PDF | Learning curves (LCs) are deemed as effective tools for monitoring the Article (PDF Available) · January with Reads. Cite this. PDF | Purpose – The purpose of this paper is to build a curve that can portray quality Angus Jeang, (),"Learning curves for quality and productivity", Jaber, M. and Saadany, A. (), “An economic production and. learning curves were modeled per block by linear and power regression models and tested for difference The learning curve showed a significant power regression (p ;21(6) .
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Learning Curves (, pages, Klara Sjölén and Allan Macdonald) is a brand new sketch book, aimed at teaching how to really learn to sketch. Full of tips. Written by international contributors, Learning Curves: Theory, Models, and First Published DownloadPDF MB Read online. EUR EN. ISBN (pdf) References. Annex 1 – Participants of the workshop “Learning Curves for Policy Support, held on 8th European Commission, ; IEA, ). A mature system.
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Management Science 44 7 : — The learning curve: A new perspective. International Transactions in Operational Research 7 6 : — As organizations gain operating experience, organizational performance improves, although at a decreasing rate.
Scholars have frequently used the power curve to model this relationship in manufacturing contexts. In these models, the logarithm of unit cost decreases linearly as a function of the logarithm of cumulative number of units produced Yelle The decrease in cost i. For an overview of forgetting and learning from others, see Argote This chapter focuses on learning from own experience.
The disappointing implication of the typical use of the power curve is that management can only accelerate learning from own experience by producing more. There are several limitations to this traditional view of the learning curve.
Adapted from Bohn, R. Hence, the learning rate should be treated as an endogenous variable. In other words, management is actually responsible for managing the rate of improvement. Second, experience—typically measured by cumulative production volume—is not the only source for learning.
Organizations can engage in deliberate learning activities such as quality improvement projects. Yet, there is an actual learning process inside the learning curve. Learning results from experience and deliberate activities. It can yield better organizational knowledge, and better organizational knowledge can persuade organizational members to modify behavior.
Changed behavior, in turn, can improve organizational performance Bohn see Figure 2. None of these steps are trivial. Scholars have merely scratched the surface in terms of studying these steps. No single empirical study has incorporated all of the steps.
This chapter reviews empirical findings in the literature in terms of i different sources for learning, and ii partial assessments of the steps that make up the actual learning process inside the learning curve. The chapter concludes by identifying opportunities for future research that should provide insights for organizations to better manage their learning curves. Is cumulative volume the most relevant proxy, or would a variant of cumulative volume be a more accurate measure of experience?
Second, to what extent can organizations gain a competitive learning curve advantage from specialization?
Third, what factors contribute to the rather significant variation in learning rates from own experience? Nature of Experience The learning curve literature has focused on three common experience variables: 1 cumulative volume, 2 calendar time, and 3 maximum volume. As mentioned before, cumulative production volume is the typical experience variable Yelle ; Argote Repetition allows organizations to gain experience and to fine tune operations.
When a plant is scaling up production, the production system faces significant challenges and new situations. Factory personnel need to figure out how to solve such challenges during scale-up. Only two studies have compared learning curve estimations with all three measures of experience. Interestingly, both studies concluded that maximum volume was the best measure. Mishina found that the learning curve estimation for bomber airplanes suffered from autocorrelation with cumulative volume and calendar time, but not with maximum volume.
Future research should assess whether these findings generalize beyond airplane and tirecord manufacturing. Not all experience is necessarily equally effective in improving organizational performance. First, organizations could learn significantly from their own failures Cannon and Edmondson Whenever a production unit produces defective units, such defects provide opportunities to learn from and improve the production system.
Li and Rajagopalan found that the cumulative number of defective units is statistically more significant than the cumulative number of good units in explaining learning curve effects. For manufacturing contexts that require defective units to be reworked, Jaber and Guiffrida propose a model that incorporates rework time.
Depending on the evolution of rework time, a learning curve may continue to improve, plateau, or deteriorate. Future learning curve research is needed to empirically investigate the role of defects and rework. Second, experience can accumulate at the individual, team, and organizational levels. Recently, scholars have investigated the impact of team experience.
In addition to organizational experience measured with the usual cumulative volume variable, organizations with stable teams could potentially reach higher performance levels. In stable teams, team members learn how to better coordinate work with one 26 Learning Curves: Theory, Models, and Applications another, because team members learn 1 who is best at performing which role, and 2 to trust one another.
Scholars have found that team experience is a significant driver for learning curves in health care Reagans et al. Sinclair et al.
Their study suggests that cumulative volume provides an indication of future volume. Research and development projects—not cumulative volume—were the real source of cost reduction. Task homogeneity, coupled with a higher frequency of repetition, allows a factory to learn more quickly from its experience. Several learning curve studies have investigated the benefits of specialization.
In the U. In a study of incidents and accidents in the U. Heterogeneous causes allow for deeper analysis. The authors concluded that focus helped specialist airlines to analyze heterogeneous causes. Interestingly, however, it might not be optimal to focus as much as possible.
An experimental study showed that some degree of variation yields faster learning rates. Schilling et al. An analysis of an offshore software services operation confirmed the potential benefits of related variation.
According to Narayanan et al. However, investments in learning new tasks can impede short-term productivity. Average specialist airlines did not learn faster than average generalist airlines. However, the best specialist airline did learn faster than the best generalist airline.
So, focus provides an opportunity for faster learning, but there are no guarantees for superior performance. A promising area for future work would be to investigate under what conditions does a particular level of specialization result in faster learning from a certain type of experience? Variation in Learning Rates It has been well documented that organizations show tremendous variation in learning rates.
Dutton and Thomas , for example, graphed a distribution of learning Inside the Learning Curve: Opening the Black Box of the Learning Curve 27 rates from a sample of over studies. Understanding the dynamics that cause learning curve heterogeneity has been an important area for learning curve research.
In a study of adoption of minimally invasive cardiac surgery in sixteen hospitals, Pisano et al. The authors used case data from two hospitals to explore differences that might have contributed to variation in learning rates. The two hospitals differed markedly in terms of: i their use of formal procedures for new technology adoption, ii cross-functional communication, iii team and process stability, iv team debrief activities, and v surgeon coaching behavior.
In a follow-up study, Edmondson et al. Wiersma investigated how four conditions affected learning rates across twenty-seven regions in the Royal Dutch Mail Company.
A higher degree of temporary employees, a higher level of free capacity, a higher degree of product heterogeneity, and less conflicting concerns about other performance measures all had a favorable impact on the learning rate. It will be worthwhile for future research to further quantify conditions that vary across organizations and include such quantitative data in learning curve analyses.
They can also engage in a more pro-active approach to managing learning curves. Examples of deliberate activities include both pre-production planning before a process starts, as well as industrial engineering after a process starts.
Levy found that prior experience and training explain differences in the estimated learning rates for individual workers. This was a landmark study even though the explanatory variables prior experience and training did not evolve over time. Adler and Clark made the next step by incorporating longitudinal variables for deliberate activities in productivity learning curves: cumulative engineering activity and cumulative training activity. In one production department, engineering activity enhanced productivity while training activity disrupted productivity.
In a second production department, the exact opposite occurred. Thus, deliberate learning activities can both help and hurt. The authors provided some case-based explanations for these surprising findings. For example, if producibility concerns trigger engineering activity, engineering activity enhances productivity.
On the other hand, if product performance concerns trigger engineering changes, such changes could be disruptive. Hatch and Mowery studied the impact of cumulative engineering in yield learning curves in semiconductor manufacturing. Yield learning curves for processes in the early stages of manufacturing were driven by cumulative engineering as opposed to cumulative volume.
In more mature processes, cumulative engineering and cumulative volume were both sources for learning to improve yields.
Further studies into the relationship between operating time and quality of surgery are needed. Level of evidence: Level II, prognostic study.
Kristian Bjorgul, Phone: Wendy M. Novicoff, Phone: Khaled J. Saleh, Phone: National Center for Biotechnology Information , U. Journal List Int Orthop v. Int Orthop.
Published online Feb Kristian Bjorgul , 1, 2 Wendy M. Novicoff , 1 and Khaled J. Saleh 3. Author information Article notes Copyright and License information Disclaimer. Corresponding author. This article has been cited by other articles in PMC. Abstract The aim of this study was to identify and characterise learning curves in hip fracture surgery. Introduction The existence of a learning curve in acquiring technical skills in orthopaedic surgery is established in the literature, but has not been studied in detail as it relates to an individual resident acquiring basic orthopaedic skills without prior experience.
Materials and methods The data for this study was extracted from a larger prospective study on hip fractures in a community hospital serving a population of , people.
Open in a separate window. Statistical analysis Each surgeon performed a series of operations and the procedures in each group of operations were assigned ascending numbers.
Type of surgery Number of procedures Mean operating time minutes Minimum operating time minutes Maximum operating time minutes Cannulated screws 37 10 IM nail, no locking screw 54 15 IM nail with locking screw 70 27 Hemiarthroplasty residents 92 40 Hemiarthroplasty orthopaedic surgeons 72 40 Discussion This study examined operating times to determine whether a learning curve or learning effect could be measured for hip fracture surgery.
Footnotes Level of evidence: Contributor Information Kristian Bjorgul, Phone: References 1. Delaunay C, Kapandji AI.
Paillard P. Hip replacement by a minimal anterior approach. Swanson TV. Posterior single-incision approach to minimally invasive total hip arthroplasty. The AO distal locking aiming device: De VJ, Vandenberghe D. Acute percutaneous scaphoid fixation using a non-cannulated Herbert screw.
Chir Main. Rosenkranz J, Babst R. A special instrument: Oper Orthop Traumatol. Learning curves in orthopaedic surgery: Ann R Coll Surg Engl.
Hansen TB, Snerum L. Elektra trapeziometacarpal prosthesis for treatment of osteoarthrosis of the basal joint of the thumb. Statistical evaluation of learning curve effects in surgical trials. Clin Trials. Statistical assessment of the learning curves of health technologies. Health Technol Assess.
Spine J. Anatomic comparison of the Roy-Camille and Magerl techniques for screw placement in the lower cervical spine. Evaluation of humeral head replacements using time-action analysis. J Shoulder Elbow Surg. Gross M. Innovations in surgery. A proposal for phased clinical trials. J Bone Joint Surg Br. Navigation reduces the learning curve in resurfacing total hip arthroplasty. Clin Orthop Relat Res. From a more evolutionary perspective, some authors have proposed that teaching should be studied when it improves learning of a task that would otherwise be impedingly difficult for learners to achieve 35 , 36 , In other words, easy skills may be learned without any teaching, whereas hard to acquire skills might induce teaching.
One challenge when comparing pedagogical strategies within and between species is that they will be very skill-specific, and most technical challenges require unique solutions with some strategies benefiting from instruction and others not. Therefore, it could be very misleading and uninformative to compare pedagogical strategies across different skills. For example, fishing for termites requires inserting a stick into the opening of a mound and removing the stick with the termites biting on it - a straightforward technique with no pedagogical interactions observed between mother and infant in Gombe chimpanzees In contrast, nut cracking requires the subject to bring three different objects together, the nut, the anvil, and the hammer.
Each item must possess specific visible and invisible physical properties so that once the correct physical strength is applied, the user can crack open the nuts. Because of the difficulties researchers have in agreeing on a simple and clear definition of teaching for humans, let alone one that could apply across species 35 , 40 , 41 , we recorded all interactions coming from the expert or requested by the apprentice to the experts that include any of the nut-cracking behavior elements These interactions include facilitating access to tools or nuts, and providing information or correcting errors in the apprentice with or without demonstrations.
We based our study on direct observations to prevent ourselves from imposing western pre-conceptions regarding what teaching should look like when using self-reporting interviews Cross-species comparison in skill acquisition Cross-species comparisons are quite challenging because the species often live in quite different environments where important differences in living conditions, prior knowledge, and ecological conditions prevail We also must consider that life-history traits differ between species, among which maturation rates, weaning age, age of parturition, and life span can affect the acquisition of skills.
Therefore, a direct comparison between species without accounting for life-history traits might be completely misleading 36 , Chimpanzee life-history is shorter than that of humans, whereby the former possesses a shorter life span, and matures and reproduces earlier 44 , This point is important as the subadult phase is generally considered as the prime period for learning during their lifespan.
As maturation may not follow a linear development over time, there is no single easy way to control for maturation differences between species. Some have suggested correcting for the different life spans observed in the species compared. This way the longer-lived species would be assigned comparatively similar values to those shorter-lived ones On the other side, most comparative experimental psychological studies tend to simply ignore this and do not attempt to correct for age of maturation differences among the studied species By doing so, sequences of developmental stages of different cognitive traits could be compared, while comparison of the rates of appearance of different traits would be misleading.
In our study, we also wanted to account for important population differences regarding maturation within the two species. For example, age of first reproduction varies in traditional human societies from Since age of first reproduction is often considered to be one of the main life-history markers 48 , we will present our comparisons between the two species both with absolute age estimates and by correcting age with population-specific age of first reproduction in the Mbendjele Results Number of nuts cracked per minute For the number of nuts cracked per minute, both models with absolute and relative age, respectively revealed the development to reach adult performance earlier in chimpanzees at an age of about 10 years as compared to humans ca.
Adult performance seemed higher in humans, though the difference was not statistically significant.