You can achieve greater technical proficiency with demand forecasting techniques by completing an additional 60 hour curriculum; 15 hours for a two-day Instructor-led, hands-on training Workshop and 45 hours comprised of six (6) self-paced, e-learning workshops combined with self study. 

	Time	Instructor-led Hands-on Training Workshop
Day 1	8:30 AM	Registration
	9:00	Part 1 - The Demand Forecasting and Planning Cycle 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 Structures 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
		Computer Workshop 15: Data-driven Baseline Forecasting with Exponential Smoothing. Cases: Ice Cream and Tourism Industry
		Part III - Data Mining, Data Exploration and Data Quality Predictive analytics - 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
		Computer Workshop 16: Data Exploration, Outlier Correction and Predictive Visualization. Case -  Healthcare Industry
		Readout of Computer Workshops 14 - 16
	PM	Lunch
		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 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
		Coffee/Tea Break
		Computer Workshop 18: Forecasting With Seasonal ARIMA Models. Case: Telecommunications Industry
		Readout of Computer Workshops 17 and 18
Day 2
	8:30 AM	Recap of Day 1
		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
		Coffee/Tea Break
		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 Volatility: Root Cause Analyses and Exception Handling. Cases: Ice Cream and Tourism Industry (cont'd)
	PM	Lunch
		Readout of Computer Workshops 19 and 20
		Part VIII - Improving Forecasts With Subjective 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
		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).
		Coffee/Tea Break
		Computer Workshop 21 (cont'd): GLOBL Case: Simulating The Forecasting Cycle
		Workshop Take-aways Closing Remarks

 

After you complete this two-day instructor-led workshop, you will need to complete and submit six (6) self-paced computer workshop exercises re-enforcing the management principles, forecasting techniques and best practices used by demand forecasters worldwide to qualify for CPDF_Master certification. Upon completion of this phase your CPDF designation will increase your proficiency and stay competitive in your discipline, while enhancing your salary opportunities of your chosen profession. To register, complete e-Learning Registration form.