How to Forecast Demand Despite COVID?

The article below is a summary of one of my LinkedIn posts. If you are interested in such debates, let’s connect!

I would like to thank the following people for their insightful remarks in the original discussion: Valery Manokhin, Nick Cronshaw, Robert van Dijk, Thomas Meersseman, Wassim Tabbara, Archit Patel, Chris Davies, Joris De Smet, Aleksandra Barteczek, Paul Balcaen, Karl-Eric Devaux, and Rohit Anand.

❓ COVID shook supply chains in 2020. How should you forecast future demand when everything is changing and you lack relevant data?

🥉Flag Outliers (Simple Solution)

The most straightforward response to an unusual demand-period is to flag it as an outlier. As the demand over the COVID months was exceptional, we can assume that it is not representative of future demand (in a post-COVID world). A safe bet would be to flag those months as outliers in your forecasting engine. Often, overwriting the demand in periods with outliers by the latest previous forecast will do the trick (see my article on outlier detection for more info).

❗ Pay attention to seasonality H1 2020 was heavily impacted by COVID. Even if you manually tweak H2 2020 forecasts, H1 2020 might impact early H1 2021 as your model will learn and apply new seasonal parameters. Cleaning H1 2020…