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      • Department of Management
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      Investing in quality under autonomous and induced learning

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      Author(s)
      Serel, D. A.
      Dada, M.
      Moskowitz, H.
      Plante, R. D.
      Date
      2003
      Source 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
      Article
      Item Usage Stats
      206
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      222
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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
      Costs
      Decision making
      Investments
      Learning systems
      Process engineering
      Resource allocation
      Product performance
      Quality control
      Permalink
      http://hdl.handle.net/11693/24467
      Published Version (Please cite this version)
      http://dx.doi.org/10.1080/07408170304415
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      • Department of Management 639
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