Application Of Learning Curves Research Articles

Learning Curve as Effective Approach to Study Efficiency and Productivity in Business Process

Learning is a process/technique increasing knowledge or skill through study or experience. Learning helps to become aware of situation/state through observing or hearing about whether it is process, functioning, operational procedure etc.. The learning curve refer as the experience curve, the cost curve, the efficiency curve, or the productivity curve. It is in various form increasing, diminishing learning curve how a process(Business/Operation) is improved during time due to learning and increased proficiency/efficiency. The learning curve theory explains, it is a process which helps to study the task performed is taking less time and resources used and productivity depends upon performance due to competency/efficiency as process will be learned. A learning curve is explained the rate of improvement in techniques and gives best result in production process. In business, the slope of the learning curve represents the rate at which learning new skills/knowledge add cost-savings technique in business. In this article, the focus is on to study types and application of learning curve in business as efficiency and experience effect cost and productivity respectively.

Learning curve applications in Industry 4.0: a scoping review

This study aimed at identifying applications of learning curve (LC) modelling at individual, group, and organisational levels in Industry 4.0 (I4.0) environments. For that, a scoping review on four databases was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. Our results indicated that LCs are more prominently adopted in I4.0 to model learning at the individual level using technologies oriented to sensing and communication (e.g. big data, IoT, wireless sensors, cloud computing, remote control, or monitoring). However, the effect of a few processing and actuation technologies, such as augmented/virtual reality, collaborative robots, and machine learning/AI, on learning seems promising. Further, despite the number of studies investigated, few explicitly described the LC model used to represent the impact of I4.0 technologies on learning. Our findings allowed the proposition of five research directions. Literature on both LC and I4.0 is still fragmented, poorly addressing their relationship. As I4.0 is an innovative approach that allows more extensive information exchange and processing, new ways of using I4.0 technologies to expedite data collection, which has always constrained LC practical applications, should be devised to close the gap between I4.0 and learning.

Safe implementation of robotic-assisted minimally invasive direct coronary artery bypass: application of learning curves and cumulative sum analysis.

BackgroundLearning curves are inevitably encountered when first implementing an innovative and complex surgical technique. Nevertheless, a cluster of failures or complications should be detected early, but not deter learning, to ensure safe implementation. Here, we aimed to examine the presence and impact of learning curves on outcome after robotic-assisted minimally invasive direct coronary artery bypass (RA-MIDCAB).MethodsA retrospective analysis of the first 300 RA-MIDCAB surgeries between July 2015 and December 2020 was performed. Learning curves were obtained via logarithmic regression for surgical time. Cumulative sum (CUSUM) analysis was performed for (I) major complications including MI, stroke, repeat revascularization, and mortality, and (II) other complications, including prolonged ventilation, pneumonia, pleura puncture, lung herniation, pericarditis, pleuritis, arrhythmia, wound complications, and delirium. Expected and unacceptable rates were set at 12% and 20%, respectively, for major complications, and at 40% and 60% for other complications, based on historical data in conventional coronary artery bypass grafting (CABG).ResultsDemographic characteristics did not differ between terciles, except for more smokers in the first tercile, and less hypercholesterolemia and more complex procedures in the third tercile. The mean surgical time for all operations was 258±81 minutes, ranging from 127 to 821 minutes. A learning curve was only observed in the first tercile. Subgroup analysis revealed that this learning curve was only observed for procedures consisting of single internal mammary artery (SIMA) with 1 or 2 distal anastomoses but not with bilateral internal mammary arteries (BIMA) or more than 2 distal anastomoses. CUSUM analysis showed that the cumulative rate of major and other complications never crossed the lines for unacceptable rates. Rather, the lower 95% confidence boundary was crossed after 50 cases, indicating improvement in safety.ConclusionsThese results suggest that integration of RA-MIDCAB in the surgical landscape can be safely achieved and complication rates can quickly be reduced below those expected in traditional CABG. Collective experience plays a key role in overcoming the learning curve when more complex procedures and cases are introduced.

Quality Insights: Learning, forgetting, and relearning quality: a half-life learning curve modelling approach

Learning curves are used extensively in business, science, technology, engineering, and industry to predict system performance over time. Although most of the early developments and applications were in the area of production engineering, general applications can be found in all areas of applications. Quality engineering and technology, in particular, offer a fertile area for the application of learning curves. This paper applies the concept of half-life of learning curves to quality management. This is useful for predictive measures of quality system performance. Half-life is the amount of time it takes for a quantity to diminish to half of its original size through natural processes. The approach of half-life computation provides additional decision tool for researchers and practitioners in quality engineering and technology. Derivation of the half-life equations of learning curves can reveal more about the properties of the various curves, with respect to the unique life-cycle property of product quality.

Application of Learning Curves for Didactic Model Evaluation: Case Studies

The success of (online) courses depends, among other factors, on the underlying didactical models which have always been evaluated with qualitative and quantitative research methods. Several new evaluation techniques have been developed and established in the last years. One of them is â??learning curvesâ??, which aim at measuring error rates of users when they interact with adaptive educational systems, thereby enabling the underlying models to be evaluated and improved. In this paper, we report how we have applied this new method to two case studies to show that learning curves are useful to evaluate didactical models and their implementation in educational platforms. Results show that the error rates follow a power law distribution with each additional attempt if the didactical model of an instructional unit is valid. Furthermore, the initial error rate, the slope of the curve and the goodness of fit of the curve are valid indicators for the difficulty level of a course and the quality of its didactical model. As a conclusion, the idea of applying learning curves for evaluating didactical model on the basis of usage data is considered to be valuable for supporting teachers and learning content providers in improving their online courses.

Half-Life of Learning Curves for Information Technology Project Management

Learning curves are used extensively in business, science, technology, engineering, and industry to predict system performance over time. Although most of the early development and applications were in the area of production engineering, contemporary applications can be found in all areas of applications. Information technology project management, in particular, offers a fertile area for the application of learning curves. This paper applies the concept of half-life of learning curves to information technology project management. This is useful for predictive measures of information technology system performance. Half-life is the amount of time it takes for a quantity to diminish to half of its original size through natural processes. The approach of half-life computation provides an additional decision tool for researchers and practitioners in information technology management. Derivation of the half-life equations of learning curves can reveal more about the properties of the various curves with respect to the unique life-cycle property of information technology.

Acceptance of Technology by Texas Museums: An Application of Learning Curve

There are over 3100 museums in the United States. Of these, around 320 are in Texas. These museums represent a cross section of American cultural values and contribute a great deal to attract visitors internationally and domestically. They are also used as a tool of educating the public through various activities. Since 1994 an increasing number of Texas museums have been connected to the Internet and are becoming members of the global marketplace. However, there are many museums that lack the tools and expertise to participate in the digital revolution. The objective of this exploratory research is to investigate the speed of acceptance of new technology, specifically the Internet, by Texas museums. The theoretical framework is related to the application of a learning curve. As in many industrial and service organizations, learning facilitates the acceptance of new techniques. According to the proponents of a learning curve, learning rapidly increases in the first periods but the later periods typically show a leveling of the speed of acceptance. In order to achieve the objective of this research a random stratified sample of Texas museums was taken and a questionnaire was sent to test several hypotheses related to the acceptance of new technology, including barriers to enter the global market. The results indicate that acceptance of new technology by Texas museums is in line with a typical learning curve. However, many obstacles, including resource availability and lack of a niche in the marketplace, hinder further development. There are also two important factors that affect the acceptance. One of them is the unparalleled population growth in or around the large metropolitan areas like Dallas, Houston, San Antonio, and Austin. The second is related to the dramatic change in the cultural fabric of Texas due to immigration, mainly from Mexico. Future research on technological acceptance should take into account these structural changes and wider use of the Spanish language in Texas.

An Alternative Procedure for Estimating Unit Learning Curves

Abstract : The unit learning curve plays a prominent role in DOD cost analysis. In those cases where the model accurately describes the real-life situation, i.e., when the model is properly applied to the data, it can be a powerful tool for predicting unit production costs. There are, however, some unique estimation problems inherent in the model. The usual method of generating predicted unit production costs attempts to extend properties of least squares estimators to non-linear functions of these estimators. The result is biased estimates of unit production costs. Another problem common to many learning curve applications is estimating lot midpoints and slope coefficients when both estimates depend on each other and both quantities are unknown. This paper addresses the two problems discussed above and presents an alternative procedure for estimating unit learning curves.