Demand Forecasting:Time Series Models presentation
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13-05-2010, 11:57 AM


Demand Forecasting: Time Series Models




PResented By:
Professor Stephen R. Lawrence
College of Business and Administration
University of Colorado



Forecasting Horizons

Long Term
5+ years into the future
R&D, plant location, product planning
Principally judgement-based



Medium Term

1 season to 2 years
Aggregate planning, capacity planning, sales forecasts
Mixture of quantitative methods and judgement


Short Term

1 day to 1 year, less than 1 season
Demand forecasting, staffing levels, purchasing, inventory levels


Quantitative methods

Short Term Forecasting:
Needs and Uses
Scheduling existing resources
How many employees do we need and when?
How much product should we make in anticipation of demand?


Acquiring additional resources

When are we going to run out of capacity?
How many more people will we need?
How large will our back-orders be?
Determining what resources are needed
What kind of machines will we require?
Which services are growing in demand? declining?
What kind of people should we be hiring?



Types of Forecasting Models

Types of Forecasts
Qualitative --- based on experience, judgement, knowledge;
Quantitative --- based on data, statistics;



Methods of Forecasting

Naive Methods --- eye-balling the numbers;
Formal Methods --- systematically reduce forecasting errors;
time series models (e.g. exponential smoothing);
causal models (e.g. regression).
Focus here on Time Series Models
Assumptions of Time Series Models
There is information about the past;
This information can be quantified in the form of data;
The pattern of the past will continue into the future.



Forecasting Examples

Examples from student project and implimentations:
Demand for tellers in a bank;
Traffic on major communication switch;
Demand for liquor in bar;
Demand for frozen foods in local grocery warehouse.



Example from Industry: American Hospital Supply Corp.

70,000 items;
25 stocking locations;
Store 3 years of data (63 million data points);
Update forecasts monthly;
21 million forecast updates per year.
Simple Moving Average
Forecast Ft is average of n previous observations or actuals Dt :
Note that the n past observations are equally weighted.
Issues with moving average forecasts:
All n past observations treated equally;
Observations older than n are not included at all;
Requires that n past observations be retained;
Problem when 1000's of items are being forecast.


Simple Moving Average

Include n most recent observations
Weight equally
Ignore older observations
Moving Average
Example:

Moving Average Forecasting

Exponential Smoothing I
Include all past observations
Weight recent observations much more heavily than very old observations:
Exponential Smoothing I
Include all past observations
Weight recent observations much more heavily than very old observations:
Exponential Smoothing I
Include all past observations
Weight recent observations much more heavily than very old observations:
Exponential Smoothing I
Include all past observations
Weight recent observations much more heavily than very old observations:
Exponential Smoothing: Concept
Include all past observations
Weight recent observations much more heavily than very old observations:
Exponential Smoothing: Math
Exponential Smoothing: Math
Exponential Smoothing: Math
Thus, new forecast is weighted sum of old forecast and actual demand
Notes:
Only 2 values (Dt and Ft-1 ) are required, compared with n for moving average
Parameter a determined empirically (whatever works best)
Rule of thumb: a < 0.5
Typically, a = 0.2 or a = 0.3 work well
Forecast for k periods into future is:
Exponential Smoothing
Example:

Exponential Smoothing

Complicating Factors
Simple Exponential Smoothing works well with data that is moving sideways (stationary)
Must be adapted for data series which exhibit a definite trend
Must be further adapted for data series which exhibit seasonal patterns
Holtâ„¢s Method:
Double Exponential Smoothing
What happens when there is a definite trend?
Holtâ„¢s Method:
Double Exponential Smoothing
Ideas behind smoothing with trend:
``De-trend'' time-series by separating base from trend effects
Smooth base in usual manner using a
Smooth trend forecasts in usual manner using b
Smooth the base forecast Bt
Smooth the trend forecast Tt
Forecast k periods into future Ft+k with base and trend
ES with Trend
Example:

Exponential Smoothing with Trend

Winterâ„¢s Method:
Exponential Smoothing
w/ Trend and Seasonality
Ideas behind smoothing with trend and seasonality:
De-trendâ„¢: and de-seasonalizetime-series by separating base from trend and seasonality effects
Smooth base in usual manner using a
Smooth trend forecasts in usual manner using b
Smooth seasonality forecasts using g
Assume m seasons in a cycle
12 months in a year
4 quarters in a month
3 months in a quarter
et cetera
Winterâ„¢s Method:
Exponential Smoothing
w/ Trend and Seasonality
Smooth the base forecast Bt


Smooth the trend forecast Tt

Smooth the seasonality forecast St
Winterâ„¢s Method:
Exponential Smoothing
w/ Trend and Seasonality
Forecast Ft with trend and seasonality

Smooth the trend forecast Tt

Smooth the seasonality forecast St
ES with Trend and Seasonality
Example:

Exponential Smoothing with
Trend and Seasonality

Forecasting Performance
Mean Forecast Error (MFE or Bias): Measures average deviation of forecast from actuals.
Mean Absolute Deviation (MAD): Measures average absolute deviation of forecast from actuals.
Mean Absolute Percentage Error (MAPE): Measures absolute error as a percentage of the forecast.
Standard Squared Error (MSE): Measures variance of forecast error
Forecasting Performance Measures
Mean Forecast Error (MFE or Bias)
Want MFE to be as close to zero as possible -- minimum bias
A large positive (negative) MFE means that the forecast is undershooting (overshooting) the actual observations
Note that zero MFE does not imply that forecasts are perfect (no error) -- only that mean is on target
Also called forecast BIAS
Mean Absolute Deviation (MAD)
Measures absolute error
Positive and negative errors thus do not cancel out (as with MFE)
Want MAD to be as small as possible
No way to know if MAD error is large or small in relation to the actual data
Mean Absolute Percentage Error
(MAPE)
Same as MAD, except ...
Measures deviation as a percentage of actual data
Mean Squared Error (MSE)
Measures squared forecast error -- error variance
Recognizes that large errors are disproportionately more expensive than small errors
But is not as easily interpreted as MAD, MAPE -- not as intuitive
Fortunately, there is software...

download full presentation
leeds-faculty.colorado.edu/lawrence/Tools/FORECAST/forecast.ppt
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15-10-2010, 02:25 PM



.ppt   Human Resource Demand Forecasting Lecture 4.ppt (Size: 1.36 MB / Downloads: 81)
Human Resource Demand Forecasting

By
Neha Mhaskar



Managerial Judgment

In this managers sit together, discuss and arrive at a figure which
would be the future demand for labor. Two approaches are there
bottom-up and top-down.

In bottom-up approach, line managers submit their departmental
proposals to the top managers who finally decide the forecast.

In top-down approach, top managers prepare the same.



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