FORECASTING
Operations Management
Dr. Ron Lembke
Demand Management
• Coordinate sources of demand for supply chain
to run efficiently, deliver on time
• Independent Demand
▫ Things demanded by end users
• Dependent Demand
▫ Demand known, once demand for end items is
known
Affecting Demand
• Increasing demand
▫ Marketing campaigns
▫ Sales force efforts, cut prices
• Changing Timing of demand
▫ Incentives for earlier or later delivery
▫ At capacity, don’t actively pursue more
Predicting the Future
We know the forecast will be wrong.
Try to make the best forecast we can,
▫ Given the time we want to invest
▫ Given the available data
• The “Rules” of Forecasting:
1. The forecast will always be wrong
2. The farther out you are, the worse your forecast
is likely to be.
3. Aggregate forecasts are more likely to accurate
than individual item ones
Time Horizons
Different decisions require projections about
different time periods:
• Short-range: who works when, what to make each
day (weeks to months)
• Medium-range: when to hire, lay off (months to
years)
• Long-range: where to build plants, enter new
markets, products (years to decades)
Forecast Impact
Finance & Accounting: budget planning
Human Resources: hiring, training, laying off
employees
Capacity: not enough, customers go away angry,
too much, costs are too high
Supply-Chain Management: bringing in new
vendors takes time, and rushing it can lead to
quality problems later
Qualitative Methods
• Sales force composite / Grass Roots
• Market Research / Consumer market surveys &
interviews
• Jury of Executive Opinion / Panel Consensus
• Delphi Method
• Historical Analogy - DVDs like VCRs
• Naïve approach
Quantitative Methods
Time Series Methods
0. All-Time Average
1. Simple Moving Average
2. Weighted Moving Average
3. Exponential Smoothing
4. Exponential smoothing with trend
5. Linear regression
6. Seasonality, with a Trend
Causal Methods
Linear Regression
The Human Element
• Colbert says you have more nerve endings in
your gut than in your brain
• Limited ability to include factors
▫ Can’t include everything
▫ More factors = more parameters = more places to
make potential errors
• If it feels really wrong to your gut, maybe your
gut is right
Time Series Forecasting
Assume patterns in data will continue, including:
Trend (T)
Seasonality (S)
Cycles (C)
Random
Variations
Evaluating Forecasts
How far off is the forecast?
Forecasts
Demands
What do we do with this information?
Measuring the Errors
Period
A-F
Method 1
A-F
Method 2
1
100
10
2
-100
10
3
100
10
4
-100
10
5
100
10
6
-100
10
7
100
10
8
-100
10
9
100
10
10
-100
10
RSFE
0
100
• Method 1 forecasts are low,
high, etc.
• Method 2 forecasts always too
low.
• Running Sum of Forecast
Errors, RSFE
▫ Sum of all periods
▫ Also known as the Bias
n
RSFE   At  Ft
t 1
Evaluating Forecasts
n

Mean Absolute MAD  (1 / n)
At  Ft
Deviation
t 1
Mean Squared
Error
n
MSE  (1 / n)  At  Ft 
2
t 1
Mean Absolute

MAPE  (1 / n)
Percent Error

n

t 1
At  Ft
At

 100

MAD of examples
Period
|A-F|
Method 1
|A-F|
Method 2
1
100
10
2
100
10
3
100
10
4
100
10
5
100
10
6
100
10
7
100
10
8
100
10
9
100
10
10
100
10
MAD
100
10
• MAD shows that method 1 is
off by a larger amount
• Method 2 was biased
• However, overall, Method 2
seems preferable
n
MAD  (1 / n) At  Ft
t 1
RSFE
Tracking Signal 
MAD
n
RSFE   At  Ft
t 1
Forecast Error
Tracking Signal
4
Upper
Limit
0
-4
Forecast Period
Lower
Limit
• If >4 or <-4 something is wrong
• Top should sum to 0 over time. If not, forecast is
biased.
Summary
•
•
•
•
•
•
Rules of forecasting
Time Horizons
Discussion of Qualitative Methods
List of Quantitative Methods
Importance of Human instincts
Assessing Forecast Accuracy
▫
MAD, MAPE, MSE, RSFE, Tracking Signal