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The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from the literature and to assess which learning curves are most suitable to describe learning in order picking.

An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis.

It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar‐El et al. and the three‐parameter hyperbolic model are suitable to approximate the learning effect. The Stanford B model and the time constant model led to unrealistic results.

The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules.

The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects.

The origin and applications of learning curves. Learning curves were first noticed by aircraft producers before World War II. The time taken to assemble a new type of aircraft diminished with each successive airframe built. Typically, the time taken for assembly had dropped by 20% each time the cumulative output had doubled. Later, similar “start up effects” were noticed in many other industries, and were used for contract negotiations and for cost control. Prices of new products, especially chemicals, often fell with time and output according to some kind of “learning curve”. However exceptions to the “learning curve” were sometimes found. Assembly times sometimes ceased to drop, or dropped at different rates at different sites.

The purpose of this paper is to demonstrate that cybernetic theory explains learning curves and sets the curves as legitimate and efficient tools for a pro-active energy technology policy.

The learning system is a non-trivial machine that is kept in non-equilibrium steady state at minimum entropy production by competitive, equilibrium markets. The system has operational closure and the learning curve expresses its eigenbehaviour. This eigenbehaviour is analysed not in calendar time but in the characteristic time of the system, i.e., its eigentime. Measured in eigentime, the minimum entropy production in the steady-state learning system is constant. The double closure mechanism described by Heinz von Förster makes it possible for the learning system to change (adapt) its eigenbehaviour without compromising its operational closure.

By obeying basic laws of second order cybernetics and of non-equilibrium thermodynamics the learning system self-organises its learning to follow an optimal path described by the learning curve. The learning rates are obtained through an operator formalism and the results explain observed distributions. Application to solar cell (photo-voltaic) modules indicates that the silicon scarcity bubble 2005-2008 produced excess entropy corresponding to costs of the order of 100 billion US dollars.

Grounding technology learning and learning curves in cybernetics and non-equilibrium thermodynamics open up new possibilities to understand technology shifts through radical innovations or paradigm changes.

Learning curves are legitimate and efficient tools for energy policy and industrial strategy.

Grounding of technology learning and learning curves in cybernetic and thermodynamic theory provides a stable theoretical basis for applications in industry and policy.

Evaluates the benefits and problems of applying the experience curve in two very different service organisations. The first case shows how an experience curve has been calculated at a macro (organisation) level for British Airways over a 20 year period, including the time at which it was privatised. The second example shows an application over the first year of operation of a high‐volume paperwork processing operation within a financial services organisation. These studies demonstrate that experience curves can be applied to great effect in high volume service organisations, but a single measure of output needs to be established. The paper also shows how different phases and rates of learning may be linked to organisational and technological change, and discusses how an experience curve might be used to monitor improvement and establish future cost‐related performance objectives.

This paper focuses on the management of the learning curve in overseas capacity expansions. The purpose of this paper is to unravel the direct as well as indirect influences on the learning curve and to advance the understanding of how these affect its management.

The paper builds on the offshoring, capacity expansion and learning curve literature. The existing scholarship often lacks detailed insights into the factors surrounding the globalisation of production, and how constructing and operationalising new capacities overseas should be implemented. The paper employs qualitative methodology and draws on a longitudinal, factory-level analysis of an in-depth case study of a Danish wind turbine manufacturer.

This study goes beyond a simplistic treatment of the lead time and learning required to establish a new capacity. The authors examined the dimensions of the learning process involved in a capacity expansion project and identified the direct and indirect labour influences on the production learning curve. On this basis, the study proposes solutions to managing learning curves in overseas capacity expansions. Furthermore, the paper concludes with measures that have the potential to significantly reduce the non-value-added time when establishing new capacities overseas.

The paper uses a longitudinal in-depth case study of a Danish wind turbine manufacturer and goes beyond a simplistic treatment of the lead time and learning required to establish a new capacity.

The results of an empirical study which has investigated the cost dynamics of a group of private warehouses is presented. Specifically the article tests the applicability of using a learning curve to plan and evaluate a private warehouse′s average labour cost per case performance. To test the applicability of using a learning curve in this industry, the study has analysed the empirical performance of a small sample of private warehouses over a five‐year period. A regression model of the private warehouse′s average labour cost per case performance shows that they do follow the standard log‐linear learning curve relationship between unit cost and cumulative units. The regression model shows that the private warehouses have a learning rate of 87 per cent. An example application shows that including the study′s results in a private‐versus‐public warehouse selection problem, using a discounted cash‐flow analysis, increases the private ware‐house′s internal rate of return by approximately 10 per cent.

This study seeks to examine how the quantitative semantics of the learning curve phenomenon can be employed in order to derive monetary information for team learning observed within knowledge‐intensive production environments.

Software development is selected as an identical example of a team‐based, knowledge‐intensive production environment. The interaction of learning rate of the developer teams and the improvements on their average solving time (i.e. productivity) is modelled as a Lotka‐Volterra predator‐prey interacting populations system establishing a causal relationship between the human capital (HC) of organizational teams and the observed learning curve effects on their performance. In addition, empirical evidence illustrates that the estimated learning rates capture the entire range of team learning effects on performance fluctuations caused by the HC.

The fluctuations on the learning rates can be interpreted as a result of the HC variability across the population of developer teams. Hence, the cost implications of the HC within knowledge‐intensive production environments can be rationalised using the quantitative semantics of the learning curve phenomenon

The learning curve is associated with the cost side of the organizational income‐generating process limiting its potential valuation applications for team learning observed within the context of the production environments.

The study offers a theoretical justification, supported by empirical evidence, for employing the mathematical expression of the learning curve paradigm to rationalize the financial consequences of team learning observed within production environments.

The applicability of learning curve theory to the construction industry has been investigated by several studies; however, the outcomes are characterised by inconsistent, rather sporadic patterns. Therefore, the purpose of this paper is to explore the effect of learning on concrete masonry blockwork labour productivity in recurring building floor cycles.

Repetitive blockwork labour inputs from 52 multi-storey residential buildings were collected and analysed using the straight-line learning curve model. The cumulative average labour input for each recurring floor and its corresponding cycle number were modelled using the least squares method.

According to the learning curve theory principles, labour inputs are expected to decrease by a certain percentage as the floor cycle number within each building observed increases. Nonetheless, the patterns emerged from this study provide little evidence for that.

Contrary to several previous findings which have asserted the significance of the learning concept to construction productivity, the results obtained for the activity investigated suggest that there is no potential context for the theory to be used as a useful tool to quantify productivity improvement, or to provide for a practical project management observation and control system.

Notwithstanding the numerous research into the effect of learning on construction activities, this study is unprecedented in examining the applicability of the theory to concrete masonry blockwork labour productivity in building construction. It can thus assist in achieving reliable planning, determining the plausibility of correlating past performances or predicting future expenditures, and appraising the potentiality of the learning phenomenon as a useful tool to quantify productivity improvement over the repetitive cycle process of such a distinct construction activity.

Learning behaviors related to quality improvement in manufacturing systems (i.e. reduction of defectiveness over production cycles) are widely investigated. Many different approaches have been introduced to describe the link between the learning mechanism and quality performance of a plant. In a previous study by the same authors, a set of learning “composition laws” for two basic structures were defined to provide a tool to forecast the behavior of complex manufacturing systems composed by a network of elementary processes. This paper presents an empirical investigation about these learning composition laws on a real case in the field of automotive exhaust‐systems manufacturing.

Considering the technology learning system as a non‐trivial machine, this paper seeks to take a first step to ground experience and learning curves in cybernetic theory.

Assuming operational closure, feedback regulation and a constant elasticity of output/input ratio to cumulative output makes it possible to calculate eigenvalues for the self‐reflecting loop in the learning system.

The results imply a zero mode learning rate of 20 per cent with higher modes providing learning rates smaller than 8 per cent. The results reproduce the grand features of technology learning.

The NTM approach provides basis for work to understand improvements in grafted technologies and effects on learning from radical innovations.

Further inquiries into the learning system need complementary organisational analysis.

Based on the theory of the non‐trivial machine, this paper takes the first step to ground the experience and learning curves in cybernetic theory.