What’s the Best Way for Large, Disparate Teams to Communicate?

Prior to System/360’s launch in 1964, computers were largely singular and fixed units, so changing one component meant changing the computer entirely. The System/360 family of mainframe computers, in contrast, had compatible software and standardized hardware that allowed its products to be easily reconfigured or upgraded.

This new system was so different in form that IBM had to restructure its organization to build it.

“When we have a big change in technology,” says Niko Matouschek, a professor of strategy at the Kellogg School, “the question is, ‘How do we think that’s going to change the way firms are organized?’”

In IBM’s case, the company decided that producing computers with this kind of design would require a more modular production process. So it divided its engineers into separate teams, each team tasked with designing a different component.

But this introduced a new challenge: communication. With so many teams working separately on their projects, all of which would ultimately need to be united, team coordination and communication became significantly more important; the company had to ensure that the necessary information—and only the necessary information—was shared across these newly established teams.

“Designing optimal communication networks is a very complicated problem,” Matouschek says. “If you’ve got hundreds of people and they have to decide who to tell their information to, and who should do what next—there’s a gazillion different possibilities.”

Matouschek partnered with Kellogg colleagues Michael Powell and Bryony Reich to develop a mathematical model to shed some light onto the problem. They focused specifically on communication within the context of companies that, like IBM, build products by assembling separately produced parts, or modules.

They found that the optimal way for people at these organizations to communicate likely resembles a “core–periphery structure,” in which the organization has a central group of teams that share nearly all their information with each other and a group of peripheral teams that rarely share their information with each other.

“In the core, we speak a lot with each other,” Matouschek says. “People in the periphery may talk with us, but they’re not talking much to each other. That’s a core–periphery structure.”

He notes that this communication structure is not an uncommon one even outside of manufacturing. On online social networks like Facebook, for example, a person has their main circle of friends who interact with each other often. The person also occasionally engages with some looser social connections, but those connections almost never interact with each other.

“We see this type of core–periphery communication in all sorts of environments,” he says, “and it is a nice result that, of all the millions of crazy different approaches that could be optimal, what is optimal might actually look like this.”

Adjusting to change

Beyond computing, companies across a wide range of industries—from residential homes to phones and airplanes—build their products by combining separately made, replaceable parts. This technique, called modular production, has allowed companies to adapt their products to customer preferences, outsource their manufacturing, and scale production.

With this technique’s resurgence in recent decades, Matouschek, Powell, and Reich turned to mathematical modeling to understand the optimal way for a company that uses modular production to have its teams communicate about their work.

They created a model with a couple of key characteristics. First, for the many decisions the company is expected to make, the model assumes that those made within teams require more coordination than those made between teams. And second, it assumes that each instance of communication comes at the cost of time and energy.

The model’s challenge, then, is to design a communication network that best balances these trade-offs—ensuring that people can share information as effectively as possible while expending as few resources as possible. In theory, the right communication network would give a company the best chance of maximizing its profits.

Optimizing communication

The model considered numerous communication strategies. One popular strategy, for instance, is to have a tree-like hierarchy in which people at the bottom report information to their direct boss, who then communicates the information to their boss until all the important information reaches the top. Another approach resembles a matrix: people communicate information to everyone within their division, as well as everyone in related divisions.

For companies that use modular production, where separate teams manage distinct parts of the product, another common approach is for each team to simply adopt a “modular communication” strategy, where they communicate with other teams on an as-needed basis.

But according to the model, this kind of modular, or siloed, communication could be problematic.

“The model cautions against the common prescription that firms with modular production functions ought to adopt modular communication,” Matouschek says. “The model suggests this will lead to coordination problems.”

Among all the communication strategies that the model considered, Matouschek and colleagues found that the most efficient involved organizing teams into two different types: the core and the periphery. In the core, teams share almost all of their information with each other, whereas in the periphery, teams only communicate with each other and with the core to discuss the most critical information.

“People who happen to be in the core, they communicate a lot; they tell each other everything,” he says. “But they might or might not tell a lot of information to people who are in these different peripheries.”

This structure ensures an organization’s core teams are kept fully informed of important information, while its peripheral teams have as much time as possible to focus on their main work. In other words, it maximizes resource efficiency while minimizing the risk of lapses in communication.

Though the findings are clear, they remain theoretical, according to Matouschek, who views them less as practical advice than as groundwork for future exploration. “The model is miles removed from reality,” he says. “But it does suggest that there are good solutions and bad solutions, and that good ones kind of look like this.”

Debating the possibilities

There’s a maxim in the world of software programming that goes, “Adding manpower to a late software project makes it later.” That is, recruiting more people to help complete a task can sometimes make it take longer because of the extra time it takes people to coordinate.

That’s a lesson about which Fred Brooks wrote years after he had led the development of software for IBM’s System/360. During his tenure, he instituted a rule requiring everything to be recorded into a central book that was available to everyone on the team. In terms of a communication model, Brooks’s approach was basically for everything to be communicated to everyone.

“Brooks says something like, ‘That turned out to be a disaster, because within months, the book was taller than the Empire State Building,’” Matouschek says. “It speaks to the idea that there’s a real problem in communication, which is, it takes a lot of resources.”

Indeed, Brooks’s approach, though effective at transmitting information, was likely unsustainable over the long haul.

Several years later, another well-known software engineer, David Parnas, came out advocating a different approach. He suggested dividing programmers into separate groups that communicated regularly within teams but rarely across teams. From his perspective, project coordination could be made more effective by giving up on some communication links entirely.

Sound familiar?

While not a perfect match, Parnas’s strategy offers a much closer approximation to the core–periphery approach suggested by the model. Between the Brooks approach and the Parnas one, “our model,” Matouschek notes, “says you want to do the latter rather than the former.”