Time

Instructor-led Hands-on Training Workshop

Day 1

8:30 AM

9:00

Part 1 - The Demand Forecasting and Planning Process in the Supply Chain

• What is demand forecasting, demand panning and demand management?
• Why is demand forecasting so important?
• Role of demand forecasting in the supply chain
• Establishing a forecasting work cycle - the PEER model
• Factors affecting demand (good factors) 

Workshop 14: Defining the Target - Creating a Demand-Driven Model of the Business

Part II - Data Framework for Creating Forecast Decision Support Systems

• Ways to characterize demand activity
• Time horizons, lead-times and dimensions of a forecast
• Units of measure used to quantify demand
• A framework for secure data and information management
• Determining customer forecasting needs by organization
• Internal factors likely to influence a forecast
• Designing a demand forecasting framework for data

Coffee/Tea Break

Part III - Big Data: Data Mining, Exploration and Data Quality

• Predictive analytics and predictive visualization - something new?
• Methodologies for large-scale data exploration
• Decision trees - progressive class distinction
• Basic statistical tools for summarizing data
• Traditional and nonconventional measures of variabililty
• Intelligent dashboards
• Data framework for on demand planning (SaaS)
• Identifying criteria for assessing data quality
• Handling exceptions in large datasets
• Demand Forecaster as Data Scientist
• Data process framework and checklist

PM

Lunch

Computer Workshop 16: Data Exploration, Outlier Correction and Predictive Visualization. Case -  Healthcare Industry

Readout of Computer Workshops 14 - 16

Part IV - Forecasting with ARIMA Time Series Models

• Creating a flexible, model-building strategy for ARIMA models
• Recognizing forms of stationarity (level) and non-stationarity (trending and seasonal) in time series
• Detecting autocorrelation in time series
• Identifying nonseasonal ARIMA models
• Comparison of forecasts with prediction limits
• Implementing non-seasonal ARIMA models
• Creating an ARIMA modeling checklist

Computer Workshop 17: How to Create Short-term Trend Models. Case: Residential Construction Industry

Part V:  How to Create Model-based Seaonal Forecasts and Seasonal Adjustments

• Decomposition programs for seasonal adjustment
• Identifying and implementing seasonal ARIMA models
• Creating waterfall charts for model evaluation
• Forecast test measures for multiple ARIMA models
• Best practices for ARIMA modeling

Computer Workshop 18: Forecasting With Trend/Seasonal ARIMA Models. Case: Telecommunications Industry 

Coffee/Tea Break

Readout of Computer Workshops 17 and 18

Part VI: Designing Regression Models for Forecasting

• Finding a linear association between two variables
• Checking ordinary correlation with a nonconventional alternative
• What are regression model assumptions
• What is a "best" fit
• The least-squares assumption demystified
• The ANOVA table output for regression analysis
• Paring the output for use in forecasting
• Creating forecasts and prediction limits

Computer Workshop 19: Using Causal Models for  Advertising and Promotion Analyses. Case: Retail Industry

  PM

Lunch

Part VII: Working with Residuals and Forecast Errors to Improve Forecasting Performance

• Dealing with lack of normality in time series regression modeling
• Looking out for 'Black Swans'
• How good was the fit and what does it say about forecasting
• Dealing with nonrandom patterns in residuals
• Impact of error term assumptions on prediction interval estimation
• Creating prediction intervals for forecast monitoring
• Using prediction limits for quantifying uncertainty in forecasts
• A checklist for multiple linear regression modeling

Computer Workshop 20:   Taming Uncertainty with Root Cause Analyses and Exception Handling. Cases: Workshop Participant Industry (cont'd)

Coffee/Tea Break

Part VIII - Improving Forecasts With Informed Judgment

• What is structured judgment?
• When to make judgmental adjustments and overrides to forecasts
• The Delphi method
• The forecasting audit
• A framework for setting forecasting job standards
• Functional integration
• Performance measurement
• Planning for process improvement
• Overcoming barriers and closing gaps
• Forecast horizon
• Melding quantitative and qualitative approaches for forecast development and process improvement
• Creating the final forecast with Chance and Chance numbers

Workshop 21: GLOBL Case:  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 & Chance).  

• Workshop Take-aways
• Closing Remarks