IIF | CPDF II | Master Level - Forecasting Models and Performance Measurement

This is a certification program for demand forecasters and planners working in supply chain industries. The International Institute of Forecasters (IIF). This workshop is the masters level of the CPDF program is a 200 hours curriculum comprised of three modules.  The CPDF qualification will address multidimensional job roles in demand forecasting such as data display and validation, database management, dashboard display, understanding quantitative and qualitative projection techniques, model creation and execution, forecast accuracy measurement, model and forecaster performance analysis, organization, and collaborative planning.

Each Level of the CPDF program consists of both instructor-led workshop training hours, and independent hours to be accomplished through self-paced e-learning environment.

WHY STUDY WITH US?

1. International trainers
2. Trainers have long and global experience in demand management and forecasting.
3. High quality and excellent style of delivery with participative debate and discussion, case studies.
4. E-learning service through a unique Online Web Platform designed exclusively for CPDF Students.
5. 100% Student pass rate, endorsed by past and present students in the region.
6. Abilities to enhance local demand date with international experience and theories.
7. Interchange demand forecasting experience management with local culture and knowledge.

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Public Classes | In-house customised sessions 

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This workshop will benefit practitioners in all industries specifically with the following job responsibilities:

• Demand Forecasters
• Supply Chain Managers
• Demand Planners
• Supply Planners
• Production Managers
• Operations Managers
• Financial Analysts
• Market Analysts
• Researchers
• Forecasters
• Economists
• Strategists
• Marketing and Sales Managers

Day 1

Part I – The Demand Forecasting and Planning Cycle in The Supply Chain

• What is demand forecasting, Planning and Management?
• Why is demand forecasting so important?
• Role of demand forecasting in the supply chain
• Establishing A Forecasting Cycle– PEER Model Factors affecting demand forecasting (good factors)

Workshop 1: Targeting the Environment: Creating Drivers of Demand for Product/Service Forecasting. | Cases: Automobile and Energy Industry

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 measures used to quantify demand
• A framework for secure data and information management
• Determining customer forecasting needs by organization Internal factors likely to influence forecast
• Designing a demand forecasting framework for data

Workshop 2: Large-Volume, Data-driven Baseline Forecasting With Exponential Smoothing. | Cases: Ice Cream and Tourism Industry

Part III – Big Data: Mining, Exploration and Quality

• Predictive analytics– something is new?
• Methodologies for large-scale data exploration
• Decision Trees – progressive class distinction
• Basic statistical tools for summarizing data
• Traditional and nonconventional measures of variability Intelligent dashboards
• Data framework for on demand planning (SaaS)
• Identifying criteria for assessing data quality
• Handling exceptions in datasets
• Demand Forecaster as Data Scientist
• Data Process Framework and Checklist

Workshop 3: Data Exploration, Outlier Correction, and Predictive Visualization. | Case: Healthcare Industry

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 non-seasonal ARIMA Models
• Comparison of forecasts with prediction limits Implementing non seasonal ARIMA Models Creating an ARIMA modeling checklist

Workshop 4: How to Create Short-term Trend Models. | Case: Residential Construction Industry

Part V – How to Create Seasonal Forecasts and Seasonal Adjustments

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

Workshop 5: Forecasting with Seasonal ARIMA Models.  | Case: Telecommunications Industry

Day 2

Part VI– Designing Regression Models for Forecasting

• Findind linear association between tow variables
• Checking ordinary correlation with nonconventional alternatives
• What are regression model assumptions?
• What is a "best-fit"?
• The least quiare assumption demystified
• The ANOVA table output for regression analysis
• Paring the output for the use of forecasting
• Creating forecasts and prediction limits

Workshop 6: Using Casual Models for Advertising and Promotion Analysis

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 determination
• Creating prediction intervals for forecast monitoring
• Using prediction limits for quantifying uncertainty in forecasts
• A checklist for multiple linear regression

Workshop 7: Taming Volatility— Root Cause Analysis and Exception Reporting.  | Cases: Ice Cream and Tourism Industry

Part VIII - Improving Forecasts with Subjective Judgment

• What is structured judgment?
• When to make judgmental adjustments to forecasts
• Judgmental traps in forecasting
• The Delphi Method The forecasting audit
• A framework for setting forecasting 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 Change and Chance numbers

Workshop 8: GLOBL Case: Simulating The Forecasting Cycle. Global Electronics Manufacturer (a fictitious company) provides consumer electronic technology products to a broad range of customers worldwide Participants can use their own data and prepare forecasts and prediction limits using univariate exponential smoothing and multiple linear regression models.

Workshop Take-Aways and Closing Remarks

• Degree or Job experience
• Reasonable experience in MS Excel
• Acceptable level of English language