The COVID-19 pandemic has radically impacted consumer behavior, creating enormous disruption and uncertainty in many industries’ operations. Companies have seen demand surge, plummet, or sometimes both, and the resulting confusion makes it hard for their supply chain teams to react.
When a highly disruptive event like this occurs, historical data might be insufficient, even irrelevant, for modeling the future. The consequences of this temporal break are far-reaching — time series forecasting methods, a mainstay of the forecaster’s toolkit, may no longer be a viable prediction option, as they rely on the assumption that the past is indicative of the future. But today, the future is unlike the past, which means forecasters need to rethink their approach.
This article contains our data science team’s collected thoughts on how to improve your demand forecasts in the wake of COVID-19. If you’re a fellow data scientist, check out these tips to help you support your organization in these challenging times.
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