AIBest PracticeDemand ForecastingSustainability


Jun 10 2022 ...

Shoptalk Europe 2022: Innovation, sustainability, and transparency

Nextail CEO & Co-Founder, Joaquín Villalba, was invited by Shoptalk Europe 2022 organizers to take to the stage as a featured speaker and panelist on the “Innovative Supply Chain & Merchandising” session of the event.

The track featured solutions focused on supply chain and merchandising optimization such as Nextail, Whywaste, Evrythng, and Competera. Moderated by Anne Mezzenga of Omni Talk, the panel touched upon how organizations are addressing supply chain and merchandising challenges, recommendations for developing more permanent post-pandemic solutions, as well as improving environmental responsibility.

Common causes that slow down retail decision making

Addressing the Shoptalk Europe audience, Joaquín described how the common culprits responsible for slow retail decision making, a lack of data and an excess of people involved in the decision-making process, are already being solved thanks to digital decisions enabled by a bottom-up approach (vs. a traditional top-down approach) to leveraging data.

Retailers who do so are able to remain completely flexible by decoupling their buys from their assortments and allocations in-season to actually manoeuvre their inventory to meet demand based on product performance right now, not based on what they thought it would be like 9-12 months ago.

Two examples of capabilities enabled by such a bottom-up approach to this effect were highlighted:

  • Hyperlocal, probabilistic forecasts
  • Dynamic fallbacks

Hyperlocal, probabilistic forecasts identify hidden seasonality patterns

Giving the back-to-school season in Spain as a typical seasonality context, Joaquín explained that it, like other well-known and widely applied seasonality assumptions, tend to be.

On the other hand, hyperlocal and probabilistic forecasts could make it clear, for example, that what might be true in one point of sale, isn’t necessarily true for all. A particular mall might increase sales for backpacks significantly more than in a flagship store, as shoppers prefer the convenience of air-conditioned, indoor shopping during summer months. And an ecommerce store for the same retailer might not show much difference in sales at all during this period. Perhaps the opposite would be true in the winter during end-of-season sales.

These hyper-local differences become clear when data is used to automate and digitally transform these types of insights and decisions.

Dynamic fallbacks for predicting demand when data is scarce

To solve for a lack of robust data for calculating predictions, another typical hurdle in the path to quick retail decision making, Joaquín described how dynamic fallbacks can save the day in a digital world.

For example, size distribution differs at each point of sale and is calculated through the use of historical data on product performance at each location. But when introducing new products or product families, this can be a challenge since this historical data simply does not exist.

When this happens, merchandise planning solutions such as Nextail, use product performance data from comparable products identified in the same point of sale to predict demand for different sizes or look at the same product in similar points of sale.

But what if there are no comparable in-store products? In this case, algorithms can expand the analysis dynamically to similar products or groups of products at the country or regional level.

Just another way in next generation solutions provide retailers the flexibility and fast decision making they need to adjust in-season.

Automation reduces misalignment and decision cycles

When too many people are involved in decision making, especially that related to inventory allocation, Joaquín mentioned that this becomes particularly tricky when interests are misaligned.

When decisions are digitally transformed, they can be automated based on meritocratic principles in which inventory is allocated to where it is most likely to perform. That way, conflicting interests, as well as the decision cycles themselves, can be reduced.

According to Joaquin Villalba, Nextail CEO, “In this reality, you don’t need to interpret the data, the algorithms do. You don’t need to agree on potential future scenarios. The system does.”

An example of a major retailer using Nextail to achieve such flexibility is Hackett London. On the one hand, the brand has reduced the high stock levels and shipment frequency that they used to rely on as a method to protect against uncertainty and slow decision making.

And through automation and a strong predictive system, they are spending 75% less time carrying out replenishments. The brand has even been able to reduce international transfers by 50%, increasing the sustainability of their operations.

Achieving transparency & a call for new talent

Finally, merchandising and supply chain transparency, from a Nextail perspective, is not just providing data for all, but rather in a way that drives decisions.

Since Nextail works with a wealth of cross-customer data, it can be aggregated and anonymized so that it can be used to better understand what is happening both at the most granular level and across the industry as a whole.

This type of transparency can be used for making macro predictions and for helping retailers truly take high and low-level decisions for the good of the industry.

“In the end, digital decision making is a way in which you are more agile, so you can less of the planet’s resources to provide better customer experience,” Joaquín remarked.

But what will really make the difference is retailers heading the call for the appropriate talent who will be able to help, whether they be data scientists, analysts, or digital experts who can ready teams for digitally transformed, automated decision making for the ultimate agility, sustainability, and transparency. You can read about the retailers leading in digital talent hires here.

Find out how Nextail is helping retailers embed sustainability into the core of their merchandise planning operations.