Big ideas where management, technology meet

MIT Sloan Management Review 

Sunday MBA provides ideas on running better businesses and succeeding in the modern workplace, this week from MIT Sloan Management Review.

When you talk business, starting with Peter Drucker is always a smart move. In his book, “Management: Tasks, Responsibilities, Practices,” Drucker defined the work of business leaders by its three principal tasks: to deliver financial results, make work and workers productive, and manage a company’s social effects and responsibilities. That’s all, and of course, that’s a lot.

There’s been a lot of change since Drucker’s magnum opus was published in 1974. Technological advances have transformed the world in myriad ways, large and small. But new technology hasn’t fundamentally changed Drucker’s tasks. Instead, it is giving rise to new and better ways and means of executing and achieving them.


Here’s some reading to help you identify big ideas and new tactics at the intersection of technology and management.

The mobile method to uncovering abuse in the supply chain: The good news about global supply chains is they offer competitive and cost advantages. The bad news is that the financial and reputational risks associated with such supply chains have increased exponentially. Ignorance is a flimsy defense when a garment factory collapse in Bangladesh kills and injures thousands of people.

How can a company gain an unvarnished view of what’s happening in far-flung supply chains? One way is to connect with everyone working in the supply chain by tapping into the extraordinarily high penetration of mobile phones globally. That’s what a nonprofit named Good World Solutions is doing with a program that it calls Labor Link, associate editor Bouree Lam reports in The Atlantic. Labor Link allows companies to question employees about their workplaces and working conditions directly, and employees can respond without fear of reprisals.

Reengineering reprised with machines: If you’ve been around for a while, you probably remember reengineering and its catchphrase “Don’t automate, obliterate” with mixed emotions. A lot of inefficient business processes were redesigned and rebuilt, a lot of businesses reaped the rewards, and a lot of people experienced a lot of pain. Well, reengineering is back in a new, and hopefully kinder and gentler, form.

Machine-reengineering, as H. James Wilson, Allan Alter, and Prashant Shukla of the Accenture Institute for High Performance call it in, applies machine-learning algorithms to the work of business process improvement.


The authors studied more than 30 machine-reengineering pilot projects and found “evidence of significant, even exponential, business gains” in five kinds of processes: customer service, risk and compliance, finance, capability development and management, and most commonly, marketing and sales.

Managing in the Fourth Industrial Revolution: For a glimpse of what the Fourth Industrial Revolution really means for management, read Siemens chief executive Joe Kaeser’s interview in strategy+business. Kaeser touches on each of Drucker’s management tasks — talking about how he delivers results, boosts productivity, and manages social effects in an era of digitization.

Siemens employs 17,500 software engineers, Kaeser said. One thing they’re building is virtual digital factories. “Together with Boeing, we simulate the whole development and engineering process for new airplanes,” he said. Sounds like machine-reengineering might be past the pilot stage.

What to do with all the data: “There were countless industry and academic publications describing what data science is and why we should care, but very little information was available to explain how to make use of data as a resource. We find that situation to be just as true today as we did two years ago, when we created the first edition of the field guide.” So starts the second edition of Booz Allen Hamilton’s The Field Guide to Data Science.

The great thing about this free e-book is that it sets readers — particularly managers who aren’t well versed in data science — on the path to putting data to work. Big data cognoscenti will probably yawn, but if you don’t know discrete wavelet transforms from genetic algorithms, The Field Guide will clue you in.

Better yet, if you know your company needs to harness the insights hidden in the tsunami of data rushing by but don’t know how to begin, one section of the guidebook is devoted to the elements of a data science capability and options involved in developing one.

This article is adapted from “Tech Savvy” by Theodore Kinni. Copyright 2016 MIT Sloan Management Review. All rights reserved