Learning Curve

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Kevin F. Mccardle - One of the best experts on this subject based on the ideXlab platform.

  • the stochastic Learning Curve optimal production in the presence of Learning Curve uncertainty
    Operations Research, 1997
    Co-Authors: Joseph B. Mazzola, Kevin F. Mccardle
    Abstract:

    Theoretical analyses incorporating production Learning are typically deterministic: costs are posited to decrease in a known, deterministic fashion as cumulative production increases. This paper introduces a stochastic 1earning:Curve model that incorporates random variation in the decreasing cost function. We first consider a discrete-time, infinite-horizon, dynamic programming formulation of monopolistic production planning when costs follow a Learning Curve. This basic formulation is then extended to allow for random variation in the Learning process. We also explore properties of the resulting optimal policies. For example, in some of the stochastic models we analyze optimal production is shown to exceed myopic production, echoing a key result from the deterministic Learning-Curve literature. In other of the stochastic models, however, this result does not hold, underscoring the need for extended analysis in the stochastic setting. We also provide new insights in the deterministic setting: for example,...

  • A Bayesian Approach to Managing Learning-Curve Uncertainty
    Management Science, 1996
    Co-Authors: Joseph B. Mazzola, Kevin F. Mccardle
    Abstract:

    This paper introduces a Bayesian decision theoretic model of optimal production in the presence of Learning-Curve uncertainty. The well-known Learning-Curve model is extended to allow for random variation in the Learning process with uncertainty regarding some parameter of the variation. A production run generates excess value above its current revenue for a Bayesian manager in two ways: it pushes the firm further along the Learning Curve, increasing the likelihood of lower costs for future runs; and it provides information, through the observed costs, that reduces the uncertainty regarding the rate at which costs are decreasing. We provide conditions under which one of the classical deterministic Learning-Curve results-namely, that optimal production exceeds the myopic level-carries over to the extended framework. We demonstrate that another classical deterministic Learning-Curve result-namely, that optimal production increases with cumulative production-does not hold in the Bayesian setting.

Joseph B. Mazzola - One of the best experts on this subject based on the ideXlab platform.

  • the stochastic Learning Curve optimal production in the presence of Learning Curve uncertainty
    Operations Research, 1997
    Co-Authors: Joseph B. Mazzola, Kevin F. Mccardle
    Abstract:

    Theoretical analyses incorporating production Learning are typically deterministic: costs are posited to decrease in a known, deterministic fashion as cumulative production increases. This paper introduces a stochastic 1earning:Curve model that incorporates random variation in the decreasing cost function. We first consider a discrete-time, infinite-horizon, dynamic programming formulation of monopolistic production planning when costs follow a Learning Curve. This basic formulation is then extended to allow for random variation in the Learning process. We also explore properties of the resulting optimal policies. For example, in some of the stochastic models we analyze optimal production is shown to exceed myopic production, echoing a key result from the deterministic Learning-Curve literature. In other of the stochastic models, however, this result does not hold, underscoring the need for extended analysis in the stochastic setting. We also provide new insights in the deterministic setting: for example,...

  • A Bayesian Approach to Managing Learning-Curve Uncertainty
    Management Science, 1996
    Co-Authors: Joseph B. Mazzola, Kevin F. Mccardle
    Abstract:

    This paper introduces a Bayesian decision theoretic model of optimal production in the presence of Learning-Curve uncertainty. The well-known Learning-Curve model is extended to allow for random variation in the Learning process with uncertainty regarding some parameter of the variation. A production run generates excess value above its current revenue for a Bayesian manager in two ways: it pushes the firm further along the Learning Curve, increasing the likelihood of lower costs for future runs; and it provides information, through the observed costs, that reduces the uncertainty regarding the rate at which costs are decreasing. We provide conditions under which one of the classical deterministic Learning-Curve results-namely, that optimal production exceeds the myopic level-carries over to the extended framework. We demonstrate that another classical deterministic Learning-Curve result-namely, that optimal production increases with cumulative production-does not hold in the Bayesian setting.

F Mccardlekevin - One of the best experts on this subject based on the ideXlab platform.

Magnus Nilsson - One of the best experts on this subject based on the ideXlab platform.

Frans Van Workum - One of the best experts on this subject based on the ideXlab platform.

  • Learning Curve and postoperative outcomes of minimally invasive esophagectomy.
    Journal of thoracic disease, 2019
    Co-Authors: Linda Claassen, Frans Van Workum, Camiel Rosman
    Abstract:

    Surgical innovation is necessary to increase surgical effectiveness and to decrease postoperative complications, but can be associated with Learning Curves. The significance of surgical Learning Curves is increasing and it is important to take surgical Learning Curves into account when interpreting outcome data that is acquired during an implementation period. This may especially be the case for a technically challenging procedure like minimally invasive esophagectomy (MIE). This review article provides an overview of the published literature that has described a Learning Curve for MIE, with particular interest in the relationship between the Learning Curve and postoperative complications. Twenty two studies reported Learning Curves of different types of MIE. These studies showed that the length of the Learning Curve of MIE can be significant, but most studies are single center studies of limited methodological quality. In addition, several Learning Curve analysis methods are used but a clear recommendation regarding the preferred method is lacking. Most studies use intraoperative parameters (e.g., operative time) to define the length of the Learning Curve. However, significant Learning Curve effects have been found for clinically more relevant parameters (e.g., anastomotic leak), especially for Ivor Lewis MIE. These studies suggest that patient safety can be substantially compromised during Learning Curves. To increase patient safety and shorten the Learning Curve, evidence based and effective safe implementation programs are necessary.

  • Learning Curve and associated morbidity of minimally invasive esophagectomy a retrospective multicenter study
    Annals of Surgery, 2017
    Co-Authors: Frans Van Workum, Marianne H B C Stenstra, Gijs H K Berkelmans, Annelijn E Slaman, Mark I Van Berge Henegouwen, Suzanne S Gisbertz, Frits J H Van Den Wildenberg, Fatih Polat, Tomoyuki Irino, Magnus Nilsson
    Abstract:

    Objective:To investigate the morbidity that is associated with the Learning Curve of minimally invasive esophagectomy.Background:Although Learning Curves have been described, it is currently unknown how much extra morbidity is associated with the Learning Curve of technically challenging surgical pr

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