Food & Beverage デジタルブレイン

A leader in the food and beverage industry and currently in the top largest food and beverage company in the world.

Inaccurate forecast and low adoption by the sales organization was a challenge for this customer. Moreover, they were unable to use drivers of demand to better predict the future.


Reduction of food waste and reduction of inventory.



The forecast accuracy was low and lagging indicators were predominantly being used in the forecasting process.


With o9, this company was able to incorporate internal and external drivers of demand into o9’s highly differentiated ML forecasting capabilities.

Manual Number Crunching

Demand planners were spending the majority
of their time crunching Excel spreadsheets, not having the time to focus on what truly matters.


With o9, the grunt work was removed by enabling data-driven exception workflows.

Misaligned Forecasts

This company was generating a demand forecast, a financial forecast and a commercial forecast based on different datasets and assumptions.


With o9, this company moved to a single forecasting engine that will drive plans and decisions, creating cross-functional alignment.



The o9 Enterprise Knowledge Graph was used to build market knowledge models (competitors, consumers, retailers, distributors) and demand-knowledge models that are incorporating leading indicators of sell-out (trade promotions, marketing initiatives, weather, competitor pricing and promotions, Nielsen and IRI data). o9 leveraged its open architecture by using best-in-class algorithms from R and Python to get to an optimal forecast.


Blue Yonder and SAP APO.



  1. o9’s highly differentiated knowledge graph allows for the incorporation of leading indicators of demand and turning those indicators into more accurate forecasts and commercial insights.
  2. o9’s scalable and cloud native platform with highly differentiated open architecture leveraged best-in-class analytics.
  3. Amazing user experience of the platform allowed for high user adoption by the sales organization.


  1. Significant improvement of forecast accuracy and a much more consistent bias.
  2. Significant inventory improvement.
  3. Reduction of lost sales.