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

Client
Distributor of exotic fruits, berries and vegetables
Sector
Retail, distribution & logistics
Functional area
Operation and strategic sales forecasting
Platforms
Anaplan, Integrated ML Forecasting module
Machine learning algorithms and Anaplan transform forecasting and the S&OP process

“Launching an ML-enabled sales forecasting has been part of a large initiative to transform the S&OP process in the company. Our demand planning team got hold of not only a more high quality forecast, factoring in features and patterns which were never leveraged by the statistical methods and analysts, but a user interface in Anaplan to review, correct and finalize the ML forecast in Anaplan.”

Head of Planning
01
BUSINESS CHALLENGE
A large distributor of exotic fruits and berries is selling the produce in top national retailers as well as HORECA. ... Read more

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 Chiefly, this is crucial for a short-term sales forecast, which used to be generated by an outdated IT tool and demand planners.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.

02
SOLUTION
The Planingo team developed and launched a ML-enabled sales forecasting model for different forecasting horizons and an interface to manage ... Read more

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.
03
BUSINESS VALUE
The forecast accuracy increased by 13 p.p. compared to the existing statistical forecast. The operational forecast accuracy grew compared to ... Read more
  • 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.