A neural perspective on the paint and coatings value chain
Neural capabilities will create exponential value across the value chain of the paint and coatings industry.
For example, as the demand for eco-friendly coatings with smart features like anti-UV-ray, anti-corrosion, anti-fungal, and heat-resistant began to increase, major players in the industry have invested around 1 - 3.5% of their overall sales in R&D to develop innovative products and reduce the time to market, to capture higher market share. Neural traits developed by performing analytics in a connected ecosystem will speed up the development cycle by generating insights from customer data, identifying requirements, pain points, and user sentiments. With greater collaboration through cloud-based tools within and across organizations, institutes, laboratories, and startups, a partner ecosystem will evolve, where information on novel materials and research work can be accessed quickly. Besides, AI and ML technology can generate significant benefits in product development and testing. These technologies will assist in virtual product formulation by developing digital weather twins for paints. These digital twins can simulate the impact of environmental and other factors on the paint and coatings sector, thereby mitigating risks and reducing time, money, and effort to launch new products.
We believe paint and coatings companies must focus on making their manufacturing operations flexible, agile, and resilient, especially after the pandemic, as reducing costs and improving productivity have become crucial to avoid squeezing margins. Smart factories equipped with a well-connected ecosystem of people, process, and technology impart a cognitive capability, where self-monitoring and controlling critical parameters by equipment make autonomous operations a reality. Paint and coatings manufacturers can also leverage AI and ML to receive intelligent and timely alerts on diagnostic, predictive, and prescriptive maintenance for improving machine availability, quality, and throughput. A good example is that of Asian Paints: they created a digital twin for production processes using AI on the data collected from various assets. Digitalization reduced the cycle time for the firm by 7%.