1. Data Mining and Forecast Management
MGMT E-5070
Applied Management Science for Decision Making, 1e © 2012 Pearson Prentice-Hall, Inc Philip A. Vaccaro , PhD
Applied Management Science for Decision Making, 2e © Pearson Learning Solutions Philip A. Vaccaro , PhD

2. What is Forecasting? Forecasting is the art and science of predicting
It may involve taking
historical data and
projecting it into the
future by means of a
mathematical model.
It may also be an
intuitive prediction.
It may also be a
mathematical model
adjusted by good
judgement.
Forecasting
is the art
and science
of predicting
future events.

3. Forecasting is Data Mining Too !
Data mining is the process of extracting
patterns or correlations among dozens
of fields in large relational data bases.
With the amount of data doubling every
three years, it is becoming increasingly
important for transforming data into in-
formation, which in turn, can be used to
increase revenues, cut costs, or both.
Data mining uses simple and multi-
variate linear, and non-linear regression
models as well as hypothesis testing.

4. Simple Linear Regression
Data Mining Example
one or more
Simple Linear Regression
models
A grocery chain analyzed local buying
patterns.
They discovered that when men bought
diapers on Thursdays and Saturdays,
they also tended to buy beer.

5. Data Mining Example Further analysis showed that these men A Multiple
Regression
Model
Further analysis showed that these men
usually did their weekly grocery shopping
on Saturdays. On Thursdays, however,
they only bought a few items.

6. Data Mining Example The retailer concluded that the men purchased beer
to have it available for the upcoming weekend.
The grocery chain could use this newly discovered
information in various ways to increase revenue.
For example, they could move the beer display closer
to the diaper display. And, they could make sure that
beer and diapers were sold at full price on Thursdays!
INFORMATION to KNOWLEDGE to DECISION !

7. A Word of Advice… There is seldom a single superior forecasting
method. One firm may find exponential
smoothing to be effective. Another firm may
use several models, and a third firm may combine
both quantitative and subjective methods.
Whatever approach works best should be used.

8. Forecasting Time Horizons
1. Short-range forecast : Time span of up to 1 year but generally
less than 3 months. It is used for planning purchasing, job
scheduling, workforce levels, job assignments, and product-
ion levels.
2. Medium-range forecast : Time span of 3 months generally to
3 years. It is useful in sales planning, production planning /
budgeting, cash budgeting, and analysis of various operating
plans.
3. Long-range forecast : Generally 3 years or more in time span.
It is used in planning for new products, capital expenditures,
facility location or expansion, and research and development.

9. The Strategic Importance of Forecasting
Good forecasts are of critical importance in all
aspects of a business.
The forecast is the only estimate of demand
until actual demand becomes known.
Forecasts of demand therefore, drive the
decisions in many areas.

10. Forecast Impacts Human Resources
Hiring, training, and terminating workers all depend
on anticipated demand. If the HR department must
hire additional workers without warning, the amount
of training declines and the quality of the workforce
suffers.

11. Forecast Impacts Capacity the resulting shortages
When capacity is inadequate,
the resulting shortages
can mean undependable
delivery, loss of customers,
and loss of market share.
When capacity is in excess,
costs can skyrocket.

12. Forecast Impact Supply Chain Management
In the global marketplace, where expensive
parts for Boeing 787 jets are manufactured
in dozens of countries, coordination driven
by forecasts is critical.
Scheduling transportation to Seattle for final
assembly at the lowest possible cost means
no last-minute surprises that can harm already
low profit margins.

13. Product Life Cycle Influence
Products and even services, do not sell at a constant level
throughout their lives. Most successful products pass
through four stages : introduction, growth, maturity, and
decline.

14. Product Life Cycle Influence
Products in the first two stages
of the life cycle need longer
forecasts than those in the
maturity and decline stages.
Forecasts that reflect life cycle
are useful in projecting different
staffing levels, inventory levels,
and factory capacity as the
product passes from the first
to the last stage.

15. Forecasting Caveats Forecasts are seldom perfect. Outside factors we
cannot predict or control often impact the forecast.
Most forecasting techniques assume that there is
some underlying stability in the system.
Product family and aggregated forecasts are more
accurate than individual product forecasts.
This approach helps balance the over and under
predictions of each.

16. Service Sector Forecasting
Barber Shops
Expect peak flows on Fridays and Saturdays.
Many call in extra help on the above days.
Most are closed on Sunday and Monday.

17. Service Sector Forecasting
Flower Shops
When Valentine’s Day falls on a weekend, flowers cannot be
delivered to offices, and customers are likely to celebrate
with outings rather than flowers ( low sales ) .
When Valentine’s Day falls on a Monday, some celebration
will have taken place on the weekend ( reduced sales ) .
When Valentine’s Day falls in midweek, busy midweek work
schedules make flowers the optimal way to celebrate
( higher sales ).

18. Service Sector Forecasting
Fast Food Restaurants
Use point-of-sale computers that track sales
every 15 minutes.
May use the moving average technique to
minimize the error of the 15-minute forecasts.
The forecasts are used to schedule staff, who
begin in 15-minute increments, not the 1-hour
blocks as in other industries.

19. Hourly Sales at a Fast-Food Restaurant
20%
15%
10% 5%
Percent of Sales by Hour of Day
( Lunchtime )
( Dinnertime )

20. Monday Calls at a FedEx Call Center
12%
11%
10% 9% 8% 7% 6% 5% 4% 3% 2% 1% 0%
A.M P.M.

21. Service Sector Forecasting
Federal Express
makes 1-year and 5-year models to predict number of service
calls, average handle time, and staffing needs.
breaks the forecasts into weekday, Saturday, and Sunday,
and then uses the Delphi Method and time-series analysis.
tactical forecasts are monthly, and use 8 years of historical
daily data. They predict caller volume by month, day of the
week, and day of the month.
the operational forecast uses a weighted moving average and
6 weeks of data to project the number of calls on a 30-minute
basis.

22. Service Sector Forecasting
Federal Express
Fed Ex’s forecasts are consistently
accurate to within 1% to 2% of actual
call volumes.
This means that coverage needs are
met, service levels are maintained, and
costs are controlled.

23. Forecasting Fundamentals & Models
TYPES
Time – Series Models
Causal Models
Qualitative Models

24. Weighted Moving Average, Exponential Smoothing,
Time-Series Models
Predict the future by using historical data.
Assume that what happens in the future is
a function of what has happened in the past.
Moving Average,
Weighted Moving Average,
Exponential Smoothing,
Trend Projection

25. Ice cream sales, for example, might depend on the season,
Causal Models
Incorporate variables or factors that
might influence the quantity being
forecasted into the forecasting model.
The most common causal model is
regression analysis.
Ice cream sales, for example,
might depend on the season,
average temperature,
day of the week, and so on.

26. Qualitative Models Incorporate judgmental or subjective factors
into the forecasting model.
Opinions by experts, individual experiences,
and other factors are expected to be very
important.
Used when accurate quantitative data are
difficult to obtain.
EXAMPLES ARE
THE DELPHI METHOD,
SALES FORCE COMPOSITE,
AND
CONSUMER MARKET
SURVEY

27. 1. The Delphi Method Three types of participants: decision makers,
staff personnel, and respondents.
The decision makers make the actual forecast.
The staff personnel prepare, distribute, collect,
and summarize a series of questionnaires and
survey results.
The respondents are those whose judgements
and values are being sought. They provide in-
put to the decision makers before the forecast
is made.

28. The
Delphi Method

29. 2. Sales Force Composite Each salesperson estimates what sales
will be in his or her region.
These forecasts are reviewed to ensure
that they are realistic.
These forecasts are combined at the
district and national levels to reach an
overall forecast.

30. 3. Consumer Market Survey
This method solicits input from customers
or potential customers regarding their future
purchasing plans.
It can help not only in preparing a forecast
but also in improving product design and
planning for new products.

31. Consumer
Market
Survey

32. Consumer
Market
Survey

33. Types of Forecast Models
Regression
Analysis
Multiple
naïve approach
arithmetic mean
moving average
weighted average
weighted - moving
average
exponential smoothing
trend projections
decomposition
delphi method
jury of executive
opinion
sales force
composite
consumer market
survey
Time – Series
Methods
Qualitative
Models
Causal Methods

34. Cost vs. Accuracy Tradeoff
HIGH
TOTAL of
MODEL COST
and
FORECAST
ERROR COST
ECONOMETRIC
MODELS M O D E L C S T
CAUSAL
MODELS
OPERATING COSTS
DUE TO INACCURATE
FORECASTS
SOPHISTICATED
TIME
SERIES
SIMPLE
TIME
SERIES
QUALITATIVE MODELS
THE
OPTIMAL REGION
LOW
DECLINING ACCURACY 0%
100%