| This week, how resilient companies do better in bad times. Plus, Eric Schmidt on recognizing the power of your people, and how Freeport-McMoRan went deep on agile and AI. |
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| It’s a rare business that’s prepared for a pandemic like the coronavirus. The massive scale of the outbreak and its sheer unpredictability make it challenging for executives to respond. At the same time, higher volatility—from geopolitics, economic cycles, climate risk, and many other forces—has become business as usual for many companies. |
| What leaders need during this crisis are behaviors and mindsets that keep them from overreacting to yesterday’s developments and help them look ahead. These behaviors include creating a network of teams rather than following the typical top-down command-and-control structure. |
| Making networks work. In this model, some parts of the network pursue actions that take place outside regular business operations. Other parts identify the crisis’s implications for routine business activities and make adjustments, such as helping employees adapt to new working norms. In many cases, the network of teams will include an integrated nerve center covering four domains: workforce protection, supply-chain stabilization, customer engagement, and financial stress testing. |
| And you have to be resilient. Not everyone fares the same in times of crisis or economic slowdown. During the 2008 downturn, we traced the paths of more than 1,000 publicly traded companies and found that about 13 percent of those companies fared materially better than the rest. We called those companies “resilients”—and we were intrigued. What made them different? |
| It turns out the resilients were in good shape to start: they entered the downturn ahead, dipped less, and widened their lead in the recovery. They created flexibility by cleaning up their balance sheets before the trough, which helped them be more acquisitive afterward. There is little evidence to suggest that the resilients were better at timing the market, but they cut costs ahead of the curve and were prepared earlier, moved faster, and cut deeper when recessionary signs were emerging. |
| Looks can be deceiving. In the three boom years before 2007, the resilients actually underdelivered slightly on total returns to shareholders (TRS). However, they opened up a slight lead in TRS relative to their sector peers during the downturn and extended this lead through the recession. By 2017, the cumulative TRS lead of the typical resilient had grown to more than 150 percentage points over the nonresilients. |
| How did they know when to take these actions? It seems that some companies have developed a superior analytical muscle and are just more externally oriented. And they truly understand the concentration risks within their own business models. We see a wide variance of practice, with some global companies not quite understanding that a downturn in one part of their operations will absolutely be correlated with another part of their operations. They don’t see the full magnitude of the impact. |
| It’s too early to say how long or deep any downturn could be. Our question is, what is the right time to start preparing? It’s when the board and management get concerned that something might happen. And rather than waiting for it to happen, to act on it. |
| And remember: although crises change, preparation is timeless. Here’s a piece from 2009 on how with the right mindset and actions, business leaders can be strategic when considering their next moves. It holds up pretty well. |
| For our evolving coverage of COVID-19’s implications for business, check here. |
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| OFF THE CHARTS |
| Consumer goods and not-so-goods |
| The consumer-goods industry has some catching up to do when it comes to digital maturity. Among 11 industries analyzed in the latest McKinsey Digital Quotient survey, consumer goods ranked third lowest. We’ve found that following four core elements of a successful digital and analytics strategy can help these players deliver impact at scale. |
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| INTERVIEW |
| Power from the people |
| Eric Schmidt, the former chairman of Alphabet, is regularly tapped for his views on the future of tech. Yet he’s quick to point out that it’s the people behind the technology who make the difference. He recently spoke at a McKinsey conference about the dizzying speed of disruption, as well as how to nurture and position people to harness technological dynamism for the greater good of organizations and society. |
| “When I think about how we’re going to solve the problems ahead of us, the answer is people. We need to acknowledge how powerful people are, particularly those who are willing to take risks and drive societal change.” |
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| MORE ON MCKINSEY.COM |
| How banks can ease the pain of negative interest rates | Over time, negative interest rates hurt profitability by eroding banks’ net-interest margins. But with better governance and data collection, treasurers can staunch the effects of margin erosion. |
| Transit investments in an age of uncertainty | Why spend big money, for decades, on traditional transit when mobility technologies are changing so quickly? We look at ways cities and rail operators can shape the mobility system to incorporate these new technologies. |
| Building world-class LNG players | Liquefied-natural-gas (LNG) organizations that capture the most value will be the ones committed to significant investments in building and maintaining best-in-class LNG trading, marketing, and optimization capabilities over the next few years. |
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| WHAT WE’RE DOING |
| A mining company goes deep on agile and AI |
| How copper-mining giant Freeport-McMoRan achieved next-level performance with help from McKinsey data scientists and agile coaches. |
| The mood was apprehensive as data scientists, metallurgists, and engineers from Freeport-McMoRan filed into the control room of a copper-ore concentrating mill in Bagdad, Arizona, on the morning of October 19, 2018. They had come to learn what would happen when they cranked the big mill up to a work rate that had never been tried. |
| The possibility of causing problems at the mill weighed on everyone’s mind. The team members had initially resisted the idea of running the mill faster. They wanted to keep the stockpile of ore that feeds the mill from dropping below the minimum size they had long maintained. Their concern was that a too-small stockpile would hamper the mill’s performance. |
| Whether the minimum stockpile size actually helped the mill run better was another matter. No one really knew for sure. Nor could the mill’s managers and staff say what would happen if the stockpile shrank to less than the traditional minimum. What they did know is that a custom-built artificial-intelligence model, loaded with three years’ worth of operating data from the mill and programmed to look for operational tweaks that would boost output, kept saying copper production would rise if the mill were fed with more ore per minute. |
| To the mill operators, that notion sounded logical enough—except that it didn’t account for the minimum stockpile size they had in mind. But the model didn’t know, or care, about minimum stockpile size or any of the mill operators’ other ideas about how the mill ought to be run. |
| With permission from company executives, the crew members at the Bagdad site decided to turn up the pace of the mill as the model had suggested. They also prepared to ramp up mining and crushing activities so the stockpile of ore wouldn’t run out. |
| At ten o’clock in the morning, a technician clicked a control on his computer screen to speed up the system of conveyor belts carrying chunks of ore from the crusher to the stockpile and from the stockpile to the mill. Everyone in the room kept watch on the 13 oversize monitors in the control room, which were lit up with readings from hundreds of performance sensors placed around the mill. The quantity of ore grinding through the mill rose. No warnings went up. |
| Twelve hours passed. The mill held steady. Even when its stockpile of ore dipped below the usual minimum, the accelerated delivery of ore from the crusher and the mine allowed the mill to keep going. As the weeks went by, the mill sustained the faster pace with no loss of efficiency. The data model had been right: the mill could handle more ore than its operators thought. |
| “That was the breakthrough we’d been looking for,” Justin Cross, the Bagdad site’s general manager, told us. “Once we started to run the mill at full speed, we knew we could get results from more of the recommendations that the model was making.” |
| For more about how the mining company applied AI techniques and agile principles, keep reading here. |
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| BACKTALK |
| Have feedback or other ideas? We’d love to hear from you. |
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