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Case 2 |
Case 3 |
Case 4 |
|||
| $\ell$ | $P\{L=\ell\}$ | $\ell$ | $P\{L=\ell\}$ | $\ell$ | $P\{L=\ell\}$ |
3 | 0.3 | 2 | 0.2548 | 2 | 0.35 |
4 | 0.4 | 3 | 0.2162 | 3 | 0.24 |
5 | 0.3 | 4 | 0.1776 | 4 | 0.13 |
| 5 | 0.1390 | 5 | 0.05 |
||
| 6 | 0.1005 | 6 | 0.05 |
||
| 7 | 0.0619 | 7 | 0.05 |
||
| 8 | 0.0500 | 8 | 0.05 |
||
| 9 | 0.05 |
||||
| 10 | 0.02 |
||||
| 11 | 0.01 |
||||
$E\{L\}$ |
4 |
4 |
4 |
||
$Var\{L\} |
0.60 |
3.13 |
5.60 |
||
The performance is measured with the $\beta$ service level. The following Table summarizes the simulation
results.
Case |
1 |
2 |
3 |
4 |
$\beta$ |
86.5% |
84.4% |
81.1% |
79.9% |
It appears that the randomness of the replenishment lead time may have a significant influence on the performance of an inventory node. This negative influence increases with the coefficient of variation of the lead time. If it is not possible to eliminate the causes of the lead time variations, then safety stock is required.
Additional information are available in the book.
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Date of last change: 17.05.2008.
