Mid and long-term demand forecasting in times of COVID-19

The impact of Covid-19 poses many challenges for supply chains. One of these challenges is the capacity of forecast models to remain accurate in volatile circumstances. When using statistical forecasting, demand prediction is based on historic sales. This method works effectively in an environment with stable conditions. Due to Covid-19, the environment is anything but stable. Sales drop tremendously (e.g. in the tourism sector) or have huge peaks (e.g. at grocery stores). Since we have never seen the impact of a global pandemic on demand, forecast models that rely purely on statistics will perform poorly. This happens because statistical models may not be quick enough to incorporate the new information, or because the models rely heavily on previous behavior. Therefore, we propose a method to incorporate information about the future into the statistical baseline forecast.

Proposed solution: rubber duck curve

The proposed solution is the rubber duck curve forecast adjustment. The figure below is called the rubber duck curve, because it has the shape of the beloved bath toy. The idea is simple and intuitive. The method divides the time horizon into four moments: regular sales, disruption, recovery, and new normal. The method assumes that the regular sales will follow the patterns of the statistical forecast in a stable environment. Then, the impact of Covid-19 kicks in, causing a decline in demand. A period of disruption starts. As measures to control the infection are eased, the sales are expected to recover. This recovery phase can be perceived in one period, or over a period of time. It is also possible that there is no recovery and the new normal picks up directly from the disruption phase. The new normal is defined as the period with new stable sales. From this point on, the statistical forecasting methods can take over again. Beware that the impact of disruption can also have an opposite effect. Thus, a positive effect on the sales.

Application and implementation

The method is a great concept, but how do you apply it to your forecasting process? To be able to apply this method you need to have knowledge about the expected impact of the virus. This knowledge is represented by three key moments in the curve: start of disruption, start of recovery, and start of the new normal. Each moment is characterized by a starting point in time, and an expected percental change in sales with respect to the forecast before the disruption started. If you have identified each moment, you can impute the expected behavior to your statistical forecast. The statistical forecast incorporates the trend and seasonal effects, so the future forecast has the right demand pattern adjusted with the knowledge about the expected impact.

The way in which these moments are specified is through a template. Information about the expected disruptions is gathered here. The template is flexible to allow the specification of each of the moments at the right hierarchy level. This ensures that the adjustments match the level at which companies can expect the disruption. Additionally, this allows for a fast adjustment of the entire portfolio, without having to provide an expected change at the lowest hierarchy levels.


The figure below shows an example of rubber duck adjustments and a filled-in template. The first columns of the template indicate the hierarchy level which is impacted by Covid-19. Then, the next three columns indicate ‘when’ a new phase starts, and the last three columns indicate what the percental change is.  The graph shows that starting on May 2020 there is a change of -30% in the expected demand; the disruption phase has started. Then, in November 2021 the recovery period starts with a change of 15% in comparison to the reference forecast. Finally, the new normal starts on February 2021 and the sales are expected to stabilize again. From this moment on, the statistical forecasting method can take over again and rubber duck adjustments are no longer needed.


Adding the rubber duck curve to the statistical forecast can provide a good foundation for future demand planning. Consequently, there is no need to adapt each individual forecast in an enrichment tool manually due to unrealistic mid/long term statistical forecast figures. Furthermore, the template can be updated easily during each forecast cycle based on the latest insights. Lastly, the rubber duck curve can be implemented fast and can be easily reapplied in case of a another Covid-19 wave. 

In conclusion, combining the power of statistical forecasting with the knowledge about expected impact on your business as a rubber duck curve, provides you with a Covid-19-proof method for forecasting.

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