Six Sigma Plus Methods
H. E. Cook,* T. Moseley, J. L. Freeman, D. R. Herington
General Electric Co.; University of Illinois

Design of experiments, Taguchi methods, QFD, long- and short-term statistical process control, target costing, and value engineering are important elements in the quality management process known as Six Sigma. Although these tools should be closely connected, they remain apart because they have been developed independently from each other. The objective of this research is to integrate these tools into a single formalism for total quality management. Our research currently focuses on modeling variance, assessing the significance of design and manufacturing factors on variance reduction, and using profitability as the objective function for industrial experiments.

Strategic Quality Deployment and Value Benchmarking
H. E. Cook,* E. Cowan, C. Bush, M. Pozar, M. Neidlinger, A. Wu, M. Lee
C. J. Wicall Gauthier Professorship; University of Illinois; Ford Motor Co.; Caterpillar, Inc.

The objectives of this research are (1) to develop the relationships between the metrics of quality, cost, lead time, and innovation and the traditional bottom line functions of return on investment and market share; (2) to generate a fundamental and encompassing definition of quality that includes considerations of value and cost and apply it to the entire product realization process; and (3) to explore the role of organization structure and corporate culture on manufacturing effectiveness. Initial results, based upon a market model that incorporates value as well as the more traditional elements of cost and price, show that a single universal metric governs manufacturing effectiveness. The new quality function being developed yields the traditional Taguchi formalism as a limiting case.

Design for Machinability
D. L. Thurston*
CMI (through Center for Machine-Tool Systems Research)

Machining operations affect cost, cycle time, surface finish, tolerance, weight, quality and the environment. Specialists evaluate designs for machinability to minimize cost or maximize quality, but tradeoff decisions are difficult. The proposed solution is to formulate a mathematical model of the cause-and-effect relationships between design decisions and overall product performance. Controllable decision variables include material, geometry, cast-in vs. machined features, machining process selection, fixturing, cutting fluids, feed rate and speed. The objective is to identify the set of design and machining decisions which achieves the best possible combination of product attributes.

Environmentally Conscious Design and Manufacturing
D. L. Thurston,* J. V. Carnahan
National Science Foundation, DMI 95-28629

This project develops a rigorous new method for integrating quantitative decision analysis over the entire range of product design, manufacture, use, and disposal. Specifically, the methodology combines statistical manufacturing process control with life-cycle analysis and concurrent multiobjective design optimization. Pollution and its removal cost are treated as product defects. This project significantly expands on previous work to develop design tools that can be used by any industry. Procedures for classes of manufacturing processes and their resultant waste streams are specified. The best combination of strategies is identified, including specification of the product design, materials, manufacturing process design, and manufacturing process control settings.

Integrating Customer Preferences into Green Design and Manufacturing
D. L. Thurston*
Motorola (through Center for Machine-Tool Systems Research)

"Green" products must compete in the marketplace against many rivals. If consumers do not purchase them, they do not succeed in their goal of environmental protection. Preferences for "environmentally friendly" products have been difficult to assess, since customers' stated willingness to pay often differs from their actual purchasing practices. This project incorporates information about customer preferences into environmentally conscious design and manufacturing. It expands upon mathematical models of the concurrent design process, and addresses issues of cost, manufacturing cycle time, product quality (measured in terms of defect rate), product size and weight, volatile organic compound (VOC) production, recycling, disassembly, and other environmental impacts.