Revolutionizing Supply Chain Management for a Major Paint Manufacturer in India

The customer is one of India's largest paint manufacturers with a presence in multiple countries. The company serves both B2C and B2B business segments, including Decorative, Automotive, General Industrial, and Coatings. The company was growing rapidly and was facing challenges in managing its demand and supply planning processes due to high dependency on manual activities and Excel spreadsheets. The company primarily relied on the Annual Operating Plan (AOP) to determine future demand, which meant they were unable to keep up with the latest market trends. There was limited collaboration between sales, marketing, and supply chain teams, leading to inaccuracies in a heavily regional, promo-driven market.

One of India's largest paint manufacturers, with a presence in multiple countries and serving both B2C and B2B business segments, was facing significant challenges in managing its demand and supply planning processes. The company was growing rapidly, and its existing processes, heavily reliant on manual activities and Excel spreadsheets, were unable to support this growth. The company primarily relied on the Annual Operating Plan (AOP) to determine future demand, which meant they were unable to keep up with the latest market trends. There was limited collaboration between sales, marketing, and supply chain teams, leading to inaccuracies in a heavily regional, promo-driven market. The stocking of depots was controlled by basic automation and overridden by sales team-based manual replenishment requests, leading to slow-moving inventory and stockouts. With a limited planning horizon (one month) and a weekly production plan, the procurement teams struggled to estimate the inventory requirements for raw materials, leading to stockouts or excess inventory with teams operating in silos.

The company partnered with o9 to leverage its advanced machine learning analytics and consensus forecasting capabilities to drive improvements in forecast accuracy. The manual work was removed, and a complete overview was generated using o9’s integrated platform for inventory planning, procurement planning, and master planning. The company was able to leverage o9’s optimization engine, providing an efficient way to arrive at inventory requirements based on matching demand, supply, and inventory levels across time horizons. The Enterprise Knowledge Graph was used to implement a complete end-to-end Integrated Business Planning (IBP) process. This includes demand planning in collaboration with sales and marketing, distribution planning, master planning, procurement planning, and S&OP. The manual Excel-based planning and homegrown solutions were replaced with o9's platform.

• The implementation of o9's platform brought about significant operational improvements for the company. The advanced machine learning analytics and consensus forecasting capabilities of o9's platform improved forecast accuracy, enabling the company to keep up with the latest market trends. The integrated platform for inventory planning, procurement planning, and master planning removed the need for manual work and provided a complete overview of the company's operations. The optimization engine allowed the company to efficiently determine inventory requirements based on matching demand, supply, and inventory levels across time horizons. The implementation of a complete end-to-end Integrated Business Planning (IBP) process facilitated better collaboration between sales, marketing, and supply chain teams, reducing inaccuracies in a heavily regional, promo-driven market.

• Reduction in inventory, specifically slow-moving inventory.
• Reduction in lost sales.
• Improved forecast accuracy.