Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

An Analysis of Learning Curve Theory & Diminishing Rates of Learning

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boones Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boones Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense end-items. However, further empirical analysis revealed deficiencies in the theoretical justifications of why and under what conditions Boones Learning Curve more accurately models observations. This research also discovered that diminishing learning rates are present but not pervasive in the sampled observations. Additionally, this research explored the theoretical and empirical evidence that may cause learning curves to exhibit diminishing learning rates and be more accurately modeled by Boones Learning Curve. Only a limited number of theory-based variables were useful in explaining these phenomena. This research further justifies the necessity of a diminishing learning rate model and proposes a framework to investigate learning curves that exhibit diminishing learning rates

A New Learning Curve for Department of Defense Acquisition Programs: How to Account for the "Flattening Effect" [video]

A video presentation with accompanying slides.Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boone's Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boone's Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense (DoD) end-items. However, further empirical analysis revealed deficiencies in the theoretical justifications of why and under what conditions Boone's Learning Curve more accurately models observations. This research also discovered that diminishing learning rates are present but not pervasive in the sampled observations. Additionally, this research explored the theoretical and empirical evidence that may cause learning curves to exhibit diminishing learning rates and be more accurately modeled by Boone's Learning Curve. Only a limited number of theory-based variables were useful in explaining these phenomena. This research further justifies the necessity of a diminishing learning rate model and proposes a framework to investigate learning curves that exhibit diminishing learning rates.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

A New Learning Curve for Department of Defense Acquisition Programs: How to Account for the "Flattening Effect"

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boone's Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boone's Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense (DoD) end-items. However, further empirical analysis revealed deficiencies in the theoretical justifications of why and under what conditions Boone's Learning Curve more accurately models observations. This research also discovered that diminishing learning rates are present but not pervasive in the sampled observations. Additionally, this research explored the theoretical and empirical evidence that may cause learning curves to exhibit diminishing learning rates and be more accurately modeled by Boone's Learning Curve. Only a limited number of theory-based variables were useful in explaining these phenomena. This research further justifies the necessity of a diminishing learning rate model and proposes a framework to investigate learning curves that exhibit diminishing learning rates.Prepared for the Naval Postgraduate School, Monterey, CA 93943.Naval Postgraduate SchoolApproved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

International audienceThis is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection

Report of the Topical Group on Physics Beyond the Standard Model at Energy Frontier for Snowmass 2021

International audienceThis is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection

Report of the topical group on physics beyond the standard model at energy frontier for snowmass 2021

This is the Snowmass2021 Energy Frontier (EF) Beyond the Standard Model (BSM) report. It combines the EF topical group reports of EF08 (Model-specific explorations), EF09 (More general explorations), and EF10 (Dark Matter at Colliders). The report includes a general introduction to BSM motivations and the comparative prospects for proposed future experiments for a broad range of potential BSM models and signatures, including compositeness, SUSY, leptoquarks, more general new bosons and fermions, long-lived particles, dark matter, charged-lepton flavor violation, and anomaly detection