Case Studies | SaaS B2B

Predicting fuel demand for greener logistics

Fuel Demand Prediction

About

A SaaS B2B business working with Cloud Combinator on AWS. The client is anonymised at their request.

Challenge

The challenge had three focus areas.

Turning raw vehicle data into accurate predictions

The data the client supplied had to be prepared and used to train a model that could reliably predict fuel consumption per vehicle. Accuracy here is the difference between useful guidance and noise.

Making predictions easy to consume

The model needed to be reachable through a simple API that returned a prediction to the user and stored each query and result for future analysis.

Landing it in the client's environment

The solution had to be deployed into the client's own AWS account as a standalone ML project, with the internal lead able to continue developing it.

Solution

We ran the engagement through our Cloud Accelerator, moving from understanding the product to a deployed, reviewed model in the client's own account.

84%

Of the European transport market are SMEs, the client's core users

~$1,100

Projected monthly AWS run cost (projected)

1

Queryable fuel-prediction API endpoint delivered

By the numbers:

  • 84% - Of the European transport market are SMEs, the client's core users
  • ~$1,100 - Projected monthly AWS run cost (projected)
  • 1 - Queryable fuel-prediction API endpoint delivered
Changes

The engagement delivered a trained fuel-consumption prediction model into the client's AWS environment, queryable via API with every prediction stored for ongoing analysis, and validated Amazon SageMaker as the platform for the client to keep building on.

  • Predictions on demand — A trained model returns a vehicle's predicted fuel consumption through a single API call.
  • Built to improve — Queries and results are stored in Amazon S3, creating a growing dataset the client can use to refine the model.
  • Model selection, not guesswork — Candidate models were compared in SageMaker and the most accurate was deployed.
  • Deployed where it runs — The solution was stood up in the client's own AWS account as a standalone project.
  • Handover — We trained the client's technical lead on using the project and completed a Well-Architected review of the stack.

With a working prediction model and a Well-Architected foundation, the client can fold fuel forecasting into its analytics dashboard and extend it across more vehicles and demand scenarios as its customer base grows.

AWS Stack

Amazon SageMaker

For training, selecting and deploying the fuel-consumption model.

Amazon S3

For storing training data and every query and result.

Amazon API Gateway

For exposing the model endpoint to users.

AWS Lambda

For supporting functions around the prediction pipeline.

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