Investing in Quality Under Autonomous and Induced Learning

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.

Economic design of EWMA control charts based on loss function

“…More details are given in Appendix B. The expected quality cost per unit when the process is in control is same as that for the mean chart, i.e., for linear loss, J 0 is obtained from (12), for quadratic loss J 0 is determined from (14), and for the case of exponential loss function, J 0 is same as (21). Let σ 1 = ρσ 0 denote the standard deviation of the process when it is out of control, ρ ≥ 1.…”

“…We consider linear, quadratic, and exponential loss functions which are commonly used in the literature (e.g. [10][11][12]). For a given deviation from target, the implied quality cost depends on the loss function used.…”

Exact probability distribution function for the volatility of cumulative production

“…(4) from the general recursion relation we need to recall Eqs. (5) and (22). ρ a is given by a narrow distribution around 0 and variance σ a 2 and correspondigly ρ yt is defined by the meanȳ t = log( t j=0 e −jg ) and σ t 2 .…”

Dynamic Cost Reduction Through Process Improvement in Assembly Networks

“…Fine (1988) studies the optimal machine inspection problem to improve quality. Li and Rajagopalan (1998) and Serel et al (2003) consider deterministic models in which the firm benefits from learning by doing and process-and-quality improvement efforts. We do not consider quality as a different dimension in our model.…”