Berger Paints India Limited is an Indian multinational paint company based in Kolkata, West Bengal. It ranks among the largest paint manufacturing company in India and globally. The company claims to hold a dominant position in the industry, including a 20% market share in the Indian decorative segment and the leading spot in India’s industrial protective coatings sector.
For Berger Paints, one of the key business challenges was managing demand volatility across a diverse portfolio of products, markets, channels, and geographies. Traditional forecasting models relied heavily on historical data and manual interventions, making it difficult to accurately predict demand in a market influenced by seasonality, economic factors, and rapidly changing customer buying patterns based on different demographics.
“As a result, we often faced challenges in balancing inventory, production planning, raw material procurement, and service levels. Forecast inaccuracies could lead to excess inventory in some locations, stock shortages in others, higher working capital requirements, and missed market opportunities,” says Partha Protim Mondal, CIO, Berger Paints.
In response to these challenges, Berger Paints reconsidered its supply chain’s planning layer. The company launched an AI-driven planning platform that started with supply and demand planning before expanding the digital foundation into logistics and warehouse management.
“The objective was to create an AI-driven, data-led planning ecosystem that could enhance forecasting accuracy, improve visibility across the supply chain, and enable more informed decision-making,” says Mondal.
However, the change meant more than just putting in place a new technological platform. It involved altering the way the company plans, reacts, and makes supply-chain choices using data. Rather than attempting to transform the entire supply chain simultaneously, Berger Paints chose to begin with the planning layer.
Building an Integration-First Architecture
At Berger Paints, an integration-first architecture was implemented to ensure that the new AI-driven platform, called o9, became an extension of the existing digital ecosystem rather than a standalone planning platform. The objective was to enable seamless information flow across planning, execution, and inventory management processes.
At the core of the integration is Oracle ERP, which serves as the system of record for master data, transactional data, manufacturing information, inventory balances, and organizational structures. Relevant data elements such as items, locations, plants, warehouses, and inventory positions are extracted from Oracle ERP and synchronized with o9 through defined integration interfaces between Oracle & o9.
“From a supply chain perspective, o9 consumes Oracle eBusiness data (inventory, pending orders etc) to generate demand and supply plans, sourcing recommendations, and factory allocation decisions. These plans are then synchronized back into execution systems for operational fulfilment. During rollout, the platform utilized algorithm-driven factory allocation and forecasting capabilities while continuing to interact with Oracle-based order and execution processes,” Mondal says.
The integration architecture was designed to ensure that newly created locations, products, and planning parameters in Oracle are automatically reflected in o9, maintaining a single version of the truth across the organization.
“For enterprise integration with periphery systems of end-to-end supply chain solution like logistics (logistics for both primary and secondary sales), we leveraged Oracle Integration Cloud (OIC) as the strategic middleware layer alongside traditional API based integration. OIC provides secure, near real-time connectivity between Oracle ERP and business applications, reducing point-to-point integrations and enabling scalable data exchange across the ecosystem,” says Mondal.
Demand and Supply Planning
The initial implementation focused on demand forecasting, depot demand planning, factory sourcing and supply allocation. One of the significant changes was the move from manual, indent-driven replenishment towards forecast-driven planning. The platform’s algorithms could consider factors such as demand, factory capacity, geography and product availability when supporting factory sourcing and supply allocation decisions.
This helped create a more consistent planning process across the network. The implementation was subsequently extended across both Berger Paints’ Retail and Industrial businesses.
For Berger Paints, planning became the foundation on which other supply-chain processes could be improved. Procurement, manufacturing and logistics remained important parts of the end-to-end model, but strengthening demand and supply planning first allowed the organization to establish better data quality, forecasting discipline and supply synchronization before expanding further.
Changing Established Processes
One of the biggest lessons from the implementation was that the technology itself was not the most difficult part. Mondal shared that the bigger challenge was changing established processes and building confidence in system-driven planning.
A paradigm where forecasts and sourcing suggestions increasingly came from an integrated planning environment required planners, sales teams, depot staff, and supply-chain partners to adjust.
There were also complexities around factory-product mapping, manufacturing constraints, product availability, inter-depot transfers and master-data quality.
The company addressed these challenges through a combination of executive sponsorship, training, process standardization, and phased implementation.
“The leadership played a major role in communicating that the new planning platform was not just another IT system but would become part of the organization’s standard planning process. Training programs were held before go-live, and additional support was provided to users who needed it,” says Mondal.
The company also leveraged dedicated support mechanisms, comprising on-site teams, escalation channels, and WhatsApp-based assistance. An AI-enabled chatbot was introduced to further improve the user experience.
Measuring the Impact
AI/ML has played its most significant role in demand forecasting and integrated supply-chain planning.
Instead of spending significant time consolidating information across spreadsheets and manual processes, the planning team can now spend more time evaluating scenarios and making business decisions. The larger value, therefore, lies not simply in automated forecasting but in creating a more responsive planning process.
Forecast quality, inventory control, planning flexibility, service performance, and supply chain visibility have all improved at Berger Paints.
However, the business has not released precise performance metrics.
With AI-driven demand sensing helping planners better account for market trends, seasonality, and geographic variations, forecast accuracy continues to be one of the key metrics. Inventory management has also benefited from the transition towards forecast-driven planning and improved visibility across factories, depots and warehouses. The organization has also seen greater efficiency in its S&OP process.
For the finance function, the focus extends beyond forecast accuracy.
Working-capital optimization remains a key measure, with inventory levels, inventory turns and inventory carrying costs being tracked alongside service performance and productivity. The broader objective is to ensure that better planning translates into healthier inventory, stronger availability and more efficient use of working capital.
The Next Steps
The transformation did not stop with demand and supply planning. As planning became more integrated, the need for stronger execution capabilities became increasingly important. This influenced Berger Paints’ investments in Warehouse Management Systems across its warehouses and plants. The focus included inventory visibility, FIFO-based dispatch, inventory management, and picking efficiency.
The company has also worked with logistics platforms such as Pando and Mojro. In effect, the initial planning transformation created a foundation for a broader digital supply-chain architecture. Better demand visibility could support better supply decisions, while better supply planning created the need for execution systems that could respond with similar levels of visibility and discipline.
Key Takeaways
Mondal’s advice for CIOs embarking on a similar transformation is to start by treating supply chain digitization as a business transformation rather than a technology implementation.
The first step, he says, is to define the business problem clearly. For Berger Paints, that meant addressing demand variability, planning complexity and the limitations of manual forecasting.
The second is to invest early in data and process standardization. Advanced analytics and AI cannot deliver their intended value if master data, sourcing rules, factory mappings and planning ownership remain inconsistent.
The third is to adopt a phased approach. Berger began with demand planning before expanding into supply planning and subsequently supporting broader supply-chain initiatives.
The fourth is change management. Executive sponsorship, user training, communication, and hands-on support were critical to building confidence in the new model.
“Technology platforms such as o9 are powerful enablers, but success ultimately depends on process alignment, data quality, organizational readiness, and executive sponsorship,” Mondal adds.
