Abstract
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.
Original language | English (US) |
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Pages (from-to) | 545-555 |
Number of pages | 11 |
Journal | IIE Transactions (Institute of Industrial Engineers) |
Volume | 35 |
Issue number | 6 |
DOIs | |
State | Published - Jun 2003 |
Externally published | Yes |
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering