Investing in quality under autonomous and induced learning
Date
2003Source Title
IIE Transactions (Institute of Industrial Engineers)
Print ISSN
0740-817X
Electronic ISSN
1545-8830
Publisher
Taylor & Francis
Volume
35
Issue
6
Pages
545 - 555
Language
English
Type
ArticleItem Usage Stats
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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.
Keywords
CostsDecision making
Investments
Learning systems
Process engineering
Resource allocation
Product performance
Quality control