Professional Case Study

Operational Missort Analytics

Creating repeatable visibility into where package-processing failures occurred, what was driving them, and where corrective action could reduce unnecessary cost.

This is a sanitized case study. No proprietary employer data, internal screenshots, customer information, confidential business rules, or production code are published here.

The problem

Packages sent to the wrong postal destination could create service failures and financial assessments. The business needed a repeatable way to identify where those failures were occurring, which customers and locations were involved, and the underlying causes.

My role

I designed and developed the analytical solution across data integration, SQL logic, historical structures, reporting, validation, and stakeholder communication. I worked with Quality, Operations, Master Data, Postal teams, Customer Service, and subject-matter experts to make sure the analytical logic matched actual network behavior.

Technical approach

  • Integrated source data into SQL Server and designed reusable analytical structures.
  • Developed SQL logic, views, stored procedures, and summary datasets for recurring analysis.
  • Automated processing through ETL and scheduled jobs.
  • Created reporting that allowed users to analyze issues by date, customer, distribution center, postal location, sort location, and reason.
  • Validated causal classifications with operational and quality stakeholders before relying on them for corrective action.
$3.3MDocumented cost reductions, achieving 133% of the initiative's goal.

Why the project mattered

The value was not simply another report. The solution created a common analytical view of a difficult operational problem and gave teams a way to repeatedly identify the drivers behind failures so they could target corrective actions.

Public technical recreation

Synthetic technical demo coming next.
A recreated dataset will demonstrate the SQL model, KPI logic, and Power BI analysis without exposing employer information.