Abstract
The main applications presented in the following example are the monitoring of a dynamically changing process scheme; the implication of the simulation of dynamic planning capabilities; and the potential use of the simulation results for process-related decision-making.The example represents an NPD environment, where process activities are derived from the product structure and the relations between product components, which are subject to changes during the design as new knowledge becomes available.
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Notes
- 1.
The implementation of a dynamic process scheme was done using Matlab (by Mathworks). The process presentation employs Simulink (within Matlab). Simulink could not be used for simulation since the Simulink engine (like most workflow engines) cannot stop the process, change its scheme, and continue.
- 2.
The illustrative data at this state do not represent the data given by the company.
- 3.
Skewness measure is given by \( \gamma = {\frac{{\sqrt {n(n - 1)} }}{n - 2}}{{\sqrt n \sum\limits_{i = 1}^{n} {(x_{i} - \bar{x})^{3} } } \mathord{\left/ {\vphantom {{\sqrt n \sum\limits_{i = 1}^{n} {(x_{i} - \bar{x})^{3} } } {(\sum\limits_{i = 1}^{n} {(x_{i} - \bar{x})^{2} } )^{3/2} }}} \right. \kern-\nulldelimiterspace} {(\sum\limits_{i = 1}^{n} {(x_{i} - \bar{x})^{2} } )^{3/2} }} \)(Whitley 1994).
- 4.
In the current example D(H)Â =Â 7. When using different time scale (as done for simulating learning in Sect. 5.4.8), then the inequality becomes 600Â <Â D(H)Â <Â 800.
- 5.
Maximal parallelism, minimal impact of multiple iterations, and minimal process time, see sect. sect. 11.6
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Karniel, A., Reich, Y. (2011). Implementation Example. In: Managing the Dynamics of New Product Development Processes. Springer, London. https://doi.org/10.1007/978-0-85729-570-5_12
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