When it comes to the retail and fashion industry, speed and agility are key to success. For businesses that rely heavily on riding the waves of new and emerging trends, it is imperative that they employ predictive models, from monitoring trigger events to periodic forecasting, in order to help them stay ahead of their competitors.
Fashion retailers are aware that it is no longer enough to monitor trends and track purchases in the present. The most pioneering companies are looking to the future by deploying data-driven predictive models that improve supply chain efficiency, cut waste and — crucially — keep them ahead of the competition.
Perhaps the most transformative effects of technology in regard to forecasting, are not on the demand side, but in logistics.
Predictive logistics for proactive supply chains
For retail clients moving goods around the world, there is nothing more important than predictability, according to Rafael Almoguera, Global Account Manager (Lifestyle) at Maersk. “What is vital is that when you say it will be 28 days, it is always 28 days sharp,” he says. “That way, the customer can plan around those timeframes and optimise their supply chain.”
To ensure that kind of reliability, retailers are increasingly asking their logistics partners to predict potential disruptions, such as a strike, a storm, or congestion at a port.
“(Companies) do not want to wait a week before finding out there could be a delay,” says Almogeura. “They want to know immediately, so they can start planning.”
Retailers adopt technology-driven solutions
TechStyle Fashion Group is one such company that invested early in data science. The global fashion retailer, whose brands include Fabletics, Savage X Fenty and ShoeDazzle, is perhaps best known for its membership-based model, which combines data on industry trends with a move towards personalisation.
This model allows the company to collect data on customer preferences and formulate predictions on how demand patterns are likely to change.
“We are constantly looking for trends in customer behaviour and feeding that to the buyers, who are then putting in the orders. That’s not fully automated — it’s still a creative process — but it is data-driven,” says Meera Bhatia, President of Expert Services at TechStyle Fashion Group.
Bhatia says the company’s technology achieves this by constantly tracking the location of its products, be they in transit or at the destination warehouse.
“Say someone puts in an order on our site,” she explains. “Our software will look at our multiple warehouses and say – based on where the customer is and inventory availability – which warehouse to send that order into. Then we can get the product to the customer as quickly as possible.”
Additionally, TechStyle’s data analysis platform can also optimise the transit process in real time by assessing which carrier should be used to send any given product.
For these kinds of fashion retailers, their core business really is in logistics.
Finding a competitive edge
Greater efficiency through better forecasting can also give a boost to companies’ working capital. “To give an example, you may have US$50m permanently on container ships moving from one place to another,” explains Almoguera. “If you can reduce the transit time and the lead time on your supply chain, you may be able to reduce that to US$40m on the water.”
This also lowers the risk of items going out of fashion before they are delivered. “For these kinds of fashion retailers, their core business really is in logistics,” says Almoguera. “The capacity to have shorter cycles, compared with their competitors, relies on shorter lead times and optimised supply chains. To have that, you need to be able to predict ahead.”
How predictive models benefit businesses
Data-driven technologies used for business forecasting can offer significant insights, support efficient supply planning and ensure streamlined supply chain operations through:
Inventory management: Predictive analytics and forecasting solutions allow brands to study trends and plan inventory based on possible increases or decreases in demand expectations.
Resource allocation: Studying market predictions provides businesses to alter production scale, decide on logistics options and target key markets.
Customer satisfaction: Being prepared for changes in demand allow retail and fashion to reach markets ahead of the competition and provide the end customer with faster delivery times.
Adopting sustainable logistics through localisation
Sustainability concerns have been growing across all sectors in recent years, and this trend has only accelerated since the start of the Covid-19 pandemic. When virus containment measures brought global trade to a near standstill, companies were urged to simplify and localise their supply chains.
“A move towards localisation is the only way to reach the sustainability goals the fashion industry has set for itself,” says Lisa Morales-Hellebo, Co-founder and General Partner of New York-based REFASHIOND Ventures.
Morales-Hellebo argues that moving away from fast fashion is an essential next step, but one which will only come about if the infrastructure is localised. That is not necessarily bad news for fashion retailers, however.
Almoguera agrees that near-sourcing is a “clear trend”, with European companies increasingly sourcing goods from nearby Turkey or Morocco, for example, rather than more distant markets. “It means we might have to be more efficient when it comes to door-to-door operations, optimising assets by not having huge container ships moving all the time,” he says.
Thus, a new norm is being established. As businesses build more resilient supply chains, employ predictive models and bring production closer to home, they are increasingly able to respond to trends and world events faster than ever before.
We are constantly looking for trends in customer behaviour and feeding that into the buyers, who are then putting in the orders.
Produced by (E) BrandConnect, a commercial division of The Economist Group, which operates separately from the editorial staff of The Economist and The Economist Intelligence Unit. Neither (E) BrandConnect nor its affiliates accept any responsibility or liability for reliance by any party on this content.
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