Company Size
200-1,000
Region
- Pacific
Country
- Australia
Product
- DataRobot
Tech Stack
- Artificial Intelligence
- Machine Learning
- Cloud Computing
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Retail
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Demand Planning & Forecasting
- Inventory Management
Services
- Cloud Planning, Design & Implementation Services
- Data Science Services
About The Customer
Harris Farm Markets is a family-operated grocery retailer specializing in fruit and vegetable, gourmet meat, seafood, and bakery. With more than 25 physical stores, Harris Farm Markets aims to reconnect Aussies with the joy of food and bring the best that nature has to offer to their customers each day. The company operates in New South Wales, Australia, and has an expanding geographic footprint. It runs about 20,000 SKUs, or stock keeping units, with a subset of concurrent fresh produce running at 1200. The company sought a solution to better manage its perishable inventory and meet consumers’ demand for variety and freshness.
The Challenge
Harris Farm Markets, a grocery retailer in New South Wales, Australia, faced significant challenges in managing its perishable inventory due to unpredictable supply caused by wildfires and sudden spikes in demand due to COVID. With over two dozen stores and an expanding geographic footprint, the chain needed a way to consistently meet consumers’ demand for variety and freshness. The task of predicting demand for their 20,000 SKUs, including a subset of concurrent fresh produce running at 1200, was too vast for a manual approach. The company sought a solution that could provide accurate predictions with minimal labor on the part of the IT team.
The Solution
Harris Farm Markets implemented DataRobot, an artificial intelligence and machine learning platform, to generate accurate demand predictions. The system took into account a wide range of data points, from seasonal impacts to customer numbers. DataRobot initially came online with around 100 AI models focused primarily on demand forecasting for fresh produce. Predictions from these models were fed into a custom-built buyers’ app that Harris Farm had already been using. The scoring pipeline was set to automatically run in a two-hour window every night, based on data from the previous day. A few months in, Harris Farm tweaked the model to target deployments by store, allowing the chain to turn off deployments for a store undergoing renovations, for example.
Operational Impact
Quantitative Benefit
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