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Forecasting
OPS 370
Forecasting - Chapter 4
Forecasting
2
Forecasting - Chapter 43
What to Forecast?
Short Term(0-3 Months)
Demand for IndividualProducts & Services
Medium Term(3 Months – 2 Years)
Demand for Product & Service Families
Long Term(>2 Years)
Total Sales, New Offerings
Forecasting - Chapter 44
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)
Forecasting - Chapter 45
Qualitative Methods
• Executive Judgment
• Sales Force Composite
• Market Research/Survey
• Delphi Method
Forecasting - Chapter 46
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– Random Variation– Trend– Seasonality– Composite
Monthly Champagne Sales
0
200
400
600
800
1000
1200
1400
1600
0 12 24 36 48 60 72 84
Time (t)
U.K. Airline Miles
0
2000
4000
6000
8000
10000
12000
14000
16000
180001 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94
Month
Th
ou
san
ds
of
Mile
s
U.K. Airline MilesUK Airline MilesTh
ou
san
ds o
f M
iles
Forecasting - Chapter 49
Forecasting StepsData Collection
Data Analysis
Model Selection
Monitoring
Collect Relevant/Reliable Data
Be Aware of “Garbage-In, Garbage Out”
Forecasting - Chapter 410
Forecasting StepsData Collection
Data Analysis
Model Selection
Monitoring
Plot the Data
Identify Patterns
Forecasting - Chapter 411
Forecasting StepsData Collection
Data Analysis
Model Selection
Monitoring
Choose Model Appropriate for Data
Consider Complexity Trade-Offs
Perform Forecast(s)
Select Model Based on Performance Measure(s)
Forecasting - Chapter 412
Forecasting StepsData Collection
Data Analysis
Model Selection
Monitoring
Track Forecast Performance (Conditions May and Often Do
Change)
Forecasting - Chapter 413
Time Series Models
• Short Term– Naïve – Simple Moving Average– Weighted Moving Average– Exponential Smoothing
Forecasting - Chapter 414
Forecasting Example
• L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e., quantitative) forecasting technique.
Forecasting - Chapter 415
Forecasting Methods
• Naïve
Forecasting - Chapter 416
Forecasting Methods
• Naïve (Excel)
=C4
=C5
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 Demand1 1252 1753 1504 1505 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$4*C5
Forecasting Methods• Weighted Moving Average Example
• Assume n = 2, W1 = 0.7, W2 = 0.3Week Demand
1 1252 1753 1504 1505 160
Forecasting Methods
• Exponential Smoothing
Forecasting Methods
• Exponential Smoothing
Month Actual Forecast ErrorJan (1) 200 200 0Feb (2) 300 200 100Mar (3) 200 230 -30Apr (4) 400 221 179May (5) 500 275 225Jun (6) 600 343 257Jul (7) - - -
Forecasting Methods
• Exponential Smoothing (Excel)
Initial forecast
=D4+$G$4*(C4-D4)=D5+$G$4*(C5-D5)
Forecasting Methods• Exponential Smoothing Example• Assume a = 0.4
Week Demand1 1252 1753 1504 1505 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 DemandMonth Actual Forecast Error
Jan 200 200 0Feb 300 200 100Mar 200 230 -30Apr 400 221 179May 500 275 225Jun 600 343 257
Average Error121.8
Determining Forecast Quality
• Why is Average Error a Deceiving Measure of Quality?
• Better Measures:
Mean Absolute Deviation
Mean Squared Error
Root Mean Squared Error
n
eMAD
n
t 1
n
eMSE
n
t 1
2
Determining Forecast Quality
MAD
MSE
Measure of Bias: Tracking Signal =
Sum of Errors/MAD=731/131.8 = 5.55
*OK if between -4 and +4
Determining Forecast Quality
Week Demand Forecast1 125 --2 175 --3 150 1504 150 162.55 160 150
155
For this MA(2) forecast. What is MAD, MSE, and TS?
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– b = slope
• Use “least squares” regression equations to estimate a and b.– Or …
Y a bX
Excel for Linear Regression
Use SLOPE Function
Use INTERCEPT Function