Toward a Theory of Continuous Improvement and the Learning Curve

Zangwill, Willard I.; Kantor, Paul B.

doi:10.1287/mnsc.44.7.910

Cited by 207 publications

(137 citation statements)

References 15 publications

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“…The first stream primarily focuses on finding the optimal control policies to improve the production process while minimizing operating cost, e.g., Porteus (1986), Fine andPorteus (1989), Marcellus andDada (1991), Dada and Marcellus (1994), Chand et al (1996), where process improvement is typically measured in effective capacity (Spence and Porteus 1987), amount of defects (Marcellus and Dada 1991), or general cost of failures (Chand et al 1996). The second stream of the literature focuses on the interaction of process improvement with the firm's knowledge creation and learning curve, e.g., Fine (1986), Zangwill and Kantor (1998), Carrillo and Gaimon (2000), Terwiesch and Bohn (2001), Carrillo and Gaimon (2004). This stream establishes theoretical foundations for the evaluation of process improvement benefits.…”

Section: Literaturementioning

confidence: 99%

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Wang

Gilland

Tomlin

2010

M&SOM

325

202

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Surveys suggest that supply chain risk is a growing issue for executives and that supplier reliability is of particular concern. A common mitigation strategy observed in practice is for the buying firm to expend effort improving the reliability of its supply base. We explore a model in which a firm can source from multiple suppliers and/or exert effort to improve supplier reliability. For both random capacity and random yield types of supply uncertainty, we propose a model of process improvement in which improvement efforts (if successful) increase supplier reliability in the sense that the delivered quantity (for any given order quantity) is stochastically larger after improvement. We characterize the optimal procurement quantities and improvement efforts, and generate important insights. For random capacity, improvement is increasingly favored over dual sourcing as the supplier cost heterogeneity increases but dual sourcing is favored over improvement if the supplier reliability heterogeneity is high. In the random yield model, increasing cost heterogeneity can reduce the attractiveness of improvement, and improvement can be favored over dual sourcing if the reliability heterogeneity is high. A combined strategy (improvement and dual sourcing) can provide significant value if suppliers are very unreliable and/or capacity is low relative to demand.

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Section: Literaturementioning

confidence: 99%

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Wang

Gilland

Tomlin

2010

M&SOM

325

202

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“…Although it might be expected that the graph would have linear character (which would be logical and theoretically acceptable if we do not consider human behaviour factor), presented graph is slightly non-linear and can be better approximated with quadratic curve than the straight line. This can be explained by the 'learning curve' effect [31], as well as by the Hawthorne effect [32] (people tend to work faster/better when they are aware of the fact that somebody is measuring and assessing their productivity), although the latter one is questionable and still a subject of discussions in scientific circles [33]. …”

Section: Coefficient Of Distributionmentioning

confidence: 99%

Quantitative method for evaluating applicability of designed reinforcement pattern

Milajić

Prokić²,

Beljaković

et al. 2015

Teh. vjesn.

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Original scientific paper Methods for optimal design of the reinforced concrete structures presented in literature usually do not consider detailed reinforcement pattern, or if they do, it is either too generalized or the optimal solution is always the one with the lowest amount of the reinforcement, disregarding its applicability in reality, at the building site. In order to develop adequate apparatus for numerical estimation and easier choice of the most appropriate solution from the practical point of view, a field research was conducted at the building sites for obtaining data about realistic time needed for placing and fixing reinforcement in different formations. Obtained results were normalized and functional correlations between them were established. Testing results confirmed that proposed approach can help a designer to choose the most applicable solution among several theoretically acceptable ones and that the developed reinforcement pattern coefficient can be successfully used in solving complex optimization problems. Keywords: applicability evaluation; optimization; reinforcement pattern; reinforced concrete structures Kvantitativna metoda za ocjenu primjenljivosti projektirane sheme armiranjaIzvorni znanstveni članak U metodama optimalnog projektiranja armiranobetonskih konstrukcija koje se mogu naći u literaturi obično se ne razmatra detaljna shema armiranja ili je, ako se i razmatra, to ili previše uopćeno, ili se za optimalno rješenje uvijek usvaja ono s najmanjom količinom armature, bez obzira na njegovu primjenljivost u stvarnosti, tj. na gradilištu. U cilju razvoja odgovarajućeg matematičkog aparata za numeričku ocjenu i lakši odabir odgovarajućeg rješenja s praktične točke gledišta, provedeno je terensko istraživanje na gradilištima kako bi se prikupili podaci o realnom vremenu potrebnom za postavljanje i fiksiranje različitih rasporeda armature. Prikupljeni rezultati su normalizirani i između njih su uspostavljene funkcionalne korelacije. Rezultati testiranja su potvrdili da prikazani pristup može pomoći projektantu kod odabira najprimjenljivijeg rješenja između nekoliko teorijski prihvatljivih te da se izvedeni koeficijent sheme armiranja može uspješno primijeniti pri rješavanju složenih optimizacijskih problema.

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“…Indeed research into developing better learning models continues (Zangwill and Kantor (1998); Zangwill and Kantor (2000)). The transfer, and use, of learning curve technology from manufacturing to other arenas is also regarded as important, for example into food service operations (Reis (1991)), in pizza production (Darr et al (1995)), and musical instrument and apparel manufacture (Baloff (1971)).…”

Section: The Learning Curvementioning

confidence: 99%

Learning in disrupted projects: on the nature of corporate and personal learning

Howick

Eden

2007

International Journal of Production Research

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The majority of large, complex projects suffer disruptions. These can have unexpected and significant impacts on a project, resulting in excessive time and cost overruns. Disrupted projects therefore require careful management in order to minimize the impact of a disruption. One element of a project for which disruptions will have a particular impact is on any anticipated learning gains. Loss of learning from disruptions can often be very significant. This lost learning may be from individual workers (personal) or by the organization (corporate). An understanding of the impact of a particular disruption on learning is required to enable effective management of that disruption. This paper argues that improved management of learning in disrupted projects may come from the disaggregation of personal and corporate learning from the typically used aggregated learning curve presumptions. The literature is reviewed to explore whether a method of disaggregation can be determined from existing propositions about the behaviour of learning curves. The explorations demonstrate that the disaggregation of a learning curve is complex. The contribution of this paper derives from developing an understanding of the role of asymptotes in constructing learning curves, and the nature of the interaction between personal and corporate learning. These two aspects are argued to be crucial aspects in managing the impact of disruptions on learning. The main motivation for this work is to provide managers with an understanding of the disaggregation process, in order that they can think through the impact of a disruption on learning in their own projects

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Toward a Theory of Continuous Improvement and the Learning Curve

Cited by 207 publications

References 15 publications

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Mitigating Supply Risk: Dual Sourcing or Process Improvement?

Quantitative method for evaluating applicability of designed reinforcement pattern

Learning in disrupted projects: on the nature of corporate and personal learning

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