35A00210
Operations Management
Lecture 3
Forecasting
Lecture 3
Importance of forecasts
Forecasting
Importance of forecasts
Forecasting methods
Real world forecasting
It is hard to know what the future
holds
Society
Companies forecast many things
Political environment
This ‘telephone’ has too many shortcomings to be seriously
considered as a means of communication.
Values and social changes
- Western Union internal memo, 1876.
Economic indicators
“Everything that can be invented has been invented”
- Charles Duell, Commissioner, US Patent Office, 1899
Raw material prices
“Who the hell wants to hear actors TALK?”
Competitor behaviour
- Harry M. Warner, Warner Brothers, 1927
Industry
Technology developments
Price development
Company
Product cycles
"Stocks have reached what looks like a permanently high plateau“
- Irving Fisher, Professor of Economics, Yale University, 1929
Weather
“I think there is a world market for maybe five computers.”
Demand
Progress in productivity
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Consumer behavior
- Thomas Watson, chairman of IBM, 1943
Learning diffusion
“We don’t like their sound, and guitar music is on the way out.”
- Decca Records, 1962, from Beatles
Product development time
“There is no reason anyone would want a computer in their home”
Quality issues
- Ken Olson, President, Chairman, and founder of DEC, 1977
Advertising impact
In a computer, “640K ought to be enough for anybody.”
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Bill Gates, 1981
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How valuable right information
would be? - case B-to-B marketplaces -
Operations are based on forecasts
Forecasts often used in planning process,
decision making and resource allocation
Expectations were really high…
- Forrester:
$1.8 Trillion in 2003
- Goldman Sachs:
$1.5 Trillion in 2004
- Yankee Group: $3.0 Trillion in 2004
- AMR Research:
$5.7 Trillion in 2004
- Gartner Group:
$7.3 Trillion in 2004
- long term: location, capacity, technology etc. questions
- short term: production planning, material management, hiring and
scheduling employees, allocating transportation etc.
Competition and speed of development
increased the importance lately
- wrong decisions cost more and more
Forecasting the future and success go pretty
much hand-in-hand
- good forecast is easy to use, reliable, accurate, timely and
meaningful
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Operations are based on forecasts
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Lecture 3
Forecasting
- case EuroDisney -
Fourth Disney-park in the world
- in Europe nothing equivalent
- forecasts had to be done based on US parks data
The park was build for larger number of visitors
- Actual number of visitors 15-25% less than expected
Forecasting methods
Operations planned for different consumer
behavior
- park visitors used 10% less money than expected
Economic disaster
Quick operations decision made to cover losses
- lower prices, cost cutting, targeted investments (shopping mall,
restaurants, congress facilities)
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More loses (forecasts again too optimistic)
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Forecasting methods
Quantitative (Objective)
Time-series methods
-
naive
straight line
moving average
exponential smoothing
classical decomposition
Causal methods
- regression and correlation
- econometrics
- leading indicators
Time-series methods
In time-series, forecasts are based on a model
that fits the past data.
Qualitative (Judgmental)
Executive opinion
Expert opinion
Delphi-method
“Build up” -method
Market research
Customer panels
Test marketing
History analog
Life-cycle analog
- basic assumption is that the future will be no different
- both structural and trend changes will cause problems
Various methods exist/used.
- differ from each other on how many past periods are taken into
consideration and what are the relative weights of each period
In the real world used mostly for short term
forecasting
- simple and yields “good enough” results
- in practice, demand’s cycle component is quite hard to estimate due
to large data requirement and random variation of the data
Intuition, ”Gut feel”
Guessing
Unofficial ones
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Time-series methods
- naive and straight line -
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Straight line works in stable
environment - case alcohol in Australia-
In the naive forecast it is assumed
that the next period’s demand is
same than current’s one
- easy, cheap and fast method
- in some cases very efficient
- risks are naturally high
units
• In the straight line forecasting it
is assumed that the trend in
demand will continue
t
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• ”lets budget 3% increase for next year too”
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Time-series methods
- exponential smoothing -
Time-series methods
- moving average -
The forecast is based on the latest period’s
actual sales and the latest period’s forecast
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7
6
- latest actual sales figures will be weighted with so called ”alfa” ( )
- last forecast’s weight will be 1-
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Determining the correct alfa almost an art form
- large alfa emphasizes latest period’s demand more
4
- same as naive forecast if
=1
- typical values used for alfa between 0,1 - 0,3
3
- low alfa smooths the forecast
2
1999 2000 2001 2002 2003 2004
Sales
3 y. without weights
3 y. with weights
- often determined from past data (what fits the demand pattern)
Still widely used due to its simplicity
- no subjectivity needed as in giving weights to past periods demands
Moving average can be calculated for any number of periods!
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Low alfa smooths the forecast
Exponential smoothing example 1
- theoretical example 2 -
Assumed if
not given
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Determining alfa using historical data
Exponential smoothing example 4
- theoretical example 3 -
Software company has sold its own platform-product for four years
(sales volumes can be found from the table below). What is the sales
forecast for year 2013 using exponential smoothing with 0,1 alfa?
Assumed when
2009 forecast not given
Forecast for 2013
=
alfa * Sales in 2012 + (1-alfa) * Forecast for 2012
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Alfa’s impact on forecasts in
example 4
Exponential smoothing example 4
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NOTICE!
Excel uses the term
damping factor
( 1-alfa)
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40
30
20
10
0
2009
2010
Sales
2011
Forecast alpha 0,1
2012
2013
Forecast alpha 0,7
Low alfa unsuitable for growth industries
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Using a seasonal factor
Problem
14-4
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Evaluating the quality of timeseries forecast
Time-series methods
- classical decomposition -
Season
Methods goodness is measured with a forecasting
error
forecast
Year 1
Sales
Sales
Trend
actual
Year 2
Must pay attention to longer than one period’s errors
- different measurements emphasize error differently
Cycle
Random
Sales
Year
Sales
Time
E.g. 5 years
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Time
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Evaluating the quality of timeseries forecast
Causal methods
problem
14-2
In causal models forecasts are based on historically
proven cause-effect relationship
- clearly the most developed method in forecasting
Regression models most often used causal models
- linear regression most widely known and used
- complex econometric models have also gained some popularity
Amount of data is not a substitute for quality
- leading indicator -variables most important (see the forest from the trees)
- time lag’s existence forgotten too often
- correlation and sample coefficient used as a “goodness measurements”
3 month moving average seems to
forecast better than
4 month moving average
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problem 14-15
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Qualitative methods
Judgmental, qualitative methods often used
because of time, knowledge and data problems
- Situations’ uniqueness not as common as often mentioned
Several different opinion methods used
- Executive opinion
- ”management knows best”, groupthink the main common problem
- Expert opinion (”guru-logic”)
- validity sometimes questioned
Y = 1121,2 -0,2819X
- Delphi-method
- anonymity and justification should cut down the groupthink-effect
- slow and costly method
- “Build up” -method (sales force forecasts)
- forecast is aggregated from the organization one level at the time
- intentional falsification (both up and down) often noticed
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Lecture 3
Forecasting
Qualitative methods
Qualitative data can also be based on tests
- Market research
- suffer a bit from overly optimistic consumption estimates and social bias
in answering
- Customer panels
Real world forecasting
- real motives, preferences and usage information better understood
- Test marketing
- tests products’ real demand and popularity
- Finland for example an EU test market for some high tech products
- History analogue
- e.g. new product demand pattern similar to earlier ones
- Life-cycle analogue
- e.g. ”growth in GSM-phone penetration follows Finland’s case”
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Choosing the right forecasting
method
Simple forecasting process
1. Choose forecasted variables
2. Choose forecasted time horizon
3. What are results used for?
4. Choose methods to be used
5. Collect data (evaluate chosen methods)
6. Make a forecast
7. Analyze results and act accordingly
8. Evaluate forecast’s accuracy
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A. How accurate a forecast do you need
- adjusting to forecast errors is harder in short term so it is not a bad
idea to try improve forecasting methods
B. Amount of data available
- less data means more qualitative methods
C. Amount of time and resources available
- information technology has sped up the process and lowered the
costs (e.g. Delphi)
D. Economic risks of wrong forecast
- been recently highlighted
Trade-off between cost and accuracy
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Long
2-5 years
3-24 months
0-3 month
Short
Medium
Timeframe impacts chosen method
Strategic forecasting
• market entry (demand and price levels)
• long-term capacity requirements
• new product development
Complex
5,000
mathematical
patients
and subjective
models
per year
•
•
•
Quite
ER 1,000
complex
Surgery 1,000
statistical
Other 3,00
models
aggregate planning, employment needs
overtime, subcontracting
logistical solutions
Forecasts general truth
Forecasts almost always wrong
- based on historical data, system stability as an assumption
- i.e. do not trust blindly
Important to understand how much forecasts
were wrong and why
- right direction in many cases good enough
Random
noise
Often
X-ray 500
simple
Dialyse 100
methods
Heart 20
Operative forecasting
• production planning and production
• procurement and inventory levels
• scheduling, maintenance
(e.g. time series)
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How to improve the forecast?
standardize working methods
- systematic approach to used data and
methods as well as to who forecasts
- documenting clearly what has been done
use multiple methods
- both ”top-down” and ”bottom-up”
- group methods help against overly
optimistic forecasters
consider forecast incentives
- too much intentional errors
- can you separate forecaster and the user of
forecasts?
re-evaluate the forecasts
- too often forgotten
- tendency to repeat old mistakes!
- do not forget training
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Information impacts the quality
Early sales data gives hints about how the sales will
develop
Operative
choose the correct forecasting unit
- even a couple days can improve forecast quality tremendously
- you have to understand the business
understand the difference between
sales and demand
- systems tell only part of the story
- reduced prices, stock-outs, quality problems,
campaigns etc.
Real sales
Process
Fundamental
mistake
understand the cost of wrong
forecasts
- e.g. stock-outs consistently forgotten
4000
4000
4000
3500
3500
3500
3000
3000
3000
2500
2500
2500
2000
2000
2000
1500
1500
1500
1000
1000
1000
500
500
0
use large forecasting units
500 1000 1500 2000 2500 3000 3500 4000
Original forecast
- e.g. product family’s demand’s variation
smaller than individual products
500
0
0
0
0
500 1000 1500 2000 2500 3000 3500 4000
Forecast when 20%
of sales is known
0
500 1000 1500 2000 2500 3000 3500 4000
Forecast when 80% of
sales is known
Especially useful with short product lice-cycle products
use early sales data
- try to forecast as close to the sales season
as possible and use early data
- clothes, consumer electronics, books, movies, music, seminars etc.
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How to survive in fast paced
markets?
Forecasting doesn’t have to be rocket
science - case “asking the right questions” -
Fast paced decision making process?
- new kind attitude toward forecasting
- ABB: “7-3 formula” and pure intuition (wrong forecasts accepted)
- using new information technology in information management
- eases both information handling and sharing
- trusting test market data
- e.g. web pages’ customer feedback forms, softwares’ beta-versions
- Redesigning decision making process
- e.g. emphasizing decentralized decision making
Redesigning the whole operations?
“Well do you think
it will be more than
last year?”
- product design, improving production processes, flexibility in the
amount of capacity, flexibility of product assortment, decreasing setup times, location decisions, training personnel, focusing on value
added tasks, improved supplier relations etc.
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“Do you think that is
mostly servers or directaccess storage devices?”
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