Abstract
In order to develop a framework for PCS engineering, the relevant drivers that influence PCS design need to be elicited first. Two main driver categories can be identified: structure-based drivers and variability-based drivers. Structure-based drivers (1) stem from the static production setup, whereas variability-based drivers (2) stem from the dynamic behavior of the production system. Variability-based drivers can be further separated according to the source of the variability into drivers resulting from production system variability (2a) and drivers resulting from demand variability (2b). Drivers based on production system variability have their origin within the production system or its inputs. Drivers based on demand variability have their origin at the customer. Table 3.1 illustrates this split and gives concrete examples for each driver category.
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Notes
- 1.
Note that the presented WIP limits do not include raw materials and finished goods
- 2.
See literature survey in Sect. 2.2
- 3.
An introduction to the concept of the Overall Equipment Effectiveness (OEE) can be found in Muchiri and Pintelon (2008)
- 4.
Taguchi (1986) initially derived this loss function by approximating it with the Taylor series \( L(X) = L(T + X - T) = L(T) + \frac{{L\prime(T)}}{{1!}}(X - T) + \frac{{L\prime\prime(T)}}{{2!}}{(X - T)^2} +... \)Since L(X) is minimal at X = T and L(T) = 0 the quadratic term remains as most important one
- 5.
Using \( E({X^2}) = Var(X) + {(E(X))^2} \) (explained for instance in Bosch 1998)
- 6.
Variables in lower case that are named after random variables represent, depending on the context, either realizations of the random variable, or observed values from reality used to fit a distribution for the random variable.
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Karrer, C. (2011). A Queuing Network Based Framework for PCS Engineering. In: Engineering Production Control Strategies. Management for Professionals. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24142-0_3
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