(Business 2.0) – An ant crawls out of its hill and marches toward a half-eaten Twinkie. Another treks to a puddle of water. Others plot routes to their own diminutive chores. Along the way, each lays down a pheromone trail that, over time, tells co-workers where it has been, what hazards to avoid, and which path offers the quickest way home.

If ants can run efficient supply chains with brains that weigh less than the ink in this comma, why do we humans have such trouble? The question has dogged Eric Bonabeau for years. The 34-year-old Frenchman--a scientist and student of the branch of complexity science known as chaos theory--has spent nearly a decade studying the organization, coordination, and work habits of social insects. Ant colonies are so efficient, Bonabeau deduced, because they lack centralized control; no single ant boss runs the business. Bonabeau took this notion a step further in his 1999 book, Swarm Intelligence, in which he described how the study of an organization's "ants," its myriad individual parts, could help businesses find solutions to problems that elude ordinary top-down analysis: how the late arrival of a single package can derail an entire supply chain, say, or why adding a lane to a highway can often worsen traffic jams.

It all sounded great on paper, but until recently Bonabeau hasn't had the chance to prove that his theories can work in practice. When hired by a client company, the consultancies applying Bonabeau's ideas--notably Santa Fe's Biosgroup, where Bonabeau got started, and Icosystem, the firm he founded after leaving Biosgroup--create algorithms that generate super-realistic simulations of all of an operation's moving parts. In other words, they plug in virtual employees, products, and customers, order them to do different things, and watch what happens. Bonabeau's "agent-based modeling" techniques are now helping tighten supply chains, speed drugs to market, and even design better unmanned drones for the Pentagon.

Bonabeau's ants, for example, can be found crawling all over Air Liquide, the French industrial gas giant. The company supplies liquid oxygen, nitrogen, and other gases to 10,000 customers from more than 300 sources through 30 depots, using 200 trucks and 200 trailers. It's a supply chain that can create 3 trillion daily combinations among all its constituent parts; it took 22 full-time logistics analysts nearly half a day to generate a delivery schedule that would get every product to its destination on time.

Working with Biosgroup, Air Liquide chose to run agent-based simulations to see how they could draw up more efficient delivery routines. Air Liquide trucks were programmed, like ants, to find the shortest routes, or to follow the equivalent of pheromone trails, meaning that subsequent trucks were ordered to retrace shortcuts found by others. Then, using reams of data from Air Liquide's business operations, Biosgroup engineers retested their computer simulations until they found the most efficient combination of rules. The result: Just one Air Liquide analyst is needed to create daily shipping and production schedules across its numbingly complex supply chain in about two hours. "There's no way a human could look at all the possibilities out there," says Air Liquide senior project manager Clarke Hayes. "So we turn the ants loose."

Southwest Airlines, too, has benefited from Bonabeau's ideas. The carrier called on Biosgroup to simulate the various parts of its cargo shipping business: its aircraft, destinations, the types of cargo, ground personnel, and so on. The simulations proved what Southwest's logistics managers suspected all along: It's sometimes more efficient not to take the shortest route, as long as fewer hands actually touch the cargo in the process. Southwest claims it saves $2 million a year in labor costs because of that insight.

Agent-based modeling has evolved beyond ant behavior, and so too has Bonabeau. After leaving Biosgroup in 2000, he soon founded Icosystem in Cambridge, Mass., and began to apply his algorithms to new business applications. "Inspiration comes from ants, but we'll use any rules to help solve problems," he says. To get Eli Lilly's drugs to market faster, Icosystem engineers recently built a model of the company's sprawling clinical development processes, using people, drugs, regulatory hurdles, and other individual parts to simulate the laborious task. By finding and applying the right rules, Bonabeau hopes to speed Lilly's development time by as much as 80 percent. To better understand how people choose their own health plans, Icosystem is also devising models for insurance powerhouse Humana.

Even the U.S. Office of Naval Research is using Icosystem to help it design smarter unmanned aerial vehicles. Because UAVs are controlled by a centralized command on the ground, they tend not to work well as a team. They bunch up, miss large areas, or fail to respond to enemy threats. So Bonabeau and his colleagues have created simulations in which virtual drones follow hundreds of different rules: Stay away from other drones, fly to areas no other drones have covered, and so on. Initially, the goal is to help drones communicate directly with each other. But by 2020, Pentagon planners hope to create entire swarms of unmanned vehicles that communicate and attack in concert.

Of course, it takes a certain corporate mind-set to cotton to swarm intelligence. "Managers don't like to give up control," Bonabeau admits. Moreover, these simulation games require math and programming smarts that can come only from experts, as well as a wealth and breadth of transaction and other business data to make the resulting scenarios meaningful to decision-makers. Bonabeau's clients pay as much as $200,000 a month for his services.

Despite the costs, more and more organizations are aspiring to be like ant colonies. IBM recently began experimenting with its own agent-based modeling programs for its future e-commerce software. The European Union is also funding a three-year modeling research project at the Santa Fe Institute, the hotbed of complexity science.

Beyond modeling, Bonabeau says, the ultimate goal of the technology is to solve problems before they happen--not unlike, for example, "autonomic" computing systems that seek out and fix software glitches without any human intervention. But as Bonabeau admits, advancing to the next phase means he'll have to do better than just delivering more efficient shipping routines for his customers. "We need a company to make $1 billion from this technology," he says. That's one scenario for which Bonabeau has yet to find the perfect algorithm. --THOMAS MUCHA