What is demand sensing and is it now more important than ever?
I like telling stories; sharing real-life examples that help us illustrate and comprehend ideas and concepts that may otherwise sound too theoretical.
Today’s story is about a well-known, iconic consumer packaged goods brand in Canada…Robin Hood Flour. For those not familiar with it, the Robin Hood brand name has been around since 1909. The flour, made by the Horizon Milling division of Cargill, is part of a product line that is available through both foodservice/industrial and retail distribution.
Back on April 22nd, CBC News published an article about some challenges that the company was experiencing as consumers were struggling to find Robin Hood Flour on store shelves. I am a frequent buyer of this product, so the article caught my attention.
Figure 1: Surge in demand sees Robin Hood flour running in some troubles
Let’s delve into the situation that contributed to the company’s challenges. I know some of you may say, “It’s easy to look back and reconstruct the story when you know the ending.” Absolutely, it’s easier! The purpose of sharing this story is not to poke holes, but instead to question whether this would (or could) happen to us as well and learn from it.
For a minute, let’s be retrospective and imagine ourselves in a similar situation. For those of you who like movies, this might seem like the plot of “The Butterfly Effect,” the 2004 sci-fi thriller (starring a young Ashton Kutcher).
Here’s the timeline…
Event 1: Mid-March
Figure 2: Google searches “How to Make Bread”
Figure 2 provides a snapshot of Google search interest for the phrase, “how to make bread.” In the US, searches increased significantly after President Trump declared a state of emergency on March 13th. Similar spikes occurred in other countries, including Canada.
Event 2: Late March
In late March, we observe a similar spike on Amazon. People are searching for flour and yeast…interesting!
Figure 3: Amazon searches “Buy Yeast and Flour”
Event 3: Early April
Let’s continue connecting the dots…
On March 31st, Stackline, a retail intelligence and software company based in Seattle, WA, published a cool article that ranked the top 100 fastest growing and declining categories in e-commerce.
As you might expect, the number one fastest growing category is disposable gloves, a category whose e-commerce sales in March 2020 grew by 670% versus the same period in 2019! But, unexpectedly (at least for me), the second fastest growing category is bread machines, with a 652% increase. Bread machine sales up, searches for how to make bread spiking, and searches for yeast and flour increasing…what a cool coincidence! Amazing!
Figure 4: Top 100 Fastest Growing & Declining Categories in E-commerce
Event 4: Late April
Finally, on April 22nd, CBC News publishes an article highlighting that Robin Hood’s supply chain challenges are not the result of a flour shortage but instead the result of packaging constraints.
According to CBC’s source, Robin Hood had plenty of flour but had run out of bags in which to package the product. This is why the company decided to temporarily sell its product in nondescript white bags, instead of its iconic yellow bag, in some stores.
What a sequence, folks.
This is the speed of today’s data-driven economy. Could Robin Hood and its packaging supplier have done better in anticipating a demand surge? Maybe. But criticism is not the point here. This example illustrates that the data economy in which we are living moves at an extremely fast pace.
Consumer behaviour changes so rapidly that every player in the supply chain must find ways to be connected to real-time data if they want to understand and prepare for what is happening in the market now. This real-time connection must include demand and supply signals.
Now, let’s connect this story with the subject of this article…demand sensing.
Demand sensing is an activity that sits under the demand planning umbrella. In a nutshell, the core principle of demand sensing is that planners can do a better job of anticipating and planning for short-term changes in demand by considering what has happened in the last few hours or days. Ideally, demand sensing reduces latency issues associated with traditional time-series forecasting methods which assume, correctly in most cases, that history will repeat itself over longer periods of time.
Would demand sensing have helped Robin Hood’s packaging supplier get an earlier signal about most recent consumer demand patterns, and consequently trigger a heads-up to expedite its supply chain?
Do you think that the very recent past is the best predictor of the very near future?
Does your company have the tools to monitor your supply chain performance near real-time?
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Until next time supply chainers!