AI Case Study
Danone reduces forecast error and lost sales by 20 and 30 percent respectively and achieves a 10 point ROI improvement in promotions with machine learning
Danone implemented ToolsGroup's machine learning solution with the aim of identifying how promotional and media events affect their sales. They also needed to secure a more accurate forecast of demand, which is necessary given that their fresh products face volatile demand and a short shelf life. The solution leverages machine learning to predict demand variability and planning. Danone managed to create an efficient planning coordination between various departments such as marketing, sales, account management, supply chain and finance and has also reported a 20% reduction in forecast error, 30% reduction in lost sales, and a 10 point ROI improvement in promotions.
Consumer Goods And Services
Food Beverage And Drugs
ToolsGroup's demand modeling creates a reliable baseline, then uses machine learning to adjust the baseline by identifying the effect of stimuli and demand indicators at a detailed channel level (prnewswire).
"When Danone launched their Disc’Over project, they looked for a partner who could provide technology to identify the shared characteristics of promotional and media events and predict their lift on baseline sales, Danone chose ToolsGroup. The ToolsGroup system uses machine learning technology to analyze demand variability and improve demand planning. The new forecasting capability proved to be a key functionality for not only improving the forecasting process, but also for creating an effective planning process between marketing, sales, account management, supply chain and finance.
For Sales and Marketing, the new system provides reliable detailed modeling of trade promotion uplift. It allows campaign operational planning in which the promotion and media event is allocated to the account and provides support to customer plan definition for key accounts.
For Supply Chain, it improves efficiency and inventory balance, allowing them to satisfy promotions and media uplifts with timely production and balanced inventory deployment to achieve the target service levels for detailed (channel/store level) supply chain execution. Predictive Commerce enabled a cohesive planning process to improve danone's forecast" (ToolsGroup).
According to prnewswire, ToolsGroup reports that Danone's "trade promotion forecasting covered a wide range of fresh products characterized by dynamic demand, short shelf life and the need for accurate demand forecasting." Moreover, according to ToolsGroup case study, "Danone offers a wide range of fresh healthy food products impacted by trade promotions and media events. More than 30% of their volume is sold on promotional offers (discounts, leaflets, displays, hostesses, etc.), accounting for nearly 70% of forecast inaccuracy. Danone also spends a lot on media advertising, which significantly impacts sales and forecasts.
Danone’s forecasting, planning and execution for these promotional and media events was somewhat ad hoc. Four functional departments: sales, demand forecasting, account planning and finance had trouble properly coordinating and communicating their goals, plans and expected outcomes, producing uneven results and unpredictable emergencies."
ToolsGroup reports that their client, Danone, "improved performance across a range of business metrics from front-end forecasting to back-end production planning, including:
* A 20% reduction in forecast error, increasing forecast accuracy to 92%
* A 30% reduction in lost sales, increasing service levels to 98.6%
* A 30% reduction in product obsolescence
* A 6% increase in Net ROI in 2011, improved further to 8% in 2012
* A 36% improvement in net uplift from promotions in 2011, improved further to 55% in 2012
* Demand planners workload reduced by 50% and refocused to higher value-added activities
* Exceeded service level target of 98.7% for 37 consecutive months in a row"
"The ToolsGroup system uses machine learning technology to analyze demand variability and improve demand planning.
ToolsGroup's machine learning technology creates a major improvement in demand visibility, forecast quality and level of forecast detail, which are all critical for reliable supply chain planning. It harnesses the power of machine learning to accurately model demand in difficult forecasting scenarios such as trade promotions, new product introductions, extreme seasonality and product cannibalisation."