Dr. Yossi Sheffi is the Elisha Gray II Professor of Engineering Systems at the Massachusetts Institute of Technology and Director of the MIT Center for Transportation and Logistics (CTL). His most recent book is “The Magic Conveyor Belt: Supply Chains, A.I., and the Future of Work.”
Throughout human history, people have worried about technology taking away their jobs. From the automated textile looms and “horseless carriages” of the past to the package-handling robots and driverless vehicles of today, automation has elicited fear and resentment toward job-stealing machines. This is understandable, as during the series of industrial revolutions, many jobs were diminished or replaced by machines. Yet other jobs were created over time.
Some research suggests that transport, warehousing, and manufacturing may automate the fastest, leading to a large reduction in supply chain-related employment for low-skilled workers. Just a few examples of the kind of automation the airfreight industry is already seeing or may soon experience include self-driving forklifts, palletising and depalletising robots, automated transport of ULDs within air cargo facilities, and robotic process automation (RPA) of administrative transactions.
New workplace technologies can disrupt jobs in three ways:
1. By “de-skilling,” or enabling less-skilled (and lower-paid) workers to accomplish something that previously required a higher-skilled (and higher-paid) worker. Examples include the use of digital maps and vehicle routing algorithms.
2. By enhancing the productivity of workers in their existing jobs, thereby reducing the number of workers required for a given volume of production. For example, robots that bring items for packing and shipment to workers save time and reduce the physical effort required to handle cargo.
3. By eliminating jobs altogether. For instance, aircraft used to require a cockpit crew of five (pilot, co-pilot, navigator, flight engineer, and radio operator), but technology automated almost all aspects of the last three positions and eliminated those jobs, leaving only the pilot and co-pilot.
While the march of technology eliminates some jobs, it can be demonstrated that technological development results in many more jobs than it eliminates. It is not clear, however, how many jobs will be lost versus how many new ones will be created. What is fairly clear is that the new jobs will differ from those we are accustomed to. Those new jobs typically require new or different skills that displaced workers don’t always have. In a world with growing levels of automation and shifting technologies, many workers will need new or upgraded skills in order to retain, regain, or improve their employment.
Even if many workers stay in the same jobs with the same job titles, their jobs will not be the same. They will likely delegate to automation many of the repetitive tasks that consumed sizable parts of their time. They will need to use a growing array of timely data about both the overall environment and each task instance. They will be expected to spot deviations from normal operations that might be caused by defects in some process, or a wrong execution of some task by an algorithm, while taking into account changes in the broader environment that may necessitate an override. And they will be expected to help determine whether a potential anomaly is something to fix, a change to adapt to, or just a blip to ignore.
Today, almost everyone can operate a smartphone, tablet, or personal computer, but that knowledge will not be sufficient. As more work uses data-driven approaches, workers and managers will need more numerical literacy, which is the ability to use and understand mathematics, as well as statistical literacy, which is the ability to understand and reason with data and statistics. Seeing the data on a smartphone is one thing, but understanding the implications of those data and taking the right action in response is another.
How can companies—not just in airfreight but in any supply chain-related business—ensure their frontline workers are ready for the future? To be efficient and productive they must hire and employ high-performing employees. This may require helping employees acquire the skills they need to provide value in a complex, technology-driven world.
Achieving these goals necessitates training and educating a spectrum of three broad categories of people. The first are existing, productive workers who need ongoing, incremental re-skilling or up-skilling to maintain a stable or improving career path. The second are displaced workers who need substantive retraining to get their next job or career. The third are young people entering the workforce who need a foundation of skills that suit both their aptitudes and the prevailing demand for human labor in the economy.
A critical challenge for the future of supply chain talent revolves around entry-level workers. Companies need to develop entry-level trainees into experienced workers who can handle exceptions and make the decisions that require human judgment and nuance. Yet if technology greatly reduces or eliminates entry-level jobs by automating all the “easy” tasks, how does a company ensure it has a pool of mid-level workers with five years’ experience?
One way is through the apprentice system, by which young people learn a trade or profession while working in a company. The modern German apprentice system is a 2- to 3.5-year program that combines about 70% work in a company with 30% training in a vocational school, and provides salaries and subsidies for living expenses. Over half of German high school graduates go into this system, and about two-thirds of the apprentices then accept a full-time job with the employer that originally hired them.
Another is to use advanced technologies to enable them to provide value. For example, the use of AI-enabled augmented reality (AR) allows the employment of people with limited or no experience, because they are instructed by the software to do certain tasks. For example, warehouse workers wearing AR goggles see the physical environment with overlaid instructions, such as which items to pick and where they are located.
A third solution is on-demand learning. These are training-delivery systems designed to quickly teach a person when they need to solve a specific problem or perform a new task. They deliver small, modular blocks of knowledge that might consist of a short video lecture or text, a quick exercise or problem set to confirm the student’s understanding, and rapid feedback on performance and any misunderstandings. Bite-sized lessons and self-tests can quickly build or refresh incremental skills.
Maximising the performance of both people and technology is vital. Automation can help handle routine tasks, so that people can concentrate on the more fulfilling parts of their jobs. Artificial intelligence and digital tools can augment people, enabling them to handle jobs they could not do in the past. Timely and affordable education and knowledge can help workers and managers cope with technological change. Future generations can make the most of automation by collaborating with the technology to create fulfilling and well-paying jobs, affordable products and services, and a bright future.
. His most recent book is “The Magic Conveyor Belt: Supply Chains, A.I., and the Future of Work.”){kind=link}