Do you remember the movie “A Beautiful Mind”?
The film is based on mathematician Dr. John Nash’s life, and, during one part, attempts to explain how Nash got the idea for his equilibrium theory as a part of game theory. In the scene Dr. Nash is at a bar with three pals, and they are all enraptured by a beautiful blond woman who walks in with her friends.
While his friends banter about which of them would successfully woo the woman, Dr. Nash concludes they should do the opposite – Ignore her. “If we all go for her,” he says, “we block each other and not a single one of us is going to get her. That’s the only way we win.” That’s the moment when he formulated his idea.
The idea that pops into Dr. Nash’s head at that moment is very instructive in the innovation process. Often, real innovation happens because you are in a situation and you’re paying attention, or listening, and you just connect the dots. It’s the subconscious mind at work, finally coming to grips with something you’ve likely been pondering for a while.
It’s a great film and a beautiful story.
Here’s another beautiful story of essentially the same approach that was used to create the breakthrough thinking and solution in demand planning at store level – which, as we know, drives the entire Flowcasting process.
In retail, forecasting at store level, systemically, has been a major challenge for a long time. Not only do most retailers have millions of store/item combinations, they also need to deal with virtually every imaginable sales pattern. But, by far, the largest challenge, is the large number of slow selling items – accounting for 50%+ of virtually any retailers assortment.
The main issues with slow selling items is twofold: finding a selling pattern amongst sparse data, and ensuring that the forecast reflected the somewhat random nature of the actual sales.
The hero in our true story is named Darryl. Darryl is the architect of the RedPrairie Flowcasting solution (now part of JDA) and, specifically, the profile-based, randomized integer forecasting approach that has simplified retail store level forecasting to a beautiful, elegant, intuitive process that does something incredible – it works and is very low touch.
The baseline forecasting process works like this:
In Darryl’s approach, unlike that of other attempts, he first calculates an annual forecast by item/store. Then simple user defined sales thresholds automatically doing the following:
- Determine what time period to use to forecast in (weeks, months, quarters, semi-annual)
- Determine which level of already pre-aggregated history to use to spread the annual forecast in the time period
- Determine whether to convert the forecast into integers – which he randomizes by store/item, ensuring that the same item across many stores will not have an integer forecast in the same week
How did he think of this? Well, similar to Dr. Nash, he found himself in a situation where someone said something very interesting and it sparked his thinking and helped him connect the dots.
Rumour has it that Darryl was walking around a Canadian Tire store years ago and was talking to the owner of the store. They approached a section of the store and the owner grabbed a particular product and said something like, “I don’t know when we’ll sell these, all I know is that we’ll sell two every quarter”!
BOOM! The idea for a different time period for forecasting by item/store popped into Darryl’s head and this event triggered the thinking and eventual development of the baseline forecasting process.
This is a significant development – so much so that it has been patented and is now available with the JDA product solution set. What it has done is obsolete the need for multi-level forecasting approaches that, to date, have been the norm in attempting to create store/item level forecasts.
This approach is simple, intuitive, elegant and is computationally blazingly fast – another key requirement in retail store level forecasting.
Oh, and it also works. We implemented this exact approach during our very successful implementation of Flowcasting at Princess Auto. The solution is forecasting items in all varying time periods and is creating store/item forecasts for products that sell from 1 unit a year at store level, to over 25,000 units a year.
Even more important is that the people that would become demand planners (with no prior knowledge or experience in demand planning) would understand and become proficient using this approach. Just another benefit of simplicity.
John Nash looks like any other bloke. But, without a doubt, he’s got a beautiful mind.
The hero in our story, Darryl is just like John. If you met him, you’d immediately think he’s another Vermont farmer who’s good with hydraulics. But behind those coveralls and hay-stained hands is…
A beautiful mind.