Oct 5 2021 ...
Understanding the Basics: A Guide to Bottom-Up Merchandise Planning
What is meant by a “top-down” approach?
At the risk of sounding trite, the best way to understand what a bottom-up approach is, it’s important to understand what it is not.
Traditionally, when making decisions about how much product to produce, buy, allocate, and later rebalance, retailers used whatever data they had on hand to determine what sales performance they could expect.
Generally speaking, that data was historical data on the past sales performance of products or stores. Then, using traditional tools such as open-to-buys or WSSIs, retailers would use these estimates to make core decisions about their inventory management. In other words, these decisions were high-level, generalized estimates usually being dispatched from HQ, and hence a “top-down” approach.
The challenge with over-reliance on “top-down”
In the past, it made perfect sense to approach merchandising solely from the top down. Retailers had to use the data they had on hand and were able to collect and interpret with currently available tools. But no matter how good your data is, or how much of it you have, it’s only as good as the tools you use and the approaches you take.
A major issue with pure top-down approaches is their distance from reality. By basing decisions on oversimplified data and then forcing them down on current merchandising execution, it’s basically a recipe for misalignment, especially when reality doesn’t unfold as you expected it to.
For example, what happens when some external factor, such as bad weather, keeps customers from heading to physical locations in the middle of the season? What if demand over online channels suddenly picks up out of nowhere?
Top-down approaches defined months earlier don’t have “listening” capacity necessary for adjusting course as reality plays itself out. And this is essential for keeping up with the most subtle changes that occur because of their much bigger impact on the bottom line.
Overgeneralization leads to overcompensation
Since top-down approaches can’t measure subtle real-time changes in demand across a retailer’s network, how have they dealt with this uncertainty in the past?
In order to make sure they can meet demand wherever it appears, retailers relying on top-down approaches have overcompensated by producing, buying, and allocating in excess. The problem is that later in the season, having excess stock leads to the common retail culprits of overflowing storage, heavy discounting, and worst of all, waste. Bad for the environment and bad for the bottom line.
The role of bottom-up, hyper-local, intelligence
For flexible and agile merchandising operations, decision-making has to work the other way around, or in the very least use bottom-up intelligence to inform high-level strategy. Advanced tech like AI and predictive analytics make it possible to collect, manage and process the data necessary to account for the demand of each and every SKU-store-day combination and feed that information upward for timely decision-making.
The benefits of incorporating hyper-local, bottom-up intelligence address many of the drawbacks of relying solely on a top-down approach.
1. A better grasp on reality
Since you are able to track data on each and every SKU across your entire network, you are so much closer to the reality of demand and can capture changes and shifts and adapt to them faster. So instead of buying products and trying to allocate them to the best of your ability (the old approach), you can make allocation decisions based on what is selling right now, essentially decoupling the buying and allocation processes altogether.
This truer vision of the market enables you to make subtle changes to your strategy or even bigger decisions such as increasing the frequency of product introductions, for example.
2. No need to rely on overgeneralizations
The nature of top-down decision-making is overgeneralized, simply because you’re trying to predict the future with very little actual information.
A bottom-up approach allows retailers to work with data at the most granular hyper-local level. Nextail can take this a step further and actually aggregate this data taking into account business-specific rules such as differing store capacities, visual merchandising requirements and more.
3. Reliable demand forecasts and predictions
The more granular the data you are able to collect and process over time, the stronger forecasts and predictions will become if you are applying machine learning algorithms such as predictive analytics. Of course, the more confidence you have in these predictions, the more likely you are to change strategy if necessary.
4. Unified visibility into sales and channel performance
Working with a bottom-up approach also makes it possible to gain visibility of performance across your entire network, including physical stores, online channels, and even warehouses. By centralizing this information, your whole team is privy to insights, and not just limited to one part of your business or another.
Within Nextail, for example, HQ users can access daily reports on top products, top stores, key KPIs, with store teams accessing the same reports from their smart devices.
5. Greater Customer-Centricity
Simply put, a good customer experience depends on the availability of the products that customers want to buy. Since each store and channel profile is different and continually changing, you must be able to detect even the most subtle differences in demand. With an agile retail methodology in place, you can make hyper-local assortment decisions, putting customer demand at the very center of your strategy.
Using a more efficient, less generalized approach to merchandise planning, retailers are able to increase their sustainability by confidently reducing safety stock and by ending up with less excess stock at the end of the season. With Nextail specifically, they can reduce order volumes by 20% while lowering stock-outs by 60%.
If you’re ready to take the next step in transforming your fashion retail operations, here are 7 essential considerations for what to look for in a merchandise planning platform.