Global Battery Manufacturer Unlocks Insights from SAP Data to Improve Demand Forecasting in Google Cloud

1 min read
Jun 27, 2023


Global Battery Manufacturer




Canada, USA

For this global manufacturer and distributor of consumer and automotive batteries, enhancing customer service levels without sacrificing efficiency was key to achieving their goals. The ability to analyze and predict manufacturing and sales demand would significantly improve resource planning, product delivery and customer satisfaction—but amalgamating the necessary data to make those decisions was proving difficult. The company’s SAP environment could manage its manufacturing processes and financial controls, but it was difficult and costly to source and incorporate third-party data like population demographics, weather forecasts, and other outcome-influencing information into one analytics environment.

What we did

Pythian was engaged to implement a data environment leveraging Google Cloud to integrate the client's SAP environment with multi-sourced third-party data. This would help to deliver a substantial improvement in forecasting intelligence and ensure that customer needs were reliably met.

Pythian determined that the Google Cloud Cortex Framework, specifically designed to consolidate data sources, could effectively consolidate and analyze the manufacturer’s data to achieve its goals.

Pythian managed the entire implementation of the Google Cloud Cortex Framework and its integrations, including building dashboards to provide visuals of the data and providing training for the in-house IT team.


Technologies used 

  • Google Cloud Cortex Framework
  • Google Cloud BigQuery
  • Google Cloud
  • Google Cloud Composer
  • Looker
  • AecorSoft

Key Outcomes

Pythian successfully deployed Cortex-enabled demand forecasting in the customer’s SAP environment, resulting in an analytical model that now combines third-party data—such as long-term weather forecasting and population data— with their ERP data, to better predict and plan for demand fluctuations in the battery product and automotive product categories.

  • More accurate production and product placement in the warehouse to fulfill store-level inventory needs.
  • Development of a strong foundation for further transformation and the ability to use machine learning for different product lines in other parts of the business.
  • Ability for the marketing team to kick-start insights, adapt to changing market demands and reduce time to value.

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