Allocation of quality improvement targets based on investments in learning

Purchased materials often account for more than 50% of a manufacturer's pro duct nonconformance cost. A common strategy for reducing such costs is to a llocate periodic quality improvement targets to suppliers of such materials . Improvement target allocations are often accomplished via ad hoc methods such as prescribing a fixed, across-the-board percentage improvement for al l suppliers, which, however, may not be the most effective or efficient app roach for allocating improvement targets. We propose a formal modeling and optimization approach for assessing quality improvement targets for supplie rs, based on process variance reduction. In our models, a manufacturer has multiple product performance measures that are linear functions of a common set of design variables (factors), each of which is an output from an inde pendent supplier's process. We assume that a manufacturer's quality improve ment is a result of reductions in supplier process variances, obtained thro ugh learning and experience, which require appropriate investments by both the manufacturer and suppliers. Three learning investment (cost) models for achieving a given learning rate are used to determine the allocations that minimize expected costs for both the supplier and manufacturer and to asse ss the sensitivity of investment in learning on the allocation of quality i mprovement targets. Solutions for determining optimal learning rates, and c oncomitant quality improvement targets are derived for each learning invest ment function. We also account for the risk that a supplier may not achieve a targeted learning rate for quality improvements. An extensive computatio nal study is conducted to investigate the differences between optimal varia nce allocations and a fixed percentage allocation. These differences are ex amined with respect to (i) variance improvement targets and (ii) total expe cted cost. For certain types of learning investment models, the results sug gest that orders of magnitude differences in variance allocations and expec ted total costs occur between optimal allocations and those arrived at via the commonly used rule of fixed percentage allocations. However, for learni ng investments characterized by a quadratic function, there is surprisingly close agreement with an "across-the-board" allocation of 20% quality impro vement targets. (C) 2001 John Wiley & Sons, Inc.