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Sachin Kheveria

The paint and coatings industry has historically remained slow in adopting the latest technologies, and even if technology was utilized, it was restricted to specific activities only. However, slowly but steadily, the industry is breaking through its aversion to change. That said, the industry dynamics in recent times have also changed radically due to increased volatilities stemming from shifting demands, evolving regulations, and challenges like raw material price fluctuations, demand-supply disruption, and geopolitics amplified by the pandemic, among other factors. These challenges are forcing paint and coatings manufacturers to redefine their business models to stay competitive and sustain market dominance.

To grow, we believe that the paint and coatings industry will need to develop traits based on Neural ManufacturingTM –connected, cognitive, and collaborative functions. Manufacturers that develop and adapt to these neural capabilities can generate valuable insights and demonstrate resilience, adaptability, and purpose-driven behaviors, all of which will drive intelligent and faster decision making. Emerging technologies like data analytics, artificial intelligence (AI) and machine learning (ML), blockchain, cloud, and more will a play crucial role in helping the industry cultivate these traits to transform their operations, offerings, and customer experience.

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%.

The supply chain of paint and coatings is expensive and complex. Volatilities are further amplified by disruptions caused by price fluctuations, high shipping costs, longer lead times, shorter product life cycle, and more. To manage these challenges, manufacturers are adopting digital technologies across their supply chain to gain the much-needed agility and transparency in their operations. Supply chain visibility will ensure resilience, reduce variability, and enable quick and effective decision making, resulting in cost reduction and ensuring on-time delivery.

Advanced analytics on external factors can predict price fluctuations and enable cognitive procurement. Technologies like the internet of things (IoT) and cloud computing can provide intelligent insights on real-time locations of resources, condition of vehicles, driver performance, and more. Technology like blockchain, thanks to its connected ecosystem, can provide transparency on financial transactions, material movement, and inventory stocks across the supply chain, which eliminates the possibility of fraud.

Personalized customer experience is by far the most important element impacting a customer’s buying decision. Paint and coatings retailers need to invest judiciously in delivering unique brand experiences to customers. For example, retailers can create virtual store experiences by personalizing store ambience and product layout tailored to their customers’ preferences. This is done using augmented, virtual, and extended reality. In addition, retailers can use AI and ML in neural networks to optimize discounts and prices offered to individual customers based on their preference and purchasing history, thus offering curated shopping experiences.

The approach matters

To embed neural traits in their business models and operations, paint and coatings manufacturers will need a focused, planned, and holistic approach to transform into a smart, agile, flexible, and resilient ecosystem. This ecosystem of people, process, and technology will need to connect, collaborate, and mature over time to enable manufacturers to achieve cognitive capabilities.

About the author

Sachin Kheveria
Sachin Kheveria is an assistant consultant with TCS' Manufacturing business unit. He has more than seven years of industry experience working with TCS’ customers in addressing supply chain challenges in the areas of transportation, logistics, inventory, procurement, and fleet management across process and discrete manufacturing. He holds a Bachelor’s degree in Mechanical Engineering and Master’s degree in Supply Chain and Operations Management from the National Institute of Industrial Engineering (NITIE), Mumbai, India.
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