This post was authored by Adam Wasserman, Solutions Architect at Databricks and Clinton Ford, Staff Partner Marketing Manager at Databricks.
The importance of supply chain analytics
Rapid changes in consumer purchase behavior can have a material impact on supply chain planning, inventory management, and business results. Accurate forecasts of consumer-driven demand are just the starting point for optimizing profitability and other business outcomes. Swift inventory adjustments across distribution networks are critical to ensure supply meets demand while minimizing shipping costs for consumers. In addition, consumers redeem seasonal offers, purchase add-ons and subscriptions that affect product supply and logistics planning.
Supply chain analytics at ButcherBox
ButcherBox faced extremely complex demand planning as it sought to ensure inventory with sufficient lead times, meet highly-variable customer order preferences, navigate unpredictable customer sign-ups and manage delivery logistics. It needed a predictive solution to address these challenges, adapt quickly and integrate tightly with the rest of its Azure data estate.
“Though ButcherBox was cloud-born, all our teams used spreadsheets,” said Jimmy Cooper, Head of Data, ButcherBox. “Because of this, we were working with outdated data from the moment a report was published. It’s a very different world now that we’re working with Azure Databricks.”
How ButcherBox streamlined supply chain analytics
ButcherBox uses Azure Databricks to generate its Demand Plan. When Azure Data Factory (ADF) triggers the Demand Plan run, Azure Databricks processes supply chain data from Azure Data Lake, vendor data and Hive caches. New outputs are stored in a data lake, then Azure Synapse updates Demand Plan production visualizations.
Batching/microbatching orchestration with Azure Databricks