Artificial Intelligence and the Supply Chain

Stephen DeAngelis

August 22, 2012

The human race has progressed in fits and starts. Great leaps forward can always be traced to advances in technology. That doesn’t mean that everyone is happy with such progress. MIT business school researchers Erik Brynjolfsson and Andrew McAfee, for example, conclude that job creation isn’t keeping pace technological advances resulting in increased unemployment. [“When Machines Do Your Job,” by Antonio Regalado, Technology Review, 11 July 2012] On the other hand, McAfee, told Regalado, “There is a closely related phenomenon, which is the massive increases in productivity brought on by digital technology.”

 

Despite the effect that technology has on jobs, technological progress is not going to stop. In fact, it is probably going to increase as machines begin learn and assist technologists in making the next-generation of machines even better and more productive. As the global population ages and the global birth rate slows, humankind will eventually welcome the productivity that technology generates. Today, however, McAfee insists “that if you are a ‘routine cognitive worker’ following instructions or doing a structured mental task,” your job is probably a good target for a technological takeover.

 

The real problem, of course, is not the technology but the fact that our educational and training systems are not keeping pace. Jeff Owens, the chief executive of Advanced Technology Services, a manufacturing equipment maintenance company in Peoria, Illinois, told the Financial Times, “People coming out of high school just don’t have the skills necessary to work in this industry.” [“US industrial groups partner for training,” 21 August 2012] As a result, Owens helped establish a training program that works with community colleges to create workers skilled enough to tackle tomorrow’s jobs. Owen’s went to state, “They have to have skills around hydraulics, electronics, computers, software. Community colleges were not training to the level we needed for our world – machine maintenance. They were training for welders, for machine operators. Our focus is on the guys who service and repair the machines.”

 

McAfee says that the good news is that if train people to work with machines “in a very, very automated and digitally productive economy you don’t need to work as much, as hard, with as many people, to get the fruits of the economy. So the optimistic version is that we finally have more hours in our week freed up from toil and drudgery.” Technology has always had a focus on helping relieve humans of mundane work. Because it is drudgery, mundane work often results in mistakes that result from boredom. Machines don’t get bored so they make fewer mistakes. When Regalado asked McAfee, “Which is further advanced, the automation of intellectual work or of physical tasks?” McAfee responded, “The automation of knowledge work is way, way farther along. … But it feels to me as if we are starting to turn a corner.” Commenting on McAfee’s interview with Regalado, Karen Hanna, concludes “that artificial intelligence can and will be used to automate mundane tasks that knowledge workers now do, thus freeing up their time to do more high-value or more interesting tasks–a belief shared by most AI researchers today.” [“Will Artificial Intelligence and Technology Automation Kill Jobs?” Midsize Insider, 18 July 2012]

 

McAfee told Regalado, “The data available to help a robot is big data, and it’s exploding. The sensors have been progressing along a Moore’s Law trajectory. And the physical pieces of a robot, the actuators and so on, have gotten a lot better too. So it seems the ingredients are all in place for the robots to start getting into the economy.” Tam Harbert, a freelance journalist, agrees that “big-data is about to have a big effect on a lot of industries.” [“Big-Data, Big Problem,” EBN, 21 August 2012] Harbert goes on to note that “the supply chain seems to be choking on the data it already has. It’s hardly ready to digest even more data in many different forms.” When big data collides with mundane tasks, a perfect scenario for artificial intelligence systems emerge. Harbert cites an article written by Lora Cecere in which she writes:

“Today’s supply chains are more complex than before. While the structured data and the systems that use them will not go away, new forms of data offer new opportunities for companies to solve previously unanswered problems. These new data types—from mapping and GPS sensors, to voice, images and video—do not fit into traditional applications or data models. That’s the bad news. The good news, as we learned in a survey of 53 IT and supply chain managers, is that companies are beginning to recognize that they have a problem and that they need to respond. While there is a general lack of understanding of big data terms and technologies, there is an awareness that supply chain best practices are moving from insights into supplies to leveraging insights into demand.”

Harbert goes on to write, “As a journalist, I’m covering big-data stories in all sorts of industries, but I haven’t heard of any big-data projects in the supply chain.” Hopefully, that is about to change. In the latest newsletter published by Cecere’s new company, Supply Chain Insights, Cecere writes:

“Often the best supply chain solutions come from the foxholes of wartime. This is the case of Enterra Solutions. While we have talked about applying artificial intelligence to the supply chain for many years, Enterra Solutions is the first company that we have seen that is making it work. We first saw this at Conair where Enterra used rules-based ontologies to automate shipping compliance and the management of supply chain visibility in the long, and ever-changing, supply chain. Early adopters are now starting to use the Enterra Solution to mine demand insights data and to power supply chain visualization. We think supply chain learning systems are just around the corner.”

You can find that comment in the “Search of Cool” section of the newsletter. Concerning this section, Cecere writes:

“In Search of Cool highlights the supply chain technologies that we think are cool. This section of our website is not pay-for-play. Instead, we comb the world looking for new technologies. The postings are purely based on our beliefs that these technology vendors are meeting three criteria:

  • A new technology approach to solving a supply chain problem
  • A bright idea that is validated by customer references
  • Unique solution for a business problem

However, the list should not be seen as an endorsement. New technologies and implementations carry risks, and these solution providers are pushing the edges.”

As President/CEO of Enterra Solutions®, I’m obviously grateful to have the company receive recognition from an analyst as well-respected as Cecere. I’m also grateful that despite of any perceived risks that Conair might have seen, it was willing to implement a solution that is “pushing the edges.” As Cecere noted, the kind of solutions Enterra works on often combines artificial intelligence and big data analytics. We obviously believe these kinds of solutions will be implemented more frequently as companies understand how they can relieve management personnel from having to focus on mundane tasks so that they can concentrate on more productive activities.

 

Regardless of where technology takes us in the future, goods will have to be produced and distributed. As Cecere noted above, supply chains continue to get more complicated and complex. To deal with that complexity, supply chain professionals will require Sense, Think, Act, and Learn® systems that can help them respond to emerging challenges in a timely manner.