Operations – in class week 3.
Simple moving average
months = t =October, t+1 = November, t+2= December etc…
N = number of time period = get all demand data plus them and get the average for ‘’N’’
Moving averages = another way of forecasting data into the future.
Methods
The nave approach (last year’s data) that’s what next year’s data going to be.
Simple moving average – straightening the curves, best used when data points are
relatively stable during time. Smooths out bumps simple moving average will smooth it
out. The sum of enrolment in previous n periods / n. the more time periods that you add the
smoother the line will be. (Three period moving average) only through 3 periods.
Weighted moving average – emphasises data that is closer to the present. Analyse data
more than the year before that. Data from most recent time period is more useful than the
later time period data. Any weights can be used but the sum must be = 1. Actual demand x
Weight (add them all up, no division required). Weights are in percentages. Weights should
be shifted to forecast other periods.
Weighted average and weighted moving average = check more about it.
Exponential smoothing = allows us to create a new average from and old average using the
most recent demand.
Value of smoothing constant = how much the calculation weights recent demand (smooths
