Case Studies > Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting

Blue Bottle Coffee Enhances Ordering Accuracy and Reduces Waste with ML-Driven Demand Forecasting

Technology Category

Analytics & Modeling - Machine Learning
Analytics & Modeling - Predictive Analytics

Applicable Industries

Food & Beverage
Recycling & Waste Management

Applicable Functions

Procurement
Sales & Marketing

Use Cases

Predictive Replenishment
Predictive Waste Reduction

Services

System Integration
Training

About The Customer

Blue Bottle Coffee is a renowned coffee roaster and retailer with a global presence. The company operates an international network of cafes in the US and Asia, dedicated to delivering delicious coffee to its customers. BBC believes in making life more beautiful through the taste of its coffee. The company is a leader in the third-wave coffee movement, which emphasizes high-quality, sustainable coffee production and brewing. BBC's commitment to sustainability is evident in its efforts to reduce food waste and improve operational efficiency across its chain of cafes.

The Challenge

Blue Bottle Coffee (BBC), a global coffee roaster and retailer, faced a significant challenge in managing the supply of pastries across its international network of cafes. The company was using a manual ordering system, where cafe leaders estimated the required quantity of pastries based on historical sales data, current inventory, and growth projections. This system was effective when BBC had a few cafes, but with over 70 cafes worldwide, it became inefficient and inaccurate. The inaccuracies led to either under-ordering, causing sell-outs and customer dissatisfaction, or over-ordering, resulting in food waste and profit loss. The suboptimal utilization of pastries was also affecting BBC's bottom line. Therefore, BBC needed a scalable, precise, and predictive ordering solution to improve pastry ordering accuracy, reduce food waste, and meet its sustainability goals.

The Solution

BBC partnered with Provectus, an Artificial Intelligence consultancy, to develop a Machine Learning (ML) powered predictive ordering system. The system was designed to generate accurate future pastry demand by analyzing historical sales data, current inventory, and growth projections. Provectus enhanced BBC's machine learning models and orchestrated computational workflows and data processing pipelines for faster and more efficient data/ML manipulations. The predictive ordering system comprised three pipelines: Model Data Pipeline for data migration, Training Pipeline for model training and enhancement, and Forecasting Pipeline for demand prediction. The system used Amazon SageMaker for training and generating predictions, and Amazon S3 and Amazon RDS for data storage and reporting. A user-friendly UI was also developed, allowing BBC's cafe leaders to check and manually edit demand forecasts if necessary.

Operational Impact

The implementation of the ML-powered predictive ordering system has brought significant operational benefits to Blue Bottle Coffee. The system has improved the accuracy of pastry ordering, leading to less under- or overstocking. This has resulted in happier customers who can enjoy a variety of pastries anytime, and cafe leaders who can better manage food utilization. The system has also saved cafe leaders' time, as they no longer have to spend hours estimating the quantity of pastries to order. Furthermore, the system aligns with BBC's zero waste goals and financial bottom line, as it has reduced overstock waste, leading to cost savings and a lower ecological footprint. The system has also paved the way for BBC's growth in new markets by enabling it to operate more efficiently and sustainably.

Quantitative Benefit

Improved pastry ordering accuracy by 8% in July compared to the previous month
Reduced overstock waste, resulting in significant cost savings
Enabled efficient and accurate ordering of pastries, minimizing stockouts and waste