Learning Curve Model Research Articles

Learning Curve Analysis of Wind Power and Photovoltaics Technology in US: Cost Reduction and the Importance of Research, Development and Demonstration

The strategic transition from fossil energy to renewable energy is an irreversible global trend, but the pace of renewable energy deployment and the path of cost reduction are uncertain. In this paper, a two-factor learning-curve model of wind power and photovoltaics (PV) was established based on the latest empirical data from the United States, and the paths of cost reduction and corresponding social impacts were explored through scenario analysis. The results demonstrate that both of the technologies are undergoing a period of rapid development, with the learning-by-searching ratio (LSR) being greatly improved in comparison with the previous literature. Research, development, and demonstration (RD&D) have contributed to investment cost reduction in the past decade, and the cost difference between high and low RD&D spending scenarios is predicted to be 5.5%, 8.9%, and 11.27% for wind power, utility-scale PV, and residential PV, respectively, in 2030. Although higher RD&D requires more capital, it can effectively promote cost reduction, reduce the total social cost of deploying renewable energy, and reduce the abatement carbon price that is needed to promote deployment. RD&D and the institutional support behind it are of great importance in allowing renewables to penetrate the commercial market and contribute to long-term social welfare.

Grouping workers with similar learning profiles in mass customization production lines

Mass Customization (MC) implies in large product variety and reduced production batch sizes. Tasks that rely on human ability are especially affected in this context as workers must quickly adapt to the requirements of new models. That adaptation process takes place differently in each worker, justifying the development of strategies to group workers with similar learning profiles. This paper proposes a framework to form homogeneous groups of workers based on their learning profiles by integrating learning curve (LC) modeling and cluster analysis. For that, performance data are collected and modeled through LCs, such that LC parameters quantify workers’ adaptation to tasks. Principal Components Analysis (PCA) is applied to the dataset consisting of the LC parameters; PCA outputs give rise to an importance index that guides a backward parameter selection process. After each LC parameter is removed from the dataset, a new grouping using the Fuzzy C-Means (FCM) clustering technique is carried out using the remaining LC parameters, and the quality of the formed groups is assessed by means of three metrics. When applied to a shoe manufacturing process, 8 out of the original 29 LC parameters were retained, and used to insert workers into two clusters. The reduced subset of parameters elevated the quality of the clustering procedure by 29.4% (from 0.476 to 0.616) according to the Silhouette Index; similar improvements were indicated by the other two metrics. The retained parameters are related to workers’ performance at the first repetition and previous experience, highlighting the importance of well-designed training programs.

Learning only buys you so much: Practical limits on battery price reduction

Wide deployment of electric vehicles (EVs) would greatly facilitate global de-carbonization, but achieving the emission targets depends on future battery prices. Conventional learning curves for manufacturing costs, used in many battery projections, unrealistically predict battery prices will fall below $100/kWh by 2030, pushing EVs to hit price parity with internal combustion engine vehicles (ICEVs) in the absence of incentives. However, in reality, essential materials costs set practical lower bounds on battery prices.Our 2-stage learning curve model projects the active material costs and NMC-based Lithium-ion battery pack price with mineral and material costs as the respective price floors. The improved model predicts nickel-manganese-cobalt (NMC) battery prices will fall only to about $124/kWh by 2030 – much cheaper than today, but still too expensive to truly compete with ICEVs, due primarily to the high prices of cobalt, nickel, and lithium. Our results suggest that stabilizing raw materials prices and/or stimulating R&D activities on alternative battery chemistries will be important to achieve environmentally sustainable EV-based ground transportation at an attractive price.

Learning rate and subjective mental workload in five truck driving tasks

Both learning curve models and subjective mental workload are useful tools for determining the length of training for new workers and predicting future task performance. An experiment was designed to collect the task completion times and subjective mental workload of five driving tasks including (a) reverse into garage, (b) 3-point turn, (c) parallel parking, (d) S-curve and (e) up-down-hill. The results indicated that task completion times of truck driving can be predicted with a learning curve. Practice significantly reduced the mental workload rating. However, the novice trainees tended to have a more significant reduction because, compared to experienced trainees, they tended to give greater or lower workload scores than the experienced trainees before and after practice, respectively. The current study may not be complete enough to provide guidelines for a training programme, but it is adequate to suggest that learning rate and workload measure can serve as indexes for factoring in the individual differences.Practitioner summary: Learning curves can be used to determine the length of training for new workers and performance standards for a particular task. Learning rate and mental workload were found to be important measures for comparing individual differences in order to better design a training programme. However, mental workload must be evaluated by experienced participants.

A game-based virtual machine pricing mechanism in federated clouds

In a federated cloud environment, diverse pricing schemes among different IaaS service providers (ISPs) form a complex economic landscape that nurtures the market of cloud brokers. Although pricing mechanisms have been proposed in the past few years, few of them address the issue of competitive and cooperative behaviours among different ISPs. In this paper, we employ the learning curve to model the operation cost of ISPs, and introduce a novel algorithm that determines the cooperative pricing mechanism among different ISPs. The cooperation decision algorithm uses the operation cost computed based on the learning curve model and price policies obtained from the competition part as parameters to calculate the final revenue when outsourcing or locally satisfying users' resource requests. Extensive experiments are conducted in a real-world federated cloud platform, and the experimental results are compared with three existing pricing mechanisms. Our experimental results show that the proposed pricing mechanism is effective to improve resource utilisation as well as reduce the profit loss caused by request rejection.

From Laboratory to Production: Learning Models of Efficiency and Manufacturing Cost of Industrial Crystalline Silicon and Thin-Film Photovoltaic Technologies

Efficiency and manufacturing cost are two key elements for the photovoltaic (PV) industry. In this paper, we look at the time-dependent evolution of efficiency and manufacturing cost for PV devices. For efficiency improvements, the empirical model developed by Goetzberger et al. is applied to describe the time-dependent improvements for a concrete technology in laboratory cell's research and development, and the learning factor c is extracted. The application of the Goetzberger's model is extended to industrial PV modules. It is forecasted that cadmium telluride (CdTe) and copper gallium indium diselenide (CIGS) will have the average module efficiency in 2020 of 17.9% and 16.4%, respectively. Over 21.8% commercial module efficiency is projected with n-type Si interdigitated back contact technology, while average module efficiencies of 17.4%, 18.4%, and 19.4% are projected for conventional p-type multi-, mono-, and mono-passivated emitter and rear cell (PERC), respectively. For the manufacturing cost, we parameterized the learning curve model of manufacturing cost for industrial crystalline silicon (c-Si), CdTe, and CIGS technologies. As projected by the learning curve, the manufacturing cost of c-Si and thin-film modules may reach 0.2 $/Wp or below, when the cumulative production reach 1 TW. The learning rate for Si (24.2%) is greater than CdTe (19.1%) and CIGS (8.1%).

Applications of learning curves in production and operations management: A systematic literature review

The occurrence and characteristics of human learning and forgetting are extensively researched in many fields. Thus, the literature on learning curves is abundant. Within production and operations management, learning curves can describe the performance improvement of workers due to repetitions or experience, which makes them a useful tool for managerial decision making. Human learning is very relevant to manufacturing firms that are labor-intensive, especially where labor is costly. Also, technology and organization learning frequently occur in many organizations, even when manufacturing is not labor-intensive. It is not surprising, therefore, that, despite the plethora of prior work in learning curves, research in this important area continues to appear in the production and operations management literature. A comprehensive, systematic review of the literature, however, has never been conducted.This article aims at giving a general overview of learning curves in production and operations management. It maps this specific research area, synthesizes its research findings, highlights its key application areas and explores future research directions. This work achieves this goal by presenting the results of a systematic literature review on the applications of learning curves in production and operations management. First, a framework that includes typical learning curve models is developed. The fundamental characteristics of learning curves and their applications in production and operations management are then identified. This framework is used to categorize the literature. This study also presents a discussion of the most important and informative articles in each of the major categories, as well as highlighting future research opportunities.

Pathways Toward Enhanced Techno-Economic Performance of Flow Battery Systems in Energy System Applications

Redox flow batteries have shown great potential for a wide range of applications in future energy systems. However, the lack of a deep understanding of the key drivers of the techno-economic performance of different flow battery technologies—and how these can be improved—is a major barrier to wider adoption of these battery technologies. This study analyzes these drivers and provides an extensive comparison of four flow battery technologies, including the all-vanadium redox (VRB), iron–chromium, zinc–bromine, and polysulfide–bromine flow batteries, by examining their current and projected techno-economic performances. We address the potential for performance improvements and resulting cost reduction by developing a component-based learning curve model. The model considers the near-term learning rates for various subcomponents of each of the four battery technologies as well as their technological improvements. The results show that (i) both technological improvements in the performance parameters as well as mass production effects could drive significant cost reductions for flow battery systems; (ii) flow battery systems could be cost-effective in a variety of energy system applications in the near future; and (iii) from a techno-economic perspective, VRB systems are more suitable for the applications that require low energy and high power capacities.

Carbon capture and storage (CCS) retrofit potential of coal-fired power plants in China: The technology lock-in and cost optimization perspective

Carbon capture and storage (CCS) has been discussed intensively in China; however, the CCS technology lock-in risk has been neglected for a long time and may have a negative impact on understanding the CCS application potential. Thus, from the perspective of avoiding a technology lock-in, a learning curve model and a cost-optimization model are employed in this study to explore the total cost of CCS commercialization and the national and provincial CCS retrofit potential of coal-fired power plants in China. The results show that if the second-generation CCS technologies are not commercially applied by 2040, coal-fired power plants in China may face a huge risk of being locked in by the first-generation technologies with a retrofit potential of only 0–143.63 GW (GW = 106 kW) and a cost of 13.39 billion USD. Advancing the CCS commercialization time to 2030 can reduce the technology lock-in risk greatly and increase the CCS retrofit potential to 431.01–499.90 GW, which would cost 54.3 billion USD. Considering the cost input, the technology lock-in risk, and the CCS retrofit demand, 2035 is regarded a suitable time for CCS commercialization in China with a retrofit potential of 143.63–431.04 GW and 31.46 billion USD cost input. Moreover, at the regional level, there is a great CCS retrofit potential of coal-fired power plants in Shaanxi, Hebei, and Inner Mongolia. Policymakers should provide greater support for the second-generation CCS technologies and promote them actively in 2030–2035, especially in Shaanxi, Hebei, and Inner Mongolia, to achieve CCS commercialization and control the CO2 emissions of coal-fired power plants in China.

Learning Patterns of Pivoting Neuromuscular Control Training-Toward a Learning Model for Therapy Scheduling.

The goal of this study was to investigate the learning patterns in leg pivoting neuromuscular control performance over six-week pivoting neuromuscular control training (POINT) and to estimate how many sessions at beginning are needed to estimate the overall pivoting neuromuscular control learning curve. Twenty subjects (ten females, ten males) participated in 18 sessions of POINT (three sessions per week for six weeks) program using an off-axis elliptical trainer. Performance measures including pivoting instability and stepping speed were quantified for each study session during a stepping task while subjects were asked to control pivoting movements under a slippery condition. Learning curve relating the pivoting instability to training sessions was quantified by the power law and by the exponential curve as a function of sessions or days with three parameters: The limit of learning, rate of learning, and learning capacity. The power and exponential learning models characterized the learning curves similarly with no differences in [Formula: see text]. No significant sex differences were found in the limit of learning, rate of learning, and learning capacity. Based on [Formula: see text] and RMSE, data from the first three study sessions might be enough to estimate the pivoting neuromuscular performance over the whole training period. The findings showed that subjects' motor skills to improve pivoting instability followed the learning curve models. The findings and models can potentially be used to develop more effective subject-specific therapy scheduling.

Using the learning curve theory in the investigation of on-site craft gangs’ blockwork construction productivity

Purpose The purpose of this paper is to investigate how on-site blockwork craft gangs’ learning impacts productivity within the production environment on-site to optimise their productivity. Design/methodology/approach The research is adopting a quantitative method with the observation of seven craft gangs’ blockwork with an average of five members in each gang, using the learning curve model application in a 17-storey tri-tower construction project in Nigeria. The linear regression method was employed in the analysis stage of this study using labour-recorded productivity time input as the dependent variables. Findings The paper provides empirical insights about the significance of on-site craft gangs’ learning. The overall blockwork craft gangs learning observed at the site level shows an average learning rate of 94.21 per cent resulting in 5.79 per cent improvement gains. Research limitations/implications Due to the nature of the study and the research question, the observations in this research study were limited to FCDA construction project in Nigeria. The limitation of this scenario is that the research results may lack generalisability. Therefore, there is the need for further study on the learning rate. Practical implications This research study includes the implications for the development of on-site blockwork craft gangs learning; the significant impact of learning rate of 94.21 per cent resulting in 5.79 per cent improvement gain can be used in the planning and to fast track the productivity of craft gangs’ construction. Originality/value This paper identified the need to improve construction productivity through craft gangs’ on-site learning with the application of the learning curve theory.

Constructing supplier learning curves to evaluate relational gain in engineering projects

Learning curve models analyze how experience relates to performance over time, and rates vary widely across industries, organisations, and processes. Very little empirical research addresses learning curves for suppliers in engineering projects. Hence, we consider learning within strategically managed buyer–supplier relationships, focusing on the potential for ‘relational gain’ in terms of learning performance for longer term strategic suppliers (LTSP). A mixed-method approach is adopted employing 2 phases. First, empirical performance data is collected as well as contextual information regarding the managerial actions behind the learning curve. Second, the empirical data is analysed using the time constant learning curve model. Different supplier relational categories are mobilised to assess any relational gain in the rates of learning. The findings give learning parameters across different categories, showing that LTSPs outperform other less collaborative categories across a range of learning and performance dimensions. Suppliers who are not formally part of the firm’s development programme, but are requested by clients or have specialist capabilities, do not perform well at the outset of a project but accelerate rapidly along the learning curve. There is a need for project based organizations to both strategically manage long term relationships with suppliers, as well as quickly integrate new suppliers. Better performing learning curves act as benchmarks to show the relative relational gains to be achieved by investment and integration approaches.

Automating the Use of Learning Curve Models in Construction Task Duration Estimates

AbstractStandard scheduling tools for construction projects with repetitive tasks assume that labor productivity remains constant throughout the project lifetime. None of these tools accommodate th...

Predicting field reliability based on two-dimensional warranty data with learning effects

ABSTRACTUnderstanding the field reliability of a sold product is crucial to both managers and engineers for monitoring product quality and improving warranty service design. When practitioners model warranty data, they it often assume that the lifetimes of products manufactured on different days is homogeneously distributed (i.e., product reliability remains the same over time). Based on a two-dimensional warranty data set collected from an automobile manufacturer, we find that the reliability of products improves over time. A log-linear regression model on the failure rate of the products is proposed by considering the usage rate and manufacturing day as covariates. The maximum likelihood approach is used to estimate the parameters. The results show the existence of learning effects in reliability in the early stage of manufacturing. A learning-curve model is then used to predict the reliability of new items produced.

Understanding cost reduction of China's biomass direct combustion power generation—A study based on learning curve model

Abstract During the critical transition towards cleaner production (CP), the developing and transition countries should pay extra attention to displace traditional biomass utilization with CP concepts such as biomass direct combustion power (Bio-DCP). China has witnessed impressive growth in its Bio-DCP sector from 2006 to 2014. The further industrial expansion and policy making of Bio-DCP in China and other countries in transition call for a deeper understanding of cost reduction mechanisms. In this study, the learning curve models (i.e., electricity production cost as a function of multiple factors including cumulative installed capacity, operation & maintenance costs, feed-in-tariff, and cogeneration) were developed to (i) empirically estimate the learning rates of China's Bio-DCP, and (ii) investigate the impacts of past technological developments and policies on the cost development. Main findings include: (i) The overall learning rate of China's Bio-DCP is 4.54% over the period 2006-2014; (ii) The raised feed-in-tariff in 2010 shows a positive effect on the learning of Bio-DCP; (iii) The circulating fluidized bed projects show a higher learning rate compared to that of the grate boiler projects. Based on the above results, suggestions regarding the Bio-DCP's future development are offered, including discriminative subsidies, biomass waste supply chains, cogeneration, and multi-industry parks.

Can technological learning significantly reduce industrial air pollutants intensity in China?—Based on a multi-factor environmental learning curve

Abstract This paper introduces a methodology to examine the effect of technological learning on industrial air pollutants intensity in China. It is based upon the theory of learning curves and environmental learning curves. In this way it is possible to estimate the effect of technological learning on industrial air pollutants intensity in two steps. First, we build a multi-factor environmental learning curve model to investigate the relationship between technological learning and industrial air pollutants intensity. At the second stage of the analysis, we assess whether and how different types of technological learning can reduce industrial air pollutants intensity based on a mediating effect model. The results show that there is a causal relationship between technological learning and industrial air pollutants intensity. Moreover, different types of technological learning have the same influence way on industrial air pollutants intensity: learning-by-doing can significantly reduce industrial air pollutants intensity through energy efficiency; and the same is true with learning-by-researching and learning-by-importing. In addition, different types of technological learning have different influence degree on industrial air pollutants intensity: learning-by-doing is the leading contributor to the reduction in industrial air pollutants intensity, while the contribution of learning-by-researching is the smallest.

Alternation of parties in power and economic volatility: testing the rational partisan hypothesis and policy learning hypothesis

Right and left parties have distinct macroeconomic preferences that could create different levels of volatility during their executive tenures. But rational partisan theory argues that, because actors in the economy anticipate ruling party behavior, partisan differences only matter when election outcomes are uncertain. We argue that policy risk from ruling parties extends beyond elections, leading to important variation in growth volatility that occurs during a ruling party’s tenure. Building on theories of policy risk and learning, we argue that after elections, economic actors still face uncertainty about the policies of new ruling parties. With time in power, new ruling parties build policy track records, reducing policy risk and, thus, volatility. We estimate a learning curve model of ruling party duration’s effect on the variation in quarterly GDP growth rates. Using data from 44 democracies between 1981 and 2009, we find that learning processes extend beyond the conclusion of uncertain elections.

Analysis of the learning curve of the surgical procedure for the treatment of thoracic disc herniation using anterolateral transthoracic approach with the aid of image-guided system.

In surgery, the learning process represents one of the critical topics in the development of a young surgeon where there is often no standardized learning program. The learning curve is defined by plotting proficiency as a function of time, or the number of repetitions. In this study, we analyzed the learning process for a rare and surgically demanding pathology such as thoracic disc herniations (TDHs) treated with an antero-lateral trans-thoracic approach. We analyzed a consecutive series of patients admitted with a diagnosis of TDHs. All patients underwent surgery using a standard antero-lateral trans-thoracic approach, assisted by navigation system based on intraoperative 3D imaging performed with O-Arm™ (Medtronic; Dublin, Ireland) system. Twenty-one patients underwent surgery from January 2012 to June 2017. The time analysis shows a progressive reduction in time from the first to the last case with continued improvement in operating time but progressively less dramatic, this is explicable considering the classic learning curve model. Predictably, a slow learning curve was charted - typical for a technically demanding procedure associated with a rare incidence of pathology. A learning curve is an easy and efficient way to gauge the growth of a surgeon. After 21 cases we obtained an intermediate profile of learning curve between steep and shallow. Despite different alternatives for speeding up the learning process it is not always possible to standardize and reflect them in a learning process. Today, the use of technology is often considered in this but does not always affect the process itself.

An Evaluation of Investment in a PV Power Generation Project in the Gobi Desert Using a Real Options Model

This paper presents a policy benefit model of a photovoltaic (PV) power generation project based on real options analysis (ROA) and the two-factor learning curve model. The main purpose is to examine the investment behavior of developing a PV project in the Gobi desert considering multiple uncertain factors. We take the environmental cost of desertification control into account for the first time in the literature. Four other uncertain factors are thermal power cost, PV power generation cost, carbon prices, and government subsidy. A binary tree method is applied to solve the proposed model, and we obtain both unit decision value and optimal investment time. Our baseline scenario illustrates that ROA is more effective than net present value (NPV) analysis when dealing with uncertainty. Our simulation results show that the government could suffer a loss in accordance with the existing subsidy policy when investing in a PV project. Therefore, the subsidy should be gradually reduced. Finally, the influence of the subsidy policy on decision value is discussed, and an appropriate subsidy is determined accordingly.

Faeyee: allocation of constrained business resources for production of competitive products from cost accounting perspectives

Synopsis Faeyee Electronics was an electronics company that manufactured and sold smartphones including XS and XT types. The management of Faeyee wanted to pursue the maximum contribution margin as much as possible, especially since they were faced with limited resources. It was necessary to apply analytical tools and cost accounting concepts to study this case including cost-volume-profit analysis, learning curve analysis, regression analysis, definitions of competitive products, constrained non-linear optimization, and contribution margin. Research methodology The case uses business analytics tools and cost accounting concepts, including regression models and constrained optimization approaches, to study how to maximize business outcomes, such as contribution margin and profits when limited business resources are available. The company and individuals are disguised. Relevant courses and levels This case can be used in any junior-, senior-, masters- or MBA-level managerial accounting course. Students need to have at least one course of introduction to statistics or instructors review required statistics concepts or techniques before assigning this case. Students are exposed to the challenges of deriving learning curve models, using regression analysis to study collected data and allocating limited resources to maximize contribution margin.