A New Tool for Six Sigma in Manufacturing

Robert L. Horst, P.E.—Consultant and Founder
Peak Productivity USA, P.O. Box 681, Lancaster PA 17608

horst@peaksigma.com

A proprietary¹ Six-Sigma Isogrammetric Analysis Method (SSIAM™) is a new tool for achieving lean and efficient manufacturing. Graphing data on isograms can visually present factory-floor personnel with the sigma-level metric and level-of-defectives actually achieved, says Robert L. Horst of the technical consultancy Peak Productivity USA, in a recent issue of SME Manufacturing Engineering magazine.

The Motorola six-sigma model defines a criterion for excellence, promising extremely high yields, with a maximum of 3.4 defectives-per-million (dpM). Defectives are defined as errors, faulty parts or actions, and out-of-tolerance variables.

A unique feature of the six-sigma peak-yield ideal is that it acknowledges an acceptable degree of drift (process shift) of variables from target, and permits a defined zone of variation. No process adjustments need to be made when the collected data stay within the limits of ±1½ sigma, as long as manufacturing specifications are consistent with a process tolerance of ±6σ corresponding to a process capability index (Cp) of 2. An alternative ±4½σ tolerance yields a maximum of 1.3 defectives-per-thousand (dpK) and corresponds to Cp = 1.5.

Another feature of the six-sigma model is the relevance of short-term versus long-term data collection. To meet the 6σ criterion, short-term data need to exhibit a standard deviation that fits with the process tolerance. Long-term data may (and likely will) exhibit process drift across the allowable ±1½σ zone, yielding a centered distribution with larger standard deviation. To achieve a high production yield, the focus is always on reducing the data scatter represented by the spread of the Gaussian bell curve.

The power of sigma-level performance analysis for the improvement of manufacturing processes kicks in where the usefulness of statistical process control (SPC) diminishes. SPC is a powerful analytical tool for out-of-control process/product variables, but it's inadequate for quantitative analysis of "in-control," high-yield processes.

SPC practice always focuses on centering the mean value, on reducing process shift to a minimum. Standard deviation is used to establish upper and lower control limits of ±3σ, representing 99.7% yield for a perfectly in-control, centered-on-target process.

The value of sigma-level analysis is in the quantification of variable data with respect to required process tolerances rather than intrinsic control limits assigned by SPC rules. It's an approach that leads to the discovery of rogue variables that prevent the achievement of high yields.

In the broad perspective, knowledge of sigma-level performance for key variables--in every production process--alerts operators and signals management, leading to fact-based decision-making and corrective actions that are essential for achieving higher productivity.

Peak Productivity USA's proprietary approach is called the Six Sigma Isogrammetric Analysis Method (SS IAM™). It uses isograms of constant process yield to determine the probable sigma-level metric (yield) associated with measurable process variables in a production process.

Mean-value shift (in σ units) and ratio (tolerance-to-σ) are plotted, respectively, as X, Y coordinates on isogrammetric graphs. Data points show the sigma metric and the achieved level-of-defectives, without the encumbrance of reference tables and associated calculations that, likely, are not practical for use by production personnel.

The SSIAM^™ tool is particularly effective for high-yield processes where statistical sampling and inspection methods tend to miss the relatively few defectives, and where process variables are supposedly "in control" by SPC rules.

Isogrammetric analysis merges SPC and sigma-level measurement criteria. When implemented on-line in factories, it provides real-time feedback in terms of probable level-of-defectives from variables data, informing plant-floor employees of the need to take action to stabilize production processes. Incremental increases in process yield will produce calculable savings in materials, energy, and labor, and can mean the difference between profit and loss on the balance sheet.

Read the complete article published in SME Manufacturing Engineering magazine—"Measuring and Achieving Six Sigma Performance," July 2004.

The author, Robert L. Horst, PE, is a Life Fellow of IEEE, a Senior Member of SME, and the founder of Peak Productivity USA.