Innovation is just as important for business survival as food is for a balanced diet, to paraphrase a quote by Fran Lebowitz. On a macroeconomic level, innovation is today believed to be the main driver of economic progress and well-being. It is also expected to be an important factor in meeting global challenges in domains such as the environment and health [1]. A report from the Congressional Research Service to the U.S. Congress writes that "It is widely accepted that technological progress is responsible for up to one-half of the growth of the U.S. economy" [2]. The Boston Consulting Group has produced numbers showing that innovation indeed is the largest contributor to economic growth, as can be seen in the following graph from The Economist [3].

Statistically, innovative companies are creating more intellectual capital, jobs, growth, and wealth than less innovative companies. Increased globalization, communication and technology availability are increasing competition and shortening product cycles. This is not only increasing the need for innovation, but the need to innovate at an increasing rate. For instance, since the life cycle of consumer goods in the United States fell by half from 1992 to 2002, companies needed to innovate twice as fast just to keep up [4]. Companies that do not innovate effectively may be destroyed by those that do, and as a result, the alternative to growth is not stagnation, but decline.

Clearly, innovation is important. Very important.

References

[1] Innovation and Growth, Rationale For An Innovation Strategy, Organisation For Economic Co-Operation And Development, May 2007.

[2] Industrial Competitiveness and Technological Advancement: Debate Over Government Policy, Updated August 1, 2007.

[3]Special Report on Innovation, the Economist, Oct 11th 2007.

[4] Gil Cloyd, chief technology officer at Procter Gamble, as reported in Revving up; How globalisation and information technology are spurring faster innovation, Special Report on Innovation, the Economist, Oct 11th 2007. Oct 11th 2007

More reading

A Culture of Improvement: Technology and the Western Millennium by Robert Friedel, The MIT Press, 2007.

Innovation Nation by John Kao, Free Press, 2007.

Zoom: The Global Race to Fuel the Car of the Future by Iain Carson and Vijay V. Vaitheeswaran, Twelve (Hatchette Book Group USA, 2007).

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I have two of Erno Rubik’s Cubes on my desk. The first is the type you’re probably most familiar with, where each surface has nine colored squares, giving 3 × 3 × 3 in total. This small and simple cube has one million million million distinct patterns (or configurations, if you want). I don’t know about you, but this large number surprised me.

I also have another type of Rubik’s Cube on my desk: a not so common 4 × 4 × 4 variation. The number of distinct patterns this cube can yield is 10⁴⁰. (Read about the combinatorics of Rubik’s Cube at the Math Forum @ Drexel.) A simple addition of a few squares added
enormous complexity to the patterns available.

Working on many of today’s business issues is inherently complex too, both in terms of the processes that are in place, and mathematical complexity. The facts and decision points available can make the “combinatorial complexity” so high that it’s impossible to resolve some situations properly, with any degree of confidence. So what do we do then? Rely on instinct and “gut feel”?

Consider the situation when a company like UPS or United Airlines wants to plan routes through its network. Even a modest number of flight legs results in a massive set of routing options. For example, 25 individual legs lead to more than 60,000 alter-natives; while 40 legs lead to more than 73 million.

But these examples are actually pretty small. Where I live, in the province of British Columbia in Canada, forestry is a very important contributor to the economy. So let us consider a mid-sized sawmill capable of processing 25 orders at any one time, using seven different machines, with ten workers directly involved in production. Each order may use three to four machines and most job types can be serviced by more than one machine. The number of possible production plans for this seemingly simple example is staggering! For a single order, the number of plans is more than 39 million, while for four orders it grows to 2,497,280,186,667,030,000,000,000,000. For ten orders it is more than the estimated number of atoms in the Universe (which is 10⁸⁰).

At Actenum, we calculated the number of possible schedules in a television broadcasts for a large sports league to be 10⁶⁴⁰. Numbers like these are really inhuman, in the sense that we cannot really fathom them.

This phenomenon is known as combinatorial explosion. Unfortunately, it is pervasive in modern businesses. Most people involved with resource allocation, scheduling, planning, routing, product configuration, production, yield management, warehouse management, and logistics face the complexity issue every day.

Preparing a schedule for the sawmill example given above is, of course, possible (it’s done every day), but the schedule isn’t necessarily a good one. When order volumes increase, or a machine is out of service, the pressure to resolve the situation is intense, and using simple methods to plan production may result in unreliable production control, failure to meet promised delivery deadlines, and failure to predict shortfalls in production resources.