Advanced retail tech picks up where WSSI leaves off

How today’s retailers can overcome the limitations of legacy merchandising

A new era requires new solutions.

Today’s merchandisers need more. They’re facing a blended online and offline landscape and customer demands that make it feel like the goalposts keep moving.

To succeed, brands need business models that are dynamic, customer-centric, and anticipatory. But why are some reluctant to let go of legacy retail tools and methods?

One such method that deserves study is the “weekly sales and stock intake” (WSSI). A standard industry merchandising process, WSSI seems to have stood the test of time, but it’s not perfect.

Let’s look at WSSI and how advanced retail technology can step in to fill the gaps it leaves.

WSSI, a retail merchandising classic

The Burton retail group popularized WSSI, pronounced “whizzy”, in the UK back in the 1970s. Since then, merchandisers around the world have widely adopted it as a standard procedure for reporting sales and stock intake. So standard, in fact, you may use it and not even know it.

Chart showing the implication of WSSI in each step of merchandising planning
Retailers have used WSSI for essential steps in merchandise planning.

If you’re more familiar with the Open to Buy plan (OTB), think of WSSI as OTB performed weekly.

Retail organizations generally implement WSSI through large and expensive, dedicated packages or simply through Excel spreadsheets. Buying and merchandising planners break data down into weeks and display figures for the whole year.

Excel sheet showing an example of WSSI
An example of WSSI carried out over spreadsheets.

They then use the WSSI-generated figures for constant decision making based on past, present, and probable future achievement against budget. Senior management teams also use WSSI outputs for visibility and control for high-level decision making.

WSSI whizzes: Why people like it

WSSI has been popular because the premise is simple and it’s fairly easy for everyone to understand. WSSI reports usually bring together historical and forecasted sales, committed purchase, opening stock, stock coverage, and availability. Any actions taken during the week are reflected in next week’s report, so it continually evolves.

WSSI has an important role in helping ensure stock flow availability throughout the season and can add value, especially if done right. But just because it’s the way things have always been done, doesn’t mean it’s the best way.

What today’s merchandisers actually need

WSSI’s limitations and where advanced retail tech can step in

WSSI can provide a great deal of stock and budgetary insight but is limited to solving merchandising needs of simpler times.

Overview of merchandising needs, limitation of WSSI in solving those needs and tech solutions addressing those limitations

1. The ability to collect, store, and process massive amounts of granular data

Today’s merchandisers require a full, unified vision of stock, online and off. Since they need to account for thousands of products over several locations, they need a level of complexity that WSSI can’t achieve.

To avoid over-complicating reporting, merchandisers limit the level of detail required from teams in WSSI reporting, sacrificing granularity. For example, WSSI is typically calculated by channel, isolating stock without a unified view. How can merchandisers rely on an oversimplified version of reality, when they use it for constant decision making?

To be fair, some automated WSSI tools for data collection exist, which is a step up from doing it manually. But once you have the data, how can you wrap your head around all of it?

The solution: Cloud-based data storage

Cloud-based data storage makes it possible to store and retrieve massive amounts of retailer data and process it within a matter of minutes. Without having to sacrifice granularity for the sake of ease, retailers get the fully bottom-up view of stock they need to tap into full-sales potential.

Empowered merchandisers can zoom in on new metrics like daily SKU-store stock calculation and the sales impact of each out-of-stock SKU instead of relying on basic store clustering.

2. A stronger, more complete demand forecast

Customer demands change so fast that retailers need to anticipate them, not react to them. The problem is, the very nature of WSSI means having to overlook key factors that impact forecasting.

Blindspots can lead to stock misalignment and subsequent reactivity to unforeseen changes in demand. An incomplete view also means that WSSI can’t inform retailers of what might have been sold, had stock been correctly allocated.

With only past data to inform them, buyers and merchandisers must make interpretations and decisions based on intuition and trading experience rather than on larger trends and forecasts identified by richer, faster, more granular data.

The solution: High-performing machine learning algorithms

High-performance machine learning algorithms let retailers account for crucial factors like the weight of each day of the week on sales, size curves at store and product level, promotions, and even restrictions like key size, minimum displays, trips, etc.

Merchandising forecasts are further enriched when they account for sales that have potentially been lost due to low product availability, since these “lost sales” have such a huge impact on top line. With this understanding, retailers have a more complete view of true sales performance.

3. Forecasts for new products or those with missing historical data.

What would fashion be without the psychological draw of “newness”? Introduction of new product is fundamental, but can be especially tricky during the buying and first allocation processes.

The WSSI methodology only allows you to look at an item’s past sales and performance data to figure out how much more you need or where to send it.

The solution: Computer vision

Computer vision, a type of artificial intelligence, can identify “comparable” products that have similar features to build forecasts for new items. By extracting product features (e.g. print, category, size, long-sleeved, pleated, etc.) and grouping them by similarities, merchandisers can forecast for new products using historical data from comparable products.

Nextail’s demand forecast autocorrects when historical data is missing, for example due to data synch errors. Nextail will either “fill the gaps” by using other days as a baseline and projecting the weight of each day of the week, or by going back in time to when the product was available in the store.

And what about the times when there are completely new designs that have no similar products? Nextail can solve for that too. Contact us and we’ll be happy to explain how.

4. More flexibility and fewer calendar restrictions.

While there are certainly weeks with no surprises, there are outliers. Unexpected factors like weather events or even how weekends and holidays fall throughout the year, can make one week or year entirely different from the next, tremendously impacting numbers.

If you’re forecasting based on weeks with high volatility or something unexpected happens, you can’t rely on weekly data to give you an accurate picture of what to expect.

The solution: Advanced analytics

Predictive analytics can provide retailers with both short and long-term forecasts that include individual day, weekly, and weekday seasonality to improve their accuracy even further. Forecasting for a specific day of the week and even specific date is going to give retailers a much better idea of what they can expect in terms of sales.

Beyond forecasts, prescriptive analytics allow Nextail to output improved, data-driven merchandising decisions. With WSSI, all you get is information, and you need to decide, while Nextail decides for you.

5. To avoid and identify errors quickly.

At this point we know that WSSI reporting leaves a lot of room for key errors. Aside from the considerations it overlooks and the granularity it is unable to achieve, it can take up to 4–6 weeks of trading in a 26-week season for a “tolerable” forecast accuracy.

This vision already gives merchandisers an incomplete or erroneous picture of demand that they’re using for constant decision making. If you can’t identify errors and take them into account and counterbalance them, they’ll continue to haunt future forecasts and decisions.

Nextail’s demand forecast goes one step further by identifying discrepancies between the stock retailers think they have, and what they actually have, and store managers can play a strong role in making sure stock is aligned.

The solution: Machine learning algorithms

Machine learning algorithms make it possible to quickly identify SKU-level stock discrepancies, such as “ghost stock”, which is when stock shown in the system isn’t actually present in the store. Shrinkage like this has an important effect on replenishment decisions.

Nextail will alert store managers to conduct inventory on these items or, if the retailer prefers, will take the stock as zero and replenish automatically.

The final verdict

WSSI has been a hit for a long time, and for good reason.

The problem is that, while WSSI worked well with technology available years ago, limitations and inflexibility mean that retailers no longer gain a competitive edge. And while WSSI can aid them by monitoring stock levels and flow, it’s not great at working as an intelligence tool for driving merchandising decisions. It gives you the information, but you have to make the decisions.

Nextail helps retailers become more agile and “data-forward” in this new era of merchandising. Now they have an automated way of capturing sales and performance for a unified, bottom-up vision of stock. A better demand forecast means, better stock levels for improved margins, and the ability to manage budgets with reality, not guesswork.

Embracing uncertainty doesn’t have to be a scary process. Visit our website to find out how you can become an empowered, data-forward retailer with Nextail!

Nextail is a smart platform for retail merchandising. Developed by retail experts, it delivers agile data-driven decisions to meet increasing consumer demands.

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