Making the six sigma leap using SPC data [quality]

Implementation of sigma-level analysis provides a quantum leap in management´s understanding and control of the quality process in manufacturing. Six sigma performance is a realizable objective and is highly desirable for enterprise profitability, as well as for safety and health when product failure can cause catastrophic results. A proprietary isogrammetric analysis methodology (IAM) uses level-of-defectives isograms to discover and display the sigma-level performance of processes using statistical process control (SPC) data and process tolerances. While SPC alone is a powerful analytic tool for out-of-control process/product variables, it is inadequate as a mechanism for quantitative analysis of in-control high-yield processes. This is illustrated by analysis of electronic hybrid-circuit wire bond data using both SPC and IAM. Isogrammetric analysis is a new tool for auditing manufacturing performance (and supplier data), and provides identification of rogue variables that prevent the achievement of high yields in critical processes. Typical performance goals in the manufacture of technical products and materials are six sigma (⩽3.4 defectives-per-million) and 4½ sigma (⩽1.3 defectives-per-thousand). The easy-to-use tool gives a statistical measurement and graphic display of achieved sigma level, calculated from variables data for products and processes. The outcome alerts plant-floor operators to needed process adjustments or shutdown, and signals operations management to the incapability of processes and the need for systems re-engineering for cost reduction and profit improvement