Operations
Management
Topic 6 Forecasting
What is Forecasting?
Process of
predicting a future
event
Underlying basis
of
all business
decisions
Hmm. you
gonna get an A for
this subject
Production
Inventory
Personnel
Facilities
2
Forecasting Time Horizons
Short-range forecast
Up to 1 year, generally less than 3 months
Purchasing, job scheduling, workforce
levels, job assignments, production levels
Medium-range forecast
3 months to 3 years
Sales and production planning, budgeting
Long-range forecast
3+ years
New product planning, facility location,
research and development
3
Seven Steps in Forecasting
Determine the use of the forecast
Select the items to be forecasted
Determine the time horizon of the
forecast
Select the forecasting model(s)
Gather the data
Make the forecast
Validate and implement results
4
Types of Forecasts
Economic forecasts
Address business cycle inflation rate,
money supply, housing starts, etc.
Technological forecasts
Predict rate of technological progress
Impacts development of new products
Demand forecasts
Predict sales of existing products and
services
5
Strategic Importance of
Forecasting
Human Resources Hiring, training,
laying off workers
Capacity Capacity shortages can
result in undependable delivery, loss
of customers, loss of market share
Supply Chain Management Good
supplier relations and price
advantages
6
The Realities!
Forecasts are seldom perfect
Most techniques assume an
underlying stability in the system
Product family and aggregated
forecasts are more accurate than
individual product forecasts
Forecasting Approaches
Qualitative Methods
Used when situation is vague
and little data exist
New products
New technology
Involves intuition, experience
e.g., forecasting sales on
Internet
8
Forecasting Approaches
Quantitative Methods
Used when situation is stable
and historical data exist
Existing products
Current technology
Involves mathematical
techniques
e.g., forecasting sales of color
televisions
Overview of Quantitative
Approaches
1. Naive approach
2. Moving averages
3. Exponential
smoothing
4. Trend projection
5. Linear regression
Time-Series
Models
Associative
Model
10
Time Series Forecasting
Set of evenly spaced numerical data
Obtained by observing response
variable at regular time periods
Forecast based only on past values,
no other variables important
Assumes that factors influencing past
and present will continue influence in
future
11
Demand for product or service
Components of Demand
Trend
component
Seasonal peaks
Actual
demand
Average
demand over
four years
Random
variation
|
1
|
2
|
3
Year
|
4
Figure 4.1
12
Trend Component
Persistent, overall upward or
downward pattern
Changes due to population,
technology, age, culture, etc.
Typically several years
duration
13
Seasonal Component
Regular pattern of up and
down fluctuations
Due to weather, customs, etc.
Occurs within a single year
Period
Week
Month
Month
Year
Year
Year
Length
Number of
Seasons
Day
Week
Day
Quarter
Month
Week
7
4-4.5
28-31
4
12
52
14
Cyclical Component
Repeating up and down movements
Affected by business cycle, political,
and economic factors
Multiple years duration
Often causal or
associative
relationships
0
10
15
20
15
Random Component
Erratic, unsystematic, residual
fluctuations
Due to random variation or
unforeseen events
Short duration and
nonrepeating
16
Naive Approach
Assumes demand in next
period is the same as
demand in most recent period
e.g., If January sales were 68,
then February sales will be 68
Sometimes cost effective and
efficient
Can be good starting point
17
Techniques for Averaging
Moving average
Weighted moving average
Exponential smoothing
18
Moving Average Method
MA is a series of arithmetic means
Used if little or no trend
Used often for smoothing
Provides overall impression of data
over time
Moving average =
demand in previous n periods
n
19
Moving Average Example
Month
January
February
March
April
May
June
July
Actual
Shed Sales
10
12
13
16
19
23
26
3-Month
Moving Average
(10 + 12 + 13)/3 = 11 2/3
(12 + 13 + 16)/3 = 13 2/3
(13 + 16 + 19)/3 = 16
(16 + 19 + 23)/3 = 19 1/3
20
Shed Sales
Graph of Moving Average
30
28
26
24
22
20
18
16
14
12
10
Moving
Average
Forecast
Actual
Sales
|
J
|
F
|
M
|
A
|
M
|
J
|
J
|
A
|
S
|
O
|
N
|
D
21
Weighted Moving Average
Used when trend is present
Older data usually less important
Weights based on experience
and intuition
Weighted
=
moving average
(weight for period n)
x (demand in period n)
weights
22
Weights Applied
Period
3
Last
month
Weighted Moving
Average
2
1
6
Month
Actual
Shed Sales
January
February
March
April
May
June
July
10
12
13
16
19
23
26
Two months ago
Three months ago
Sum of weights
3-Month Weighted
Moving Average
[(3 x 13) + (2 x 12) + (10)]/6 = 121/6
[(3 x 16) + (2 x 13) + (12)]/6 = 141/3
[(3 x 19) + (2 x 16) + (13)]/6 = 17
[(3 x 23) + (2 x 19) + (16)]/6 = 201/2
23
Moving Average And
Weighted Moving Average
Weighted
moving
average
30
Sales demand
25
20
Actual
sales
15
Moving
average
10
5
|
Figure 4.2
|
F
|
M
|
A
|
M
|
J
|
J
|
A
|
S
|
O
|
N
|
D
24
Potential Problems With
Moving Average
Increasing n smooths the forecast
but makes it less sensitive to
changes
Do not forecast trends well
Require extensive historical data
25
Exponential Smoothing
Form of weighted moving average
Weights decline exponentially
Most recent data weighted most
Requires smoothing constant ()
Ranges from 0 to 1
Subjectively chosen
Involves little record keeping of
past data
26
Exponential Smoothing
Remember This!!!!!!!!
New forecast = Last periods forecast
+ (Last periods actual demand
Last periods forecast)
Ft = Ft 1 + (At 1 - Ft 1)
where
Ft = new forecast
Ft 1 = previous forecast
= smoothing (or weighting)
constant (0 1)
27
Choosing
The objective is to obtain the most
accurate forecast no matter the
technique
We generally do this by selecting the
model that gives us the lowest forecast
error
Forecast error = Actual demand - Forecast value
= At - Ft
28
Common Measures of Error
Mean Absolute Deviation (MAD)
|Actual - Forecast|
MAD =
n
Mean Squared Error (MSE)
(Forecast Errors)2
MSE =
n
29
Exponential Smoothing
Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
30
Exponential Smoothing
Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 142)
31
Exponential Smoothing
Example
Predicted demand = 142 Ford Mustangs
Actual demand = 153
Smoothing constant = .20
New forecast = 142 + .2(153 142)
= 142 + 2.2
= 144.2 144 cars
32
Exponential Smoothing
Example 2
Demand for the last four months
was:
Predict demand for July using each of these methods:
(A)
1) A 3-period moving average
2) exponential smoothing with alpha equal to .20 (use nave to
begin).
(B)
3) If the naive approach had been used to predict demand for April
through June, what would MAD have been for those months?
33
Exponential Smoothing
Example 2
A) 1.
2.
(8+10+8)/3 = 8.33 (July Forecast)
Use nave to begin
Month
B)
Deman
d
Forecast
March
April
May
10
June
6 + 0.2(8 6) = 6.4
6.4 + 0.2(10 6.4) = 7.12
7.12 + 0.2(8 7.12) = 7.296
Month
March
April
May
June
Demand
10
Nave
10
Error
+2
+2
-2
MAD
6/3
= 2.0
34
Exponential Smoothing with
Trend Adjustment
When a trend is present, exponential
smoothing must be modified
Forecast
including (FITt) =
trend
Exponentially
smoothed (Ft) + (Tt)
forecast
Exponentially
smoothed
trend
35
Exponential Smoothing with
Trend Adjustment
Ft = (At - 1) + (1 - )(Ft - 1 + Tt - 1)
Tt = (Ft - Ft - 1) + (1 - )Tt - 1
Step 1: Compute Ft
Step 2: Compute Tt
Step 3: Calculate the forecast FITt = Ft + Tt
36
Other Examples
Moving Average
Weekly sales of ten-grain bread at the local organic food
market are in the table below. Based on this data,
forecast week 9 using a five-week moving average.
Wee
k
Sale
s
415
389
420
382
410
432
405
421
(382+410+432+405+421)/5 = 410.0
37
Other Examples
Exponential Smoothing & MAD
Jim's department at a local department store has tracked the sales of a product
over the last ten weeks. Forecast demand using exponential smoothing with
an alpha of 0.4, and an initial forecast of 28.0. Calculate MAD.
Period
Demand
24
23
26
36
26
30
32
26
25
38
Other Examples
Exponential Smoothing
Period
Demand
Forecast
Error
Absolute
24
28.00
23
26.40
-3.40
3.40
26
25.04
0.96
0.96
36
25.42
10.58
10.58
26
29.65
-3.65
3.65
30
28.19
1.81
1.81
32
28.92
3.08
3.08
26
30.15
-4.15
4.15
25
28.49
-3.49
3.49
10
28
27.09
0.91
0.91
Total
2.64
32.03
Average
0.29
3.56
39
