The demand latency hangover in retail is headline news this week. While it is not labeled as such, the dark clouds hang low over multiple retailers threatening profitability.

What is the demand latency hangover? Demand latency is the time from consumer demand shifts to visibility by a corporation to take action in their supply chain. Using conventional demand processes, the time to translate market demand to replenishment can be weeks, even months. This is too long to drive a profitable response. In contrast, a market-driven response (modeling based on market factors) drives a more accurate and quicker signal to align supply (a reduction of 70-80% in time to sense).

On May 16th, the Home Depot reported its biggest revenue miss in more than 20 years, lowering the Company’s future forecast for this fiscal year. The last quarterly miss of this magnitude was in November 2002.While the retailer attributed cold weather and falling lumber prices to a decline in fiscal first-quarter sales, the retailer is experiencing a shift in consumer spending away from investments in home repair to services like travel and entertainment. The question is, “Why did Home Depot not sense this shift sooner to give better market guidance?” The answer is the demand latency hangover.

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The story at Target is similar. Target reported flat sales on February 28, 2023, with actuals lower than the analyst prediction of a .2% increase in sales. The reason? Shoppers pulled back on discretionary spending. Previously, at the end of the quarter ending June 30, 2022, Target’s TGT net earnings fell 90% while operating margin declined to 1.2% missing the forecasted margin estimate of 2%. The company forecasted a $200M inventory write-off of inventory at the end of June 2022, that is still a work in process. Target, like Home Depot, drove a flat-footed response in their supply chain: they did not sense the sharp reversal in consumer buying behavior.

The traditional thinker sees supply chains as a sequential process to deliver reliable supply. The views are very supply-centric. The largest issue through the last thirty-six months of disruption was demand—sensing and shaping demand based on shifts in consumer buying behaviors at the speed of business. The problem is that traditional supply chain management solutions focus on mining historic demand patterns; and as such, are ineffective in translating shifts in markets at the speed of business.

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While over 90% of companies have demand management solutions for planning, less than 1% use market drivers and manage the demand flows to reduce demand latency (time to sense market changes). As a result, the effectiveness of supply chain planning and customer service rates lower than the traditional roles of manufacturing and transportation.

The lack of demand sensing, or the use of market insights, to drive the retail response results in a demand latency hangover. As a result, supply is out-of-step with shifts in market demand by weeks and often months. This results in mistakes in investor forecasts, inventory holding strategies, and merchandising programs.

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While many might wave their hands and say, “Couldn’t this be solved through the use of deep learning in artificial intelligence?” I say, not so fast. We must rethink our approach to demand management and unlearn the bad habits of using latent data to forecast future sales. There is no substitute for a market-driven approach to demand management, and those that are savvy clearly understand the difference between a market-driven approach and a marketing-driven or sales-driven program. The issue is that a market-driven approach to reduce the demand hangover, requires admitting that most of our current systems and processes to manage demand are legacy requiring a refresh.