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

Client
Distributor of exotic fruits, berries and vegetables
Industry
Distribution and Logistics
Functional area
Demand Planning, ML-based Forecasting
Solution
Anaplan, ML
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. 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 Chiefly, this is crucial for a short-term sales forecast, which used to be generated by an outdated IT tool and demand planners.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.

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 Chiefly, this is crucial for a short-term sales forecast, which used to be generated by an outdated IT tool and demand planners.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

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