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Chapter 04 Forecasting

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Forecasting at Tupperware ... Three Key Factors for Tupperware ... The final step is Tupperware's version of the 'jury of executive opinion' ... – PowerPoint PPT presentation

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Title: Chapter 04 Forecasting


1
Operations Management
Chapter 4 - Forecasting
2
Outline
  • Global Company Profile Tupperware Corporation
  • What Is Forecasting?
  • Forecasting Time Horizons
  • The Influence of Product Life Cycle
  • Types of Forecasts

3
Outline Continued
  • The Strategic Importance of Forecasting
  • Human Resources
  • Capacity
  • Supply-Chain Management
  • Seven Steps In The Forecasting System
  • Forecasting Approaches
  • Overview of Qualitative Methods
  • Overview of Quantitative Methods

4
Outline Continued
  • Time-series Forecasting
  • Decomposition of a Time Series
  • Naïve Approach
  • Moving Averages
  • Exponential Smoothing
  • Exponential Smoothing with Trend Adjustment
  • Trend Projections
  • Seasonal Variations in Data
  • Cyclical Variations in Data

5
Outline Continued
  • Associative Forecasting Methods Regression And
    Correlation Analysis
  • Using Regression Analysis to Forecast
  • Standard Error of the Estimate
  • Correlation Coefficients for Regression Lines
  • Multiple-Regression Analysis

6
Outline Continued
  • Monitoring And Controlling Forecasts
  • Adaptive Smoothing
  • Focus Forecasting
  • Forecasting In The Service Sector

7
Learning Objectives
  • Forecasting
  • Types of forecasts
  • Time horizons
  • Approaches to forecasts
  • Moving averages
  • Exponential smoothing
  • Trend projections
  • Regression and correlation analysis
  • Measures of forecast accuracy

8
Forecasting at Tupperware
  • Each of 50 profit centers around the world is
    responsible for computerized monthly, quarterly,
    and 12-month sales projections
  • These projections are aggregated by region, then
    globally, at Tupperwares World Headquarters
  • Tupperware uses all techniques discussed in text

9
Three Key Factors for Tupperware
  • The number of registered consultants or sales
    representatives
  • The percentage of currently active dealers
    (this number changes each week and month)
  • Sales per active dealer, on a weekly basis

10
Forecast by Consensus
  • Although inputs come from sales, marketing,
    finance, and production, final forecasts are the
    consensus of all participating managers
  • The final step is Tupperwares version of the
    jury of executive opinion

11
What is Forecasting?
  • Process of predicting a future event
  • Underlying basis of all business decisions
  • Production
  • Inventory
  • Personnel
  • Facilities

12
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

13
Influence of Product Life Cycle
Introduction Growth Maturity Decline
  • Introduction and growth require longer forecasts
    than maturity and decline
  • As product passes through life cycle, forecasts
    are useful in projecting
  • Staffing levels
  • Inventory levels
  • Factory capacity

14
Product Life Cycle
Figure 2.5
15
Product Life Cycle
Product design and development critical Frequent
product and process design changes Short
production runs High production costs Limited
models Attention to quality
Forecasting critical Product and process
reliability Competitive product improvements and
options Increase capacity Shift toward product
focus Enhance distribution
Standardization Less rapid product changes more
minor changes Optimum capacity Increasing
stability of process Long production runs Product
improvement and cost cutting
Little product differentiation Cost
minimization Overcapacity in the industry Prune
line to eliminate items not returning good
margin Reduce capacity
Figure 2.5
16
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 product

17
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 advance

18
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

19
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

20
Forecasting Approaches
Qualitative Methods
Quantitative Methods
  • Used when situation is vague and little data
    exist
  • New products
  • New technology
  • Involves intuition, experience
  • Used when situation is stable and historical
    data exist
  • Existing products
  • Current technology
  • Involves mathematical techniques

21
Overview of Qualitative Methods
  • Jury of executive opinion
  • Pool opinions of high-level executives, sometimes
    augment by statistical models
  • Delphi method
  • Panel of experts, queried iteratively
  • Sales force composite
  • Estimates from individual salespersons are
    reviewed for reasonableness, then aggregated
  • Consumer Market Survey
  • Ask the customer

22
Overview of Quantitative Approaches
Quantitative Forecasting
Associative Models
Time Series Models
Linear Regression
Trend Projection
Moving Average
Exponential Smoothing
23
Time Series Forecasting
  • Set of evenly spaced numerical data
  • Obtained by observing response variable at
    regular time periods
  • Forecast based only on past values
  • Assumes that factors influencing past and present
    will continue influence in future

24
Time Series Components
25
Components of Demand
Figure 4.1
26
Overview of Quantitative Methods
  • Naïve Approach
  • Moving Averages
  • Exponential Smoothing
  • Trend Projection
  • Linear Regression

27
Naive Approach
  • Assumes demand in next period is the same as
    demand in most recent period
  • e.g., If May sales were 48, then June sales will
    be 48
  • Sometimes cost effective and efficient

28
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

29
Moving Average Example
Actual 3-Month Month Shed Sales Moving Average
January 10 February 12 March 13 April (13
12 10)/3 11.6 May June July
30
Moving Average Example
Actual 3-Month Month Shed Sales Moving Average
January 10 February 12 March 13 April 16 (10
12 13)/3 11.6 May (16 13 12)/3
13.6 June July
31
Moving Average Example
Actual 3-Month Month Shed Sales Moving Average
January 10 February 12 March 13 April 16 (10
12 13)/3 11.6 May 19 (12 13 16)/3
13.6 June (19 16 13)/3 16 July
32
Moving Average Example
Actual 3-Month Month Shed Sales Moving Average
January 10 February 12 March 13 April 16 (10
12 13)/3 11.6 May 19 (12 13 16)/3
13.6 June 23 (13 16 19)/3 16 July (23
19 16)/3 19.3
33
Graph of Moving Average
34
Weighted Moving Average
  • Used when trend is present
  • Older data usually less important
  • Weights based on experience and intuition

35
Weighted Moving Average
Weights Applied Period 3 Last month 2 Two
months ago 1 Three months ago 6 Sum of weights
(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
36
Moving Average And Weighted Moving Average
Figure 4.2
37
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

38
Exponential Smoothing
New forecast last periods forecast a (last
periods actual demand last periods
forecast)
Ft Ft 1 a(At 1 - Ft 1)
where Ft new forecast Ft 1 previous
forecast a smoothing (or weighting)
constant (0 ? a ? 1)
39
Exponential Smoothing Example
  • During the past 8 quarters, the Port of Baltimore
    has unloaded large quantities of grain. (a
    .10). The first quarter forecast was 175.
  • Quarter Actual
  • 1 180 2 168 3 159 4 175 5 190
  • 6 205
  • 7 180
  • 8 182
  • 9 ?

Find the forecast for the 9th quarter.
40
Exponential Smoothing Example
Ft Ft-1 0.1(At-1 - Ft-1)
Forecast,
F
t
Quarter
Actual
a?
(
.10)
175.00 (Given)
1
180
2
168
175.00 .10(180 - 175.00) 175.50
3
159
4
175
5
190
6
205
41
Exponential Smoothing Example
Ft Ft-1 0.1(At-1 - Ft-1)
Forecast,
F
t
Quarter
Actual
a?
(
.10)
175.00 (Given)
1
180
2
168
175.00 .10(180 - 175.00) 175.50
3
159
175.50 .10(168 - 175.50) 174.75
4
175
5
190
6
205
42
Exponential Smoothing Example
Ft Ft-1 0.1(At-1 - Ft-1)
Forecast,
F
t
Quarter
Actual
a?
(
.10)
4
175
174.75 .10(159 - 174.75) 173.18
5
190
173.18 .10(175 - 173.18) 173.36
6
205
173.36 .10(190 - 173.36) 175.02
7
180
175.02 .10(205 - 175.02) 178.02
178.02 .10(180 - 178.02) 178.22
8
182
178.22 .10(182 - 178.22) 178.58
?
9
43
Effect of Smoothing Constants
44
Impact of Different ?
45
Choosing ?
Our objective is to obtain the most accurate
forecast
We generally do this by selecting the model that
gives us the lowest forecast error
Forecast error Actual demand - Forecast
value At - Ft
46
Common Measures of Error
47
Comparison of Forecast Error
48
Comparison of Forecast Error
49
Comparison of Forecast Error
50
Comparison of Forecast Error
Actual a .10
a .50 Tonnage
Rounded Absolute Rounded Absolute Quarter Unloade
d Forecast Deviation Forecast Deviation
  • 1 180 175 5 175 5
  • 2 168 176 8 178 10
  • 3 159 175 16 173 14
  • 4 175 173 2 166 9
  • 5 190 173 17 170 20
  • 6 205 175 30 180 25
  • 7 180 178 2 193 13
  • 8 182 178 4 186 4
  • 84 100
  • MAD 10.50 12.50
  • MSE 194.75 201.50

51
Linear Trend Projections
Fitting a trend line to historical data points to
project into the medium-to-long-range
(Linear trends can be found using the least
squares technique)

where y forecast value (dependent
variable) a y-axis intercept of the regression
line b slope of the regression line t the
time period (independent variable)
52
Least Squares Method
Least squares method minimizes the sum of the
squared errors (deviations)
Figure 4.4
53
Least Squares Method
Equations to calculate the regression variables
y a bt

Where,
54
Least Squares Example
The trend line is

y 56.70 10.54t
Sty - nty St 2 nt 2
b
10.54
a y - bt 98.86 - 10.54(4) 56.70
55
Least Squares Example
Trend line, y 56.70 10.54t

56
Least Squares Requirements
  • We always plot the data to insure a linear
    relationship
  • We do not predict time periods far beyond the
    database
  • Deviations around the least squares line are
    assumed to be random

57
Associative Forecasting
Used when changes in one or more independent
variables can be used to predict the changes in
the dependent variable
Most common technique is linear regression
analysis
We apply this technique just as we did in the
time series example
58
Associative Forecasting
Forecasting an outcome based on predictor
variables using the least squares technique
59
Associative Forecasting Example
60
Associative Forecasting Example
61
Associative Forecasting Example
Sales 1.75 .25(payroll)
If payroll next year is estimated to be 600
million, then
Sales 1.75 .25(6) Sales 325,000
62
Standard Error of the Estimate
  • A forecast is just a point estimate of a future
    value
  • This point is actually the mean of a
    probability distribution

Figure 4.9
63
Standard Error of the Estimate
where y y-value of each data point yc compute
d value of the dependent variable, from the
regression equation n number of data points
64
Standard Error of the Estimate
Computationally, this equation is considerably
easier to use
We use the standard error to set up prediction
intervals around the point estimate
65
Standard Error of the Estimate
Sy,x .306
The standard error of the estimate is 30,600 in
sales
66
Correlation
  • How strong is the linear relationship between the
    variables?
  • Correlation does not necessarily imply causality!
  • Coefficient of correlation, r, measures degree of
    association
  • Values range from -1 to 1

67
Correlation Coefficient
68
Correlation
  • Coefficient of Determination, r2, measures the
    percent of change in y predicted by the change in
    x
  • Values range from 0 to 1
  • Easy to interpret

For the Nodel Construction example r .901 r2
.81
69
Multiple Regression Analysis
If more than one independent variable is to be
used in the model, linear regression can be
extended to multiple regression to accommodate
several independent variables
Computationally, this is quite complex and
generally done on the computer
70
Multiple Regression Analysis
In the Nodel example, including interest rates in
the model gives the new equation
An improved correlation coefficient of r .96
means this model does a better job of predicting
the change in construction sales
Sales 1.80 .30(6) - 5.0(.12) 3.00 Sales
300,000
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