PRODUCTIONS/OPERATIONS MANAGEMENT
Download
Report
Transcript PRODUCTIONS/OPERATIONS MANAGEMENT
3-1
Forecasting
Forecasting
3-2
Forecasting
FORECAST:
A statement about the future value of a variable of
interest such as demand.
Forecasts affect decisions and activities throughout
an organization
Accounting, finance
Human resources
Marketing
MIS
Operations
Product / service design
3-3
Forecasting
Uses of Forecasts
Accounting
Cost/profit estimates
Finance
Cash flow and funding
Human Resources
Hiring/recruiting/training
Marketing
Pricing, promotion, strategy
MIS
IT/IS systems, services
Operations
Schedules, MRP, workloads
Product/service design
New products and services
3-4
Forecasting
Assumes causal system
past ==> future
Forecasts rarely perfect because of
randomness
Forecasts more accurate for
groups vs. individuals
Forecast accuracy decreases
as time horizon increases
I see that you will
get an A this semester.
3-5
Forecasting
Elements of a Good Forecast
Timely
Reliable
Accurate
Written
3-6
Forecasting
Steps in the Forecasting Process
“The forecast”
Step 6 Monitor the forecast
Step 5 Prepare the forecast
Step 4 Gather and analyze data
Step 3 Select a forecasting technique
Step 2 Establish a time horizon
Step 1 Determine purpose of forecast
3-7
Forecasting
Types of Forecasts
Judgmental - uses subjective inputs
Time series - uses historical data
assuming the future will be like the past
Associative models - uses explanatory
variables to predict the future
3-8
Forecasting
Judgmental Forecasts
Executive opinions
Sales force opinions
Consumer surveys
Outside opinion
Delphi
method
Opinions of managers and staff
Achieves a consensus forecast
3-9
Forecasting
Time Series Forecasts
Trend - long-term movement in data
Seasonality - short-term regular variations in
data
Cycle – wavelike variations of more than one
year’s duration
Irregular variations - caused by unusual
circumstances
Random variations - caused by chance
3-10 Forecasting
Forecast Variations
Figure 3.1
Irregular
variatio
n
Trend
Cycles
90
89
88
Seasonal variations
3-11 Forecasting
Naive Forecasts
Uh, give me a minute....
We sold 250 wheels last
week.... Now, next week
we should sell....
The forecast for any period equals
the previous period’s actual value.
3-12 Forecasting
Naïve Forecasts
Simple to use
Virtually no cost
Quick and easy to prepare
Data analysis is nonexistent
Easily understandable
Cannot provide high accuracy
Can be a standard for accuracy
3-13 Forecasting
Techniques for Averaging
Moving average
Weighted moving average
Exponential smoothing
3-14 Forecasting
Moving Averages
Moving average – A technique that averages a
number of recent actual values, updated as new
values become available.
n
MAn =
Ai
i=1
n
Weighted moving average – More recent values in a
series are given more weight in computing the
forecast.
3-15 Forecasting
Moving Averages
Moving average –
n
MAn =
Period
1
2
3
4
5
6
Ai
i=1
n
Sales Average Forecast for next period
40
41
42
123/3
41
?
?
?
3-16 Forecasting
Moving Averages
Moving average –
n
MAn =
Period
1
2
3
4
5
6
Ai
i=1
n
Sales Average Forecast for next period
40
41
42
123/3
41
42
?
?
Actual
41
42
42
3-17 Forecasting
Moving Averages
Moving average –
n
MAn =
Period
1
2
3
4
5
6
Ai
i=1
n
Sales Average Forecast for next period
40
41
42
123/3
41
42
125/3
41.67
44
?
Actual
41
42
42
44
3-18 Forecasting
Moving Averages
Moving average –
n
MAn =
Period
1
2
3
4
5
6
Ai
i=1
Sales Average Forecast for next period
40
41
42
123/3
41
42
125/3
41.67
44
128/3
42.67
?
n
Actual
41
42
42
44
43
3-19 Forecasting
Moving Averages
Moving average –
n
MAn =
Period
1
2
3
4
5
6
Ai
i=1
n
Sales Average Forecast for next period
40
41
42
123/3
41
42
125/3
41.67
44
128/3
42.67
43
129/3
43
Actual
41
42
42
44
43
43
3-20 Forecasting
Simple Moving Average
Actual
MA5
47
45
43
41
39
37
MA3
35
1
2
3
4
5
6
7
8
n
MAn =
9
Ai
i=1
n
10 11 12
3-21 Forecasting
Weighted Moving Averages
Weighted Moving average –
WMAn=
Period
1
2
3
4
5
6
Sales
40
41
42
?
?
?
Weight
20%
30%
50%
Result
n
A*wi
i=1
Forecast
Actual
3-22 Forecasting
Weighted Moving Averages
Weighted Moving average –
WMAn=
Period
1
2
3
4
5
6
Sales
40
41
42
?
?
?
Weight
20%
30%
50%
n
A*wi
i=1
Result Forecast Actual
8
12.3
21
41.3
42
3-23 Forecasting
Weighted Moving Averages
Weighted Moving average –
n
WMAn=
Period
1
2
3
4
5
6
Sales
40
41
42
42
?
?
Weight
20%
30%
50%
A*wi
i=1
Result Forecast Actual
8.2
12.6
21
41.8
44
3-24 Forecasting
Weighted Moving Averages
Weighted Moving average –
n
WMAn=
Period
1
2
3
4
5
6
Sales
40
41
42
42
44
?
Weight
20%
30%
50%
A*wi
i=1
Result Forecast Actual
8.4
12.6
22
43
43
3-25 Forecasting
Weighted Moving Averages
Weighted Moving average –
n
WMAn=
Period
1
2
3
4
5
6
Sales
40
41
42
42
44
43
Weight
20%
30%
50%
A*wi
i=1
Result Forecast Actual
8.4
13.2
21.5
43.1
43
3-26 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
• Premise--The most recent observations might
have the highest predictive value.
Therefore, we should give more weight to the
more recent time periods when forecasting.
3-27 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
Weighted averaging method based on previous
forecast plus a percentage of the forecast error
A-F is the error term, is the % feedback
3-28 Forecasting
Exponential Smoothing
Ft = Ft-1 + (At-1 - Ft-1)
Ft
= Next Period
Ft-1= Previous Period
= Smoothing Constant
At-1 = Actual Result
Previous Period
3-29 Forecasting
Exponential Smoothing - Problem
Ft = Ft-1 + (At-1 - Ft-1)
Ft = Result of formula
Ft-1= 42
= Smoothing = .10
At-1 = 44
3-30 Forecasting
Exponential Smoothing - Problem
Ft = Ft-1 + (At-1 - Ft-1)
Ft = 42 + .10(44-42)
Ft = 42 + .10(2)
Ft = 42 + .20
Ft = 42.20
3-31 Forecasting
Exponential Smoothing - Problem
Ft = Ft-1 + (At-1 - Ft-1)
Ft = 43 + .10(42.20-43)
Ft = 43 + .10(-.80)
Ft = 43 + -.080
Ft = 42.92
3-32 Forecasting
Example 3 - Exponential Smoothing
Period
Actual
1
2
3
4
5
6
7
8
9
10
11
12
Alpha = 0.1 Error
42
40
43
40
41
39
46
44
45
38
40
42
41.8
41.92
41.73
41.66
41.39
41.85
42.07
42.36
41.92
41.73
Alpha = 0.4 Error
-2.00
1.20
-1.92
-0.73
-2.66
4.61
2.15
2.93
-4.36
-1.92
42
41.2
41.92
41.15
41.09
40.25
42.55
43.13
43.88
41.53
40.92
-2
1.8
-1.92
-0.15
-2.09
5.75
1.45
1.87
-5.88
-1.53
3-33 Forecasting
Picking a Smoothing Constant
Actual
Demand
50
= .4
45
= .1
40
35
1
2
3
4
5
6
7
Period
8
9 10 11 12
3-34 Forecasting
Common Nonlinear Trends
Figure 3.5
Parabolic
Exponential
Growth
3-35 Forecasting
Linear Trend Equation
Ft
Ft = a + bt
0 1 2
Ft = Forecast for period t
t = Specified number of time periods
a = Value of Ft at t = 0
b = Slope of the line
3 4 5
t
3-36 Forecasting
Calculating a and b
n (ty) - t y
b =
2
2
n t - ( t)
y - b t
a =
n
3-37 Forecasting
Linear Trend Equation Example
t
Week
1
2
3
4
5
2
t
1
4
9
16
25
t = 15
t = 55
2
( t) = 225
2
y
Sales
150
157
162
166
177
ty
150
314
486
664
885
y = 812 ty = 2499
3-38 Forecasting
b =
Linear Trend Calculation
5 (2499) - 15(812)
5(55) - 225
=
12495-12180
275 -225
812 - 6.3(15)
a =
= 143.5
5
y = 143.5 + 6.3t
= 6.3
3-39 Forecasting
Associative Forecasting
Predictor variables - used to predict values of
variable interest
Regression - technique for fitting a line to a set
of points
Least squares line - minimizes sum of squared
deviations around the line
3-40 Forecasting
Linear Model Seems Reasonable
X
7
2
6
4
14
15
16
12
14
20
15
7
Y
15
10
13
15
25
27
24
20
27
44
34
17
Computed
relationship
50
40
30
20
10
0
0
5
10
15
20
25
A straight line is fitted to a set of sample points.
3-41 Forecasting
Forecast Accuracy
Error - difference between actual value and predicted
value
Mean Absolute Deviation (MAD)
Mean Squared Error (MSE)
Average absolute error
Average of squared error
Mean Absolute Percent Error (MAPE)
Average absolute percent error
3-42 Forecasting
MAD, MSE, and MAPE
MAD
=
Actual
forecast
n
MSE
=
( Actual
forecast)
2
n -1
MAPE =
( Actual
forecas
nt
/ Actual*100)
3-43 Forecasting
Period
1
2
3
4
5
6
7
8
MAD=
MSE=
MAPE=
Example 10
Actual
217
213
216
210
213
219
216
212
2.75
10.86
1.28
Forecast
215
216
215
214
211
214
217
216
(A-F)
2
-3
1
-4
2
5
-1
-4
-2
|A-F|
2
3
1
4
2
5
1
4
22
(A-F)^2
4
9
1
16
4
25
1
16
76
(|A-F|/Actual)*100
0.92
1.41
0.46
1.90
0.94
2.28
0.46
1.89
10.26
3-44 Forecasting
Controlling the Forecast
Control chart
A visual tool for monitoring forecast errors
Used to detect non-randomness in errors
Forecasting errors are in control if
All errors are within the control limits
No patterns, such as trends or cycles, are
present
3-45 Forecasting
Sources of Forecast errors
Model may be inadequate
Irregular variations
Incorrect use of forecasting technique
3-46 Forecasting
Choosing a Forecasting Technique
No single technique works in every situation
Two most important factors
Cost
Accuracy
Other factors include the availability of:
Historical data
Computers
Time needed to gather and analyze the data
Forecast horizon
3-47 Forecasting
Exponential Smoothing
3-48 Forecasting
Linear Trend Equation
3-49 Forecasting
Simple Linear Regression