Josh Green was at a loss. In 2005, he was working at the electronics firm E Ink when his boss asked him to find a component supplier in China. But how? He could hop a flight to Asia, but where to? He couldn’t Google solid information about a supplier’s reliability or its labor standards. And it wasn’t as if E Ink’s competitors were going to tell him whom they used. “There was a shocking lack of information and transparency about the firms you might do business with,” Green says. His sister, who works in apparel, told him she was having similar problems.
The eureka moment came when Green sat down with his friend James Psota, a computer scientist at MIT. Psota felt he could design software that would do for importing what Google has done for the Web: make search smarter. In 2006, the duo founded Panjiva. Starting with the apparel industry, they gathered data from more than 200 sources — governments, private certifiers, not-for-profits. Then they digitized, cleaned, and collated that data to create detailed snapshots of more than 70,000 suppliers, each rated from 1 to 100, based on criteria such as the supplier’s environmental record and experience serving the U.S. market. The company charges for database subscriptions and also sells reports on individual suppliers.
On its most basic level, Panjiva could be likened to a dating service, matching American businesses with foreign suppliers based on several levels of compatibility. “The apparel business has always been based on contacts and trust,” says Jeff Silberman, chair of the department of textile development and marketing at New York’s Fashion Institute of Technology, whose students use Panjiva. “In that sense, Panjiva is not just innovative, it’s revolutionary.” The model has been intriguing enough to attract a stellar, diverse roster of investors, from fashion-industry icons (Diane von Furstenberg) to economic notables (ex — Treasury secretary Larry Summers) to VCs (Battery Ventures recently put in several million dollars). To date, Panjiva’s services have been used by about 90 companies, including Elie Tahari, J.Crew, Reebok, and Wal-Mart.
Kellwood, a $2 billion apparel conglomerate that markets brands including Calvin Klein and Phat Farm, had a team of four trying to gather the kind of data that Panjiva specializes in. But the searches were time-consuming and what they did find “was messy,” says Jeff Streader, the executive who used Panjiva to update Kellwood’s roster of suppliers. “Our options were to use our industry contacts or try Panjiva to identify alternatives,” he says. Panjiva not only helped Streader find new suppliers but also taught him a few things about companies he had been doing business with for years. “Some people had led us to believe their business was much larger than it really was. Other people were doing business in categories that we were unaware of,” he recalls. “So we could go back to them and say, ‘Hey, we didn’t know you were in swimwear!’ ‘Well,’ they would say, ‘you never asked.’ ” Streader now heads global sourcing at Guess and is working on adding his new employer to Panjiva’s client list.
Panjiva has similarities to Alibaba, the online marketplace much praised for linking Chinese manufacturers with American wholesale buyers. But while Alibaba acts as an online marketplace for matching buyers and suppliers, it doesn’t verify all the information provided by the companies it lists.
The trustworthiness of Panjiva’s data will be a keystone to its success. In theory, firms shouldn’t be able to manipulate the data Panjiva is using, which come from public-sector sources such as the Department of Homeland Security and not-for-profit monitors such as Worldwide Responsible Accredited Production. But in markets like China, the largest foreign supplier of apparel to the U.S., government reports aren’t always reliable. Christine Bullen, president of the not-for-profit Global Sourcing Council, which focuses on social responsibility and sourcing, says Panjiva could “truly reduce search and verification costs.” But she adds, “If their ratings — even one — were in any way incorrect or bogus, their reputation could plummet.”
Green acknowledges this concern and cites two criteria used by Panjiva. First, is there an incentive for a data provider to manipulate the information? Second, is it likely manipulations could go unnoticed, because, for instance, the data can’t be corroborated? “If the answer to both questions is yes, then we don’t incorporate the data source,” he says. “Also, we are always transparent about our data sources so that our users can decide for themselves.”
Green and Psota never intended their business to be fashion-only, and their ambitions are much broader. Green has had discussions with brands in several other consumer sectors, including food, housewares, and office supplies, and he expects Panjiva’s database to eventually have profiles of several million suppliers in numerous industries. “For millennia, people doing business across borders made do with trust,” he says. “Now you’ve got to supplement that with information — or you’re doing business with a blindfold on.”