Forecasting
OPS 370
Forecasting
2
Forecasting - Chapter 4
What to Forecast?
Demand for Individual
Products & Services
Short Term
(0-3 Months)
Demand for Product &
Service Families
Medium Term
(3 Months – 2 Years)
Total Sales, New Offerings
Long Term
(>2 Years)
3
Forecasting - Chapter 4
How to Forecast?
• Qualitative Methods
– Based On Educated Opinion & Judgment
(Subjective)
– Particularly Useful When Lacking Numerical Data
(Example: Design and Introduction Phases of a
Product’s Life Cycle)
• Quantitative Methods
– Based On Data (Objective)
4
Forecasting - Chapter 4
Qualitative Methods
• Executive Judgment
• Sales Force Composite
• Market Research/Survey
• Delphi Method
5
Forecasting - Chapter 4
Quantitative Methods
• Time Series & Regression
• Time Series  Popular Forecasting Approach in
Operations Management
• Assumption:
– “Patterns” That Occurred in the Past Will Continue to
Occur In the Future
• Patterns
–
–
–
–
6
Random Variation
Trend
Seasonality
Composite
Forecasting - Chapter 4
Monthly Champagne Sales
1600
1400
1200
1000
800
600
400
200
0
0
12
24
36
48
Time (t)
60
72
84
UK Airline Miles
U.K. Airline Miles
U.K. Airline Miles
18000
16000
12000
10000
8000
6000
4000
2000
Month
94
91
88
85
82
79
76
73
70
67
64
61
58
55
52
49
46
43
40
37
34
31
28
25
22
19
16
13
10
7
4
0
1
Thousands
Thousands ofof
MilesMiles
14000
Forecasting Steps
Data Collection
Data Analysis
Collect Relevant/Reliable Data
Be Aware of “Garbage-In,
Garbage Out”
Model Selection
Monitoring
9
Forecasting - Chapter 4
Forecasting Steps
Data Collection
Data Analysis
Plot the Data
Identify Patterns
Model Selection
Monitoring
10
Forecasting - Chapter 4
Forecasting Steps
Data Collection
Choose Model Appropriate for Data
Data Analysis
Consider Complexity Trade-Offs
Perform Forecast(s)
Model Selection
Select Model Based on Performance
Measure(s)
Monitoring
11
Forecasting - Chapter 4
Forecasting Steps
Data Collection
Data Analysis
Track Forecast Performance
(Conditions May and Often Do
Change)
Model Selection
Monitoring
12
Forecasting - Chapter 4
Time Series Models
• Short Term
– Naïve
– Simple Moving Average
– Weighted Moving Average
– Exponential Smoothing
13
Forecasting - Chapter 4
Forecasting Example
• L&F Bakery has been forecasting by “gut feel.”
They would like to use a formal
(i.e., quantitative) forecasting technique.
14
Forecasting - Chapter 4
Forecasting Methods
• Naïve
15
Forecasting - Chapter 4
Forecasting Methods
• Naïve (Excel)
=C4
=C5
16
Forecasting - Chapter 4
Forecasting Methods
• Moving Average
Forecasting Methods
30 Day Moving Average of AAPL Price
Forecasting Methods
• Moving Average (Excel)
=AVERAGE(C4:C6)
= AVERAGE(C5:C7)
Forecasting Methods
• Moving Average Example
• Assume n = 2
Week
1
2
3
4
5
Demand
125
175
150
150
160
Forecasting Methods
• Weighted Moving
Average
Forecasting Methods
• Weighted Moving Average
=$G$6*C6+$G$5*C5+$G$4*C4
=$G$6*C7+$G$5*C6+$G$4*C5
Forecasting Methods
• Weighted Moving Average Example
• Assume n = 2, W1 = 0.7, W2 = 0.3
Week
1
2
3
4
5
Demand
125
175
150
150
160
Forecasting Methods
• Exponential Smoothing
Forecasting Methods
• Exponential
Smoothing
Month
Jan (1)
Feb (2)
Mar (3)
Apr (4)
May (5)
Jun (6)
Jul (7)
Actual
200
300
200
400
500
600
-
Forecast
200
200
230
221
275
343
-
Error
0
100
-30
179
225
257
-
Forecasting Methods
• Exponential Smoothing (Excel)
Initial forecast
=D4+$G$4*(C4-D4)
=D5+$G$4*(C5-D5)
Forecasting Methods
• Exponential Smoothing Example
• Assume a = 0.4
Week Demand
1
125
2
175
3
150
4
150
5
160
Forecasting Methods
• How to Select Value of a?
• Alpha determine importance of recent forecast
results in new forecasts
Determining Forecast Quality
• How Well Did a Forecast Perform?
• Determine Forecast Error
Error = Actual Demand – Forecasted Demand
Month
Jan
Feb
Mar
Apr
May
Jun
Actual
200
300
200
400
500
600
Forecast
200
200
230
221
275
343
Error
0
100
-30
179
225
257
Average Error
121.8
Determining Forecast Quality
• Why is Average Error a Deceiving Measure of
Quality?
n
• Better Measures:
e
Mean Absolute Deviation
MAD 

t
1
n
n
2


e
 t
Mean Squared Error
MSE 
Root Mean Squared Error
RMSE = MSE
1
n
Determining Forecast Quality
Measure of Bias:
Tracking Signal =
Sum of Errors/MAD
=731/131.8 = 5.55
*OK if between -4 and +4
MAD
MSE
Determining Forecast Quality
For this MA(2) forecast. What is MAD, MSE, and TS?
Week
1
2
3
4
5
Demand Forecast
125
-175
-150
150
150
162.5
160
150
155
Linear Regression
• <SKIP Section in Textbook on Exponential
Smoothing with Linear Trend>
Linear Trend Line
• Given Data
– Y = Values of Response Variable
– X = Values of Independent Variable
• Parameters to estimate
– a = Y-intercept
Y  a  bX
– b = slope
• Use “least squares” regression
equations to estimate a and b.
– Or …
Excel for Linear Regression
Use SLOPE Function
Use INTERCEPT Function