Coop Reduces Food Waste by Forecasting with Google’s AI and Data Cloud
Customer Company Size
Large Corporate
Region
- Europe
Country
- Switzerland
Product
- Vertex AI Forecast
- Google Cloud
- BigQuery
- Google Kubernetes Engine
Tech Stack
- PyTorch
- TensorFlow
- Extreme Gradient Boosting
Implementation Scale
- Pilot projects
Impact Metrics
- Productivity Improvements
- Cost Savings
- Environmental Impact Reduction
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Logistics & Transportation
- Business Operation
Use Cases
- Predictive Maintenance
- Supply Chain Visibility
Services
- Data Science Services
- System Integration
About The Customer
Coop is a large Swiss retailer with a rich history spanning nearly 160 years. Despite its long-standing presence in the market, Coop is committed to modernizing its operations through innovative technologies. The company has a dedicated machine learning (ML) team that began its journey in 2018 with the mission to leverage ML-powered forecasting to inform business decisions. Coop's focus is on optimizing operations to enhance customer satisfaction, reduce costs, and support sustainability goals. The company is particularly committed to becoming a zero-waste organization, integrating sustainability into all aspects of its business, from supplier selection to reducing energy, CO2 emissions, waste materials, and water usage in its supply chains.
The Challenge
Coop faced challenges with its initial on-premises forecasting environment, which was limited by cumbersome scaling and infrastructure issues. The company needed a more robust solution to operationalize machine learning outcomes beyond local machines. The goal was to optimize operations, save costs, and support sustainability goals by leveraging machine learning-powered forecasting for demand planning based on supply chain seasonality and expected customer demand.
The Solution
Coop transitioned to Google Cloud to address its forecasting challenges. The company conducted a two-day workshop with the Google Cloud team to ingest data from its data pipelines and SAP systems into BigQuery. Coop's ML team utilized Vertex AI Workbench to develop its data science workflow, aiming to train forecasting models to optimize stock levels at distribution centers. During the proof-of-concept phase, Coop's ML team compared two custom-built models against an AutoML-powered Vertex AI Forecast model. The team found that Vertex AI Forecast was faster and more accurate, achieving a 43% performance improvement over in-house models. Coop is now building a small-scale pilot for one distribution center, with plans to scale it across all centers in Switzerland.
Operational Impact
Quantitative Benefit
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