The apparel industry has been hit with a whirlwind few years, and unpredictability is now expected, but data and digitalization can help to manage risk. During the PI Apparel Supply Chain Forum 2022, speakers dove into the power of data for product development, assortment planning and production strategy.
In a keynote, Alex Thomas, vice president global quality at Gap Inc., noted that the rate of major events has accelerated from one or two every few years to two or three per year. “The more black swan events, the more issues, the less we’re able to say, ‘Hey, what do we do going forward?’” Thomas said. “When you’re moving thousands of containers of goods every day, the lack of predictability is a massive issue.”
Among the factors helping Gap drive agility is customer centricity, including feedback. As Thomas said, the traditional “three-legged stool” of quality, cost and delivery does not cut it anymore. While quality remains important, the demands are now greater. It’s no longer about acceptable quality levels. “Even the customer feedback we get on one defective garment has impact,” he said. “Product quality has to improve year-on-year.”
Across its brands, Gap Inc. receives approximately 2.5 million lines of voice of the customer data, and it is using these insights to inform product development. For instance, it knows how its customers use its products—such as the expected lifespan or the laundering frequency for a particular garment.
After receiving negative feedback, Gap makes adjustments, which might be instant or implemented the next season. For instance, if consumers feel they didn’t get the fit they expected because of how a garment was photographed, that is a faster fix. But issues about fabric used—such as pilling—inform the next production cycle.
In addition to voice of the customer insights, Gap is using predictive analytics to be more proactive. The company is focused on five V’s to improve its predictive use of data: volume, variety, velocity, veracity and value.
Gap’s agility efforts also include a control tower, which helps the Athleta, Old Navy and Banana Republic owner keep tabs on its production and potential snares, such as extreme weather. Rounding out its strategy are innovation and its corporate culture.
Thomas likened Gap’s use of predictive data to the precogs in the film “Minority Report,” who are individuals who can see the future. “We have precogs in our system, we have artificial intelligence, we have predictive capability,” he said. “We’re able to say, ‘Hey, with all these different data sets, we can predict with different levels of certainty what’s going to happen.’ This is a big part of where the supply chain is moving ahead.”
Data for risk mitigation
During a panel discussion on “Maintaining Operational Stability in an Era of Disruption,” moderated by LIM College professor of graduate studies Kenneth Kambara, panelist Dr. Ahmed Zaidi, AI and fashion researcher at the University of Cambridge, outlined the difficulties of trying to make projections based on data. “When you’re talking about predicting fashion trends a year out, six months out, the uncertainty is incredibly high, and you might as well just guess,” he said. “The idea that predictive technology is a silver bullet for being able to predict the future and figure out what trends you want to buy for is not exactly accurate.”
Data can help predict demand for core staples, such as basic T-shirts, but it is less effective at gauging upcoming trend demand, per Zaidi. He added, “We should look at technology not as a predictive measure, but as a risk mitigating measure.” For instance, AI can help pinpoint whether a particular product is riskier or a safer bet or help identify supply chain constraints, all of which can feed into a company’s decision-making.
Another best practice is flexibility, Zaidi said. In mass production, there is always some level of betting, but fashion can take inspiration from inside and outside the industry on reducing risk. Hedge funds using high-speed electronic trading and e-commerce powerhouse Shein share an approach that favors smaller bets, which enables them to react to the market. For apparel, this model helps with inventory turnover, profitability and cash flow.
“You’d rather take a bet on a sure thing,” said fellow panelist Joseph Altieri, a consultant, manufacturing expert and educator at the Fashion Institute of Technology.
Risk mitigation can also revolve around how and where products are sourced.
Altieri shared a case study in strategic sourcing choices. Even before the pandemic, the U.S.-based manufacturer of band and color guard uniforms that he works with was experiencing delays with imports both in transit and at customs. To improve the flow of raw materials, they searched for and found a textile supplier about 50 miles away from the factory. The partnership involves a “pack and hold” strategy in which materials are purchased and then stored at the mill until needed for production.
At the same time, the firm looked at its data and determined that it could slightly raise the low margins on its basic items like leggings and gloves by moving production offshore. This enabled it to focus its domestic manufacturing more on custom, made-to-measure orders that require a quick turn and command a higher margin. “It was very successful,” Altieri said. “Returns were greater on that one decision than on any marketing plan [the business owner] had ever done or any products he had introduced.”
Zaidi added that made-to-measure fashion can be priced 170 percent higher than standard goods. For on-demand production to work, companies must have the materials and capacity in place to produce when the order comes in. One solution is platforming fabrics, and data can help analyze what is a staple textile and what should be used within the season.
“Making those decisions upfront and being intelligent about it is not a new concept, but it’s something that technology definitely can help with,” Zaidi said. “It’s definitely a risk-reduction strategy that can be very beneficial for speed and agility.”