Modelling and forecasting industrial innovations via the transfer function S-shaped learning curve

Naim, Mohamed Mohamed; Towill, Denis Royston

doi:10.1007/bf01783617

Cited by 7 publications

(3 citation statements)

References 18 publications

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“…They offer qualitative description of the growth rate for real-life forecast objects, make it possible to study the way in which different factors influence this development [7,8]. S-shaped curves have been applied on a wide scale in economical and technical forecasting to model dynamics of different processes [9][10][11]. However, in order to forecast the system development, it is necessary to develop a mathematical model, which will make it possible to determine the pace of system development.…”

Section: Figure I S-shaped Curve Of Systems Development (1 Stage -Omentioning

confidence: 99%

Simulation of Technological Parameters Changing with the Satiation Effect

Rubin¹,

Polyakova²,

Gun³

2015

Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

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Dynamics of several processes quantity parameters has S-shape curve character. It is proved by the fact that process may be described by two parameters: potentional limit of the growth and resource determinating this growth. Firstly such processes were investigated in biology. Later this approach was spread on many other processes and technological systems development as well. At present time S-shaped curves are considered to be universal and sometime are used in order to describe the technological systems progress. In this paper mathematical model is carried out in order to describe S-shaped curve. S-shaped dynamics and its mathematical model make it possible to forecast processes quality parameters based on known reference points.

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Section: Figure I S-shaped Curve Of Systems Development (1 Stage -Omentioning

confidence: 99%

Simulation of Technological Parameters Changing with the Satiation Effect

Rubin¹,

Polyakova²,

Gun³

2015

Proceedings of the 2015 International Conference on Modeling, Simulation and Applied Mathematics

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“…Naim and Towill (1993) presented a family of transfer-function based learning curves. In view of the limited resources for this article we decided to base the paper at hand on simple assumptions with regard to the learning effect.…”

Section: Hypothesesmentioning

confidence: 99%

The Measurement of International Experience as a Dimension of Board Indices: Concept for an Improvement

Sommer¹

2012

IJBA

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In recent years, the internationality of enterprises, relying on adequate management resources, has gained in importance. One resource is the "International Experience of the Board" which for lack of primary data very often can be measured on the basis of secondary data only. The lack of primary data on the one hand and the public interest on the other hand leads to the creation of partly simplified board indices based on more or less random measures / variables or compositions with limited informative value. The objective of this paper is to demonstrate the need for an improvement of such indices composed of secondary data and to develop a new concept for a board index ("InEx WE "). The new index is based on the available secondary data, too, and has to be seen as a suboptimal solution but in contrast to some other indices this index offers the advantage of subdividing "International Experience" into different subareas and separate weighting factors to improve its informative value. First results of a pilot study indicate an additional informative value.

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“…The approach adopted in this paper is to embed an industrial case study, which by definition is geared to a particular market sector into the current management literature pertinent to enhancing real-world performance. As would be evident from such publications as Naim and Towill (1993) and Mukherjee et al (1998), no single case study can possibly exhibit all of the possible phenomena. So instead we concentrate on how performance monitoring and associated modelling may be exploited within such a particular environment.…”

Section: Introductionmentioning

confidence: 99%

Cusum modelling of the dynamics of process performance improvement programmes

Owen

Towill

2006

International Journal of Productivity and Performance Management

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If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service information about how to choose which publication to write for and submission guidelines are available for all. Please visit www.emeraldinsight.com/authors for more information. About Emerald www.emeraldinsight.comEmerald is a global publisher linking research and practice to the benefit of society. The company manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as providing an extensive range of online products and additional customer resources and services.Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation. AbstractPurpose -To evaluate the Cusum model as a means of rapid trend detection and dynamic response classifier in a low signal/noise environment. Design/methodology/approach -The method used was the design and implementation of an IT system for automatic on-line monitoring of press operator performance. Findings -Operators engaged following an in-house skills enhancement programme display a wide range of performance profiles. Research limitations/implications -Identifies opportunities for further management investigation of working practices identified as leading to regression plus operators obviously learning to "work smarter, not harder". Practical implications -A range of novel performance templates based on the Cusum model have been derived and tested on 18 press operators. Originality/value -The Cusum model is already well established in the literature. This case study shows that the model can be readily exploited via automatic operator performance monitoring and subsequent classification.

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Modelling and forecasting industrial innovations via the transfer function S-shaped learning curve

Abstract: Algorithms are derived for the four-parameter transfer function S-shaped curve, using a least-squared-error (LSE

Cited by 7 publications

References 18 publications

Simulation of Technological Parameters Changing with the Satiation Effect

Simulation of Technological Parameters Changing with the Satiation Effect

The Measurement of International Experience as a Dimension of Board Indices: Concept for an Improvement

Cusum modelling of the dynamics of process performance improvement programmes

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