STRENGTH IN NUMBERS

How AI Can Enable Better Leadership

BY RICHARD BOYD

As if straight from science fiction, artificial intelligence (AI), perhaps the buzzword of the 21st century, has become, for many, simply this nebulous catchall idea of computers magically making things work on their own. As a society, we’re slowly but surely overcoming our Stanley Kubrick-esque fears of AI and have come to trust and even rely on it to handle some of the biggest datacentric and information-heavy challenges that were once left up to intuition and guesstimation.

From product inventory at scale to civil engineering, global health crises to sports scouting, we have acknowledged the limitations of the human mind when it comes to digesting voluminous data points. But in the mainstream notion of AI, its utility is still largely siloed within what we consider data-dependent challenges: how much product to order, when is the optimal time to begin the commute, whether or not to refinance, and the like. Because of this, we are overlooking a larger and more intangible component of society that at its heart is also being revolutionized by AI: leadership.

Since the dawn of civilization, leadership has been the key differentiator between success and failure—of nations at war, of corporations, of government, of innovation, and of innumerable other verticals. As a society, we are constantly bombarded with quotes and dictums about effective leadership and its virtues. But the challenge in quantifying good leadership is the perceived intangibility and elusiveness of what constitutes it.

Enter data and modeling. In the human experience, leadership has been reliant largely on intuition. Certainly, people can develop better intuition through experiential learning and by being a student of their vertical, so it is no coincidence that the aforementioned leaders were successful—to a degree. But to AI, each person, each situation, and each variable is a data point ready to be analyzed. So, by strategically integrating machine learning and AI, it is instantaneously revolutionizing leadership within some of the most prominent and important areas of society—namely defense, health care, education, and human resources—ultimately leading to better outcomes for a better world.

Take the following scenarios: • The CEO of a large corporation has thousands of employees, each of whom has social and emotional needs, growth trajectories, health factors, and skills, and the company must attract and retain the right individuals to be profitable while remaining a desirable place to work. • An Army sergeant is given a squadron of soldiers with the goal of leading them through basic training, dividing them into the duties that suit them, and deploying to an overseas base for a specific mission. The sergeant must see the gaps in readiness against that mission and address them for each soldier. • A teacher has a classroom full of different students with different needs, skill levels, home situations, levels of confidence, and dietary and health stipulations, and they must all pass the same standardized test in order to keep the school funded.

In all of these cases, the leader’s success, the team’s success, and the initiative’s success are measured by the final outcome. But these “successes” all hinge on the leader’s ability to intuitively shuffle the right people into the right roles and to provide the right type of direction, incentives, and supervision. In some cases, they must even detect and prevent health, social, or emotional issues to protect the individuals and the health and morale of the group.

Having an AI system reveals the gaps across every dimension associated with successful outcomes and provides actionable leadership guidance. Leaders often have access to hundreds of snippets of information that can lead to conclusions about the needs of each person, but the volume of this information is nearly impossible for the leader to comprehensively act on.

Although leaders have certainly succeeded in these exact scenarios for centuries, failure is always a major risk, especially as other mitigating factors, such as budgets, timelines, or public health crises, begin to close in. AI gap analysis, paired with recommended actions, makes this complex analysis more actionable.

The data is readily available and massive, but humans alone are incapable of processing it adequately. As a society, making the best use of the information at our disposal is critical to achieving the best results. AI holds the key to effective leadership, and as we look to the future of the corrugated industry, we must not only embrace it but also demand it. 

Richard Boyd is founder and CEO of AI and machine learning company Tanjo Inc. and co-founder and CEO of Ultisim Inc., a simulation learning company that utilizes gaming technology and AI. He was the keynote speaker at SuperCorrExpo 2021.

The Winds of Change

BY MITCH KLINGHER

The past few years have been a “golden age” for independent converters, and I have been urging them to take advantage of the most favorable market conditions I have seen in my 30-plus years of experience with this business. It’s been a great run, and seemingly everyone did quite well. For most of you, sales and margins improved, and while some costs rose signifi cantly, in general, profi t levels improved greatly.

Th e gold standard of profi tability for independents had always been a return on sales of 15%, but over the past two years, I have seen margins of 20% and higher. Paper was scarce for a while, lead times for orders went from days to weeks, and overall, converting capacities were challenged in almost every regional marketplace. Customers were far less concerned with price and far more concerned with lead times and delivery schedules.

Well, conditions seem to be changing fairly quickly. Paper has gone from being scarce to being plentiful, and millions of tons of new capacity are scheduled to come online soon. In addition, our traditional containerboard export partners in Europe, Mexico, and South America also have millions of tons in new capacity starting up. Demand for boxes also seems to be waning, with many boxmakers reporting that their volumes have gone down in the past few months. Th e Federal Reserve seems committed to fi ghting infl ation by raising interest rates, which will certainly have a negative eff ect on the economy. Th is will likely depress box demand as well. Finally, much of the new equipment ordered in the past 18 months is beginning to be installed, and there will be additional converting capacity in almost all regions of the country.

In my experience, most independent converters lose margin when the published price of containerboard comes down. While we may be a long way from that happening, all of the capacity starting up in our markets and in our export markets in the face of slowing demand is not exactly a good omen. Our integrated friends are going to have to do a lot of heavy lifting to ensure this does not occur; this may include shuttering some older and less effi cient mills. So, in the face of increased converting capacities that will make things more competitive, cost infl ation that is still not under control, and rising interest rates and a glut of paper that threatens overall margins, what should an independent converter do?

Th e fi rst order of business involves an introspective look at your operations and some basic planning. I recommend you model your business going out 12 to 24 months with slightly decreasing sales and margins. Do a couple of cases and see what your profi t levels look like. Look at it from a cash fl ow point of view as well to ensure you have suffi cient cash reserves and liquidity. Remember, interest rates are going up, and that will have an impact on your bottom line. Th e larger companies have been sending orders to their smaller competitors, and this is likely to slow down or stop, so you need to determine how much of this type of business you are currently running and what your business may look like with less of it. You need to come up with contingency plans for what your modeling tells you may happen and consider such concerns as: 1. Is it time to go from a three-shift to a two-shift operation on some or all of your machine centers? 2. Should you consider outsourcing some business you are currently running ineffi ciently? 3. Do you have extra people anywhere in your operation? 4. Should you renew the lease on extra space you have been carrying or consolidate your operation? 5. Is it time to cut inventory levels?