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Read the two slides attached below and answer three questions (100-120 words for each) :How does the company you are following (choose any company you are interested in) handle its forecasts? Have you seen anything in their quarterly or annual reports relating to their
ability to forecast, or to their failures to forecast accurately?Assume
you sell umbrellas. How would you forecast demand for umbrellas next week? Explain how time-series forecasting and causal forecasting both might be applied. Logically, would you trust the time series forecast, a causal forecast, or some combination of the most trustworthy?Forecasting
Methods
1
Forecasting Methods
Forecasting methods can be classified as qualitative
or quantitative.
Qualitative methods general ly involve the use of
expert judgment to develop forecasts.
Such methods are appropriate when historical data
on the variable being forecast are either not
applicable or unavailable.
We focus exclusively on quantitative forecasting
methods in this class.
2
Forecasting Methods
Quantitative forecasting methods can be used when:

past information about the variable being forecast
is available,
the information can be quantified, and
it is reasonable to assume that the pattern of the
past will continue into the future.
In such cases, a forecast can be developed using a
time series method or a causal method.
3
Quantitative Forecasting Methods
Quantitative methods are based on an analysis of
historical data concerning one or more time series.
A time series is a set of observations measured at
successive points in time or over successive periods
of time.
If the historical data used are restricted to past
values of the series that we are trying to forecast, the
procedure is called a time series method.
If the historical data used involve other time series
that are believed to be related to the time series that
we are trying to forecast, the procedure is called a
causal method.
4
Time Series Methods
The objective of time series analysis is to discover a
pattern in the historical data or time series and then
extrapolate the pattern into the future.
The forecast is based solely on past values of the
variable and/or past forecast errors.
5
Forecasting Methods
Forecasting
Methods
Quantitative
Causal
Qualitative
Time Series
This is the
portion we cover
in this class
6
Regression Analysis
By treating time as the independent variable and the
time series as a dependent variable, regression
analysis can also be used as a time series method.
Time-series regression refers to the use of regression
analysis when the sole independent variable is time.
Cross-sectional regression refers to the use of
regression analysis when the independent variable(s)
is(are) something other than time.
7
Causal Methods
Causal forecasting methods are based on the
assumption that the variable we are forecasting has
a cause-effect relationship with one or more other
variables.
Looking at regression analysis as a forecasting tool,
we can view the time series value that we want to
forecast as the dependent variable.
If we can identify a good set of related independent,
or explanatory, variables we may be able to develop
an estimated regression equation for forecasting the
time series.
Discussion – Assume you sell umbrellas. How would
you forecast demand for umbrellas next week?
8
Forecasting
Corporate Applications
1
Forecasting and its Applications
▪ Forecasting is a vital function and impacts every significant
management decision
▪ In Operations Management, forecasting demand is a central
problem. Demand forecasts help decision makers determine
capacity requirements and make decisions about sourcing,
▪ Marketing relies on demand forecasts to make key decisions such
as new product planning and personnel compensation
▪ Finance and accounting use forecasts as the basis for budgeting
and cost control
▪ Project managers use forecasts to determine budgets and
schedules and to manage risk and uncertainty
▪ Forecasting capabilities within an organization are a key aspect of
2
Organizational approaches to
forecasting vary considerably
3
FORECASTING AT DISNEY WORLD
• Disney generates daily, weekly, monthly, annual, and 5year forecasts that are used by labor management,
maintenance, operations, finance, and park scheduling.
• Forecast are used to adjust opening times, rides, shows,
• 20% of customers come from outside the USA
• A staff of 35 analysts and 70 field people survey 1 million
park guests, employees, and travel professionals each
year.
• Inputs to the forecasting model include airline specials, Federal Reserve
policies, Wall Street trends, vacation/holiday schedules for 3,000 school
districts around the world.
• Average forecast error for the 5-year forecast is 5%
• Average forecast error for annual forecasts is between 0% and 3%
4
FORECASTING AT ZARA
▪ Store managers monitor what is selling daily tracking
everything from current sales trends to merchandise
customers want but can’t find in stores, then shoot orders to
the designer teams, who fashion what’s needed instantly
▪ Store managers provide word-of-mouth information on
customer wants and preferences (most competitors rely solely
on electronically collected data)
▪ Lines that are not selling well are quickly removed and popular
items are quickly replenished
▪ A quick turn around on merchandise helps generate cash, reduces
inventories and eliminates the need for significant debt
▪ Design teams create up to 1000 designs every month based
on store sales and current trends
5
CPFR SYSTEM AT WAL-MART
▪ In the past, Wal-Mart did not share its forecasts with its
suppliers. The result was forecast errors as much as 60% of
actual demand.
▪ To combat the ill effects of forecast errors on inventories, WalMart initiated the collaborative planning, forecasting, and
replenishment (CPFR) system which is capable of providing
more reliable medium-term forecasts.
▪ The system allows manufacturers and retailers to work
together by exchanging forecast information and
replenishment data to better match supply with demand.
▪ Wal-Mart and Warner-Lambert independently calculate the
demand they expect for Listerine (primary product of WarnerLambert) six months into the future, taking into consideration
factors such as past sales trends and promotion plans.
6
BENEFITS TO BOTH PARTIES
▪ They then exchange their forecasts over the Internet. If the
forecasts differed by more than a predetermined percentage,
the retailer and the manufacturer use the Internet to exchange
▪ The parties will go through as many cycles that are needed to
converge on an acceptable forecast.
▪ Wal-Mart’s stock-outs were reduced from 15% to 2%.
▪ Wal-Mart had significant increases in sales
and reductions in inventory costs.
▪ Warner-Lambert benefited by having a
smoother production plan and lower
average costs.
www.huffingtonpost.com
7
CDP SYSTEM AT UNILEVER
• Unilever has a state-of-the-art customer demand planning (CDP)
system, a business-planning process that enables sales teams (and
customers) to develop demand forecasts.
▫ The system blends historical shipment data with promotional data,
allowing information sharing and collaboration with customers.
• Unilever also conducts external market research and internal sales
projects that are analyzed, combined with retail customer
promotions, and fed into the CDP system.
• To improve the accuracy of its forecasts and reduce inventory lead
times, Unilever compares point-of-sale data with its own forecasts.
• Planners at Unilever negotiate the final numbers each week and
feed these forecasts into the CDP system.
• Overall, the CDP system has helped Unilever reduced its inventory
and improved its customer service.
8
Discussion
▪How does the company you are following hande
their forecasts?
▪Have you seen anything in their quarterly or
annual reports relating to their ability to forecast?
To their failures to forecast accurately?
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