Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
  • Measuring and Modeling Seasonality
Originally published: 19/06/2020 07:55
Last version published: 14/07/2020 06:37
Publication number: ELQ-30416-3
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Measuring and Modeling Seasonality

A set of very practical tools to analyse seasonal fluctuations

Description
Many businesses are affected by seasonal demand changes. They cause their revenues, expenses, inventories and resource requirements change through the year.

It is therefore critical to understand seasonality of your business in order to make accurate plans and budgets.

This publication contains a set of very practical tools to analyse seasonal fluctuations. They are accompanied by detailed explanations and professionally designed charts to help communicate the outcomes. The tools can be used for in-house analysis, management or shareholder reports, due diligence or consulting assignments.

These tools explain how to:

– Identify and quantify seasonal changes. This includes calculating multiple correlation between the years to determine how strong seasonality is.
– Visualize seasonal patterns on the charts
– Track current year-to-date sales (of different products) and compare them visually against historic ranges
– Remove the effect of seasonality from the numbers in the right way
– In the opposite case, apply seasonal indices to forecasted numbers
– Understand the company's performance relative to normal seasonal variation
– Build forecasts taking into account seasonality patterns

The methods provided in this publication are equally applicable to sales, expenses, balance sheet positions and physical measurements.

The publication also demonstrates how to properly build trend forecasts based on historic data. It is explained why commonly used CAGR rarely gives reliable future estimates and a method taking into account all interim reference points is given.

This Best Practice includes
1 Excel file, 1 pdf file

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Further information

Understand seasonality of a business, see the de-seasonalized performance, apply seasonal indices to forecasted numbers, visualize related figures

Businesses affected by seasonal variations

Businesses with a weak seasonality factor

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