Learning Curves in Manufacturing

Argote, Linda; Epple, Dennis

doi:10.1126/science.247.4945.920

Cited by 997 publications

(533 citation statements)

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“…2 Each component i has a cost ci. The total cost c of the design is the sum of the component costs: c = c1 + c2 + · · · + cn.…”

Section: The Modelmentioning

confidence: 99%

“…Let us now consider case (2) in which component i improves when another modifying component j that affects it is chosen (lying inside the dashed ellipse in Fig. 3).…”

Section: Interacting Components Variable Out-degreementioning

confidence: 99%

“…A typical reported value [9] is 0.8 (corresponding to α ≈ .32), which implies that the cost of the 200th item is 80% that of the 100th item. Power laws have been observed, or at least assumed to hold, for a wide variety of technologies [2,18,9], although other functional forms have also been suggested and in some cases provide plausible fits to the data 1 . We give examples of historical performance curves for several different technologies in Figure 1.…”

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The Role of Design Complexity in Technology Improvement

et al. 2009

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We study a simple model for the evolution of the cost (or more generally the performance) of a technology or production process. The technology can be decomposed into n components, each of which interacts with a cluster of d − 1 other, dependent components. Innovation occurs through a series of trial-and-error events, each of which consists of randomly changing the cost of each component in a cluster, and accepting the changes only if the total cost of the entire cluster is lowered. We show that the relationship between the cost of the whole technology and the number of innovation attempts is asymptotically a power law, matching the functional form often observed for empirical data. The exponent α of the power law depends on the intrinsic difficulty of finding better components, and on what we term the design complexity: The more complex the design, the slower the rate of improvement. Letting d as defined above be the connectivity, in the special case in which the connectivity is constant, the design complexity is simply the connectivity. When the connectivity varies, bottlenecks can arise in which a few components limit progress. In this case the design complexity is more complicated, depending on the details of the design. The number of bottlenecks also determines whether progress is steady, or whether there are periods of stasis punctuated by occasional large changes. Our model connects the engineering properties of a design to historical studies of technology improvement.experience curve | learning curve | progress function | performance curve | design structure matrix | evolution of technology T he relation between a technology's cost c and the cumulative amount produced y is often empirically observed to be a power law of the formwhere the exponent α characterizes the rate of improvement. This rate is commonly termed the progress ratio 2 −α , which is the factor by which costs decrease with each doubling of cumulative production. A typical reported value [9] is 0.8 (corresponding to α ≈ .32), which implies that the cost of the 200th item is 80% that of the 100th item. Power laws have been observed, or at least assumed to hold, for a wide variety of technologies [2,18,9], although other functional forms have also been suggested and in some cases provide plausible fits to the data 1 . We give examples of historical performance curves for several different technologies in Figure 1. The relationship between cost and cumulative production goes under several different names, including the "experience curve", the "learning curve" or the "progress function". The terms are used interchangeably by some, while others assign distinct meanings [9,29]. We use the general term performance curve to denote a plot of any performance measure (such as cost) against any experience measure (such as cumulative production), regardless of the context. Performance curve studies first appeared in the 19th century [10,6], but their application to manufacturing and technology originates from the 1936 study by Wright on aircraft produc...

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“…2 Each component i has a cost ci. The total cost c of the design is the sum of the component costs: c = c1 + c2 + · · · + cn.…”

Section: The Modelmentioning

confidence: 99%

“…Let us now consider case (2) in which component i improves when another modifying component j that affects it is chosen (lying inside the dashed ellipse in Fig. 3).…”

Section: Interacting Components Variable Out-degreementioning

confidence: 99%

mentioning

confidence: 99%

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The Role of Design Complexity in Technology Improvement

et al. 2009

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“…Alpha shows a curious mode of behavior: typically organizations learn from their experience and improve their performance over multiple projects (Argote and Epple, 1990), yet Alpha illustrates declining performance over time (see Figure 2). It began as a startup that satisfi ed its customers but over time tended to ignore good development practices in favor of rushing products to market.…”

Section: Case Study Overviewmentioning

confidence: 99%

Dynamics of concurrent software development

Rahmandad

Weiss

2009

System Dynamics Review

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In a concurrent development process different releases of a software product overlap. Organizations involved in concurrent software development not only experience the dynamics common to single projects, but also face interactions between different releases of their product: they share resources among different stages of different projects, including customer support, they have a common code base and architecture that carries problems across releases, they use the same capabilities, and their market success in early releases impacts their resources in later ones. Drawing on two case studies we discuss some of the feedback processes central to concurrent software development and build a simple simulation model to analyze the resulting dynamics. This model sheds light on tipping dynamics, the nature of inter-project feedback loops, and alternative resource allocation policies relevant to management of concurrent software development.

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“…Employees learn, that is, they improve their specific skill levels, by using the machinery that is currently employed by the firm (see e.g. Argote and Epple, 1990). The speed of learning depends positively on the general skill level of the employee (more general education leads to faster learning and adaptation), and learning only occurs when the productivity of the machinery exceeds the specific skill level of that employee, thus closing the so called skill-gap.…”

Section: Consumption Goods Sectormentioning

confidence: 99%

Bubbles, Crashes, and the Financial Cycle: The Impact of Banking Regulation on Deep Recessions

Hoog

Dawid

2017

Macroecon. Dynam.

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This paper explores how different credit market-and banking regulations affect business fluctuations. Capital adequacy-and reserve requirements are analysed for their effect on the risk of severe downturns. We develop an agent-based macroeconomic model in which financial contagion is transmitted through balance sheets in an endogenous firm-bank network, that incorporates firm bankruptcy and heterogeneity among banks to capture the fact that contagion effects are bank-specific. Using concepts from the empirical literature to identify amplitude and duration of recessions and expansions we show that more stringent liquidity regulations are best to dampen output fluctuations and prevent severe downturns. Under such regulations both leverage along expansions and amplitude of recessions become smaller. More stringent capital requirements induce larger output fluctuations and lead to deeper, more fragile recessions. This indicates that the capital adequacy requirement is pro-cyclical and therefore not advisable as a measure to prevent financial contagion.

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Learning Curves in Manufacturing

Cited by 997 publications

References 58 publications

The Role of Design Complexity in Technology Improvement

The Role of Design Complexity in Technology Improvement

Dynamics of concurrent software development

Bubbles, Crashes, and the Financial Cycle: The Impact of Banking Regulation on Deep Recessions

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