o9 Digital Brain for Food Chains

A multinational roaster and retailer of coffee, with a network of more than 30,000 coffee houses around the world.

This customer initiated a large digital transformation
program to reduce admin work for baristas so they can be customer-facing and to reduce waste through AI-Powered forecasting, assortment planning and replenishment in one single platform.

Sustainability Impact

Reduced food waste and reduction of inventory.

Business Scope Challenges

Improved Customer Focus

The baristas spent around six hours a day on ordering, inventory, forecasting etc. The company wants baristas to be more customer-focused.

Opportunity

With o9, the company was able to automate forecasting, replenishment, inventory management and assortment planning in one single platform, utilizing analytics and minimal manual input.

Inaccurate Forecasting

The company experienced significant food waste due to inaccurate forecasts. The forecasting issue is complex as each store assorts between 500 and 5,000 SKUs and demand volatility is driven by weather, assortment, pricing and local events.

Opportunity

With o9, the company was able to forecast at a Store-SKU level incorporating leading indicators such a weather and local events. For local events an app was developed to support baristas putting in their local knowledge about the market (e.g. a football game in two days from now, or a graduration event from an university in the same street). A change in weather, or a local event, will drive auto replenishment based on o9’s digital brain.

Leverage unique IP

The company has invested in data science teams and skills. These teams developed proprietary IP, such as algorithms, to predict the impact of weather on demand and store traffic.

Opportunity

With o9, these algorithms can be industrialized. Algorithms developed in Python can be incorporated into o9s platform, and as o9 is cloud native and offers a big data infrastructure, the company can now run AI algorithms at scale.

Value Delivery

Key Functionalities Implemented

The o9 Enterprise Knowledge Graph was used to do forecasting and replenishment planning at individual store level. Moreover, the company leveraged the open-source AI/ML capabilities of the platform, as its internal data analytics team uses the o9 platform to run their own algorithms.

Systems Replaced

Homegrown system (ASR).

Customer Benefits

Success Factors — 3 reasons why o9 was selected

  1. The o9 platform being considered the most advanced, flexible and sophisticated in the market.
  2. The solution is fully configurable and flexible, allowing to create new workflows on the fly.
  3. The o9 platform being open source — e.g. the data science team developed unique algorithms and unique IP (in Python) which can be industrialized in o9.

KPI Impacted

  1. Hours saved on manual planning and inventory management at a store level.
  2. Reduction of volume and dollars of food waste.
  3. Improved assortment planning and faster response to changes in demand.

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