Readers: this blog is set in the future (sometime after the year 2020). Each entry assumes there has been a 5th revolution in the US — the Revenge Revolution. More about the Revenge Revolution and author, How the 5th US Revolution Begins and About the Author. Many entries are formatted as conversations. Characters appear in a number of entries, with many entries building on previous conversations.
Occasionally I do a “sense check” about the likelihood of a Revenge Revolution. Entry #318 is the most recent “sense check.” One more note — sometimes I write about another topic that does not quite fit the theme of the blog. Those comments are available on the page titled “JRD Thoughts and Comments” as well as “Tech Tsunami”, which has more articles about how technology might affect US…and add a dimension to the Revenge Revolution.
Background to Technology Tsunami series focuses on how implementation of technology may change the family earnings structure in the US.
In “Technology Tsunami” (Entry #319) I noted that with the increased use of artificial intelligence, many current workers will need to increase skills in order to remain employed. But just what is artificial intelligence? And how can it be used? To make AI more concrete and less abstract, thought it might be interesting to allocate the next couple of entries to describing some personal experience developing AI and what happened as a result.today
First, let’s go back to define just what constitutes artificial intelligence, or AI? (Readers, please keep in mind this is not an article for an academic journal. The article is aimed at trying to help the general populace understand more about what AI is and how it might affect the workforce.)
The term “artificial intelligence,” which was first used in the 1950’s, seems to be applied to an ever-increasing range of computer-based applications. Much of AI we hear about today has been developed by applying to very large data bases sophisticated multiple regressions (regressions look for an association between one action/word and another). The algorithms that result become the foundation for software to support an AI application. What has expanded the use of AI is the availability of very large databases and much more computing power. However, as demonstrated by this example, a useful and effective AI program can be developed without an overly large database and/or staff.
A question associated with AI, “When AI is implemented, will people be replaced?” Yes, but people have always been replaced with the introduction of new technology. Farm hands were replaced by tractors and mechanical harvesting equipment. The printing press replaced scribes. The telegraph replaced the Pony Express. Trains replaced stage coaches. Cars replaced buggies…and endless other examples.
In the current wave of AI, the jobs that seem most vulnerable in the near-term are ones that involve repetition. Jobs where running a software program or using robot could perform most or all of the task. Such jobs might be assembling parts, loading/unloading shelves, providing certain types of information (clerks, including law clerks could be replaced by a more sophisticated Siri, for example), completing forms or completing some basic analysis (proof reading, financial analysis, etc.), steering vehicles and similar jobs.
The list of vulnerable jobs is quite lengthy and includes a considerable number of white-collar positions. For example, when General Motors announced in fall 2018 the intent to close five plants in the US/Canada, more white-collar workers were affected than assembly workers.
OK, how about a real-world example. In 1980…yes, that was many moons ago…I transferred to headquarters of Buick Division of General Motors. One of the staffs I managed was responsible for forecasting sales – short and long-term. The short-term forecast – 180 days – was used to set production schedules at assembly plants and suppliers.
When I arrived, the accuracy of the forecast was abysmal. Even though Buick had been in business about 75 years, it was not uncommon for forecast sales for the current month to miss actual sales by 30-40%…sometimes 50%. Such a variance made it extremely difficult to manage inventory. The forecast/actual discrepancies also caused frustrations with Buick dealers because arrival dates for cars ordered varied widely from the original schedule, which in turn frustrated customers.
To increase the accuracy of the forecast, we developed an application of AI. The AI-based forecast consisted of three key estimates: (i) industry sales; (ii) mix of sales by category – % small cars, % mid-size cars, % full-size, % SUV’s, etc. – within the industry; (iii) Buick % share within the general categories.
Unlike today, at the time most assembly plants were limited to a few models with little variation in size. Further, changing the production mix at an assembly plant could be time consuming and costly.
Buick’s solution to this dilemma (and common in the industry) was to “force” the dealers to take the mix of cars produced. Further, there was little recognition of differences in consumer preference by region of the country. Dealers in New England, where smaller cars were preferred, would end up with mix of small/large cars very similar to the dealers in say Texas, where larger cars were preferred. “Encouraging” dealers to take the production mix required the field staff to spend considerable time with the dealer and often involved some type of costly incentive – free financing, extra cash per car, etc. Dealers would then have to try to steer customers to these “unwanted” cars.
The solution to fixing the problem was conceptually simple: (i) a more accurate forecast; (ii) allowing dealers to order what cars they wanted. Improving the accuracy of the forecast was the critical first step. Doing so required building a math model that would predict more accurately upcoming changes in demand.
Previous sales forecasts had been based on changes in the rate of actual sales. Basing the forecast on “lagging indicators” – sales the past few months – is akin to trying to drive a car by looking only in the rearview mirror. Doing so reduces one’s speed and increases the chance of making a serious error. The previous method of forecasting was always “catching up” to changes in demand rather than being ahead of the curve.
Developing the AI model was remarkably easy – or so it seems now. We ran regressions of historical sales data for the industry as well as Buick. Fortunately, the auto companies had been reporting monthly sales for many years, so the data base was credible. The results of the regressions yielded useful, seasonal patterns. We also analyzed the shift in mix of sales over time. This helped determine if sales of smaller cars were increasing faster or slower than say mid-size or luxury cars. Another task was estimating how many people were switching from cars to what were then early-version SUV’s.
Finally, we had to determine Buick’s likely share of each category. At the time the overall car market was shifting to smaller cars. While Buick had competitive smaller car entries, it was more successful in larger cars. The effect of the shift in consumer preference was profound. Even though in a given month Buick could gain in market share in every major industry category compared to the previous year, that same month could show Buick’s overall share had declined compared to a year ago. That phenomenon was always fun to try to explain. “Yes, we gained market share in every category…but, no we lost market share overall.”
Within about one year of starting the AI model, the US industry experienced a major economic downturn and vehicle sales took a nosedive. The AI model helped Buick management begin to make more informed decisions about setting production schedules and marketing plans. With the implementation of the AI-model, the accuracy of the forecast improved markedly. Rather than a variance of 30-40% between actual and forecast for a given month, the variance fell to less than 5%. The improvement helped smooth production schedules, reduce short-term layoffs and/or overtime at Buick and suppliers and made lead-times for deliveries to dealers much more accurate.
The increased forecast accuracy allowed Buick to migrate to what is called a “free-expression” forecast and production schedule. Dealers were allowed much more freedom to order the number and model of cars they wanted.
The decision to migrate to “free-expression” forecast/production caused great angst among staff members tied to the old “dealers-will-order-what-we-tell-them” system. In the end, however, most everyone became a convert because the overall production volume and mix were about what the dealers wanted.
Other benefits of the AI forecast model? The field staff was able to spend more time helping dealers with marketing programs, working on customer satisfaction and finding ways to improve profitability. The dealers then started to order more cars from Buick because the turnover rate improved. In the three-year period following implementation of the AI model, Buick increased market share more than any other manufacturer, domestic or foreign. While not all the gain in market share can be attributed to the AI model, the number of new products Buick introduced during the same period was limited, so most of the gain in market share came from “non-product” activities.
What happened to employment? Buick reduced the number of field offices from 26 to 20. Buick also started a call center to increase contact with dealers located outside urban areas. The non-urban dealers still received some personal visits, but less frequently.
Use of AI also changed the skills required of the office staff. To be effective in the new environment, staff members needed more skills in math, statistics, economics and marketing. If today’s computing power were available then, we could have cut the staff in half, possibly more. Even skills of and the number of senior managers would have been affected. At the retirement party of a key sales executive, who’d grown up in the days of gut-feel and seat-of-the-pants forecasts, the retiring executive told me – after several drinks – “I never understood what you were talking about, but I trusted you.” I appreciated the compliment but was a bit taken aback by the admission.
Does this example help us look ahead for what might happen when more AI is implemented? I think so. What did this rather simple application of artificial intelligence help Buick accomplish?
- + Increased sales
- + Increased market share
- + Increased profits
- + Increased customer satisfaction (dealer and buyer)
- – Reduced employment
- – Higher skills required of employees
If you’re a shareholder and/or your compensation is tied to profits, you will view the results of implementing the AI program as positive. If you’re an employee whose job was eliminated and/or you were unable to learn the additional skills required, you will view the AI program as negative. The inherent conflict between perspectives, unless we quickly start to manage more effectively, will likely be another contributing factor to the Revenge Revolution.
(In the next entry, another real-world example of using AI – an early application of consumer profiling. While the profiling was not as sophisticated as done today by Google, Facebook, Amazon and many others, the effort allowed Buick to spend marketing dollars more effectively. We’ll also address why it is important that the output of AI programs is understood and trusted. )
Gelly: “Maybe not but you have lots of study time…and a bunch of experience in the real world using economics stuff. Remember, KISS, okay?”
Jordan: “When Siri tells you Michigan beat Indiana, that’s a form of AI.”
Gelly: “How does she do that?”
Gelly: “So for Siri, Alexa and their siblings, they’re best at providing quick access to available information. At the same time, the working stiffs most affected by Siri and siblings are people employed to provide information. The example might seem a bit dated but as a kid I remember being able to pick up the phone and have the operator get someone’s number, right?”
Gelly: “What about AI replacing some functions of what lawyers do, or at least law clerks do? Same with some portion of information gathered when visiting a doctor. Seems as if a lot of people could be replaced, or maybe have been replaced already by some form of Siri and siblings. What about using AI for tasks that are a lot more complicated than say picking up boxes or searching a database?”
Jordan: “Well, some clothes are already being made 100% or nearly 100% by machines. And over time, machines will make clothes that require more steps.”
Gelly: “Another case of the working stiff getting screwed?”
Jordan: “Excellent example. I’m impressed you remembered.”
Gelly: “Now I’m starting to get even more confused. What happened to KISS…keep it simple, stupid?”
Gelly: “First, let me make sure I understand the idea of trade between two countries. I get the part where one country might have stuff the other country needs, or makes some product more efficiently than the other country. That all seems logical. What also seems logical is that trade should be fair. Maybe I’m being naïve but shouldn’t trade between countries be like what we were all supposed to learn as kids…you know, treat your neighbor as you want to be treated?”
Gelly: “Critical such as growing and exporting coffee beans might be critical to the economy and welfare of the people of say Costa Rica? Coffee’s probably a big deal to Costa Rica but hardly of any importance to the US…other than maybe Hawaii.”
Jordan: “For countries with only a few products to export and where those products do not have much competition, tariffs might work. But, for most countries, tariffs are a high-risk poker game. While coffee can’t be grown in every country, in can be grown in many countries. Unless your country is a real big dog for that product or commodity, the country adding tariffs runs the risk of losing exports.”
Jordan: “Well, for one thing, China can then decide to add tariffs to some goods imported in China from the US – say corn or soybeans, which is exactly what they did after Trump put tariffs on Chinese steel.”
Jordan: “Some but the US steel companies did what often happens in the US when tariffs are implemented – the US companies immediately raised prices.”
Gelly: “Then, unless I’m missing something, the tariffs really end up being a tax on consumers. The government might collect revenue from the tariffs but the consumer – the working stiffs – are the ones who gets screwed.”
Jordan: “A lot Trump’s tariffs were head scratchers. In fairness, sometimes trade between countries does get out of whack. And tariffs can help resolve the issue. But tariffs are like a Band-Aid, for small wounds and to help only temporarily. There’s a better way to solve issues when trade gets out of whack…and a better way to manage trade.”
Jordan: “Yes, trade agreements. The agreements usually include what you might call a trade court. That court helps revolve issues and avoids tariffs.”
JC: “Look, I like the idea of ‘treat thy neighbor as thyself’ as the standard for behavior. But let’s not be naïve. What do we do about those people who don’t follow the rules?”
Greenie: “Until Trump. Then he and his gang basically gave the finger to everyone. He even trashed people in his cabinet who supported him from the get go. Some display of appreciation and loyalty, huh?”
Greenie: “A start would be to reinstate the 60-vote rule in the Senate for approving appointments, whether for the agencies or the courts. A 60-vote rule would force the White House to offer nominees toward the middle politically…not the extremes.”
Jordan: “On the Judicial side, even with the 60-vote rule, what about limiting tenure of Senate-approved judges? Right now these judges have lifetime appointments.”
Jordan: “Re-upping.”
Greenie: “I don’t know if the limit should be at the court level or in total. For now, let’s assume the limit applies to a specific level. Otherwise someone might get to SCOTUS with only 6-7 years left out of the 30-year limit. That doesn’t seem fair.”
Greenie: “OK, Jordan, any ideas how to stop such behavior? And what about all the obvious ethics violations by Trump, the Trump family and some cabinet officials? How do we stop that going forward?”
JC: “More teeth and more transparency. I realize there’s some information cannot be disclosed. But, and this should be a big but…no comments, please about personal appearance…the baseline should be to make the public as aware as possible of the shenanigans and unethical behavior by people inside the government, especially members of Congress and high-ranking agency personnel. The disclosures might force some people to stop.”
Greenie: “Make it in addition to impeachment. Some of the behavior will be illegal. Why shouldn’t that behavior get punished like the rest of us are subject to?”
Greenie: “I agree the president and cabinet need to pass the same end-of-year test given to 8th graders. Let me add another, ‘Duh, are you serious?’ idea.”
JC: “You mean the idea of treating your neighbor the same way you want to be treated?”
Greenie: “I don’t know if Sessions had kids or grandkids but do you think he’d want his kids or grandkids separated from their parents?”
JC: “Maybe there would a cabinet officer or some high-ranking staffer whose job it is to go around and ask ‘Would you want this whatever-idea-is-being-discussed to happen to your family?’ The person could be titled the ‘sanity-check maven.’”
JC: “Let’s hope going brain-dead is past tense. We have a new opportunity to begin rebuilding American values post Revenge Revolution. Even if it is kindergarten like, using ‘treat thy neighbor’ as a check mark for policies and legislation seems like a good way to keep things from getting too out of control again.”
Greenie: “You mean like when public pressure force king Trump to stop separating children from families at the border?”
JC: “Don’t make me laugh? Not know what an 8th grader know? Still not sure what you’re talking about.”
JC: “Now I think I see where you’re headed. What about Trump implying…or at least asking…if Canada burned down the White House in 1812? No that was the British. Gee, Donald, in case you didn’t know Canada has been a long-time friendly neighbor. Canada is north of the continental US, except for one area near Detroit, and a major trading partner until you tried to ruin the relationship.”
Jordan: “So, Greenie, exactly what are you proposing?”
Greenie: “I don’t know how we’d test for some things but by forcing candidates for Federal office and Cabinet nominees to take 8th-grade end-of-year exams, you can assume that those who pass at least paid some attention to teachers along the way. And anyone who failed…”
Greenie: “Why not be straightforward? No reason to sugarcoat. I think we give some examples of basic information that Trump and the Cabinet members did not know. There are lots of examples where it looked as if they hadn’t graduated from 8th grade and/or should have been wearing dunce caps. It was embarrassing for the country.”
JC: “Alright, now I have an idea for how to make America great again.”
Greenie: “I shouldn’t even smile at that one…but it was pretty good. Now, JC, stop the puns and tell us your idea.”
JC: “Two prongs. (i) Reconfigure existing roads into smarter roads. Smarter roads can carry more traffic with a lot less congestion; (ii) rebuild and expand the rail system to handle more passenger trains and freight traffic.”
Jordan: “Commuting by rail in metro areas is easier, more pleasant, less expensive and faster. Plus, you can work on the train.”
Jordan: “High-speed rail needs to be defined given the barriers that exist. High-speed in the Northeast corridor is not going to be like a bullet-train in Japan. Making that happen would be outrageously expensive and disruptive.”
Jordan: “Don’t know exactly but I’ll bet you’re pushing 85-90%.”
JC: “This sounds great but what about resolving the conflict between freight and passenger traffic? The little that I know about rail, the freight railroads seem to keep resisting any efforts to add passenger traffic to certain rail lines…in fact, most rail lines.”
Jordan: “Other than difference in speed between freight trains and passenger trains, I don’t know of a technical reason the two can’t share the tracks.”
Jordan: “The article was in the Harvard Business Review and written by Theodore Levitt. The title was ‘Marketing Myopia.’ An example of the myopia was the railroads viewing themselves as being in railroad business and turning thumbs down to considering being in the transportation business. As a result, the railroads lost a huge share of the logistics business to the trucking industry.”
Gelly: “Jordan, you have a call from a guy named Willie. Want to take it or should I…”
Willie: “Your voice mail said you wanted to talk about crypto-currency.”
Jordan: “In a way, the crypto-currencies remind me of the US prior to the creation of the Federal Reserve. Lots of variation in value and no one quite sure who’s in charge?”
Willie: “Another good question. A lot of the so-called crypto-currencies vanished early on. Some never got any momentum and some were never issued even after investor funds were taken.”
Willie: “A couple of crypto-currencies were backed by some assets. One was even backed by gold, but most were backed by nothing.”
Jordan: “While true that governments might have gone off the gold standard, governments do have assets…and a way to generate revenue. Governments can collect taxes. Save one or two, crypto-currencies had no assets and none had authority to collect taxes.”
Jordan: “Over the years you’ve dealt with the Federal Reserve. How did they view crypto-currency?”
Willie: “Two primary roles of the Federal Reserve are managing monetary policy and controlling the banking system. The already difficult job of managing monetary policy became much more difficult with the alternative-currency universe.”
Willie: “Most people did not fully appreciate how crypto-currencies forced the Fed’s hand. Crypto-currencies took a bigger and bigger slide out of the Fed’s monetary pie. In order to achieve the same result as before crypto-currencies, the Fed was forced to exaggerate changes in interest rates, both up and down. The exaggeration also affected Wall Street. As a result, the Fed was unhappy, Wall Street was unhappy, many investors were unhappy and the general public was unhappy.”
Willie: “Federal revenue. While the Fed is not responsible for collecting taxes, in order to manage monetary policy, the Fed needs to have a good idea of sources and uses of Federal funds.”
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