Background image for Habitat Logistics case study hero section, A delivery worker on a motorcycle seen from the side, carrying an order for a pizzeria
Habitat Logistics logo

Habitat Sees Success With ML Demand Forecasting

Case Study: Habitat Logistics

About the Company

A delivery worker con a bicycle seen from the back, carrying a food order


Philadelphia, USA




Logistics & Supply Chain

Solution Type

Demand Forecasting & Optimization for Three-Sided Marketplace

Habitat Logistics is a Y Combinator accelerated B2B delivery outsourcing platform for restaurants in the United States which works directly with restaurants for a low commission. Restaurants can receive orders from any ordering channel, and have Habitat fulfill these orders for a flat, fixed fee. Their mission is to help restaurants start, keep, and grow their business. Habitat Logistics operates in a Three-Sided Marketplace working as a link between restaurants and final consumers.


Habitat was using a set of complex business rules to manually estimate the demand forecast by hour and assign the proper amount of delivery persons. They had hired an external Data Science consultant who applied Machine Learning to computing the forecast and showed improved results but had no expertise with turning that into an actual system on the AWS Cloud that would automatically run in an hourly manner.


We deployed a team of 5 High-Performance In-House Data Experts to plan, organize, and develop all the necessary AI and data capabilities Habitat needed to solve their challenge effectively and in record time. The goal? Robust systems and capabilities, built to last and scale with Habitat’s business.

40% cost reduction in their operations implementing Data and ML pipelines

Habitat Logistics logo

“From data exploration and data architecture to developing and operating Machine Learning Systems, their team has the expertise and commitment to make any project a success.”

Mike Paszkiewicz
Chief Technology Officer at Habitat Logistics

We implemented a system that applied demand prediction algorithms to calculate hourly demand forecasts for each delivery area in each city. This system runs continually every hour and adjusted predictions accordingly for the next 7 days. To accurately calculate this demand, data is pulled, integrated and validated from several external data sources such as weather forecasts, special events, etc.

Using these hourly demand forecasts and input, we built an optimization system to adjust delivery person’s shift allocation to minimize costs while maintaining SLAs.

Finally, we implemented a real-time prediction system to estimate the time it would take to prepare a particular order. This allowed the dispatch system to minimize waiting times in the restaurant for the order to be ready.

Data and ML pipelines and models were implemented combining the use of Apache Airflow for process development, Python for application development and ML Flow for metric and model tracking. The whole Data Architecture for this system was designed, built and maintained by Mutt Data’s team.


Habitat saw Over 40% cost reduction in their deliveries. Habitat leapfrogged their data journey to operational success through and automated and optimized solution which allowed them to scale and extend their delivery business in a small window of time.

Want to Dive In Deeper?

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Increase in CPMs

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Etermax logo


Decrease In CPCs With Mutt Data’s Solution

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increase in advertising clicks without affecting organic GMV

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Sirum logo


Increase in processing capabilities

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the client witnessed a reduction in manual conciliation processes.

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ClassDojo logo


reduction in data pipeline processing and data deivery time

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Addi logo


Decrease In CPCs With Mutt Data’s Solution

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Traceable and organized process execution

See It To Believe It!

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