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Leveraging Effective Demand Forecasting and Planning for Improved Business Results in a Volatile Market

Estimating future demand is a crucial, yet incredibly challenging, element in the business process to ensure that an organization can successfully grasp every available business opportunity in the market. Doing this requires demand planners to accurately predict the pace of economic growth in the United States and abroad along with changes of lifestyle, this is a difficult task in the easiest and most stable of economic times. The rapid pace of change and the fluctuations that have gripped the market place over the past several years makes this already complex task even more challenging. In order to leverage accurate demand forecasts, demand planners are required to combine the science of forecasting with effective judgment on both internal and external factors.

Turning 'Theory' into 'Practice'?

This hands-on course is designed to specifically address the key issues in the minds of those with executive and management responsibilities in demand planning, and those that work alongside them. This course is an interactive course that takes an in depth look at forecasting and planning. Attendees will hear real world lessons learned that will enable better decision making when they return to their offices. Group activities and discussions will allow demonstration and discussions over forecasting concepts and their applicability along with an overview of real-world best and worst Forecasting practices. To translate and expedite classroom learning to real-life applications, participants are HIGHLY ENCOURAGED bringing their own laptops. DATA ANALYSIS and SOLVER MS Excel Add-In MUST BE ACTIVATED

Who should attend?

SVP’s, VP’s, Directors and Senior Managers from cross- Industry organizations with responsibilities in:   Demand Forecasting and Planning, Sales and Operations Planning Demand Management Supply Chain Supply Chain Logistics Strategic Planning Product Life-Cycle

Key areas to be covered in the programme and documentation

Recognize the Key Elements to a Successful Forecasting Process and Function

o    Establish what is required for your forecasting process and function to be successful

o    Identify what challenges your organization may face while developing a successful forecasting process and function

o    Determine what goals and objectives need to be met for a successful forecasting process

o    Deploy active coordination and leadership to the forecasting process and function to enable more efficient process management

o    Implement effective communications with all participants and users

o    Understand the four approaches to the forecasting process

§  Independent

§  Concentrated

§  Consensus

§  One Number

o    Recognize the myths about forecasting

o    Determine the impact that silo forecasting has on an organization

Case Study and Exercise:

Determine the impact that silo forecasting has on an organization.


Establish the Inputs and Outputs of an Effective Demand Plan

o    Determine the profit potential of an effective demand plan

o    Incorporate an effective Risk Management Input into demand planning

o    Link demand planning with operations planning

Case Study and Exercise:

Consensus Forecast process – a team discussion driven by a Case Study that demonstrates the value of combination of quantitative and qualitative forecasting approaches.


Introduction to statistical forecasting systems

o    Overview of SAP APO Demand Planning, Manugistics DP and similar leading forecasting systems and their statistical methods and user controls

Analyze Various Qualitative / Quantitative Forecasting Models

o    Identify how much and what data should be used for different statistical models

o    Incorporate subjective assessments of various models

Identify Various Data Patterns and Their Relationship with Demand Variability

o    Examine the impacts of the “Bullwhip Effect”

o    Recognize various types of data patterns

o    Determine ways to minimize the effects of demand variability

o    Diagnose what problems may be found in data and explore methods of treating them

o    Analyze previous sales data to identify patterns that can be used to predict future sales

Case Study and Exercise:

Seasonality, trend and level recognition, historical outliers recognition and correction. We will explore how to deploy basic normal distribution and statistical process control techniques to validate quality of the data before an attempt to generate forecasts using forecasting systems. These techniques can be used to support any forecasting software/system.


Explore Different Time Series Models

o    Determine the inherent assumptions of time series models

o    Understand when time series work and when they don’t

o    Analyze various elements of time series

Group Exercise - Exponential Smoothing and Averages:

Learn by doing. We will explore simple forecasting models by building them in MS Excel. These models are common to all forecasting systems in the market. Many forecasters create higher forecast error by simply not understanding the impact of changing the models or their statistical parameters. By building these models in excel, we will solidify knowledge of these critical base concepts.


Group Exercise and Discussion - Seasonal Decomposition:

We will analyze sales data to find patterns in past sales that we use to predict future sales. As we find a pattern, we strip it away from the data, changing the shape of the data that remains. Then we will look for new patterns and strip them away until only unexplained fluctuations remain. Understanding this technique is fundamental to successful statistical forecasting using ANY commercially available software and system.


Group Exercise and Discussion – Triple Exponential Smoothing (Holt-Winters) Model


Hands-On – Laptop required


Examine Cause and Effect Models

o    Explore regression models

o    Establish when to use regression models

o    Identify the key steps needed for the successful development of regression models

o    Determine which key assumptions are used in regression models

Group Exercise

·         Determining future sales based on various levels of radio/TV advertising

·         Regression analysis of advertising expenditure versus market share

Case Study and Group Exercise – Electric Utility Company

·         Electric utility company. Practical walk-through multiple regression forecasting technique using external data as leading indiators.

Hands-On – Laptop required


 Utilize Effective New Product Forecasts

o    Establish the success and error rates for new products

o    Determine the factors that lead to new products’ success or failure

o    Identify which issues should be considered when developing new product forecasts

o    Explore qualitative and quantitative methods used in new product forecasting

o    Discuss and understand the concepts of modeling forecasts for new products

Case Study and Group Exercise – New Product Forecast Development:

Discuss and understand the concepts of modeling forecast for new products that fall into line-extension category.


Hands-On – Laptop required


Incorporate Effective Promotion Forecasts

o    Ascertain the error rates for promotion forecasts

o    Manage the promotion forecast process for unplanned and abnormal demand

o    Determine which factors, issues and considerations are necessary for developing effective promotions forecasts

o    Recognize the “cannibalization impact” that promotions have on base / open stock SKUs

Group Exercise – Using dummy variables to forecast impact of promotional activity and moving holidays.

A practical walk-through multiple regression forecasting technique that allows to track and forecast impact of promotional activity that simple forecasting methods cannot capture.


Hands-On – Laptop required


Implement Methods to Minimize Forecasting Error

o    Examine various forecast error metrics

o    Implement an effective use of forecast error measures

o    Determine the sources of forecasting error

o    Incorporate effective error analysis and communication to enable proper remediation

o    Analyze the exception driven forecasting process

o    Recognize the relationship between MAPE / Bias, Customer Service and Inventory KPIs

Case Study and Exercise:

·         Investigating relationship between Forecasting Bias / MAPE, Customer Service and Inventory KPIs

·         MAPE vs. WMAPE vs. Forecast Quality KPIs

Hands-On – Laptop required

Benefits to you

·         Develop and refine methods and processes to link forecasting to the growth of revenue

·         Identify the key elements of a successful forecasting process and function

·         Utilize an integrated approach to forecasts across key departments to enable more accurate forecasts

·         Incorporate market intelligence into statistical forecasting

·         Recognize various data patterns and their relationship with demand variability

·         Determine the necessary inputs and outputs of an effective demand plan

·         Explore various Qualitative / Quantitative Forecasts, Time Series and Cause and Effect Models

·         Implement methods to minimize forecasting errors

·         Deploy effective new product and promotion forecasts

·         Enhance your knowledge of statistical concepts that are full applicable to all forecasting software packages and systems

·         Learning from practical examples that explore the best and worst practices in forecasting

Companies already benefiting include:

Duration of Course

2 Days Day Course

Course details

September 9-10, 2013 Chicago, IL

 For more information about this course and how to register, please contact Emily Jones at or call 312.540.3000 ext 6714

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Further information

UK Enquiries: +44 (0) 203 002 3057

Non-UK Enquiries: +420 (0)2 5570 7246

North American Enquiries: +1 312 540 3000 X6714 or

US Enquiries: +1 312 540 3000 EXT 6445
Tatawan Plengsirivat - Director Training Operations - Professional Training North America -


"This course will allow my company to develop its existing strategies to a much greater and hopefully profitable level."

Managing Director, Tarmac