Make the right product available just when the customer needs it

Transforming demand strategy at Asian Paints to reshape its
consumer focus.

Arriving at the right forecast for several hundreds of products keeping into perspective each product-line’s historical market behavioral pattern has never been easy. Adding to that complexity is the variety of shades each final-product get packaged into and the inherent seasonality attached to each of them. Such a volatile and proliferated product portfolio binds the demand planner to publish the best possible forecast output on best effort basis rather than a statistically optimal output. Catering to a volatile demand arising from thousands of dealers channeled through a complex supply network of depots across the country was getting more challenging by the day. With the adoption of advanced analytics into its demand strategy, Asian Paints could mitigate such risks while renewing its commitment toward a strong consumer-focus and an innovative spirit.

Leveraging the power of analytics
The key objective at Asian Paints was to leverage forecasting and optimization analytics to enhance its processes and achieve greater accuracy in demand planning. Streamlining supply with demand work stream was made possible because of the embedded layer of analytics in the planning ecosystem. The investment into analytics also resulted in getting the right forecast aiding in arresting the negative deviations in other attendant business functions like production, inventory, supply and distribution planning. On this analytics journey, Asian Paints partnered with SAS to develop and implement an innovative next generation solution that seamlessly integrates multiple causal variables. The joint effort facilitated in enhancing forecast accuracy by streamlining demand planning for a mix of product offerings which entailed a deeper investigation of individual product portfolio, including seasonality, rather than a plain vanilla solution across the products.

Insight to Action
Staying consistent with the key mantra of Asian Paints’ analytics journey, SAS Forecast Server served to tightly integrate well in their supply chain ecosystem to transform insights into actions. This strategic shift resulted in deploying refined and customized model outputs from SAS Forecast Server feeding as a critical input for forecast production and consumption by various business stakeholders. Tweaking the solution’s output to suit and reflect Asian Paints’ existing accuracies that were already excellent, was an inherent challenge which was addressed by effectively using cleverly architected deployment strategy incorporating complex macros and business rules. The primary goal was to ensure that right amounts of products make it to the shelves and into customer’s hands and enabling a satisfying customer service level.

Forecasting with SAS for improving accuracy
Before SAS Forecast Server could form the backbone of forecasting at Asian Paints for its paints business, SAS carefully crafted the deployment architecture to articulate the project delivery with finesse and precision. SAS designed a scalable and reliable demand forecasting methodology by carefully assessing and arriving at champion forecast models leveraging historical datasets with seasonal influences. The collaborative effort resulted in realizing the vision to achieve the best possible homogeneity within and heterogeneity across segments for the entire product portfolio. It also incorporated multiple statistical approaches and business parameters to improve forecast accuracy. Owing to the adoption of a range of forecast modeling techniques, an improvement of accuracy was delivered over the incumbent demand planning processes. Having deployed SAS Forecast Server, the demand management team envisions to build on that foundation and gradually incorporate several other tactical business decisions based on future pricing, promotions, events and stock out data. Advanced analytical sophistication and simulation based heuristics is slated to be leveraged for dynamic tracking of forecast performance with exception based intervention.

Emphasis on leveraging reliable forecasting methods can never be overstated as it leads to freeing up time to focus on demand planning for highly volatile products. More successful forecasting and production decisions ensure products are available when customers want them.