Unilever US improved connected planning by building forecasting process in Anaplan

Unilever USA is the largest and most complex operating company managing more than 200 brands that include beauty and personal care products, food and refreshments, and others. Having such a huge product portfolio the innovation forecasting process was still executed in Excel, compelling data from different sources with many participants involved. The planning team usually faced the following challenges:

• Formula errors — in many cases users did not notice formula errors in the files.
• Inconsistent customer data — In each IFT file users could change store counts resulting in inconsistent store count data.
• No feeds to Anaplan S&OP or other systems – users were required to manually enter the data in the S&OP model or Rapid Response directly.
• No feeds from SAP AMPS (Customer Plans) or shipment history into the IFT file.
• No centralized repository for the IFT projects

Unilever US team set the goal to improve innovation forecasting performance by implementing a special model –  Innovation Forecast Tool (IFT) in Anaplan. The process of creating and managing projects in the company acquired common standards and Anaplan became a single source of truth for all Users. The technical flexibility of Anaplan cloud platform allowed to quickly/easily connect IFT with the existing S&OP model as well as to integrate them with other systems in use – Rapid Response, Kinaxis, SAP– and therefore link finance, sales, and operations data together for a seamless big-picture financial planning and forecasting. As a result, the forecast is now based on real data consolidated from different sources which significantly enhanced accuracy.

• Consistency, transparency, and visibility of shared customer data across different functions;
• Minimum manual processes in data input;
• More opportunities for analysis, for example, seasonality or compare like items;
• Automated feeds to Anaplan S&OP and Rapid Response;
• Extended functionality: Customer plans and sales history;
• Enhanced collaboration and speed of performance between several divisions involved in the forecasting process;
• Precise forecast in real-time with less time effort: from 7 days to 3 days;
• More predictive insights for business decisions for different functions.