Course Overview

Smart Forecasting (Master Level — Demand Forecasting Methodologies and Performance Measurement):

  1. Establish a framework for demand forecasting in the supply chain
  2. Introduce a four-step process for streamlining the forecasting cycle
  3. Define, interpret, visualize major demand forecasting techniques.
  4. Identify appropriate accuracy measures for evaluating demand forecasting methods and models.
  5. Complement established approaches with non-traditional methods in forecasting model development and evaluation



IIF | CPDF | Certified Professional Demand Forecaster - Expert

Study Options
Skill Level
Certification Type


    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.



    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.


    Get started today!


    Public Classes | In-house customised sessions 


    DURATION: 4 days facilitation and 100 hours self study


    Booking or email us


    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.


    Day 3

    Part IX - The GLOBL Demand Forecasting and Planning Cycle in The Supply Chain

    Concepts of Change and Chance in demand forecasting
    • Role of demand forecasting in the supply chain
    • Contrasting simple, complicated and complex processes
    • Establishing a Budget Forecasting Cycle for a forecasting Simulation game
    • The PEER Model

    • Prepare recurring input data
    • Execute -models (both quantitative and qualitative)
    • Evaluate– results (resolve bias and precision)
    • Reconcile– final forecast (integrate into IBP)

    • Importance of Collaboration With Field Sales/ Customers & Partners


    Workshop 10: Targeting the Environment– How to Assess the Drivers of Demand. Define factors affecting demand for GLOBL’s product line. 


    Part X - Establishing a Data Framework for Creating a Forecast Decision Support System

    • Ways to characterize demand
      • Types of activity being forecast
      • Budget data for a rolling forecast
      • Lead –times and rolling forecast horizons
      • The on demand dashboard and forecasting system
      • Who is the customer?: Determining forecasting requirements by organization
      • Internal factors likely to influence forecast
      • Establishing a database framework for efficient storage and retrieval of data and information


    Workshop 11: Understanding the Data Structure in the Forecasting Game 


    Part XI - Preparing the Baseline Forecast for the Forecast Simulation Game

    • Improving the quality of data in preparation of a statistical forecast
    • Selecting the appropriate aggregation level at which statistical forecasting engine to create unconstrained rolling baseline forecasts
    • Allocating unit and revenue forecasts to lowest levels: SKU and Customer/Locations
    • Recognizing the implications of making subjective judgments and overrides to multi-level forecasts


    Workshop 12: Executing a Baseline Forecast for the Forecast Simulation Game


    Day 4

    Part XII - Goals and Objectives of the Forecast Simulation Game

    • Define the objectives of the forecasting cycle
    • Recognize the drivers of demand
    • Create a rolling baseline forecast for a multi-period horizon
    • Evaluate forecasting performance over the horizon with multiple metrics
    • Recognize and document adjustments and overrides necessary to reflect changes in the business environment and updated assumptions
    • Re– forecast for another multi-period horizon
    • Re-evaluate forecasts (CHANGE), associated prediction limits (CHANCE), and base assumptions and a rationale for advice to management and forecast users.


    Workshop 13: Executing a Baseline Forecast for the Forecast Simulation Game


    Part XIII - Bias and Precision: Establishing Forecast Error Metrics with Statistical Models

    • Defining bias and precision as basis for determining forecast accuracy
    • Interpreting limits in statistical models
    • Identifying accuracy measures for evaluating de-mand forecasts
    • Defining Key Performance Indicators (KPI) for use of forecasts.


    Workshop 13: Handling Exceptions: How to Use Evaluation Criteria to Measure Forecasting Performance.


    Workshop Take-Aways and Closing Remarks


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

    IIF: International Institute of Forecasting - CDPF I, II and III Certificate


    • Full attendance of hands-on workshops is required
    • Successful submission of required worksheets through e-learning system


    CPDF is not a test-based program.It’s a hand-on workshop.

    Please bring your own laptops to run the computer exercises!!


      • 36 hours facilitated sessions | 4 Full days | 08:00 - 16:30
      • 150 hours, 20 worksheets e-learning
      • Content slide manual and Excel Plug-in with Templates

    • CPDF I - Forecasting Principles and Best Practices (IIF)
    • CPDF III - Agile Forecasting Through Forecast Collaboration and Process Integration (IIF)
    • CDDP - Certified Demand Driven Planner (DDI)
    • CDDP - Certified Demand Driven Leader (DDI)

    Johannesburg and Cape Town | 4 Full day sessions |  08:00 – 15:00


    • Available on request


PO Box 153 | Bergbron | Johannesburg | Gauteng | South Africa | 1712 


+27 83 460 1175

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