This is a sanitized professional case study. No proprietary datasets, customer information, confidential business rules, internal screenshots, or production code are published here.
The business problem
A supply-cost visibility pilot was created to help the business understand where operational supplies were going and why over-ordering was occurring. The scope included rented USPS pallets, USPS #1 mail sacks, Gaylords, EO boxes, and supplies issued to customers for moving unprocessed freight to DHL.
My role
I served as the primary data and reporting developer, working closely with the project manager. Because I knew the distribution-center operation well, I was also asked for input on how the business process should be represented in the data and reporting.
Data integration & modeling
- Integrated data from multiple Oracle and SAP sources into SQL Server using SSIS.
- Designed tables, data models, transformations, and reporting structures.
- Built visibility into what was purchased, rented, on hand, received, sent out of the distribution center, and issued to customers.
- Supported analysis of excess requests, unreturned assets, and cases where supplies were distributed without a clear view of actual need.
When the data had no documentation
Some transportation tables used non-intuitive fields such as
ATTRIBUTE1
and
ATTRIBUTE2
, and there was no usable data dictionary. Rather than infer meanings, I worked cross-functionally with subject-matter experts to translate operational knowledge into defined business rules and actionable data definitions.
Knowledge transfer
When I was promoted into another role, I completed a handoff process and trained the analyst taking over support so the solution could continue after my transition.
Impact
The reporting improved inventory visibility and helped streamline supply-management processes by giving the business a clearer view of what was purchased, available, distributed, received, and consumed across the network.
What this demonstrates
Supply-chain analytics, multi-source ETL, data modeling, operational consulting, working through poorly documented data, and transferring a supportable solution to another analyst.