We all like to believe that adding the human touch to decision making is important. Business schools pride themselves are being able to educate students to become better business leaders. But some decisions actually fair worse because people get involved. Let me give you an example. Dutch sociology professor, Chris Snijders of the Eindhoven University of Technology has developed algorithms that permit computers to make routine managerial decisions based on past data. Snijders is so confident that his algorithms can do a better job than humans in making routine decisions that in 2005 he issued a challenge to any company that believes otherwise. Snijders ran experiments for two years using computer and software acquisition decisions made by purchasing managers at more than 300 organizations. When Snijders’ computer models were given the same tasks, they achieved better results in categories like timeliness of delivery, adherence to the budget and accuracy of specifications. Although I don’t believe that any company has taken on Snijders’ challenge, his work hasn’t been completely ignored. The legal department of a Dutch insurance company is using his expertise to evaluate a computer decision model it has designed to automate the routing of new cases.
An article discussing Snijders’ work notes that it builds on previous experiments that have demonstrated that mathematical models are better than humans in making a number of predictions including the success or failure of start-up businesses, criminal recidivism of parolees, and student performance in graduate school [“Maybe We Should Leave That Up to the Computers,” by Douglas Heingartner, New York Times, 18 July 2006]. Computers also trump humans, the article reports, when making medical diagnoses and picking dogs at the racetrack. Computers are also making inroads in the insurance and investment businesses. The reason that mathematical models are better than humans is that humans are generally less consistent than computer models.
The article made the point that embedded business logic allows organizations “to codify and centralize its hard-won knowledge in a concrete and easily transferable form, so it stays put when the experts move on. Models also can teach newcomers, in part by explaining the individual steps that lead to a given choice. They are also faster than people, are immune to fatigue and give the human experts more time to work on other tasks beyond the current scope of machines.” The Economist recently published an article about the pervasiveness of algorithms throughout the business world [“Business by numbers,” 15 September 2007 print edition].
“Algorithms sound scary, of interest only to dome-headed mathematicians. In fact they have become the instruction manuals for a host of routine consumer transactions. Browse for a book on Amazon.com and algorithms generate recommendations for other titles to buy. Buy a copy and they help a logistics firm to decide on the best delivery route. Ring to check your order’s progress and more algorithms spring into action to determine the quickest connection to and through a call-centre. From analysing credit-card transactions to deciding how to stack supermarket shelves, algorithms now underpin a large amount of everyday life. Their pervasiveness reflects the application of novel computing power to the age-old complexities of business. ‘No human being can work fast enough to process all the data available at a certain scale,’ says Mike Lynch, boss of Autonomy, a computing firm that uses algorithms to make sense of unstructured data. Algorithms can. As the amount of data on everything from shopping habits to media consumption increases and as customers choose more personalisation, algorithms will only become more important. Algorithms can take many forms. At its core, an algorithm is a step-by-step method for doing a job.”
This subject interests me, of course, because I founded my company, Enterra Solutions, based on the belief that automating business processes was essential to making companies more competitive in a globalized world.
“This formulaic style of thinking can itself be a useful tool for businesses, much like the rigour of good project-management. But computers have made algorithms far more valuable to companies. ‘A computer program is a written encoding of an algorithm,’ explains Andrew Herbert, who runs Microsoft Research in Cambridge, Britain. The speed and processing power of computers mean that algorithms can execute tasks with blinding speed using vast amounts of data. Some of these tasks are more mechanistic than others. For instance, people often make mistakes when they key in their credit-card numbers online. With millions of transactions being processed at a time, a rapid way to weed out invalid numbers helps to keep processing times down.”
The article goes on to talk about an algorithm developed by Hans Luhn, an IBM researcher, that almost instantaneously validates whether the numbers on a credit card are genuine. If they are, the processing can go ahead.
“The Luhn algorithm performs a simple calculation. But the real power of algorithms emerges when they are put to work on much more complex problems. As far as most businesses are concerned, these problems typically fall into two types: improving various processes, such as how a network is configured and a supply chain is run, or analysing data on things such as customer spending.”
The article explains how critical algorithms are for large companies like UPS who utilize a so-called “traveling salesman” algorithm.
“UPS uses algorithms to help deliver the millions of packages that pass through its transportation network every day in the most efficient way possible. The simplest routes are easy to draw up. If a driver has only three destinations to visit, he can take only six possible routes. But the number of possible routes explodes as the destinations increase. There are more than 15 trillion, trillion possible routes to take on a journey with just 25 drop-off points—and an average day for a UPS driver in America involves 150 destinations. The picture is further complicated by constraints such as specified drop-off and pick-up times for drivers or runway lengths and noise restrictions for aircraft. ‘Algorithms provide benefits when the choices are so great that they are impossible to process in your head,’ says UPS‘s Jack Levis. Solving this ‘travelling-salesman problem’ means a lot to UPS. For its fleet of aircraft in America, the company uses an algorithm called VOLCANO (which stands for Volume, Location and Aircraft Network Optimiser). Developed jointly with the Massachusetts Institute of Technology (MIT), it is used by three different planning groups within UPS—one to plan schedules for the following four to six months, one to work out what kind of facilities and aircraft might be needed over the next two to ten years, and one to plan for the peak season between Thanksgiving and Christmas. Getting the scheduling wrong imposes a heavy cost: flying half-empty planes or leasing extra aircraft is an expensive business. UPS reckons that VOLCANO has saved the company tens of millions of dollars since its introduction in 2000.”
Such optimization algorithms are used by a number of other sectors as well. The telecommunications industry uses algorithms to route calls through their networks. Call centers use algorithms to assign incoming calls to operators based on the nature of the questions being asked and the workloads of the operators.
“The most powerful algorithms are those that cope with continual changes. The delivery schedules for online grocers have huge ‘feedback loops’ in which the delivery times chosen by customers affect the routes that vans take, which in turn affects the choice of delivery slots made available to customers. UPS is working on a real-time algorithm for its drivers that can recalibrate the order of deliveries on the fly, in much the same way that satellite-navigation systems in cars adjust themselves if a driver chooses to ignore a suggested route. In the world of the internet, operators are looking at ways of marrying up the algorithms that find the shortest path through a network and those that control the speed with which information flows. At the moment, the routing algorithm does not talk to the flow-control algorithm, which means paths do not change even when there is congestion. According to Marc Wennink, a researcher at Britain’s BT, combining the algorithms would mean that tasks such as downloading files could become much more resilient to network disruption. It would also allow BT to make better use of its existing network capacity.”
The article notes that, in addition to optimization algorithms, statistical algorithms play an important role in business.
“Just as optimisation algorithms come in handy when people are swamped by vast numbers of permutations, so statistical algorithms help firms to grapple with complex datasets. Dunnhumby, a data-analysis firm, uses algorithms to crunch data on customer behaviour for a number of clients. Its best-known customer (and majority-owner) is Tesco, a British supermarket with a Clubcard loyalty-card scheme that generates a mind-numbing flow of data on the purchases of 13m members across 55,000 product lines. To make sense of it all, Dunnhumby’s analysts cooked up an algorithm called the rolling ball. It works by assigning attributes to each of the products on Tesco’s shelves. These range from easy-to-cook to value-for-money, from adventurous to fresh. In order to give ratings for every dimension of a product, the rolling-ball algorithm starts at the extremes: ostrich burgers, say, would count as very adventurous. The algorithm then trawls through Tesco’s purchasing data to see what other products (staples such as milk and bread aside) tend to wind up in the same shopping baskets as ostrich burgers do. Products that are strongly associated will score more highly on the adventurousness scale. As the associations between products become progressively weaker on one dimension, they start to get stronger on another. The ball has rolled from one attribute to another. With every product categorised and graded across every attribute, Dunnhumby is able to segment and cluster Tesco’s customers based on what they buy.”
The article notes that anyone surfing the Internet depends algorithms. The competition between search engines like Google, Yahoo, MSN.com, Ask.com and so forth is a competition about algorithms and which one produces the best search results. Finally, the article implies that in the information age the business with the best algorithms is going to succeed. According to expert quoted in the article, algorithms “are bound to take over the world.” Sounds good for business!