Forecasting in a Time of Uncertainty (COVID-19)

Learning Objectives

• To identify the purpose of forecasting
• To identify the problems of traditional approaches to forecasting
• To identify ways of becoming more agile in your forecasting
• To recall the Principles for Agile forecasting
• Ti recall the Methodology for Agile Forecasting

Course Description

In the current economy, there is a need to do more with less. As a result, analysis has to be quick, efficient, and provide meaningful information. Accountants are creating more forecasts because of the fast-changing business environment, yet have less time to create forecasts.

The days of “last year plus 3%” in forecasting are gone. The COVID-19 pandemic as pretty much destroyed planning and forecasting as we know it. So what do we do? And how can we forecast in a time of uncertainty? Our “Forecasting in a time of uncertainty” CPE webinar will help you build a path through the fog of uncertainty. We cannot solve all your problems, but we can give you some tips and guidelines to help you move in the right direction.

Our webinar will cover topics including:

• Revisiting the purpose of forecasting
• The problems of traditional approaches to forecasting
• Applying Agile principles to forecasting
• Criticisms of the Agile approach to forecasting
• Identifying the Levers of Performance 
• Using simulation to test possible and likely outcomes
• Using Relative Forecasting

Forecasting in a time of uncertainty” will help you become more responsive and adaptable in your forecasting methods and it will help you stress-test a wide range of possible outcomes, but there are no magic solutions.  Forecasting in a time of uncertainty is, by definition, unpredictable. This course will not give you a definite prediction – no one can do that – but it will help you get better at forecasting.

Overview

• Revisiting the Purpose of Forecasting

  10 mins
• Becoming more Agile

  17 mins
• A Methodology for Agile Forecasting

  28 mins
• Tools for Forecasting in Times of Uncertainty

  31 mins
• How do we know our data is normally distributed?

  38 mins
• Relative Forecasting

  53 mins