Friday, January 3, 2020

FedEx Needs a New Strategy

This is similar to a long-ago post about Green Mountain’s K cups. What was once a niche is now a commodity. The same thing has happened to FedEx. What was once a unique proposition—“if it has to be there on time, absolutely, positively...”—has now been commoditized since Amazon made 1 or 2 day (hour?) delivery a standard feature of their service. Remember when businesses gladly spent megatons of money to expedite stuff because of poor planning systems, inevitable processing delays or mistakes. Now quick delivery is a given as much as emails, cell phone texts, instant messaging has eclipsed snail mail, interoffice memos and faxes (“facsimiles”). One analyst that predicted the delivery/freight companies would lose market value if/when Amazon got into the delivery business—and was right despite the overall stock/equity markets increasing over the past few years—now predicts FedEx will be absorbed by another or bankrupt in the next few years.

As pointed out, FedEx spent its tax cuts, like many other companies, on buying back stock so that the stock price remains elevated instead of investing in competitive products, services, corollary channels, disruptive market offerings, etc. This too was predicted by many, especially since there was an overall cash glut by the top corporations. If they don’t know what to do with their money, and they’re stuck with the same ROI, stock prices aren’t going to increase. The only way to keep stockholders happy and their bonuses staying in the millions: repurchase company stock. It’s a no-risk, easy win achieving those outcomes.

The ‘repurchase stock’ strategy does nothing to increase total market value (price x shares). The only way to win is to focus on basic fundamentals. Has FedEx—and others like it—gone the way of buggy-whip manufacturers where it will become smaller and more niche-y for boutique customers? How do you realize that what you’re doing is no longer needed and you need to be agile and adaptive before it’s too late? Some fuel companies realized this and long time ago and invested in renewable energy sources. Accordingly, there was a hesitance to adapt to new energy sources but at least one source shows that solar energy price/watt has decreased 99% in the past decade. Coal, oil and natural gas may now or soon be the most expensive sources of energy. How do you adapt and convert your decades- and centuries-long investment in obsolete strategies?

It’s one thing to stick with core competencies and it’s another to not pay attention to market trends and disruptions. Amazon is not bullet-proof—recession-proof, disruption-proof—either. It’ll be interesting to see how they adapt to changing trends. Though Amazon is 50% of online retail, it’s only 5% of total retail. How will they adapt? By opening physical stores—like acquiring Whole Foods or piloting cashier-less grocery stores and book stores?

Even though it seemed I had a bullet-proof business providing custom stainless steel fixtures for restaurants and commercial kitchens—how could this customization, fabrication service be replaced by off-shoring or...—I was always on the lookout for disruptive technologies that could ‘radicalize’ the competitive landscape. Same with another consulting client that seemed to be in an artisan-type business: what would happen if someone sold 3D-printer plans for people to ‘sculpt’ their own commemorative plaque, trophy, etc.?

If you’re only focused on stock price, you’re not focusing on a survival, thriving strategy. As investors realize many big companies are not reinvesting in growing their market value—adapting to new strategies, strengthening market growth, operating profits, etc—they’ll stop rewarding execs by buoying stock prices by rewarding, encouraging, enabling stock repurchase plans. Then the indices will drop as stock prices collapse and then maybe FedEx and other companies will wake up and look beyond the next quarter’s horizon and beyond their narrow country-lane-wide market segment.

Monday, November 11, 2019

Weapons of Math Destruction

The post title is the title of a wonderful book by Cathy O’Neil. I can’t believe I haven’t blogged about the book before. She outlines how artificial intelligence in the form of algorithms often have unintended consequences, like sentencing of criminals based on biased perspectives. The algorithms are only as wise as the programmers, and often reflect biases of the programmers. Even today, there’s news that Apple’s trademarked credit card may have a sex-bias granting higher credit limits to males with lower credit scores than their female partners. Also, Marketplace ‘radio’ host, Kai Ryssdal, interviewed the author of “You Look Like a Thing and I Love You”, a new book about AI, wherein they demonstrated that despite lots of ‘learning’ about how people and cats appear, computer algorithms can’t accurately depict or create a picture of these living creatures.

They and I worry about driverless cars. If computers have trouble recognizing objects, legitimate hazards may go undetected while false positives (i.e. an non-hazard is identified as a hazard) generate evasive action, endangering other vehicles on the road. Likewise, if the algorithms depend on accurate signals, how well do sensors work during inclement weather? I’ve had the opportunity that radar sensors for making cruise control decisions became unavailable for use during blizzards.
They all work well during fair weather. And all algorithms can be objective if they’re built without biases, presumptions and really, really good data. As Taleb says in “Black Swan”, in order to predict the future we have to have perfect insight into the past in order to fully understand the cause and effect of the circumstances that ‘created’ our current situations. Will we ever be able to load all of the relevant data to make great decisions, not merely the significant data? Remember the scandals of the price with online shopping sites when pricing algorithms went ‘crazy’ showing prices that were insanely inflated over normal based on perceived demand and customers’ sense of affordability (i.e. how wealthy they were)?

I’m not saying algorithms are bad nor that AI won’t happen but it may be a while. Especially if a dearth of data isn’t the problem but how well it can be analyses to give great answers, not just good answers. I’ve seen presentations where AI is used to augment physicians’ own diagnostic powers through visual analysis of tumors and other physical symptoms. But note my emphasis of augmentation, not replacement. Even some other applications (like identifying guns) need human assistance (i.e. re-interpretation).