The IntraStage BlackBelt solution is designed to automate the capture, retrieval, storage, reporting and visualization of product manufacturing and quality data. BlackBelt is used by engineers and managers from manufacturing, R&D, supplier quality, and field and repair environments. The BlackBelt solution is available via a cloud-hosted solution, an on-premise installation, or a “plug and play” device.
Operations and Manufacturing/Production Engineering
Rolling out and sustaining a high-quality electronics product requires thorough analysis to stay on top of the trends and metrics for your product. IntraStage customers are reducing manufacturing costs through improved yield, reduced scrap and fewer quality escapes. Off-the-shelf BlackBelt reports such as SPC Analytics
(including Cp and Cpk, X-BarR, and Box and Whiskers charts), Gage R&R studies
such as the one from this Aerospace manufacturer, and detailed drill-downs allow you to identify any factors affecting product yield, scrap, rework, and other factors before they affect product quality. Users can quickly get to the information that matters.
Product Management and New Product Introduction (NPI)
Ramping up production quickly and resolving issues for new product manufacturing are crucial to the profitability of the product line. Product and NPI managers at companies like Motorola
are using BlackBelt to manage key information
on product quality during this critical release phase. Analyzing NPI data from multiple dimensions, analyzing the effect of limit changes and quickly sharing information between engineering, manufacturing and supplier quality helps our customers reach production volume weeks earlier. Easy drilldown and analysis gives you the information you need at your fingertips.
Field returns and manufacturing yield issues can have a dramatic effect on a product line’s profitability and brand reputation. The ability to tie field failures to original manufacturing and supplier test data helps our customers to quickly address quality issues. BlackBelt core reports (based on over a decade of experience developing analytics and workflows for complex electronics manufacturers) indicate the quality of the manufacturing process and products throughout their life-cycle. By utilizing these built-in analytics
to evaluate SPC metrics and Paretos, IntraStage customers are improving quality, reducing field failures, and minimizing scrap and wasted effort.
Supplier and CM Quality Management
A quality issue in a batch of supplier parts can mean shortages on the line, or even a production stoppage. Or worse, a quality change in the supplier might result in more subtle changes that escape existing final tests. Collaboration between OEMs, CMs and Suppliers allows you to optimize the quality of your worldwide manufacturing chain. IntraStage customers are able to see real-time information on supplier quality through inspection at the source
. Supplier Management personnel and their suppliers can identify marginal or failing components
(including performance of measurement values against spec and control limits) and address quality issues even before they arrive at the incoming dock at your assembly factory.
Field Quality and RMA Management
Existing RMA systems capture the logistics of returned materials, but what about the root cause? Which lots or batches provided a part that caused a disproportionate number of Returns? Which board revision is having a disproportionate number of early field failures? IntraStage customers are able to track the workflow of RMAs and manufacturing parts flagged for failure analysis to reduce bonepiles and capture best repair practices. An RMA manager who is focused on reducing customer returns needs to be able to quickly identify trends and actionable information derived from their RMA and Field Return data. By being able to correlate Field Quality Data metrics (like mean time between failures/MTBF, Field Return reasons, and early field failure rates), and then linking that data to the original manufacturing and supplier data, the root causes of field failures can be identified, investigated, and improved upon