Crossing the Great Forecasting Divide Part 2 – Consumption Sensing is New Forecasting Frontier

In my last post, I focused on how retailers and consumer products companies can triage through the worst of the COVID pandemic as consumers were adjusting to a stay-at-home lifestyle. I discussed using a hybrid model of humans and AI/ML-based approaches, delivering science-as-a-service, to bridge the gap and react quickly to the dramatic swings in demand.

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Crossing the great forecasting divide – make the transition with manual processes and AI/Machine Learning

It is true – forecasts from existing demand planning systems, built largely on historical data, will be significantly off, both during and after the pandemic. To quote Yogi Berra, “It’s tough to make predictions, especially about the future.” And right now, we are experiencing an event that hasn’t happened since 1918 providing very little historical experience to draw upon.

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Anticipating shifts in the Supply Chain landscape from COVID-19

The current global pandemic has not only forced immediate readjustments for CPG but will undoubtedly disrupt and transform supply chain business models permanently – or at least until the risk has reduced to an acceptable level – as consumers adapt to a new world of virus avoidance.

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Managing Fashion Retail: A Plan Forward

For almost all fashion-based retail, the current Coronavirus-related disruption is characterized more by what we don’t know versus what we do know. Length of store closures, consumer reaction when stores re-open, amount of sales transference between channels, and overall impact on demand are key unknowns. However, what is known is that when customers return to stores, there will be a lot of displaced inventory that will need to be managed. Retailers will come out of this crisis with an eye on cash position and costs, therefore managing that inventory and attracting customers to shop again will be of paramount importance.

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