AI-Driven Anomaly Detection for Supplier Defect Tracing — Case Competition

Overview

Competed in a case competition centered on using AI to catch supplier defects earlier in the manufacturing pipeline. Teams were given three pre-built solution options to choose between — my team broke from that structure and built a fourth, hybrid solution instead.

My Role

Worked across the full case: analysis, strategy, and the final pitch, rather than owning one narrow lane. Used Claude as the core analysis and strategy tool throughout — running the anomaly detection reasoning, stress-testing our assumptions, and shaping the business case for our recommendation.

Approach

The case presented three separate solution paths for anomaly detection in supplier defect tracing, each with its own cost/speed tradeoffs. Instead of picking one, we identified where each option's strengths could offset the others' weaknesses and combined them into a fourth solution — one that delivered greater cost savings with a faster implementation timeline than any single option on its own.

Outcome

Placed 4th out of the competing teams, distinguishing our submission by proposing a solution outside the given framework rather than selecting among the defaults.

Skills Demonstrated

AI-assisted analysis and strategy · anomaly detection concepts · cost/speed tradeoff evaluation · synthesizing multiple solutions into one · case pitch and communication