Operations
3
473.31
Fall 2015
Bruce Duggan
Providence University College
Summary
• Forecasting is fundamental to any planning effort.
• In the short run, a forecast is needed to predict the requirements
for materials, products, services, or other resources to respond to
changes in demand.
• In the long run, forecasting is required as a basis for strategic
changes, such as developing new markets, developing new
products, or services, and expanding or creating new facilities.
Learning Objectives
• Understand role of forecasting as a
basis for supply chain planning
• Classify:
• independent demand
• dependent demand
• Understand basic components of
independent demand:
•
•
•
•
average
trend
seasonal variation
random variation
• Understand common qualitative
forecasting techniques
• e.g.: Delphi method
• Know how to make time-series
forecasts using
• moving averages
• exponential smoothing.
• Know how to measure forecast
error
Demand Management
Dependent demand
• is the demand for a product or service caused by the demand for other
products or services
Independent demand
• is the demand that cannot be derived directly from that of other products
Demand Management
Independent Demand:
• finished goods
A
B(4)
D(2)
C(2)
E(1)
D(3)
F(2)
Dependent Demand:
• raw materials
• component parts
• sub-assemblies
• etc.
Types of Forecasts
• qualitative techniques
• subjective or judgmental
• based on estimates & opinions
• time-series analysis
• key idea:
• past demand data can be used to
predict future demand
• causal forecasting
• key assumption:
• demand is related to some
underlying factor or factors in the
environment
• simulation models
• allow the forecaster to run
through a range of assumptions
about the condition of the
forecast
Components of Demand
•
•
•
•
•
average demand for a period of time
trend
seasonal variation
cyclical variation
random variation vs. autocorrelation
Components of Demand
Qualitative Techniques in Forecasting
• market research
• sales team estimates
o (bottom up)
• executive estimate
o (top down)
• panel consensus
• historical analogy
• Delphi method
Delphi Method
1. Choose the experts to participate representing a variety of knowledgeable
people in different areas.
2. Through a questionnaire (or e-mail), obtain forecasts from all participants.
3. Summarize the results and redistribute them to the participants along with
appropriate new questions.
4. Summarize again, refining forecasts and conditions, and again develop new
questions.
5. Repeat Step 4 if necessary and distribute the final results to all participants.
Time Series Analysis
options
1. simple moving average
2. weighted moving average
3. exponential smoothing
which to choose depends on:
•
•
•
•
•
time horizon to forecast
data availability
accuracy required
size of forecasting budget
availability of qualified personnel
Time Series Analysis
1. Simple Moving Average
The simple moving average model assumes an average is a good
estimator of future behavior.
formula:
A t-1 + A t-2 + A t-3 +...+A t- n
Ft =
n
Ft = Forecast for the coming period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period, for up to “n” periods
1. Simple Moving Average Example
1. Simple Moving Average Example
2. Weighted Moving Average
Weighted moving average permits an unequal weighting on prior time
periods.
formula:
Ft = w 1 A t -1 + w 2 A t - 2 + w 3 A t -3 + ...+ w n A t - n
n
Ft = Forecast for the coming period
N = Number of periods to be averaged
A t-1 = Actual occurrence in the past period, for up to “n” periods
wt = weight given to time period “t” (must total 1)
w
i=1
i
=1
2. Weighted Moving Average Example
month sales
1
100
2
90
3
105
4
95
5
?
period
t-4
t-3
t-2
t-1
weights
0.10
0.20
0.30
0.40
F = .40(95) + .30(105) +.20(90) + .10(100) = 97.5
3. Exponential Smoothing
Premise:
• The most recent observations might have the highest predictive value.
Conclusion:
• Therefore, we should give more weight to the more recent time periods when
forecasting.
3. Exponential Smoothing Formula
Ft = Ft-1 + a(At-1 - Ft-1)
Ft = Forecast for the coming period
Ft-1 = Forecast value in 1 past time period
A t-1 = Actual occurrence in the past period
α = Alpha smoothing constant
LO5
3. Exponential Smoothing Example
Question:
• Given the weekly demand
data, what are the
exponential smoothing
forecasts for periods 2-10
using a=0.10 and a=0.60?
Assume F1=D1
month sales
1
820
2
775
3
680
4
655
5
750
6
802
7
798
8
689
9
775
10
?
3. Exponential Smoothing Example
• Answer:
• The respective alphas
colums denote the
forecast values.
Note that you can only
forecast one time
period into the future.
Week
1
2
3
4
5
6
7
8
9
10
Demand
820
775
680
655
750
802
798
689
775
0.1
820.00
820.00
815.50
801.95
787.26
783.53
785.38
786.64
776.88
776.69
0.6
820.00
820.00
793.00
725.20
683.08
723.23
770.49
787.00
728.20
756.28
Measurement of Error
Mean Absolute Deviation (MAD) refers to the average forecast error
using absolute values of the error of each past forecast.
• The ideal MAD is zero which would mean there is no forecasting error.
• The larger the MAD, the less the accurate the resulting model.
n
MAD =
å At - Ft
t=1
n
Measurement of Error
Running Sum of Forecast Errors (RSFE)
• considers the nature of the error
Tracking Signal
• a measure that indicates whether the forecast average is keeping pace with
any genuine upward or downward changes in demand
Measurement of Error
Tracking signal formula:
RSFE
TS =
MAD
Learning Objectives Review
1. How does forecasting aid effective supply chain planning?
2. Why is forecasting not necessary for dependent demand items?
3. What are the four basic components of independent demand?
4. What are some qualitative forecasting techniques that can be used
when no historical demand data is available?
5. What is the inherent assumption for moving average and exponential
smoothing forecasts?
6. What is the purpose of measuring forecast error?
End of Chapter 3
3-32
Copyright © 2013 McGraw-Hill Ryerson Limited