Investing in Quality Under Autonomous and Induced Learning

• Published in: IIE transactions Vol. 35; no. 6; pp. 545 - 555
• Main Authors: Serel, Dogan A., Dada, Maqbool, Moskowitz, Herbert, Plante, Robert D.
• Format: Journal Article
• Language: English
• Published: Norcross Informa UK Ltd 01.06.2003; Taylor & Francis Ltd
• Subjects: Learning; Mathematical models; Quality; Studies
• ISSN: 0740-817X; 2472-5854; 1545-8830; 2472-5862
• DOI: 10.1080/07408170304415

The reduction of variability in product performance characteristics is an important focus of quality improvement programs. Learning is intrinsically linked to process improvement and can assume two forms: (i) autonomous learning; and (ii) induced learning. The former is experientially-based, while the latter is a result of deliberate managerial action. Our involvement in quality and capacity planning with several major corporations in different industries suggested that it would be instructive to devise a model that would prescribe an optimal combination of autonomous and induced learning over time to maximize process improvement. We thus propose such a model to investigate the optimal quality improvement path for a company given that quality costs depend on both autonomous and induced types of learning experienced on a number of quality characteristics. Several properties of an optimal investment path are developed for this problem. For example, it is shown that decisions maximizing short-term gains may actually lead to suboptimal resource utilization decisions when total costs associated with a longer planning horizon are taken into account. Numerical examples are used to assess the sensitivity of the optimal investment plan with respect to changes in several model parameters.