Client Background

The client is one of the largest brewing companies in the United States and employs over 30,000 people. It was the world’s largest brewing company based on revenue, but third in brewing volume, before its acquisition. The division operates 12 breweries in the United States and 17 others overseas.

 

Business Objective

The client wanted to replace the legacy APO system and leverage machine learning forecast capability which can automatically enrich the forecasts with external drivers and minimize the manual enrichments.

 

Solution

Our data science team worked with client and ISV to acquire following datasets needed for forecasting on google cloud platform.

  • Orders: Data at a weekly level for each of 10,000 SKU’s
  • Macro-Economic Drivers: Consumer Price Index, LCU per USD, Retail Sales Index (USD), Industrial Production Index and Inverse Exchange Rate.
  • Weather Drivers: Weighted Precipitation, Snow, Weighted Temperature, Max Weighted Temperature and Min Weighted Temperature.
  • Marketing Spend Drivers: Digital Media Buying, Brand Events, Traditional Media Buying and Brand Promotion.
  • Promotions: We have two types of promotions in Canada, price drop promotions and goodies promotion.

 

 

Google Data proc was used to process and prepare the data needed for model training along with Vertex AI for model training.

Highly level summary of modelling process we followed is indicated below

 

 

Finally demand forecasting capability we developed had the Operational forecast – weekly forecast. The level of forecast was at Item, sales channel and monthly level. This has been implemented for Orders, Shipments and POS for the regions USA, Mexico and Canada.

 

Outcome

Forecasted values were generated in supply chain solution to provide granular view at Region, province, item and customer level. We were able to achieve accuracy levels for 90% percent for 12 weeks forward forecast on 20% percent of SKU’s that drive roughly 80% of customer business. For remaining accuracy levels were 65%.