IntraStage is a company with a unique vision to collect, organize, visualize and generate insights on the vast amounts of complex manufacturing data that is produced by companies in the complex electronics design and manufacturing industry. Our mission is to help our customers improve their product quality and manufacturing efficiency.
Our software has been designed to efficiently manage the challenge of megabyte to terabytes of Test Data and Product Quality in any format coming from R&D, Supply Chain, Repair and Manufacturing Environments. Our talented and committed team has over 100+ man years of experience and have been working with Fortune 1000 companies in the Aerospace & Defense, Consumer Electronics, Industrial and Medical Device industries since 2006. A sample of our success stories are:
- Improvement of product yield from 70 to 95% by using our SPC (Statistical Process Control) analytics.
- Re-test cost improvements of $500,000 by using our Re-test reporting.
- Improvements of up to 30% time savings in NPI (New Product Introduction) through our Limit Tuning reports.
- Improving time-to-fix of issues found by customers through our Traceability & Compliance reports.
We help our customers’ transition from legacy data collection systems/databases to a Test Data Management system. With customer service and support that puts our customers’ needs first, we help minimize costs and reduce deployment time.
At customer sites large and small, our Evaluation Teams have identified hidden silos of data that are crucial to overall product quality, and have architected time and cost-efficient methods for extracting, transforming and loading date into the dedicated test data management system. Leveraging their extensive test and enterprise software expertise, our teams interview stakeholders in Test, Supplier Quality, Contract Manufacturing Management, Operations, Information Technology, R&D and RMA/Field Quality in order to obtain a detailed picture of the factors for a customer’s overall product quality. Once we understand those factors, we can:
- Identify the data silos with highest ROI in quality improvement and yield improvement
- Identify the data silos with the easiest technical and process barriers for ETL
- Identify the analytics that have the highest impact in cost/labor savings
- Architect the data ETL process so that current and future data integrates seamlessly with other data and processes
- Map out product genealogies and life cycles
- Identify data silos that could be enriched (push-pull via API) with IntraStage data or vice-versa