Learning Curve Theory Research Articles

Electricity portfolio planning model incorporating renewable energy characteristics

Traditional electricity planning models pursue minimal costs, yet their design often results in an underestimation of the true benefits of renewable energy. This paper attempts to introduce different complementary approaches to traditional electricity planning model to incorporate various renewable energy characteristics and uses Taiwan’s electricity sector as a case study. The portfolio theory, learning curve theory and the capacity credit are applied in the proposed model to reflect characteristics of renewable energy, such as a hedge against fossil fuel price volatility, significant technological progress, and intermittent generation. Simulation results demonstrate that using renewable energies has the advantage of hedging against the volatile fossil fuel price risk as well as reducing carbon dioxide emissions. Considering the intermittency of renewable energies requires LNG-fired plants to serve as the backup generators. However, wind power can only account for limited share of total installed capacity due to the limited land resources in Taiwan. Therefore, taking intermittency into account only demonstrates a small influence of the reserve margins of the entire power system and the total generation costs.

초고층 프로젝트에서의 학습곡선효과 상쇄요인 분석 및 작업 생산성 산정 방법 제시

건설 프로젝트 내의 작업의 반복성에 주목하여 건설산업에의 학습곡선효과 적용에 관한 연구가 꾸준히 이루어졌다. 그러나 충분한 반복 작업을 가지는 초고층 프로젝트에서 학습곡선효과가 실제로 발생하는가에 대한 논란은 지속되고 있다. 이에 관해 기존의 연구들은 실제 건설 현장의 작업 생산성 데이터 분석을 통해 효과의 유무를 검증하였으나 작업 생산성의 향상 여부에만 초점을 맞추고 있을 뿐, 효과가 발생하는 과정에 관한 연구는 이루어지지 않았다. 이에 본 연구는 초고층 프로젝트에서 학습곡선효과가 발현되는 과정에 영향을 미치는 요인들을 분석하여 그 효과를 상쇄시키는 요인들을 찾고, 이를 반영한 작업 생산성 산정 모델을 제시하고자 한다. 이를 통해 기존의 이론을 보완하고 건설산업에서의 학습곡선효과 이론을 재정립하는 데 기여할 수 있다. Focusing on repetitive works of construction, many research have been conducted about application of the learning curve effect. However, it is still controversial, especially on the high-rise project, since the productivity improvement from the learning curve effects are hard to prove. In the previous research, applicability of the learning curve was mainly derived from the labor productivity data. Although the research were based on the real data, they merely concentrated on the simple conclusion that the labor productivity had improved or not, instead of the process interpretation. Therefore, the purpose of this research is to analyze the influence factors of the learning curve effect in high-rise projects and elucidate the offset factors of the effect. Based on these factors, a model for estimating the labor productivity containing the concept of process learning is suggested. Through our research, traditional learning curve theory could be compensated and re-established with having more appropriateness for high-rise projects.

Learning curve theory and innovation

Purpose – The purpose of this paper is to look at innovation and the tension which arises between proprietorship and standardization. Design/methodology/approach – The paper presents an analysis of the learning curve theory in relation to the printed circuit board industry. Findings – The measurable impact of the learning curve will be different depending on a number of factors commonly found in the printed circuit industry – degree of process maturity/automation, run lengths, and presence of customized elements requiring modifications to process flow, materials, or procedures. Originality/value – There is no escape from continuing and inexorable downward price pressure from entrenched expectations and trends. A clear understanding of how these work will confer a competitive advantage to the strategist concerned with bringing innovative technologies to the marketplace.

Relationship between building floor and construction labor productivity

Purpose – The purpose of this paper is to examine the relationship between building floor and labor productivity of the structural work including formwork installation and rebar fabrication/installation. Design/methodology/approach – The case study methodology and learning curve theory are adopted for the paper. Records from the structural work of a 20-storey apartment building were analyzed to calculate floor-based labor productivities. Findings – Labor productivity of the formwork activity increased more than twice in the first five floors. If the first cycle (floor 2) is omitted, the straight-line learning curve model shows a learning rate of 83.5 percent. Labor productivity of the rebar activity tended to increase in the first 15 floors. If the first two cycles are omitted, the straight-line learning curve model indicates a learning rate of 83.6 percent. Research limitations/implications – Future research is needed to examine and quantify factors that affect the level of learning in high-rise building construction. The relationship between building floor and labor productivity should be further investigated for other construction activities. Practical implications – Practitioners should consider the relationship between building floor and labor productivity and learning effects when planning manpower and construction duration for individual activities and for a building. Originality/value – The paper substantiates the hypothesis that labor productivity does not reach 100 percent of the normal level at the very first floors while they do not support the hypothesis that labor productivity does not reach 100 percent at the top floors.

Simulation-Based and Statistical Analysis of the Learning Effect in Floating Caisson Construction Operations

AbstractThe learning curve theory is implemented for the assessment and prediction of performance in repetitive and complex construction operations. The purpose of this study is to explore the applicability of learning curve concepts for the fabrication of floating caissons, a construction activity that has not been adequately explored in the published literature. Although standard statistics and simulation methods have been used, a direct comparison of the produced results, along with their emerging inferences, is attempted for the first time. In addition, simulation modeling is used to predict future performance within the learning curve paradigm. An extended version of an existing simulation platform (CaissonSim) is used to execute the computations. A straight-line model is applied to quantify the improvements in productivity and the dynamically changing learning rates. A comparative analysis of the results has shown that both the statistical and simulation-based approaches have yielded satisfactory re...

Knowledge as One of the Sources of Sustainable Competitive Advantages

The aim of this paper is to present some fundamental definitions of knowledge and to analyse some fundamental challenges regarding the knowledge capital of a firm. It reviews the literature pertaining to the assessment of knowledge assets. Companies have always been founded on knowledge. Veblen (1904) emphasises the importance of knowledge in companies. In 1959, Drucker defined knowledge as an importance source for the company. In 1977, Teece published the research on the transfer of knowledge and its influence on innovation. Solow (1956) and Arrow (1962) encouraged, through their learning curve theory, other economists to start considering knowledge as an economic resource. Nonaka and Takeuchi (1995), in their theory on knowledge-based organisations, further emphasised the importance of knowledge. Their thesis is that knowledge represents one of the sources of sustainable competitive advantages. Since knowledge assets are significantly important for the firm performance, this field is an exciting area for researchers and practitioners. Based on the studied literature, it can be concluded that the existing theoretical and applied research activities deal with the role of knowledge as a resource of sustainable competitive advantages for the firms.

Using optimization models to demonstrate the need for structural changes in training programs for surgical medical residents

The primary goal of a residency program is to prepare trainees for unsupervised care. Duty hour restrictions imposed throughout the prior decade require that residents work significantly fewer hours. Moreover, various stakeholders (e.g. the hospital, mentors, other residents, educators, and patients) require them to prioritize very different activities, often conflicting with their learning goals. Surgical residents' learning goals include providing continuity throughout a patient's pre-, peri-, and post-operative care as well as achieving sufficient surgical experience levels in various procedure types and participating in various formal educational activities, among other things. To complicate matters, senior residents often compete with other residents for surgical experience. This paper features experiments using an optimization model and a real dataset. The experiments test the viability of achieving the above goals at a major academic center using existing models of delivering medical education and training to surgical residents. It develops a detailed multi-objective, two-stage stochastic optimization model with anticipatory capabilities solved over a rolling time horizon. A novel feature of the models is the incorporation of learning curve theory in the objection function. Using a deterministic version of the model, we identify bounds on the achievement of learning goals under existing training paradigms. The computational results highlight the structural problems in the current surgical resident educational system. These results further corroborate earlier findings and suggest an educational system redesign is necessary for surgical medical residents.

THE OPTIMAL SIZE OF CASH BALANCE UNDER LEARNING CURVE EFFECTS

ABSTRACT Based on learning curve theory, trading cost is decreasing with increasing of accumulative finance experience related to transferring between cash and securities in cash management process. Learning curve effect will happen when a company has much more finance experience with the trading experience, trading knowledge and trading Volume. This paper constructs the model of optimal size of cash under learning curve effects in cash balance decision process. Keywords optimal size of cash, trading costs, opportunity costs, learning curve effects

Extracting Value from Learning Curves: Integrating Theory and Practice

Learning curves are used as management metrics in many industrial settings. However, their apparent simplicity masks the tremendous number of changes and opportunities that are summarized by this apparently simple exponential relationship. Through exploration of these underlying opportunities, practitioners are able to better identify and exploit opportunities. By integrating learning curve theory to include design, supply chain and life cycle management to a framework illustrating the complex series of events that comprise the seemingly parsimonious learning curve, it is possible to better see how learning curves reduce cost, expand existing markets and create new markets. Illustrations are used to assist practitioners in taking economic advantage of the theoretical contributions offered in this framework.

Economical assessment of large-scale photovoltaic power development in China

Photovoltaic (PV) power has a huge resource of supply, wide range of usage, and low impact on environment and it is considered as a key factor of the supply mix in the future for China. PV power is still in its infancy in China. It has higher cost than coal-fired power, and needs policy support. In recent years, China has launched a series of policies to stimulate PV power. These policies, however, are temporal, mainly because the total additional investment to make PV power grid parity is uncertain. After a brief review on current status of PV power development in China, this paper applies learning curve theory to study the cost reduction trends of polycrystalline silicon PV cells - main components of large-scale PV (LS-PV) power generation, then sets different scenarios to estimate time and the total learning cost of PV power’s grid parity. MESSAGE model is also applied to make a comparable estimation of the future PV installation in China. The results show that long-term subsidy policy for PV power is feasible in China with the rising cost of coal-fired power, but aggressive subsidy policy is not very efficient.

Construction of multi-storey concrete structures in Italy: patterns of productivity and learning curves

The achievement of expected site productivity is one of the main characteristics of successfully completed projects. The productivity rates of concrete construction according to the learning curve theory are discussed in this paper. The study builds upon the records of variable productivity rates achieved in the erection of 15 multi-storey concrete structures in Southern Italy and the discussion of the factors behind such variability. In this last regard, a multilevel regression analysis identifies the most important factors. The repetitive work that characterizes these structures provides distinct opportunities for productivity enhancement. Learning curve theory is applied to quantify such an improvement by using a straight-line model. The quantification of learning rates, ranging from 85% to 95%, is useful for the labour cost and time planning of future concrete structures in the region. In the case of Italian sites, the application of the learning curve would be more beneficial if more effort were spent in the planning and control of the initial construction site operations and in the constructability analysis of design documents.

Understanding wind turbine price trends in the U.S. over the past decade

On a $/kW basis, wind turbine prices in the U.S. have declined by nearly one-third on average since 2008, after having previously doubled over the period from 2002 through 2008. These two substantial and opposing trends over the past decade – and particularly the earlier price doubling – run counter to the smooth, gradually declining cost trajectories predicted by standard learning curve theory. Taking a bottom-up approach, we examine seven possible drivers of wind turbine prices in the U.S., with the goal of estimating the degree to which each contributed to the doubling in turbine prices from 2002 through 2008, as well as the subsequent decline in prices through 2010. In aggregate, these seven drivers – which include changes in labor costs, warranty provisions, manufacturer profitability, turbine scaling, raw materials prices, energy prices, and foreign exchange rates – explain from 70% to 90% (depending on the year) of empirically observed wind turbine price movements in the U.S. through 2010. Turbine scaling is found to have been the largest single contributor to the price doubling through 2008, although the incremental cost of scaling has been justified by greater energy capture, resulting in a lower cost of wind generation.

Comparison of Cumulative Average to Unit Learning Curves: A Monte Carlo Approach

Cumulative average and unit cost learning curve methodologies dominate current learning curve theory. Both models mathematically estimate the structure of costs over time and under particular conditions. While cost estimators and industries have shown preferences for particular models, this article evaluates model performance under varying program characteristics. A Monte Carlo approach is used to perform analysis and identify the superior method for use under differing programmatic factors and conditions. Decision charts are provided to aide analysts' learning curve model selection for aircraft production and modification programs. Overall, the results indicate that the unit theory outperforms the cumulative average theory when more than 40 units exist to create a prediction learning curve or when the data presents high learning and low variation in the program; however, the cumulative average theory predicts unit costs with less error when few units to create the curve exists, low learning occurs, and high variation transpires.

Revisiting the applicability of learning curve theory to formwork labour productivity

The learning phenomenon has proved applicable in various industries, especially those associated with mass productions, such as aircraft and automobile assembly. The learning process in labour‐intensive industries is assumed to be more significant in the sense that automated work is constrained by the fact that machines cannot benefit from previous experience and therefore would not ‘learn’ to run any faster or increase the rate of production. Several previous investigations into the applicability of learning curve theory to the construction industry proved the importance of this concept to labour productivity. Nevertheless, a thorough examination of the literature revealed a dearth of research into the effect of learning on the formwork operation of building floors. Consequently, the objective of this research is to explore the influence of recurring building floor configurations on formwork labour productivity. To achieve this objective, formwork labour inputs from 45 multi‐storey in situ reinforced concrete building frames were collected and analysed using the straight‐line learning curve model. The cumulative average input for each cycle, i.e. floor, and its associated cycle number were modelled using the least squares method. According to the learning curve theory, we expect the labour inputs to decrease by a certain percentage, as the cycle number increases. Based on the results obtained, there is very little evidence for that.

Critical Investigation into the Applicability of the Learning Curve Theory to Rebar Fixing Labor Productivity

The learning phenomenon has proved applicable in various industries, especially those associated with mass productions such as aircraft and automobile assembly. The learning process in labor-intensive industries is assumed to be more significant in the sense that automated work is constrained by the fact that machines cannot benefit from previous experience and therefore would not “learn” to run any faster or increase the rate of production. Several previous investigations into the applicability of learning curve theory to the construction industry proved the importance of this concept to labor productivity. Nonetheless, a thorough examination of the literature revealed a dearth of research into the effect of learning on the rebar fixing trade. Therefore, the objective of this research is to explore the influence of recurring building floor configurations on rebar fixing labor productivity. To achieve this objective, fixing labor inputs of beams and slabs from 21 multistorey residential building floors were collected and analyzed using the straight-line learning curve model. Fixing labor inputs for each cycle, i.e., floor, and its associated cycle number were modeled using the least-squares method. According to the learning curve theory, labor inputs are expected to decrease by a certain percentage as the cycle number increases. Contrary to the basic principle underlying the theory, the majority of buildings observed exhibited either an increase or a negligible reduction in labor inputs as the cycle number of recurring floors increased.

Coordinating a three-level supply chain with learning-based continuous improvement

Learning curve theory has been widely used as a managerial tool to describe and model product and process improvement. This paper investigates a three-level supply chain (supplier–manufacturer–retailer) where the manufacturing operations undergo a learning-based continuous improvement process. Improvements in the manufacturer’s operation are characterized by enhanced capacity utilization, reductions in set-ups times, and improved product quality through the elimination of rework. As a result of these continuous improvements, the manufacturer can justify a production policy that is based on more frequent, smaller lot size production. For this production policy to be practical and not sub-optimal to the supply chain, the manufacturer must integrate its lot-sizing models with the replenishment policies of its upstream raw material suppliers and the demand requirements of its downstream customers (retailers). Mathematical models that achieve chain-wide lot-sizing integration are developed and solution procedures for the models are illustrated by numerical examples. The results demonstrate that learning-based improvements in set-up time and rework allow retailers to order in progressively smaller lot sizes as the manufacturer offers larger discounts and profits and that the entire supply chain benefits from implementing learning-based continuous quality improvements. The results also demonstrate that forgetting effects lead to increases in supply chain costs.

A comparative analysis of learning curves: Implications for new technology implementation management

New technology implementation projects are notoriously over time and budget resulting in significant financial and strategic organizational consequences. Some argue that inadequate planning and management, misspecification of requirements, team capabilities and learning contribute to cost and schedule over runs. In this paper we examine how learning curve theory could inform better management of new technology implementation projects. Our research makes four important contributions: (1) It presents a comparative analysis of learning curves and proposes how they can be used to help ERP implementation planning and management. (2) Based on empirical data from four ERP implementation projects, it provides illustrations of how managers can apply the curves in different project situations. (3) It provides a theoretical basis for empirical studies of learning and ERP (and other IT) implementations in different organizational settings. (4) It provides empirical justification for the development of learning curve theory in IT implementation.

Half-Life Theory of Learning Curves for System Performance Analysis

Learning curves are used extensively in business, science, technology, engineering, and industry to predict system performance over time. Most of the early development and applications have been in the area of production engineering. Over the past several decades, there has been an increasing interest in the behavior of learning curves. This paper introduces the concept of half-life of learning curves as a predictive measure of system performance, which is an intrinsic indicator of the system's resilience. Half-life is the amount of time it takes for a quantity to diminish to half of its original size through natural processes. The common application of half-life is in natural sciences. The longer the half-life of a substance, the more stable it is. Consequently, the more resilient it is. This approach adds another perspective to the large body of literature on learning curves. Derivation of the half-life equations of learning curves can reveal more about the properties of the various curves. This paper presents half-life derivations for some of the classical learning curve models available in the literature.

Learning and Knowledge Depreciation in Professional Services

Organizational knowledge is a critical source of competitive advantage for professional service firms. Learning from experience and sustaining past knowledge are critical to the success of such knowledge-driven firms. We use learning curve theory to evaluate learning and depreciation in professional services. Our results, based on seven years of project data collected from an architectural engineering (A/E) firm, show that (a) professional services exhibit learning curves, (b) there is virtually no depreciation of knowledge and, (c) the rate of learning accelerates with experience.

Managing and predicting technological risk and human reliability: A new learning curve theory

In the use of homotechnological systems (HTSs) over the last two centuries, literally millions and millions of data points have been amassed on human deaths, injuries, losses, damages, disasters, and tragedies. The technological risk needs to be reduced by predicting the human reliability. A fundamental theory based on the learning hypothesis has been developed to provide a sound prediction, which is consistent with the theory of error correction. The rate of reduction of the error rate with increasing experience is proportional to the rate at which errors are occurring. The risk probability for any outcome or error is derived in the form of a ‘human bathtub’ curve. The probability of an outcome as a function of accumulated experience is predicted. The measure of the risk is shown to be the information entropy, which is an objective measure of order observed in organizational learning curves, which arise from the unobserved disorder and statistical fluctuations of human reliability. Thus, the technological risk is inextricably coupled to, and interwoven with, the human reliability by a prediction methodology that is validated by data.