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Lecture 6

# 33:799:301 Lecture Notes - Lecture 6: Moving Average, Time Series, Dependent And Independent Variables

Department
Supply Chain Management
Course Code
33:799:301
Professor
James King
Lecture
6

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Time Series Forecasting:
- The main purpose of a time series model is to collect and study the past data of a given
time series in order to generate probable future values for the series
- Forecast for future demand relies on understanding past demand
- Creates a baseline forecast that can be evaluated for accuracy, adjusted based on
current info and planned activity
Naive Forecasting - The forecast for the next time period is equal to the actual result in the last
time period
- Advantages: Simple, works well for mature products with consistent demand
- Disadvantages: No consideration for impacts to the demand, no adjustment for
extenuating circumstances, can throttle growth or perpetuate decline
Using a Naive Forecast model - if actual sales in August was 25,500 units, what is the forecast
for September? = 25,500
- Better than no information at all
Simple Moving Average - Uses a calculated average of historical demand during a specified
number of the most recent time periods to generate the forecast
- Adv: Provides a very consistent demand over long periods of time and smooths out
random variations
- Disadv: Fails to identify trends or seasonal effects, forecast lags behind actual demand
changes - creates shortages when demand is increasing, builds inventory during
declining periods
Weighted Moving Average - Similar to a simple moving average except that the time periods are
- Adv: More accurate than a simple moving average if actual demand is increasing or
decreasing
- Disadv: Though better than a simple moving average, this technique will still lag behind
actual demand to some degree, the challenging part of using a WMA is deciding on the
weight for each time period
Simple and Weighted Moving Averages
Key question:
- How many periods to use? - More periods, less reliance on each
- How determine the weights for each period? - Balance recent data vs. historical info
- Best to try different methods and compare to actual results to find best fit
Linear Trend Forecasting - Imposing a best fit line across demand data of an entire time series,
used as basis for forecasting future values by extending the line past the existing data and out
into the future while maintaining the slope of the line
- y=ax+b
- y=forecast or dependent variable
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