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Lecture 6

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

by OC1294395

School

Rutgers UniversityDepartment

Supply Chain ManagementCourse Code

33:799:301Professor

James KingLecture

6This

**preview**shows half of the first page. to view the full**2 pages of the document.**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

weighted to address trend

- 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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