Cloud Supply Chain Predictor on AWS because model accuracy alone does not drive business decisions.

There is a moment every data scientist eventually faces.

You spend weeks building models. The accuracy metrics look great. You walk into the meeting confident.

Then someone asks:

“What is the business impact?”

And suddenly the model metrics are not enough.

Because decision-makers do not care about accuracy scores alone.

They want to know:

• what is at risk
• where the problem is
• what action should happen next
• and how urgently they need to respond

That gap between technical performance and operational decision-making is one of the biggest problems in applied machine learning.

So for Day 30 of my 30 cloud projects, I built a Supply Chain Delay Predictor on AWS.

The system evaluates regional supply chain risk, estimates business impact and generates executive-ready operational summaries automatically.

Today it scanned 5 regions and identified:

Latin America CRITICAL risk level Estimated revenue exposure: $3.2M

Europe CRITICAL risk level Estimated revenue exposure: $3.2M

Total estimated revenue exposure: $9.1M

Results were automatically stored in DynamoDB and scheduled to run daily at 7 AM using EventBridge.

Not just model accuracy. Business impact. Operational visibility. Decision support.

AWS services used: DynamoDB, EventBridge, SES and boto3.

Full code: https://github.com/AishatAnalytics/supply-chain-predictor

Day 30 of 30 cloud projects built and documented publicly in 30 days.

What is the biggest gap between technical output and business decision-making in your field?

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