`SSL`

computes the safety stock level over lead-time for three
forecasting methods: Minimum Mean Square Error (MMSE), Simple Moving
Average (SMA) and Exponential Smoothing (ES) when the demand follows
a stationary AR(1) stochastic process.

1 |

`method` |
character string specifing which forecasting method to use, |

`phi` |
a vector of autoregressive parameters, |

`L` |
a positive lead-time, |

`p` |
the order to be used in the SMA method, |

`alpha` |
smoothing factor to be used in the ES method (0 < alpha < 1), |

`SL` |
service level. |

`SSL`

is
calculated using an estimate of the standard deviation of forecasting
error for lead-time demand
*√{Var(D_t^L-\hat{D}_t^L)}* where
*\hat{D}_t^L* is an
estimate of the mean demand over L periods after period t.

The safety stock level over the lead-time.

Marlene Silva Marchena marchenamarlene@gmail.com

Silva Marchena, M. (2010) Measuring and implementing the bullwhip effect under a generalized demand process. http://arxiv.org/abs/1009.3977

Zhang, X. The impact of forecasting methods on the bullwhip effect, International Journal of Production Economics.l, v.88, n.1, p. 15-27, 2004a.

1 2 3 4 5 |

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.