Forecasting at Tupperware

1
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

2
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

3
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

4
What is Forecasting?

• Process of predicting a future event
• Underlying basis of all business decisions
• Production
• Inventory
• Personnel
• Facilities

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Forecasting

• Why is forecasting important?

6
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

7
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

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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

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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

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Elements of a Good Forecast

• Must be timely
• Accurate with degree of accuracy stated
• Reliable and consistent
• Expressed in meaningful units
• In writing
• Simple to use and understand

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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 monitor results

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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

13
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

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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

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Jury of Executive Opinion

• Involves small group of high-level managers
• Group estimates demand by working together
• Combines managerial experience with statistical
  models
• Relatively quick
• Group-thinkdisadvantage

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Sales Force Composite

• Each salesperson projects his or her sales
• Combined at district and national levels
• Sales reps know customers wants
• Tends to be overly optimistic

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Delphi Method

• Iterative group process, continues until
  consensus is reached
• 3 types of participants
• Decision makers
• Staff
• Respondents

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Overview of Quantitative Approaches

• Naive approach
• Moving averages
• Exponential smoothing
• Trend projection
• Linear regression

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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

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Components of Demand
Figure 4.1
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Cyclical Component

• Repeating up and down movements
• Affected by business cycle, political, and
  economic factors
• Multiple years duration
• Often causal or associative relationships

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Random Component

• Erratic, unsystematic, residual fluctuations
• Due to random variation or unforeseen events
• Short duration and nonrepeating

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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

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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

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Moving Average Example
(12 13 16)/3 13 2/3 (13
16 19)/3 16 (16 19 23)/3 19 1/3
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Weighted Moving Average

• Used when trend is present
• Older data usually less important
• Weights based on experience and intuition

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Weighted Moving Average
(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
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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

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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

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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)
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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
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Common Measures of Error
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Common Measures of Error
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Comparison of Forecast Error
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Exponential Smoothing with Trend Adjustment
When a trend is present, exponential smoothing
must be modified
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Exponential Smoothing with Trend Adjustment
Ft a(At - 1) (1 - a)(Ft - 1 Tt - 1)
Tt b(Ft - Ft - 1) (1 - b)Tt - 1
Step 1 Compute Ft Step 2 Compute Tt Step 3
Calculate the forecast FITt Ft Tt
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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
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Least Squares Method
Least squares method minimizes the sum of the
squared errors (deviations)
Figure 4.4
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Least Squares Method
Equations to calculate the regression variables
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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

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Seasonal Variations In Data
The multiplicative seasonal model can modify
trend data to accommodate seasonal variations in
demand

• Find average historical demand for each season
• Compute the average demand over all seasons
• Compute a seasonal index for each season
• Estimate next years total demand
• Divide this estimate of total demand by the
  number of seasons, then multiply it by the
  seasonal index for that season

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Seasonal Index Example
43
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
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Associative Forecasting
Forecasting an outcome based on predictor
variables using the least squares technique
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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
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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
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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
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Standard Error of the Estimate
Sy,x .306
The standard error of the estimate is 30,600 in
sales
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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

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Correlation Coefficient
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Correlation Coefficient
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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
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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
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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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Monitoring and Controlling Forecasts
Tracking Signal

• Measures how well the forecast is predicting
  actual values
• Ratio of running sum of forecast errors (RSFE) to
  mean absolute deviation (MAD)
• Good tracking signal has low values
• If forecasts are continually high or low, the
  forecast has a bias error

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Monitoring and Controlling Forecasts
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Tracking Signal
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Tracking Signal Example
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Tracking Signal Example
The variation of the tracking signal between -2.0
and 2.5 is within acceptable limits
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Forecasting in the Service Sector

• Presents unusual challenges
• Special need for short term records
• Needs differ greatly as function of industry and
  product
• Holidays and other calendar events
• Unusual events

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Fast Food Restaurant Forecast
Figure 4.12