Session 8: Conclusions
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Transcript of Session 8: Conclusions
Session 8: Conclusions
Demand Forecasting and Planning in Crisis30-31 July, Shanghai
Joseph Ogrodowczyk, Ph.D.
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis 30-31 July, Shanghai 2
Conclusions Session agenda
Forecasting changes Evaluating forecasting capabilities of software A word on Microsoft Excel Creating a forecaster training program Resources
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 3
Conclusions Forecasting changes
Very difficult to forecast sharp or hard-to-reverse changes No magic bullet for forecasting quantities Need tools that will guide a forecasting process
Linear models estimate levels (quantities) of the dependent variable
Non-linear models can estimate the probability of the state of the dependent variable
Linear model
-6
-4
-2
0
2
4
6
0 2 4 6 8 10Value of Independent
Value of Dependent
Probability model
0.0
0.3
0.5
0.8
1.0
0 2 4 6 8 10 12
Good
Bad
Probability of Good
Value of Independent
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 4
Conclusions Forecasting changes
Two model types1. Modeling probabilities for future events
Statistical models called probit and logit estimate the probability that an event will occur
Independent variables are correlated with an event ISM purchasing manager’s index, price of oil, stock price
index, etc. Event is the state of the macroeconomy (Good, Bad)
As the independent variables move, the probability of the dependent variable being either Good or Bad adjusts
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 5
Conclusions Forecasting changes
Two model types1. Modeling probabilities for future events
Choosing the “right” independent variables Variables can differ in effect on various occurrences
(simple models are structurally more stable than complex models)
Try to include recent history as a basis (past events are not always an indication of future events)
Historical macroeconomic data are subject to change (re-test models for explanatory robustness)
Key independent variables can change (test the relevance of the independent variables)
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 6
Conclusions Forecasting changes
Two model types2. Decision-directed forecasting
Forecasting tool using after a major disruption Manager experience and statistical models may not be
able produce reliable forecasts Goal is to identify probable scenarios as guidelines for
making operational decisions Steps to the process
Manager identifies decision options and scenarios Management assigns probabilities to future outcomes Forecasters calculate new probabilities as new data
become available (Bayesian calculations) When one scenario becomes more likely, manager
selects the appropriate course of action
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 7
Conclusions Forecasting changes
Two model types2. Decision-directed forecasting: Example
Casino actions in Las Vegas following 9/11 Following 9/11, casinos would need to incorporate a
sharp change in macroeconomic environment Decision actions included: Business as usual (revenue
decreases are short term), Plan for short term disruption (mandatory vacations, hold on capital projects, etc.), Recovery might never fully materialize (short and long term cutbacks), New business strategy (new ventures in other industries)
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 8
Conclusions Forecasting changes
Two model types2. Decision-directed forecasting: Example
Scenario Description Business optionManager's first
probabilityA Revenues return within 3 months Business as usual 15%
BRevenues fall flat for 3 months but return by 9 months
Plan for short term disruption 35%
CRevenues fall flat for 6 months and increase at prior rate
Recovery might never fully materialize 35%
D Revenues continue to decline New business strategy 15%
Probabilities in mid-September 2001
Scenario Description Business optionManager's first
probabilityA Revenues return within 3 months Business as usual 10%
BRevenues fall flat for 3 months but return by 9 months
Plan for short term disruption 38%
CRevenues fall flat for 6 months and increase at prior rate
Recovery might never fully materialize 38%
D Revenues continue to decline New business strategy 14%
Updated probabilities in mid-October 2001
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 9
Conclusions Forecasting changes
Two model types2. Decision-directed forecasting: Example
• Probabilities are updated each month.• By January 2002, Scenarios A and D are highly unlikely• By May 2002, Scenario B is the most likely
Scenario Description Business optionManager's first
probabilityA Revenues return within 3 months Business as usual 0%
BRevenues fall flat for 3 months but return by 9 months
Plan for short term disruption 72%
CRevenues fall flat for 6 months and increase at prior rate
Recovery might never fully materialize 28%
D Revenues continue to decline New business strategy 0%
Updated probabilities in May 2002
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 10
Conclusions Forecasting changes
Two model types2. Decision-directed forecasting
• As the probabilities are updated each month, a manager can adjust the business plan
• Approach can also be used for new product introductions, responses to competitors, or new pricing strategies
• Other methods for choosing among scenarios include: Payoff tables and Decision Trees
• Techniques are available for assisting in choosing the starting probabilities
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 11
Conclusions Evaluating forecasting abilities of software
Background Companies make IT choices based on broad goals such as
supply chain management (SCM) and enterprise resource planning (ERP) needs
Demand planning software is often included with SCM or ERP systems Stand-alone forecasting software requires additional IT
knowledge, time, and funding Decision makers must balance the sophistication of the
forecasting techniques against the ease of interconnectivity of the software
What is the best way for evaluating the forecasting capabilities of the demand planning packages?
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 12
Conclusions Evaluating forecasting abilities of software
Steps in evaluation Designing a test drive
Need to test the software forecast accuracy on the organization’s data Work with vendors on a pilot study
Sample data should include a broad range of products and include all levels of the hierarchies Keep in mind any model requirements with respect to the
length of the dataset Include various demand patterns (intermittent, new product,
stable demand, etc.)
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 13
Conclusions Evaluating forecasting abilities of software
Steps in evaluation Evaluating results
Choose an evaluation period (ex post) Choose an accuracy calculation
Preference should be given to the current calculation to ensure that the software provides a reduction in error
Comparing performance Accuracy improvement may not necessarily be due to the
forecasting algorithms Other benefits from the software can include
Incorporation of point-of-sale data Ability to support Collaborative Planning, Forecasting, and
Replenishment (CPFR)
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 14
Forecasting software options
Modules in Broad Scope Statistical Software:SAS (ETS) STATA SPSS Insightful (S+FinMetrics) R
Business Forecasting Software:Autobox Stamp Forecast Pro SmartForecasts Decision Time
Demand Planning:McConnell-Chase (Forecasting for Demand)Demand Works (Smoothie)Oracle (Peoplesoft Enterprise Demand Planning)John Galt (Atlas Planning Suite)Demand Management (Demand Solutions)Delphus (Peer Planner)Modules in SAP, I2, and JDA (formerly Manugistics)
Econometrics Packages: E-Views RATS and CATS PC Give
See http://www.oswego.edu/~economic/econsoftware.htm
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 15
Conclusions A word on Excel
Overview Previous sessions of workshop used Excel
Excel can and is used for forecasting Good for quick estimates and general guideline However, Excel is not a robust forecasting tool
Specialized forecasting software (or forecasting capabilities within a demand planning software) is recommended in support of a forecasting process
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 16
Conclusions A word on Excel
Tips for using of Excel Advantages of Excel
Allows data to be visible Formulas are accessible and can be edited Calculations can be saved Scenarios can be planned using parametric analysis
Caution of Excel Excel is not a statistical software Statistical procedures do not yield accurate or precise solutions
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 17
Conclusions A word on Excel
Tips for using of Excel Guidelines
Match the tool to the job Excel is a good tool to estimate a range
Understand how to use the tool to accomplish the job Excel makes it easy for users to think they are properly
applying the wrong model to a data set Users can program statistical and forecasting equations into
Excel to obtain correct calculations Increasing the capabilities of the tool can increase the quantity
and quality of the jobs finished by the tool Consider add-ons for Excel Education for users
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 18
Conclusions A word on Excel
Tips for using of Excel Useful resources for analysis on Excel
http://www.daheiser.info/excel/frontpage.html Website documents the error and some potential solutions to
those errors Several studies documenting inconsistencies of statistical
software (McCullough 1999) SAS, SPSS (McCullough and Wilson 2005) Excel 2003 Studies also show a willingness to correct errors by SAS and
SPSS but not Microsoft (McCullough and Wilson 2002)
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 19
Conclusions A word on Excel
Nonlinear curves example In calculating coefficients of nonlinear functions (such as
exponential functions), Excel transforms the data into a line Transformation leads to incorrect calculations Optimal coefficients of a function are found by a model error
metric (such as the root mean square error or RMSE) Exponential function defined as
a: intercept (assumed to be 0)b: growth ratec: lower limitT: time (year for this example)e: mathematical constant of 2.718growth rate as a percent per year = eb-1
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 20
Conclusions A word on Excel
Nonlinear curves example Sample data
GraphSales
$0
$500
$1,000
$1,500
$2,000
$2,500
$3,000
0 5 10 15Year
Year 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Sales $301 $320 $372 $423 $500 $608 $721 $826 $978 $1,135 $1,315 $1,530 $1,800 $2,152 $2,491
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 21
Conclusions A word on Excel
Nonlinear curves example Choosing an exponential trend line in Excel Excel will find the optimal coefficient of the followingtransformed data
ln(Sales)
$5
$6
$7
$8
$9
$10
0 5 10 15Year
Year ln(Sales)1 $5.7072 $5.7683 $5.9194 $6.0475 $6.2156 $6.4107 $6.5818 $6.7179 $6.88610 $7.03411 $7.18212 $7.33313 $7.49614 $7.67415 $7.820
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 22
Conclusions A word on Excel
Nonlinear curves example Excel will convert the result from the linear function back into
the exponential function for the fit line of
where growth rate as a percent per year = e.1561-1 = 16.89%This is the annual growth rate of salesNotice that Excel assumes an intercept of zero
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 23
A word on Excel Nonlinear curves example
A statistical software will optimize the exponential function directly for a fit line of
where growth rate as a percent per year = e.1608-1 = 17.44%This is the annual growth rate of salesNotice that there is a non-zero intercept
Conclusions
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 24
Conclusions A word on Excel
Nonlinear curves example Comparing the results
Statistical Excel Parameters software Non-linear fit
a= 221.7886 237.5583b= 0.1608 0.1561c= 19.9793 0
Year Sales Statistical Statistical Excel Excelforecast error forecast error
1 $301 $280.46 -$20.54 $277.69 -$23.312 $320 $325.90 $5.90 $324.61 $4.613 $372 $379.27 $7.27 $379.45 $7.454 $423 $441.95 $18.95 $443.55 $20.555 $500 $515.56 $15.56 $518.49 $18.496 $608 $602.01 -$5.99 $606.08 -$1.927 $721 $703.55 -$17.45 $708.47 -$12.538 $826 $822.79 -$3.21 $828.17 $2.179 $978 $962.85 -$15.15 $968.08 -$9.92
10 $1,135 $1,127.33 -$7.67 $1,131.63 -$3.3711 $1,315 $1,320.51 $5.51 $1,322.81 $7.8112 $1,530 $1,547.38 $17.38 $1,546.29 $16.2913 $1,800 $1,813.84 $13.84 $1,807.52 $7.5214 $2,152 $2,126.78 -$25.22 $2,112.89 -$39.1115 $2,491 $2,494.31 $3.31 $2,469.85 -$21.15 Percent
Difference DifferenceSum squared errors $2,920.90 $4,018.70 $1,097.81 37.6%Root 54.05 63.39 $9.35 17.3%
16 $2,925.96 $2,887.12 -$38.84 -1.3%17 $3,432.90 $3,374.87 -$58.03 -1.7%18 $4,028.29 $3,945.03 -$83.25 -2.1%
Sum $10,387.15 $10,207.02 -$180.12 -1.7%
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 25
Conclusions Creating a forecaster training program
1. Program outline Program mission statement Clearly defined objectives based on ideal forecaster
characteristics Resource requirements
2. Create a measurement baseline Classify objectives into core learning areas
Forecasting and supply chain concepts Technical and software skills Process management and product knowledge Interpersonal skills
Collect data on the current ability level of the forecasters
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 26
Conclusions Creating a forecaster training program
3. Create development plan through gap analysis Define critical skills corresponding to core learning areas Compare current forecaster knowledge with critical skills Prioritize the gaps based upon criteria such as importance
of core area, size of gap, quantity of forecasters, and estimated resources needed to close the gap
4. Implementation of education program Define the content of core learning areas Outline methods used for education
Separate sessions, week long workshops, etc. Consultants or internal resources Quantity of IT support needed Use of mentoring, coaching, on-the-job training
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 27
Conclusions Creating a forecaster training program
5. Evaluation and areas of improvement for program Maintenance of forecaster education
Continuing education internally, plans for using external resources or outside education
Lessons learned from implementation Changing the course schedule or material covered Incorporating participant feedback
Defining additional supporting infrastructure Guiding principles for demand management education Roles and responsibilities of the instructors Future expectations of additions to core areas
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 28
Conclusions Resources
Forecasting portals www.appliedforecasting.com
Latest news on forecasting events, tool and papers www.forecastingeducation.com
Listing of and links to software reviews and special reports on forecasting software
www.demandplanning.net S&OP and demand management consulting and courses
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 29
Conclusions Resources
Forecasting organizations International Institute of Forecasting
www.forecastingprinciples.com Institute of Business Forecasting
www.ibf.org Both offer
Courses, seminars, and publications
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 30
Special FeaturesSpecial Features
Overcoming Challenges in Operational Overcoming Challenges in Operational Forecasting ProjectsForecasting Projects
The Organizational Politics of The Organizational Politics of Forecasting:6 Steps to Overcome BiasForecasting:6 Steps to Overcome Bias
Forecast Accuracy Metrics for Inventory Forecast Accuracy Metrics for Inventory ControlControl
The What, Why, and How of Futuring The What, Why, and How of Futuring for Forecastersfor Forecasters
Benchmarking of Forecast AccuracyBenchmarking of Forecast Accuracy plus Software Reviews, Book Reviews plus Software Reviews, Book Reviews
and briefs on Hot New Research and briefs on Hot New Research
www.forecasters.org/foresight
FORESIGHT: ConciseConcise, , objectiveobjective and and readable readable articles on issues essential to the practicing forecasterarticles on issues essential to the practicing forecaster. .
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 31
Conclusions References (Forecasting changes)
Batchelor, Roy. 2009. Forecasting sharp changes. Foresight. Spring: 7-12.
Custer, Stephen and Don Miller. 2007. Decision-directed forecasting for major disruptions: The impact of 9/11 on Las Vegas gaming revenues. Foresight. Summer: 29-35.
Jain, Chaman L. and Jack Malehorn. 2005. Practical Guide to Business Forecasting (2nd Ed.). Flushing, New York: Graceway Publishing Inc.
Sephton, Peter. 2009. Predicting recessions: A regression (probit) model approach. Foresight. Winter: 26-32.
Session 8 Joseph Ogrodowczyk, Ph.D.
Demand Forecasting and Planning in Crisis, 30-31 July, Shanghai 32
Conclusions References (Forecasting software)
Fields, Paul. 2006. On the use and abuse of Microsoft Excel. Foresight. February: 46-47.
Hesse, Rick. 2006. Incorrect nonlinear trend curves in Excel. Foresight. February: 39-43.
Hoover, Jim. 2005. How to evaluate the forecasting ability of demand-planning software. Foresight. June: 47-49.
McCullough, Bruce D. 1999. Assessing the reliability of statistical software: Part 2. The American Statistician. 53(2): 149-159.
McCullough, Bruce D. and B. Wilson. 2005. On the accuracy of statistical procedures in Microsoft Excel 2003. Computational Statistics and Data Analysis. 49(4): 1244-1252.
McCullough, Bruce D. and B. Wilson. 2002. On the accuracy of statistical procedures in Microsoft Excel 2000 and Excel XP. Computational Statistics and Data Analysis. 40(4): 713-721.
McCullough, Bruce D. 2006. The unreliability of Excel’s statistical procedures. Foresight. February: 44-45.