Strong Signals: How Real Is Real Time?

There’s been a lot written over the past couple of years on the concept of the “real-time enterprise.” Let me start by admitting that, as a mathematician by training, the term “real time” carries a lot of baggage for me.

Nevertheless, there are a lot of software engineering ideas in areas such as embedded systems and safety-critical computing that focus on how you build information-based technologies that respond very quickly to changes in their environment. Because these systems require the ability to act as fast as the changes they sense and then respond to—in other words, in real time—they generally can’t involve humans in their decision and control loops. Creating a real-time enterprise involves applying these ideas to a broad range of business systems.

Such systems are important. First, situations arise in which the ability to process a stream of complex data, identify patterns—both expected and unexpected—and then react appropriately is essential to the safe operation of a system. Electric power generation, medical monitoring, automobile emergency braking and aircraft flight controls are examples. In the business world, you can look at risk management, manufacturing quality, supply-chain operations, product profitability and customer satisfaction in the same way.

Second, systems that use rapid sense-and-respond principles can outperform systems that use more static decision rules and control models. Modern fighter aircraft are aerodynamically unstable: They can’t fly at all without their flight computers. But letting the computers microcontrol their flight surfaces greatly expands the performance envelope of the airplane. In business, we could apply the same approach to product mix, customer-specific pricing, manufacturing capacity management and logistics.

Back in the 1960s, Peter Drucker introduced the idea that business management tasks were becoming so complex and information-intensive that we needed to apply automation to them, letting people concentrate on “what to do” decisions, not on “how to do it” activities. The integrated MIS efforts of the 1970s and 1980s were often directed at this goal. We made a lot of progress in areas such as inventory management, yield management and financial risk, but the objective fell out of favor because we underestimated how much sensing would be needed, how smart the response rules would need to be, and how rapidly the business environment changes. And, as often happens, we underestimated the human change-management dimension.