance of formulation optimization in the paints and coatings industry lies in its ability to enhance the quality and effectiveness, of the final product while reducing its cost of production.
While using the traditional methods, the companies had to go through a lot of trial and error. Due to this, formulations were often suboptimal, time-consuming, and expensive. With AI, optimization has become much more efficient and accurate in recent years. AI algorithms can analyze vast amounts of data on raw materials, formulations, and product properties to identify the most effective combinations and optimize formulations in real-time.
Here are the four ways AI and machine learning help in formulation optimization –
By using AI algorithms’ predictive modeling, paints, and coatings manufacturers are able to simulate how different raw materials and formulations will interact; and then predict the performance and properties of the final product. This helps in optimizing formulations and ends the need for extensive trial-and-error testing.
Painting companies are able to analyze vast amounts of data by using AI algorithms on raw materials, formulations, and product properties to identify patterns and correlations. As a result, identifying new raw materials that can improve performance and reduce costs becomes significantly easier.
AI algorithms also optimize the production process and reduce waste and energy consumption.
The AI optimization of formulations allows chemists to respond to changing market demands and customer needs effectively.
AI algorithms allow the coatings industry to find out defects and quality issues quickly and ensure course corrections before it’s launched in the market.
Here are three ways AI-based quality control can help the painting and coating industry –
AI algorithms detect defects and quality issues quickly and accurately, which ultimately reduces the risk of releasing substandard products.
AI-based quality control systems can process large volumes of data in real-time, which reduces the time and resources required for manual inspection and testing.
AI-based quality control systems ensure that products meet consistent quality standards, regardless of variations in raw materials or production processes. It helps in improving the reliability and reputation of the manufacturer and enhancing customer satisfaction.
AI-based predictive maintenance systems use advanced algorithms to analyze data on equipment performance, such as vibration, temperature, and energy consumption.
Here are some of the advantages of using AI-based predictive maintenance in the paint and coatings industry:
AI-based predictive maintenance systems can identify potential equipment failures before they occur, enabling organizations to perform maintenance proactively. This reduces the risk of unplanned downtime and improves equipment reliability.
Predictive maintenance can help manufacturers reduce maintenance costs, which allows organizations to schedule maintenance proactively, reducing the need for emergency repairs and minimizing downtime.
By reducing downtime and ensuring that equipment is operating at optimal levels, predictive maintenance can improve overall efficiency and productivity. It helps increase the OEE (Overall Equipment Effectiveness) score of the paint manufacturer by reducing downtime and eliminating the risk of unexpected breakdowns during production.
Predictive maintenance can help prevent equipment failures that could lead to accidents or injuries, improving safety in the workplace.
Many enterprises face challenges when adopting AI to revamp their business processes. Such issues can be overcome by following a proper procedure.
AI has revolutionized the paints and coatings industry. And even if the global paints and coatings market is going to become a huge market (with a CAGR of 7.6%) by 2027, it’s doubtful how many paint and coating companies will take advantage of the magnificence of artificial intelligence.
Here’s what Rebecca Liebert, Executive Vice President of PPG said in an interview in the year 2020 – “Digital is the future, and data is how we will get there. The sooner companies embrace that, the more successful they will be.”
Liebert’s opinion still seems relevant with the advent of AI in the paints and coatings industry. More and more companies will understand the criticality of AI and digital technology in the paints and coatings industry, and better they will be able to ensure growth in the years to come.
Artificial intelligence, machine learning, and big data analytics will continue to play a vital role in the paints and coatings industry for a long time. With Industry 4.0 gaining popularity, manufacturers have to adopt digital transformation and invest in AI tools, IoT (Internet of Things) devices, etc., to survive market competition.
Consult an offshore AI consulting company and take advantage of its experienced team to achieve your long-term goals. Become a sustainable paint manufacturer and increase your brand value.
Data Engineering
AI & Machine Learning
By Use Cases
Business Intelligence & Tableau
