BABS 502
Lecture 1
February 23, 2009
(C) Martin L. Puterman
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Bookkeeping
• Your instructor
• Course guidelines
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Lectures
Assignments
Project – no exam
Contest
Software –NCSS
• Case Study
(C) Martin L. Puterman
2
What is a Forecast?
A prediction of the future
fore = before + cast = throw
Literally planning before you throw.
There is some confusion about this point
Often organizations refer to direct outputs of decisions as
forecasts. (Sometimes it is easier to use this terminology)
Example – “production forecasts” are not “forecasts”
They are subject to variability but are known to some
degree of accuracy by organization members.
(C) Martin L. Puterman
3
Course Themes
• Forecasts are necessary for effective decision
making
– Forecasting, planning and control are interrelated
• Forecasts are usually (almost always) wrong
– Quantifying forecast variability is as important as
determining the forecast; it is the basis for decision
making.
– Rare events happen and can have significant impact on
forecasts
• Scientific methods improve forecasting
(C) Martin L. Puterman
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Course Objectives
• To provide a structured and objective approach to
forecasting
• To provide hands on experience with several
popular forecasting methods
• To determine the data requirements for effective
forecasting
• To integrate forecasting with management
decision making and planning
• To introduce you to some advanced forecasting
methods
(C) Martin L. Puterman
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Why Forecast?
• It’s fun
• To look smart
• But most importantly: To make better decisions
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Investments
Inventory
Staff
Medical treatment timing
• Fact: Forecasts are usually (always?) wrong!
– Why do it then?
– Because you have to!!
• Effect of bad forecasts
– Excess costs – too much staff or stock
– Poor service –waiting lines and stockouts
(C) Martin L. Puterman
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Knowledge Base for Effective
Forecasting
• Subject Matter Knowledge
– Industry
– Market
– Demand Sources
• Statistics
• Statistical software and IT
• Interpersonal skills
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acquiring data
report writing
presentations
team work
(C) Martin L. Puterman
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Forecasting Applications
• Demand forecasts
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Whistler-Blackcomb - staffing
TELUS – capacity expansion
Worksafe BC – staffing, budgeting and reserve planning
Health Authorities – staffing, scheduling and planning
Mike’s Products - production and inventory decisions
• Price forecasts
– Teck- Cominco - production planning, ore purchase
– Vancouver Olympic Village – resale value
• New market forecasts;
– Webvan, Petfood.com, Napster
• Technology forecasts
– Intel; Nortel; TELUS; Microsoft; Google
(C) Martin L. Puterman
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Forecasting
for a
Consumer Product Distribution System
(C) Martin L. Puterman
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The Challenge
• Enhance the performance of the inventory and
distribution system for products in the US market
• Highly competitive market with highly seasonal
demand patterns
• Client’s Goal - Get the right product in the right
quantity to the right customer on time!
(C) Martin L. Puterman
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The Production/Distribution System
Co-packers
Products
Distribution
Centers
Retailers (many)
(C) Martin L. Puterman
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Modeling
• A linear programming based planning tool
• For each SKU it finds for the next 12 months:
- Optimal co-packer production levels
- Optimal distribution and transshipment plans
- Optimal distribution center (DC) inventory levels
• Developed for operational decisions but first used for
tactical/strategic decisions
• Implemented in Excel using Frontline Solver
• User friendly interface
(C) Martin L. Puterman
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Using the Model in Practice
Month
Date
Steps to Take
T–1
20th
Provide forecasts for month T to T + 12
T
5th
Estimate closing inventory at the end of month T, using
- Opening inventory of month T,
- Production schedule of month T, and
- Actual order from distributors and DC re-order suggestions in month T
Monthly input data check list, including
- Unit costs
- Production and inventory capacity
- Minimum and fixed production
From production and distribution personnel. Document the changes to the data.
6-9th
- Run the tool with updated data, review the output and re-run if necessary.
- Set production plan for month T + 1
- Document changes of actual plan from tool output and reasons of changes
10th
Provide co-packers with production plan for month T+1
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Forecasts drive the model!
• Key input – Forecasts by sales region by SKU for
next 12 months.
– Produced by regional sales representatives
– Accuracy declines over 12 month period
– Not calibrated but good in aggregate!
• But model is used in a rolling horizon approach
(C) Martin L. Puterman
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(C) Martin L. Puterman
Company logo
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Model in MS Excel
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More on Forecasting
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Forecasting is NOT a Statistical Topic
• Primary interest is not in hypothesis tests or confidence
intervals.
• Underlying models developed in statistics arena are often
used:
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regression
time series
neural networks
dynamic Bayesian systems and state space models
• Forecasts must be assessed on
– the quality of the decisions that are produced
– their accuracy
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Types of Forecasting
• Extrapolation
– Based on previous data patterns
• Assumes past patterns hold in future
– Exponential Smoothing, Trend Models, ARIMA models
• Causal
– Based on factors that might influence the quantity being forecasted
• Assumes past relationships hold in the future
– Regression
• Judgemental
– Based on individual knowledge
– Sales force composites, expert opinion, consensus methods
– Surveys and market research
• Collaborative
– Based on information available to supply chain partners
– Information sharing and partnerships
(C) Martin L. Puterman
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Forecasting Considerations
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Forecasts vs. Targets
Short Term vs. Medium Term vs. Long term
One Series vs. Many
Seasonal vs. Non-seasonal
Simple vs. Advanced
One-Step Ahead vs. Many Steps Ahead
Automatic vs. Manual
Exceptions
When to update models
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Forecasting Horizons
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Short term
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Medium term
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usually a few months to 1 or 2 years
Long term
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a few days or weeks
usually more than 2 year
Why distinguish between these?
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Different methods are more suitable in each case.
(C) Martin L. Puterman
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Some Forecasting Observations
He who lives by the crystal ball soon learns to eat ground glass.
– Edgar R. Fiedler in The Three Rs of Economic Forecasting-Irrational, Irrelevant
and Irreverent , June 1977.
Prediction is very difficult, especially if it's about the future.
– Nils Bohr, Nobel laureate in Physics
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This quote serves as a warning of the importance of testing a forecasting model out-of-sample.
It's often easy to find a model that fits the past data well--perhaps too well!--but quite another
matter to find a model that correctly identifies those features of the past data which will be
replicated in the future
There is no reason anyone would want a computer in their home.
– President, Chairman and founder of Digital Equipment Corp, 1977
640K ought to be enough for anybody.
– Bill Gates, 1981
Our sales forecasts are accurate in aggregate
– Many marketing directors
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Forecasting methods that work
• Naïve: Last Period or Same Period Last Year
• Regression
– Extrapolation
– Causal
• Exponential Smoothing
– Simple
– Trend / Damped Trend
– Holt-Winters
• Pooled methods
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Forecasting methods I don’t recommend
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Crystal balls
Tea leaves
Fortune cookies
Expert Opinion
Complex statistical models
– Box-Jenkins / ARIMA Models
– Multivariate Econometric Models
– Neural Networks
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Forecasting in Organizations
There is no forecasting department!
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Forecasting Practice in Organizations
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What quantities do organizations need to forecast?
What methods are users familiar with?
What methods have been used?
What are the impediments to using quantitative
techniques?
• What factors which make forecasting most
difficult?
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What do organizations need to forecast?
• Costs
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raw materials
semi-finished goods
wage rates and overheads
interest rates
• Sales/ Activities
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by industry, by region
by market/product, market share
by product category, by wholesaler, by retailer
new product sales
competitive position - e.g. prices, exchange rates
competitive behaviour
customer service
price
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What do organizations need to forecast?
• Technology
– new products
– new processes
– diffusion rates
• Social and Political trends
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demographics
wealth profile
welfare and health provisions
impact of technology
• Projects
– duration
– costs
– life cycle maintenance
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Top 10 impediments to effective forecasting
10. Absence of a forecasting function
9. Poor data
8. Lack of software
7. Lack of technical knowledge
6. Poor data
5. Lack of trust in forecasts
4. Poor data
3. Too little time
2. Not viewed as important
1. Poor data
(C) Martin L. Puterman
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Forecasting Challenges
• Technical Issues
– What is the best approach
• Organizational Issues
– reporting structures
– accountability
– incentive systems
• Information
– historical data not available
– timeliness and reliability
– what information is required when
• Users
– conflicting objectives
(C) Martin L. Puterman
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Top Down vs. Bottom Forecasting
Top Down - Forecast at central office
Bottom up - Forecast by sales force
Strengths
Weaknesses
Top-down
Aggregate market information included
Marketing plans
Competitive viewpoint
No responsibility accepted by sales
force
Confuses forecasts with aggregate
target setting
Politically motivated
Bottom Up
Detailed customer info
Responsibility clear for sales
Motivation
Aggregated forecast may not reflect market
plans
No easy reconciliation with corporate
financial projections
May be biased due to sales force
compensation schemes
Costs – more staff time and slower process
Questions – which is more accurate? which should be used?
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Silos and Forecasting
Production
Forecaster
Marketing
IT
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Responsibilities of Units
• Production
– Acquiring materials
– Planning and scheduling production runs
• Logistics
– Delivering products to customers
• Marketing
– Generating orders
– Creating product demand
• IT
– Acquiring software
– Integrating software
– Managing data
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Scientific Forecasting
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Scientific Forecasting
• Requires familiarity with very basic statistical
concepts:
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Mean, standard deviation, skewness and kurtosis
medians and percentiles
histograms, stem and leaf plots, box plots
scatter plots, correlation, regression
If you’re not keeping score
you are only practicing!
(C) Martin L. Puterman
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The Forecasting Process - I
• Determine what is to be forecasted and at what
frequency
• Obtain data
• Process the data
• PLOT THE DATA
• Clean the data
• Hold out some data
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The Forecasting Process - II
• Obtain candidate forecasts
• Assess their quality
– Forecast accuracy on hold out data
– Do they make sense?
– Do they produce good decisions?
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Revise forecasts
Recalibrate model on full data set
Produce forecasts and adjust as necessary
Produce report
In future - Evaluate accuracy of forecasts
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Means and Standard Deviations
Means and standard deviations are only useful for
summarizing data when it looks like it comes from a normal
distribution
3.00
2.50
2.00
1.50
1.00
0.50
0.00
-0.50
-1.00
-1.50
-2.00
-2.50
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They especially are not appropriate for summarizing time
series data with trends or seasonality.
(C) Martin L. Puterman
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Some Normal Distribution Properties
• Determined completely by its mean  and standard deviation 
• Its skewness is 0 and its kurtosis is 0
• 95% of the observations fall within 2 standard deviations (not standard
errors!) of the mean
– Useful for determining forecast ranges
– Usually forecasts are accurate to  2 standard deviations
• 95% of the observations fall below
 + 1.645 
– Useful for determining service levels of inventory policies
• When extreme outliers may occur, the normal distribution may not be
appropriate
– Such distributions are said to have long tails
– These distributions have positive kurtosis.
– The book, The Black Swan, by Nicholas Taleb addresses the practical significance of
this issue.
(C) Martin L. Puterman
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Data Patterns
Diagram 1.1: Trend -
Diagram 1.2: Seasonal -
long-term grow th or decline occuring
w ithin a series
m ore or less regular m ovem ents
w ithin a year
100
120
80
100
80
60
60
40
10
Diagram 1.3: Cycle -
Diagram 1.4: Irregular -
alternating upsw ings of varied length
and intensity
random m ovem ents and those w hich
reflect unusual events
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10
Year
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9
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Year
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40
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300
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200
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100
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Basic Modeling Concept
An observed measurement
is made up of
a systematic part
and a
random part
Unfortunately we cannot observe either of these.
Forecasting methods try to isolate the systematic part.
Forecasts are based on the systematic part.
The random part determines the distribution shape and
forecast accuracy.
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Basic Concept Again
Observed Value =
Signal “+” Noise
In non-normal (or non-additive) models the “+” may
be inappropriate
(C) Martin L. Puterman
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Forecasting Notation (p.71)
t
n
Yt
Ft+k
a specific time period
total number of observations
observed value at time t
forecasted value k periods ahead at time t
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Correlation
• Measures the strength of the (linear) relationship between
two measurements
• Often denoted by rXY
• A number between -1 and +1
• Answers question: Does one measurement contain
information about another measurement?
• Theoretically rXY = Cov(X,Y)/X Y
• From a sample rXY (see equation 2.8).
(C) Martin L. Puterman
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Autocorrelation - What is it?
• Correlation between observations at different time points
in a time series - estimated by rk
– Lag 1 autocorrelation measures the correlation between Yt and Yt-1
– Lag k autocorrelation measures the correlation between Yt and Yt-k
• Summarized in terms of an autocorrelation function (ACF)
which give the autocorrelations between observations at all
lags.
– It is often represented graphically as a plot of autocorrelation vs.
lag
(C) Martin L. Puterman
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Autocorrelation - Why is it useful?
• Can the past help predict the future?
– if autocorrelations at all lags are near zero then best
predictor is historical mean
– if all autocorrelations of differences of series are near zero
then best predictor of the future is the current value
– if autocorrelations at seasonal lags are large - suggests
seasonality in data
• An important component of the ARIMA or BoxJenkins’ method
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Autocorrelation Example 1
Autocorrelations of C2 (0,0,12,1,0)
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34
50
67
0.500
0.000
-0.500
Autocorrelations
1
-1.000
0.5
-0.8
-2.0
C2
1.8
3.0
1.000
Plot of C2
0
Time
10
21
31
41
Time
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Autocorrelation Example 2
Original
Plot of Wages
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0.500
0.000
-1.000
37
73
0
10
Time
21
31
41
Time
Difference
-0.500
0.000
0.500
1.000
Autocorrelations of Wages (1,0,12,1,0)
-1.000
19
Autocorrelations
1
-0.500
Autocorrelations
5.4
5.1
4.8
Wages
5.7
6.0
1.000
Autocorrelations of Wages (0,0,12,1,0)
0
10
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