Transcript ppt

Demand and Forecast
Dickson K.W. Chiu
PhD, SMIEEE
Text: Ballou - Business Logistics Management, 5/E (Chapter 8)
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Learning Objectives
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To understand some basic concept of demand
and forecasting
To anticipate typical problems involved in
demand and forecasting
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What’s Forecasted in the Supply Chain
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Demand, sales or requirements
Purchase prices
Replenishment and delivery times
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Some Forecasting Method Choices
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Historical projection
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Causal or associative
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Regression analysis
Qualitative
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Moving average
Exponential smoothing
Surveys
Expert systems or rule-based
Collaborative
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Typical Time Series Patterns: Random
250
Sales
200
150
Actual sales
Average sales
100
50
0
0
5
10
15
20
25
Time
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Typical Time Series Patterns:
Random with Trend
250
Sales
200
150
100
Actual sales
Average sales
50
0
0
5
10
15
20
25
Time
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Sales
Typical Time Series Patterns:
Random with Trend and Seasonal
800
700
600
500
400
300
200
100
0
Actual sales
Trend in sales
Smoothed trend and seasonal sales
0
10
20
30
40
Time
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Sales
Typical Time Series Patterns: Lumpy
Time
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Is Time Series Pattern Forecastable?
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Whether a time series can be reasonably
forecasted often depends on the time series’
degree of variability. Forecast a regular time
series, but use other techniques for lumpy
ones. How to tell the difference:
A time series is lumpy if
X  3
where
X  mean of the series
  standard deviation of series,
regular, otherwise.
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Analysis Details
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See textbook if you are interested
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Moving Average
Exponential Smoothing Formulas
Regression Analysis
Combined Model Forecasting
Note data requirements and timeliness
requirement
Tracking signal monitors the fit of the model
to detect when the model no longer
accurately represents the data => events
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Actions When Forecasting is Inappropriate
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Seek information directly from customers
Collaborate with other channel members
Apply forecasting methods with caution (may
work where forecast accuracy is not critical)
Delay supply response until demand becomes
clear
Shift demand to other periods for better supply
response
Develop quick response and flexible supply
systems, e.g., order-to-build of Dell
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Collaborative Forecasting
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Demand is lumpy or highly uncertain
Involves multiple participants each with a
unique perspective—“two heads are better
than one”
Goal is to reduce forecast error
The forecasting process is inherently unstable
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Collaborative Forecasting Key Steps
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Establish a process champion
Identify the needed information and collection processes
Establish methods for processing information from
multiple sources and the weights assigned to multiple
forecasts
Create methods for translating forecast into form
needed by each party
Establish process for revising and updating forecast in
real time
Create methods for appraising the forecast
Show that the benefits of collaborative forecasting are
obvious and real
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Summary
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Again much domain knowledge is required.
Note the data / information requirements and how IT
helps to collect / integrate the data for calculations and
decision making.
Capture forecasting signals (either determined by a
business analyst or automatically by a sub-system) as
events / exceptions / alerts and forward them to the
appropriate system and personnel for decision / action.
Collaborative forecasting as well as quick response and
flexible supply systems requires much new IT in the
process and information integration.
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