AIDemand ForecastingSustainability


Jul 23 2020 ...

Fashion Sustainability

How digital tech embeds sustainability at the core of fashion retail

From the beginning, our mission at Nextail has been to make the fashion retail world a better place: A better place for retailers to do business, for their employees to go to work every day, and for customers to find delight in discovering and purchasing the items they love.

But we also know that the fashion industry has an ethical responsibility to make more responsible choices with the world’s scarce resources and that we will have to be able to do more with less. Just as COVID-19 and current market destabilisation has accelerated transformation in fashion, it will also heighten the call for profound accountability in terms of sustainability in our industry.

The good news is that as fashion retailers adopt digital technologies, they aren’t just becoming more agile, they’re also embedding sustainability into the heart of their operations, whether they realize it or not.

Fashion sustainability in 2020: A jagged path

The year began with lofty goals and commitments

2020 was set to be a year of dramatic transformation in our industry. The start of this new decade promised to bring us even closer to Jack Ma’s notion of the “new retail” in which online, offline, logistics and data merge into a single value chain.

At the same time, the voices calling for a more ethically responsible industry were growing louder, especially in relation to fashion sustainability. As a consequence, and mostly out of good conscience, more industry members were making true commitments toward environmental and socially conscious consumerism.

At the top of 2020, industry optimism and energy was sincere and palpable.

And then COVID-19 happened.

An abrupt change of fashion retail and customer priorities

At the outset of the pandemic, all efforts were directed toward survival, rendering other “non-essential” initiatives, including fashion sustainability programs, low on the list of priorities. Understandably, these cuts were made in order to simply keep the lights on.

But as we watched the world shut down region by region, cancelled orders and supply chain breakdowns revealed our industry’s disastrous shortcomings on a mass, and very public, scale. Already exacerbating existing challenges, we now know that the pandemic also led to an estimated value of €140 billion to €160 billion unsold stock worldwide more than double the normal levels for the sector. Clearing this excess stock, both to ensure liquidity and to make room for new collections, will become a top priority.

What’s more, profound shifts in consumer attitudes, behaviors, and demands caused by the pandemic are also forcing fashion retailers to change course. In fact, 67% of consumers report that they’ll be more cautious about the scarcity of natural resources.

Looking forward, brands and retailers will have to continue following through with fashion sustainability initiatives in order to create more engagement and loyalty, and thus be able to tap into such limited spending. Going beyond eco-friendly sourcing, fashion retailers must tackle environmental impact challenges head on by looking for opportunities to embed sustainability throughout the entire value chain.

Fashion sustainability comes into sharp focus

Overproduction and waste are a result of inherited inefficiencies

The economic, environmental, and humanitarian impact that our world has both suffered and contributed to is not the result of one sole factor. But overproduction due to supply chain inefficiencies has left many fashion retailers buried under their own inventory without the agility to change course.

Matching supply and present and future demand is a challenge as old as the industry itself. And overproduction happens more often when legacy processes, operations, and mindsets render retailers unable to leverage data successfully.

The previous generation of retail accumulated a variety of inefficiencies when dealing with everyday operations. Many of these inefficiencies are specifically related to a core area in retail: the buying and merchandising function.

Buying and merchandising teams deal continuously with new products and trends, but the function has lagged in the introduction of anything new regarding how they make decisions.

In many companies, these teams still rely heavily on legacy concepts like top-down planning, pushing products to the channels, following static processes with long cycle times, using basic automation and abusing averages. Making matters worse, these processes still require extensive manual work and the support of endless spreadsheets or expensive custom-made and fast-obsolete IT developments.

Left at a disadvantage, retailers have traditionally been forced to react to demand changes rather than being able to anticipate them.

As we know, the traditional adaptation strategy to these inefficiencies is to over-purchase and over-distribute products throughout channels and geographies to meet as much demand as possible. But conducting heavy planning and then forcing execution according to that plan just doesn’t work, and even less so considering how customer journeys become more complex and involve more channels. Products that don’t sell result in wasted resources and the maintenance of the vicious overproduction-discounting cycle.

How AI is solving the inefficient fashion model at its core

While fashion sustainability programs and budgets may have been among the first slashed to free up emergency cash at the height of the pandemic, fashion retailers who had previously prioritized the digitization of their operations may still have been adopting sustainable measures.

Nowadays, advanced technology, specifically AI and machine learning help retailers embed sustainability into their supply chains. This technology not only automates manual processes and reduces inefficiencies and human error, it also provides retailers with data-driven decisions that allow them to buy 20-30% less stock while covering the same level of sales.

Probabalistic demand forecasting contributes to fashion sustainability

A major element produced by advanced analytics is that of the probabilistic demand forecast. This type of forecasting allows retailers to evaluate all possible futures to see which one has the highest probability of occurring.

New systems based on probabilistic demand forecasts can calculate the probability of each SKU being sold across channels and locations while also considering business constraints, like, visual merchandising, available space, lead times and operational costs.

Traditional forecasting methods, on the other hand, rely on looking at averages or medians and can often create stockouts of fast-moving products and overstocks of slow movers. We have seen retailers lose millions of euros a year because they manually overstock best sellers in the apparently highest selling stores instead of using an approach that can capture the best possible outcome for all of their products and channels. And traditional planning just perpetuates these mistakes over time.

What’s interesting about probabilistic forecasting is that absolute accuracy is not that relevant to significantly reduce stockouts and coverage. By assessing every single possible combination of units of sku/store sales probabilities, probabilistic forecasts embrace uncertainty and accept that absolutely anything can happen. That way, retailers can buy less inventory in the future and still lose fewer sales than before.

In fact, we’ve seen one of our largest customers reduce their buy by 20% due to this type of advanced forecasting, while others have reduced their stock coverage by 30% and stockouts by 60%.

Much like the example of the bear chasing the two runners, the exact speed that you must run in order to survive is not that relevant, you just need to run faster than the other guy. The same is true with probabilistic forecasts. We need to calculate the relative probabilities of selling an additional unit at a specific channel and location, and then have that unit sent to the store that has the highest probability of selling it.

These new capabilities have the added benefit of helping fashion retailers to return to operational stability as quickly as possible post-pandemic. When decisions are driven by fresh data and not by old plans, retailers can better adapt to shifts in demand.

Where AI and sustainability intersect

What does this all mean? Fashion companies that invest in listening to their data and in automating decisions can better avoid overuse of resources. This approach will allow them to adopt more sustainable practices such as in-season buying and localized allocation, helping them to achieve higher sell-outs.

In conclusion, the same technology that makes retailers more agile, responsive, and cost-efficient in the face of demand shifts, are the same ones that help them advance fashion sustainability by avoiding detrimental overproduction and waste.

We help retailers become more sustainable at their core. Learn more about why our work has been recognized by the World Economic Forum, naming Nextail a Technology Pioneer for this very reason.