This example may be more than illustrative, for traffic flow is not too different from the flow of products and materials through extended supply chains. In fact, physicist Dirk Helbing of the Dresden University of Technology has recently developed equations to model the dynamics of supply chains and found them to be nearly identical to those used to model traffic. Intriguingly, computations based on these equations show that interdependence within supply chains can stir up natural disruptions much like traffic jams. In essence, small variations in demand at the end of a supply chain tend to travel back upstream, growing in severity as they do and leading to sizable disruptions.
“Stop-and-go traffic,” Dr. Helbing comments, “emerges as drivers react with a time delay to a changing traffic situation in front. The frequently observed instability of supply chains occurs for similar reasons.” This natural and “emergent” instability has the potential to disrupt even the most closely managed supply chains, despite all the advances of recent years in Web-based information transfer to improve coordination.
In recent years, consultants have noted a trend — that as organizations become more efficient, they also seem to become more susceptible to small variations. This observation fits in well with Dr. Helbing’s findings, as well as the earlier work of Dr. Nagel and Dr. Paczuski. Driving the flow to be more efficient necessarily implies a lower margin for error, which brings greater instability and fluctuation in its wake. To counter this trend, it is important to find factors that can be used to control the complex, emergent dynamics of supply networks. Complexity science is no panacea, but it does suggest a way of thinking that can lead to success.
In Dr. Helbing’s model, managerial control enters through what he calls a control function, which reflects the strategy that a production manager uses in trying to adapt to varying demands and supplies. This strategy may well include information collected from the entire supply network, not only in the vicinity of one manufacturer. In studying the consequences of changes in this control strategy, Dr. Helbing has not found any one recipe for success. But what is important, he points out, is the finding that “small changes in strategy may have tremendous effects.” A slight increase in the time required to adapt production rates to a changing demand, for example, may suffice to push the system past a “tipping point” where small fluctuations suddenly explode into larger and more costly disruptions.
Dr. Helbing suggests that “these oscillations can be mitigated or even suppressed” with suitable strategies. But crafting the right strategy in the context of any particular supply line will almost certainly require a detailed exploration of its dynamics, most likely based on computational simulations that make it possible to explore emergence in a systematic way.
Investing in such exploration can pay high dividends, as Infineon Technologies in Dresden found last year when it hired Dr. Helbing to explore disruptions due to scheduling conflicts in its complex manufacturing lines for semiconductor chips. In collaboration with his student Dominique Fasold, Dr. Helbing discovered a counterintuitive but highly successful scheduling strategy that increased chip throughput by 30 percent. To put the potential of this approach in perspective, Intel estimates that similar optimization of its supply lines could save the company several billion dollars each year.
Living with Complexity
The power laws of complexity science reveal that regularity and predictability are neither as regular nor as predictable as business leaders have come to believe. Power laws suggest that today’s organizations, in following modern science beyond a misplaced fixation on predictability, face three closely related tasks: