algorithms – The OM Blog by Heizer, Render, & Munson

OM in the News: At UPS, the Algorithm is the Truck Driver

“Here’s a math problem for you,” writes The Wall Street Journal (Feb.17, 2015). Each United Parcel Service driver makes an average of 120 stops per day. There are 6,689 times 10 to the 195th power alternatives for ordering those stops! Which option is the most efficient, after considering variables such as special delivery times, road regulations, and the existence of roads that don’t appear on a map?

Even if an optimal answer exists, the human mind will never figure it out. And while experts at UPS have been giving the problem their best shot for more than a century, the company is shifting that work over to a computer platform, with 1,000 pages of coding, called Orion, which is 10 years in the making. Considered the largest operations research project in history, the $200-300 million algorithm was written by a team of 50 UPS engineers.

Orion consists of many components, including a “traveling salesman” algorithm, a tool that calculates the most efficient path between a variety of points, and geographic mapping. None of the solutions that Orion spews out are big or dramatic. It is all about saving $1-2 here and there. But in a network with 55,000 routes in the U.S. alone, that adds up. E-commerce has shifted more and more of UPS’s delivery stops to residences, and those packages are expected to make up 1/2 of all deliveries. It is a radical routing change from 15 years ago, when drivers would drop off several packages at a retailer.

Orion is expected to save the company $300-$400 million a year once it is fully implemented in 2017. (UPS saves $50 million a year by reducing by 1 mile the average daily travel of its drivers.) But reaction to Orion is mixed. For example, some drivers don’t understand why it makes sense to deliver a package in one neighborhood in the morning, and come back to the same area later in the day for another delivery. But Orion often can see a payoff, measured in small amounts of time and money that the average person might not see.

Classroom discussion questions:
1. Why is Orion so important to UPS?

2. Why is the software so complex?

OM in the News: Productivity and Technology Down on the Farm

An Iowa farmer using tractor-mounted computers to help make decisions about planting crops

We are fully aware that few of our students will be entering jobs in agriculture (see Figure 1.5 in Chapter 1 for employment figures in the U.S. manufacturing, service, and farm sectors). Yet this Wall Street Journal (Feb. 26, 2014) article on the next revolution on the farm is still worth sharing with your class. The revolution will come from feeding data gathered by tractors and other machinery into computers that tell farmers how to increase their output of crops like corn and soybeans.

Monsanto, DuPont, and other companies are racing to roll out “prescriptive planting” technology to farmers across the U.S. who know from years of experience that tiny adjustments in planting depth or the distance between crop rows can make a big difference in revenue at harvest time. Many tractors and combines already are guided by Global Positioning System satellites that plant ever-straighter rows while farmers, freed from steering, monitor progress on iPads and other tablet computers now common in tractor cabs. The same machinery collects data on crops and soil. But many farmers have haphazardly managed the information, scattered in piles of paperwork in their offices or stored on thumb drives clattering in their pickup truck ashtrays.

Algorithms and human experts crunch all the data and can zap advice directly to farmers and their machines. Supporters say the push could be as important as the development of mechanized modified seeds in the 1990s. Data-driven planting advice to farmers could increase world-wide crop production by about $20 billion a year, or about one-third the value of last year’s U.S. corn crop. The technology could help improve the average corn harvest to more than 200 bushels an acre from the current 160 bushels. Such a gain would generate an extra $182 an acre in revenue for farmers.

Classroom discussion questions:
1. Why are farmers concerned about Monsanto’s involvement?

2. How has productivity improved over the past decades on farms?

OM in the News: Hiring With Algorithms

Our Chapter 10, Human Resources, Job Design and Work Measurement, covers almost every OM aspect of dealing with employees–except how to hire them. As I think about all the instructors and staff I have hired over my 40 year academic career, I realize many personnel decisions were based on resume length and intuition. So The Wall Street Journal’s article (Sept.20, 2012) on how computer modeling is upending the way workers are hired caught my attention.

For more and more companies, the hiring boss is an algorithm. Jobs that were once filled on the basis of work history and interviews are now determined by data analysis. Under pressure to cut costs and boost productivity, employers are trying to predict specific outcomes, such as whether a prospective hire will quit too soon, file disability claims or steal.

The new hiring tools are part of a broader effort to gather and analyze employee data. Globally, spending on so-called talent-management software rose to $3.8 billion in 2011. Though hiring is a crucial business function, conventional methods are usually short on rigor. Depending on who decides, what gets candidates hired can vary wildly—from academic achievement to work experience to appearance. Managers hunches generally have little value in predicting how someone will perform on the job. The statistical approach to hiring can improve results by reducing the influence of a manager’s biases.

When looking for workers to staff its call centers, for example, Xerox used to pay lots of attention to applicants who had done the job before. Then, a computer program said that what does matter in a good call-center worker—one who won’t quit before the company recoups its $5,000 investment in training—is personality. After a short trial that cut attrition by a fifth, Xerox now leaves all hiring for its 48,700 call-center jobs to software that asks applicants to choose between statements like: “I ask more questions than most people do” and “People tend to trust what I say.”

Discussion questions:

1. What are the advantages of this software-driven approach?

2. How can algorithms help determine how much to pay workers?