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Day 1 |
8:30
AM |
Registration |
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9:00
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Part 1 - The Demand
Forecasting and Planning Cycle in the Supply
Chain
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What is demand forecasting, demand
panning and demand
management?
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Why is demand forecasting so
important?
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Role of demand forecasting in the supply
chain
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Establishing a forecasting work cycle -
the PEER model
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Factors affecting demand (good
factors) |
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Workshop 14:
Defining the Target - Creating a
Demand-Driven Model of the
Business |
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Part II - Data
Structures for Creating Forecast Decision Support
Systems
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Ways to characterize demand
activity
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Time horizons, lead-times and dimensions
of a forecast
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Units of measure used to quantify
demand
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A framework for secure data and
information management
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Determining customer forecasting needs by
organization
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Internal factors likely to influence a
forecast
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Designing a demand forecasting framework
for data |
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Coffee/Tea Break |
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Computer
Workshop 15: Data-driven
Baseline Forecasting with Exponential Smoothing.
Cases: Ice Cream and Tourism
Industry |
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Part III -
Data Mining, Data Exploration and Data
Quality
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Predictive analytics - something
new?
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Methodologies for large-scale data
exploration
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Decision trees - progressive class
distinction
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Basic statistical tools for summarizing
data
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Traditional and nonconventional measures
of variabililty
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Intelligent
dashboards
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Data framework for on demand planning
(SaaS)
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Identifying criteria for assessing data
quality
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Handling exceptions in large
datasets
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Demand Forecaster as Data
Scientist
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Data process framework and
checklist |
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Computer Workshop 16:
Data Exploration, Outlier Correction and
Predictive Visualization. Case - Healthcare
Industry |
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Readout of Computer
Workshops 14 - 16
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PM |
Lunch |
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Part IV - Forecasting
with ARIMA Time Series
Models
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Creating
a flexible, model-building strategy for ARIMA
models
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Recognizing
forms of stationarity (level) and
non-stationarity (trending and seasonal) in time
series
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Detecting
autocorrelation in time series
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Identifying
nonseasonal ARIMA models
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Comparison
of forecasts with prediction limits
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Implementing
non-seasonal ARIMA models
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Creating
an ARIMA modeling
checklist |
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Computer Workshop 17:
How to Create Short-term Trend Models. Case:
Residential Construction
Industry |
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Part V: How
to Create Seaonal Forecasts and Seasonal
Adjustments
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Decomposition programs for seasonal
adjustment
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Identifying and implementing seasonal
ARIMA models
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Creating waterfall charts for model
evaluation
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Forecast test measures for multiple ARIMA
models
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Best practices for ARIMA
modeling |
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Coffee/Tea Break
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Computer
Workshop 18: Forecasting
With Seasonal ARIMA Models. Case:
Telecommunications
Industry |
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Readout of Computer
Workshops 17 and
18 |
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Day 2 |
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8:30 AM |
Recap of Day
1 |
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Part
VI: Designing Regression Models
for Forecasting
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Finding a linear association between two
variables
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Checking ordinary correlation with a
nonconventional alternative
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What are regression model
assumptions
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What is a "best" fit
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The least-squares assumption
demystified
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The ANOVA table output for regression
analysis
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Paring the output for use in
forecasting
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Creating forecasts and prediction
limits |
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Computer Workshop 19:
Using Causal Models for Advertising
and Promotion Analyses. Case: Retail
Industry |
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Coffee/Tea Break
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Part VII: Working with
Residuals and Forecast Errors to Improve
Forecasting Performance
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Dealing with lack of normality in time
series regression modeling
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Looking out for 'Black
Swans'
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How good was the fit and what does it say
about forecasting
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Dealing with nonrandom patterns in
residuals
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Impact of error term assumptions on
prediction interval
estimation
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Creating prediction intervals for
forecast monitoring
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Using prediction limits for quantifying
uncertainty in forecasts
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A checklist for multiple linear
regression
modeling |
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Computer Workshop 20:
Taming Volatility: Root Cause Analyses
and Exception Handling. Cases: Ice Cream and
Tourism Industry
(cont'd) |
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PM |
Lunch |
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Readout of Computer
Workshops 19 and 20 |
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Part VIII - Improving
Forecasts With Subjective
Judgment
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What is structured
judgment?
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When to make judgmental adjustments and
overrides to forecasts
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The Delphi method
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The forecasting audit
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A framework for setting forecasting job
standards
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Functional
integration
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Performance
measurement
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Planning for process
improvement
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Overcoming barriers and closing
gaps
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Forecast horizon
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Melding quantitative and qualitative
approaches for forecast development and process
improvement
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Creating the final forecast with Chance
and Chance
numbers |
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Computer Workshop 21:
Simulating The
Forecasting Cycle. GLOBL case:
GLOBL Electronics Manufacturer (a fictitious
company) provides consumer electronics technology
products to a broad range of customers worldwide.
Workshop participants will prepare forecasts and
prediction limits for three product lines based on
univariate exponential smoothing and multiple
linear regression models. Objective is to prepare
a three-year forecast with quantified uncertainty
(Change and Chance). |
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Coffee/Tea Break
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Computer Workshop 21
(cont'd): GLOBL Case: Simulating The
Forecasting Cycle |
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Workshop Take-aways
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Closing
Remarks |