Overtaking Zara’s supply chain

If consumers can be inspired to buy and complete their purchase in under 60 seconds and fast fashion brands can get new lines into store within three weeks, this is how you adapt your supply chain to be as responsive.

Sixty seconds. That’s how long it would take Blair Eadie’s followers to purchase the latest look from her Instagram feed.

While you may not know Eadie, more than one million young women are intimately acquainted with her wardrobe, and they slavishly attempt to emulate it.

Blair Eadie has over 1M Instagram followers

Blair Eadie has over 1M Instagram followers

So, what if tomorrow, Eadie were to showcase a dress from your label? Cha-ching, right? Right… but only if you had the right product in the right stores at the right time.

That’s how commerce works today. GenZ is motivated not by ads or product labels but by what they see in their social media feeds. If they like it, they buy it immediately with just a few clicks.

And what could be more depressing—to profits, if nothing else—than to miss out on sales because your company can’t keep pace with consumer buying habits? For too many retailers, that’s exactly what’s happening every day.

What about Zara

The mantra of every fashion label is read and react, but not many are able to do it in real time. Pulling weekly sales results and applying those learnings to the next season isn’t enough any more—not when the most nimble competitors out there are continuously monitoring sales data with which they develop new items that will be in stores in less than three weeks.

For anyone who’s been watching Zara, this isn’t news. The fast fashion house is regarded as the gold standard for reading and reacting.

Zara’s data analysis skills and the proximity of many of its factories are certainly a critical part of the equation, but so is JIT production.

It is because Zara achieves an extremely fast turnover by producing every product in small quantities that new designs are can arrive in store so quickly.original_1496

Zara’s JIT inventory procurement and local sourcing is underpinned by highly developed forecasting systems.

Essentially, store managers collect sales data and current trends on a daily basis and send it back to head office where the information is analysed by its leading designers, who then update Zara’s clothing ranges.

At Zara, they’re afraid to not make a quick decision. At Gap, they’re afraid to make the wrong decision. They analyse and wait, analyse and wait. At Zara if it’s the wrong decision, they’ll recover and move on.

If a silhouette stalls, they’ll pivot to the new hot body. If a color is killing it, the next delivery will be awash in it. And while that sounds simple, the current systems most fashion firms have in place aren’t capable of pivoting quickly enough.

To be like Zara you need to know how you’ll marshal your resources and supply chain partners to create alternate products, if necessary.

The most effective way to do this is to pull down the walls that often exist between departments.

Building in agility

This level of interconnectedness requires data from existing PLM systems, to reach deeper and wider into the supply chain to centralise and share information.

When something’s selling, everybody needs to know because there’s a lot of things that need to be done from start to finish to get more product.

Imagine a scenario in which a factory can proactively call a brand to remind them that they have more fabric for a particular style that’s selling out rather than passively waiting for an order to materialise.

Communication like this will help fuel the on-demand supply chain to where there will be smaller deliveries that will hit stores every three weeks.

To ensure those goods are on the mark style wise, there are ways to shorten the production timeline, but moving your sourcing locally may not be an option, so JIT becomes the fall-back.

Take Uniqlo for example. It realised very quickly if it was going to expand domestically and globally, it would need to forge a partnership with its largest textile producer.

But the textile producer’s factories operated all year round, while Uniqlo worked with seasonal and not annual commitments.eu-pc-150928-stores-detail-uk-westfield-stratford-city

If a range of clothing did not prove popular with customers, they simply cut the order so they were not left with excess stock.

While this method resulted in some waste, it is a cheaper model because Uniqlo don’t have to pay the cost of operating the factory all year round.

Uniqlo solved the problem by taking the bold step of tweaking its supply chain model to accommodate the textile producer, by adopting Just-in-time inventory procurement.

It does this by analysing weekly sales patterns at all its stores and supplying clothing ranges just before they are likely to need them, which means Uniqlo hold less stock and rarely over-order.

In the unlikely event that an item of clothing does not wow its customers, it is returned and re-purposed. So if, for example, men’s cardigans go out of fashion, Uniqlo might simply convert them into jumpers or scarves, meaning that bottlenecks in the distribution section of its supply chain are drastically reduced.

If a product is selling well, Uniqlo bring in the same product and have a stable of similar styles in the same fabric to keep the floor fresh. It’s fit-approved and product-tested and ready to cut to speed production.

Also, designing into core fabrics that are positioned at the factory means merchandisers can wait until as few as three days in advance to decide which bodies and sizes to cut.

Shipping is another area where companies could rack up a time and money savings. For instance, for brands that chose to ship directly to stores, circumventing distribution centres can trim as many as three weeks from the schedule and save a significant sum for every unit.

Having the goods, factories and logistics plans in place and giving all players visibility to each part of the operation is planning to react.

Data sourced from Sourcing Journal; additional content by MIQ Logistics