Machine learning algorithms and Anaplan transform forecasting and the S&OP process

A large distributor of exotic fruits and berries is selling the produce in top national retailers as well as HORECA. Considering the nature of perishable products, growing forecast accuracy is a top priority for the business. Chiefly, this is crucial for a short-term sales forecast, which used to be generated by an outdated IT tool and demand planners.

Developing a forecasting model based on Machine learning algorithms was part of a bigger project meant to automate the Sales and Operation process (S&OP) with Anaplan, a renowned connected planning platform.

The Planingo team developed and launched a ML-enabled sales forecasting model for different forecasting horizons and an interface to manage the forecasts by the demand planning team:

• A multivariate ML model using various input data (historical sales, prices, calendar events, COVID-effect) and a combination of ML algorithms such as gradient boosting, regression, neural networks (multilayer perceptron), KNN.
• The clients’ team now have two ML forecasts: an operational one for the next 12 weeks (by days) and rolling strategic one for the next 12 months.
• An interface in Anaplan to visualize, review, correct and approve the forecast in real time.
• Fully automated integration between all input data, the ML model and Anaplan to regularly refresh the forecast without involving the external data scientists.

• The forecast accuracy increased by 13 p.p. compared to the existing statistical forecast.
• The operational forecast accuracy grew compared to the final forecast, corrected by demand planning managers.
• The S&OP process got faster thanks to the ML forecasting tool and the overall process automation in Anaplan.