Case Study: Jabil achieves predictive defect detection and reduced waste with Microsoft Azure

From the factory floor to the cloud: integrating predictive analytics with real-time manufacturing

Jabil, a global leader in manufacturing, design engineering, and supply‑chain services with operations in 102 locations, faced growing customer demand for faster, more personalized and small‑batch production that made traditional inspection methods too slow and wasteful. Rather than adding equipment or labor, the company needed a scalable, flexible way to reduce human touch, detect defects earlier, and meet tight just‑in‑time delivery expectations.

Jabil implemented an IoT and predictive‑analytics solution on Microsoft Azure (Cortana Intelligence Suite and Azure Machine Learning), connecting shop‑floor controls to the cloud and collecting over one million data points per assembly across a 32‑step process. Machine learning models anticipated and prevented the majority of circuit‑board failures early in the line (preventing more than half at step 2 and the remainder by step 6), reducing scrap and warranty costs, increasing throughput, and enabling the company to scale the approach across more facilities and data sources.

Jabil

Matt Behringer

CIO, Enterprise Operations and Quality Systems