next previous Agile Forecasting RTraining 

Training Tailored To Your Specific Requirements

Do you feel you need a refresher on data cleaning, outlier correction, regression modeling, forecast accuracy, forecast profile analysis, State Space Forecasting, or root cause analysis and exception handling?

Would you like help to improve the accuracy, reliability and credibility of automatic baseline statistical forecasting for your demand and business planning processes?

If you need to enhance the professionalism in your organization and increase opportunities for promotion, check out for more information about earning a Certified Professional Demand Forecaster (CPDF) designation.

Below you will find a suggested set of topics to cover the most essential elements of the forecasting process.

Preparation – Data Cleaning, Outlier Correction and Historical Pattern Analysis

  • Data Decomposition - Identifying drivers of demand and quantifying systematic trends and seasonality in demand history

  • Baseline Analysis - Automatic baseline forecasting, centrally developed and distributed by customer account, region or field sales representative

  • Seasonal Demand Patterns - Characterizing additive and  multiplicative seasonality in inventory and sales data 

  • Promotions and Pricing - Developing response models for promotion and pricing

  • Product Lifecycle patterns - New Product intros and launches for forecasting where there is no historical demand

  • Unusual, exceptional Events - Recognizing and coping with uncertainty through confidence intervals

Execution – Modeling Solutions

  • Customized Excel spreadsheet tools and Add-ins for basic data analysis, pattern recognition and performance reporting

  • PEER Planner® Demand Forecasting

  • PEER Planner® Requirements Planning (DRP) - Time-phased Order Forecasting

  • Intermittent demand forecasting for inventory and merchandizing applications

Evaluation – Root Cause Analysis & Exception Tracking

  • Multiple forecast accuracy measures to improve the forecasting process

  • Using forecast errors to implement ‘just-in-time’ inventory management, reserve extra production capacity and maintain extra cash reserve

  • Forecast error tracking for multiple views via ‘at a glance’ graphics to ensure accountability and commitment

  • Automatic forecast monitoring with collaborative planning to grow sales and reduce operating expense

Reconciliation – Matching Supply and Demand Needs (S&OP)

  • Identifying the key stakeholder to manage the forecasting process

  • Supporting a multifunctional consensus-based process

  • Ensuring accountability and commitment to a single number set of forecasts


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