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Published: March 1, 2005

 
 

Supermodels to the Rescue

Agent-based simulations are allowing companies to build silicon versions of themselves, piece by piece and person by person.

Illustration by eboy
Two years ago, the European packaging company SCA faced a dilemma when one of its largest and most dependable customers asked to purchase an additional 20 million boxes each year. Unable to increase its manufacturing capacity in the required time, SCA had to consider cutting back on supply to other customers. The trouble was, the high-volume customer was a tough price negotiator, and SCA earned a relatively small profit on each box. To meet its demand, SCA would lose business from other customers who seemed more profitable on a per-box basis.

How could the packaging company work through the trade-offs?

John Williams, managing director of SCA Packaging Ltd. in the U.K., found his answer through complexity science. Working with Eurobios, a European consulting firm with links to the famed Santa Fe Institute and top-heavy with physicists and mathematicians, the company built a detailed computer model of the various cutting, printing, and gluing operations involved in producing SCA’s custom-made corrugated boxes. With this, they combined models of the mechanisms for managing demand and capacity, for organizing warehouse usage and avoiding “missed deliveries,” and for dealing with unexpected processing line failures. The result was a virtual model of SCA’s operations on which the company could run “experiments” and explore the likely consequences of different decisions.

The model quickly turned up a surprise. Another of SCA’s large customers — in fact, its largest customer — paid very high prices for its boxes. But the irregularity of its ordering behavior created a hidden cost, as it forced SCA to hold a large inventory. Using the computational model, the company discovered that this customer, in the long run, wasn’t nearly as profitable as it appeared; losing its business wouldn’t be such a bad thing. So SCA dropped the customer and took on the new demands of the price-sensitive client. In just one factory alone, the packaging company’s inventory costs fell by 30 percent, and profits rose by $200,000 in the next quarter. SCA is currently rolling the model out to its other factories (roughly 100 in total), and extending it to examine supply chain and transportation issues as well.

Effective business leaders have long had to spot problems and opportunities through a forest of obscuring details and distractions. But the complexity of the global business environment routinely overwhelms the analytical capacity of even the most gifted leader. In seeking efficiency through multilayered production processes and extended supply networks, while keeping pace with rapid technological change and shifting consumer demands, contemporary managers face conflicting constraints and impenetrable webs of cause and effect. Ever more frequently, cutting through the complexity is not possible, and executives, lacking real knowledge, are forced instead to rely, however imperfectly, on instinct.

The challenge is only elevated by the growing demand for customization in products and services. Increased pricing transparency, customer mobility, and technology transfer speeds, together with continually improving supply chains, make it ever easier for customers to demand more individualized products and services. Like SCA, firms across industries find themselves faced with the choice of acceding to the demands, or losing the customer to an accommodating competitor. (See “Smart Customization: Profitable Growth Through Tailored Business Streams,” by Keith Oliver, Leslie H. Moeller, and Bill Lakenan, s+b, Spring 2004.)

In the race to offer customized solutions, most business leaders do not adequately judge the complex trade-offs that affect their bottom lines; too often, customization strategies go awry, offering too much costly service to low-profit customers, or inadequate attention to core customers. The new generation of simulation tools can help companies navigate this unfamiliar terrain, penetrating complexity and allowing them to become “smart customizers.”

 
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Resources

  1. Keith Oliver, Leslie H. Moeller, and Bill Lakenan, “Smart Customization: Profitable Growth Through Tailored Business Streams,” s+b, Spring 2004; Click here.
  2. Michael Schrage, “Here Comes Hyperinnovation,” s+b, First Quarter 2001; Click here.
  3. Eric Bonabeau, “Agent-Based Modeling: Methods and Techniques for Simulating Human Systems,” PNAS, Vol. 99 (May 14, 2002), 7280–7287
  4. Joshua Epstein and Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (MIT Press, 1996)
  5. Navot Israeli and Nigel Goldenfeld, “On Computational Irreducibility and the Predictability of Complex Physical Systems,” unpublished; Click here. 
  6. Proceedings of the Agent 2004 Conference on Social Dynamics: Interaction, Reflexivity, and Emergence (University of Chicago Press, forthcoming); Click here.
 
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