Production economics and the learning curve: A meta-analysis

Grosse, E. H.; Glock, C. H.; Müller, Sebastian

doi:10.1016/j.ijpe.2015.06.021

Cited by 90 publications

(56 citation statements)

References 75 publications

(65 reference statements)

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“…The first type focusses on summarising the literature on a certain topic without systematic selection, coding, or criteria (Rhoades, 2011, Grosse et al, 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

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Challenges and propositions for research in quality management

Fundin

Bergquist

Eriksson

et al. 2018

International Journal of Production Economics

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From its inception, the field of quality management (QM) has been characterised by practicedriven development. Although QM has reached maturity as a field of research based on empirical enquiry, its practitioners still struggle to adapt QM implementations to reap their benefits. This study aims to identify their challenges to better understand how contemporary QM research addresses them. We propose approaches to bridge the potential relevance gaps between research and practice. A Delphi study of QM practitioners in Swedish private and public organisations, several of which operate globally, generated 49 challenges. An exploratory factor analysis was performed to cluster these challenges into themes, and the literature was reviewed to investigate how each theme has been addressed in QM research.The empirical investigation identified three themes related to future QM challengesorganisations' adaptability to rapid changes in the business environment, quality as a strategic concern for business owners, and managerial ownership of quality. By analysing these challenges, six propositions for future research were proposed to reduce potential relevance gaps. Future research should focus on how to make QM a strategic concern for owners, and explore how it can contribute to organisational ambidexterity and adaptability. Research should also determine how context influences the way QM is applied, and investigate how it can improve organisational learning and innovation. Finally, research should indicate how top managers can adopt the responsibilities of quality managers, and explore the ways the principal values of QM can be better integrated into organisations.

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“…The first type focusses on summarising the literature on a certain topic without systematic selection, coding, or criteria (Rhoades, 2011, Grosse et al, 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second type is based on the systematic application of rigorous criteria for selection and analysis, and the third type focusses on an aggregate review of the results of studies of a certain topic (Rhoades, 2011, Grosse et al, 2015.…”

Section: Literature Reviewmentioning

confidence: 99%

Challenges and propositions for research in quality management

Fundin

Bergquist

Eriksson

et al. 2018

International Journal of Production Economics

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“…Learning Effect. Since the learning curve was found by Wright [10] in 1936, it has been developed into various forms [13]. According to [13], the 3-parameter exponential function has the best performance in individual models compared with other learning curves.…”

Section: Modelmentioning

confidence: 99%

“…Wright [10] initially put forward a log-linear model to study the learning effect in aircraft production. Since then, various learning curves have been introduced and applied in different areas, such as Plateau model, Stanford B model, S-curve model, Jaber-Glock learning curve model, 2-parameter exponential model, 3-parameter exponential model, and 3-parameter hyperbolic model [11][12][13]. Grosse et al [13] systematically compared performance of existing well-known learning curves using a large set of empirical data and showed how to select appropriate learning curves based on task characteristics.…”

Section: Introductionmentioning

confidence: 99%

Piecewise Linear Model for Multiskilled Workforce Scheduling Problems considering Learning Effect and Project Quality

Qin

Liu

Kuang

2016

Mathematical Problems in Engineering

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Workforce scheduling is an important and common task for projects with high labour intensities. It becomes particularly complex when employees have multiple skills and the employees’ productivity changes along with their learning of knowledge according to the tasks they are assigned to. Till now, in this context, only little work has considered the minimum quality limit of tasks and the quality learning effect. In this research, the workforce scheduling model is developed for assigning tasks to multiskilled workforce by considering learning of knowledge and requirements of project quality. By using piecewise linearization to learning curve, the mixed 0-1 nonlinear programming model (MNLP) is transformed into a mixed 0-1 linear programming model (MLP). After that, the MLP model is further improved by taking account of the upper bound of employees’ experiences accumulation, and the stable performance of mature employees. Computational experiments are provided using randomly generated instances based on the investigation of a software company. The results demonstrate that the proposed MLPs can precisely approach the original MNLP model but can be calculated in much less time.

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“…Learning curves deliver a phenomenological description of the connections between different values e.g., cumulated production and past costs and are useful for the estimation of future cost reductions by simple extrapolation. A rich review of learning curve models used in production economics was conducted in [14].…”

mentioning

confidence: 99%

Long-Term Projection of Renewable Energy Technology Diffusion

2019

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The EU aims at increasing the use of renewable energy sources (RES), mainly solar-photovoltaic (PV) and wind technologies. Projecting the future, in this respect, requires a long-term energy modeling which includes a rate of diffusion of novel technologies into the market and the prediction of their costs. The aim of this article has been to project the pace at which RES technologies diffused in the past or may diffuse in the future across the power sector. This analysis of the dynamics of technologies historically as well as in modeling, roadmaps and scenarios consists in a consistent analysis of the main parameters of the dynamics (pace of diffusion and extent of diffusion in particular markets). Some scenarios (REMIND, WITCH, WEO, PRIMES) of the development of the selected power generation technologies in the EU till 2050 are compared. Depending on the data available, the learning curves describing the expected development of PV and wind technologies till 2100 have been modeled. The learning curves have been presented as a unit cost of the power versus cumulative installed capacity (market size). As the production capacity increases, the cost per unit is reduced thanks to learning how to streamline the manufacturing process. Complimentary to these learning curves, logistic S-shape functions have been used to describe technology diffusion. PV and wind generation technologies for the EU have been estimated in time domain till 2100. The doubts whether learning curves are a proper method of representing technological change due to various uncertainties have been discussed. A critical analysis of effects of the commonly applied models for a long-term energy projection (REMIND, WITCH) use has been conducted. It has been observed that for the EU the analyzed models, despite differences in the target saturation levels, predict stagnation in the development of PV and wind technologies from around 2040. Key results of the analysis are new insights into the plausibility of future deployment scenarios in different sectors, informed by the analysis of historical dynamics of technology diffusion, using to the extent possible consistent metrics.

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Production economics and the learning curve: A meta-analysis

Cited by 90 publications

References 75 publications

Challenges and propositions for research in quality management

Challenges and propositions for research in quality management

Piecewise Linear Model for Multiskilled Workforce Scheduling Problems considering Learning Effect and Project Quality

Long-Term Projection of Renewable Energy Technology Diffusion

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