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Archive for the ‘Bidness, and Other Current Economic Realities’ Category

In our prior post, we pointed out that China is on the verge of becoming the world leader in the production, sale and implementation of robots, with a stated goal of producing at least half the nation’s own robots for manufacturing by 2025.  The takeaway from that view, outlined recently in Bloomberg BusinessWeek might be that the world has much to fear from the ascendance of this wave of Chinese bots.

But a recent counterpoint to such a robot apocalypse offered by Greg Ip of the Wall Street Journal suggests that in fact, robots aren’t destroying enough jobs, fast enough.

In short, Ip points out that by enabling society to produce more with the same workers, automation like robots becomes a major driver of rising standards of living – in effect, a productivity boost.  While some say that “this time is different” because the technological change is so profound they fear that millions of workers will be out of work or at best consigned to more menial tasks… Ip says the evidence shows we’re moving in exactly the opposite direction.

He notes that while the U.S. “has many problems, job creation isn’t one of them.”  Job creation has averaged 185,000 per month this year and unemployment is down to a ten year low.  Wage gains are even up, slightly.  Ip says that “if automation were rapidly displacing workers the productivity of the remaining workers ought to be growing rapidly.”  Instead, worker output per hour has been dismal in most sectors, including manufacturing.

When slow-growing occupations are compared to fast-growing ones in data going back to 1850 (a proxy for job creation and destruction driven by ‘technology’), they find that churn relative to total employment today is the lowest on record.

Ip’s point is that the past was, in fact, much more ‘convulsive’ than today’s job churn.  American consumption he notes is gravitating toward goods and services whose production is not easily automated.  Societies increasingly are devoting “a growing share of their income to consumption in sectors where productivity [is] stagnant.”  The idea is that robots can replace fewer things that go into GDP than we think.

As examples he cites medical breakthroughs in new, more expensive treatments rather than cheaper existing treatments, and that child-care work has soared because parents won’t leave kids in the care of a robot.  Over the past decade, “low productivity sectors” including education, health care, social assistance, leisure and hospitality have added nearly 7 million jobs, whereas information and finance, where value added per worker is 5 to 10 times higher, have cut or barely added jobs.

His conclusion: We need a change in priorities.  Instead of worrying about robots destroying jobs, we need to use them more, especially in low-productivity sectors.  While robots may one day replace truck drivers, “it’s more urgent to make existing drivers, now in short supply, more efficient,” and to be more concerned about reducing the labor, and thus the cost of energy, rather than worry about jobs added in areas like solar power.  The alternative, notes Ip, “is a tightening labor market that forces companies to pay ever higher wages that must be passed on as inflation.  And that, he notes, “is a more imminent threat than an army of androids.”

 

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As most of us have noticed by now, the pace of technology – long proceeding at a snail’s pace as generation after generation lived more or less as their parents had – has been accelerating at what to many feels like a breakneck rate.  We’ve gone from linear progress to exponential, moving from the industrial revolution to the current digital one at an ever-quickening pace.

Moore’s Law, now over 50 years old, postulated that the number of transistors per square inch on a circuit board would double every year or two since – and today, that continued pace means that exotic technologies that include AI (artificial intelligence), robots, cloud computing and 3-D printing systems are proliferating, evolving in many cases faster than we humans can keep up.  It seems like things keep getting faster, smaller and smarter.

And therein lies the downside of all this technical innovation, says Gary Smith, a logistics expert with the New York City Transit in a recent issue of APICS Magazine.  Smith believes that “the rate of technological change exceeds the rate at which we can absorb, understand and accept it.”  This is acutely true in the world of supply chain, with its deep reach into manufacturing, distribution and just business in general.

Most importantly he notes that “disruptive technologies require a workforce that adapts to new processes, new ways of learning and training systems.”  In that spirit, he suggests key considerations and qualities that are going to be important within supply chains of the future, ones that the next generation workforce can expect to have to incorporate into their work patterns.  Among them:

  • Data analysis and database development skills. The ability to analyze and produce actionable results from data using logic and fact with insightful opinions and interpretation of available data will be critical.
  • Critical thinking. It’s vital to data analysis and fact-based decision making.  The ability to quickly acquire knowledge and break it down into its logical components, and then analyze and drill for accurate and actionable conclusions matters.  You have to be able to take complex situations and break them down into their component tasks.  Or as Franklin Covey would say, “start with the end in mind.”  Critical thinking means “abstraction, systems thinking, experimentation and collaboration,” notes Gary Smith.  To wit:
  • Abstraction. The ability to discover patterns in data.  Often, lessons from one industry can be applied to another, for example.
  • Systems thinking. That is, viewing issues as a part of the whole – how issues relate to the rest of a system.  Often, the “good of the many outweighs the good of the few.”
  • Experimentation. Complex problems require trial and error, testing and experimentation.  It’s okay to fail, as that’s part of learning.  Fail fast, think differently and learn to adapt as new conditions present themselves.
  • Collaboration. It’s working with others toward the common goal.  Easier said than done.  It requires team-building and facilitation skills, along with everyone keeping their eyes on the prize.  Collaboration is particularly important in supply chain and ERP work, where silos need to be broken down and people need to cooperate and effectively communicate.

These are the critical skills companies will be looking for.  We see the need for it every day in ours, and we’re only one of many.  So in a very real sense, the future really is now.

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A bit off topic today, but we think those who work for a living and hope one day not to have to, may find this of interest in the pursuit of sound retirement investment strategies.

We are gradually learning these past few years that when it comes to making money by investing in the stock market, the lowly “exchange traded fund” (or ETF) that tracks a broad basket of stocks across many companies or sectors (the S&P 500 tracking funds being among the more obvious indexes) generally beat the returns of funds managed by human money managers.  In other words, the overall market, over time, will exceed what most of even the best money managers can do for you.

This of course is disconcerting to those who earn their living picking those stocks, or selling their funds.  Nonetheless, it’s proving true.  According to several sources we’ve reviewed, something like 86% of managed funds do NOT beat the market, or even the so-called “benchmarks” they are measured against.

The core of the problem is that it is very hard to beat the market.  Obviously.  But in this era of rocket scientist, algorithm producing, quant-based, big data stock picking… it doesn’t mean folks aren’t trying. And that may be the problem.

With today’s computing power and increasing wealth of raw data, it is possible to test thousands, even millions of data sets, ideas and trading philosophies.  The standard method of doing this involves something called “backtesting” in which someone comes up with a market hypothesis, and then looks back over, say, twenty years, to see how their strategy would have performed against real markets, with their unpredictable ups and downs, over that time.  To check the validity of their results, the technique is then checked against “out-of-sample” data, consisting of market history that was not used to create the original technique.

But in the wrong hands, things can go, well… wrong.  There is a powerful temptation to get published in finance journals among researchers, analysts and economics.  Too often, this leads to “torturing the data” as a recent Bloomberg BusinessWeek article pointed out (April, 2017).  This in turn has led to some exchange-traded funds using flawed statistical techniques according to a couple of experts at Duke University, implying that “half the financial products promising outperformance that companies are selling to clients are false.”

For example, a batch of research involving United Nations data once found that the best (backtested) predictor of stock performance in the S&P 500 was butter production in Bangladesh.  That is to say, out of millions of data sets, tested backwards in time, the one with that came closest to tracking the S&P 500’s actual market performance over time was the output of butter production in a third world nation half-way around the world.

Researches can “twist  the knobs” on their assumptions in search of a prized “anomaly” that they can write about – or sell.  They can vary, say, the “time period covered, or the set of securities under consideration, or even the statistical method,” according to Bloomberg’s Peter Coy.  Negative findings get round-filed; positive findings get published — or make into an ETF whose performance we may be relying upon for our retirement.  With enough tests, notes Coy, “eventually by chance even your safety check will show the effect you want.”

So next time you read about some great new investment strategy vetted by a Wall Street hedge fund’s top “quants” (the math wizards who come up with stuff)… take a deep breath, turn the page, and leave your long-term funds in a plain old vanilla stock market index fund from Vanguard, Fidelity or American Funds.

It will help to ensure that when it’s time to retire, your money will be ready too.

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Be sure to read our previous post beginning here on some basics of “blockchain” technology.  Today, we’ll tell you about an even newer evolution in the rapidly evolving blockchain saga.

Our title headline today is how a fellow named Joe Lubin, founder of a company called ConsenSys that develops applications for the burgeoning “Blockchain” technology, describes Ethereum.  Chances are if you’ve heard of Ethereum at all, it’s because the new platform was the victim of a $60 million hack a while back.

What you may not know is that companies including IBM, Microsoft, BP, JP Morgan and a lot of others recently attended a forum sponsored by an industry group called the Enterprise Ethereum Alliance.  Ethereum technology, while still young, comes with enormous promise, despite last year’s hack setback.  Advocates believe Ethereum “could be a universally accessible machine for running businesses,” according to a Matthew Leising of Bloomberg Businessweek.

Cornell University professor Emin Gun Sirer says “Ethereum gives you a new way for the computer to interact with the real world and how money moves.”  In effect, it’s a complete business-to-business transaction engine and database.  It’s based on the “Blockchain” concept of digital money.  The idea behind Blockchain is to create a verifiable virtual currency that can be distributed as easily as an email.  It’s an online ledger on computers distributed around the world.

We’ll spare you the details, but the idea is that every “bitcoin” distributed is tracked and verified, in a system that basically runs itself.  Its main purpose is to move currency from point A to point B.

What Ethereum adds to the Blockchain is the ability to store fully functioning programs called “smart contracts.”  So beyond moving money, users can potentially control contracts or projects, thus allowing a person to complete a job for a customer and trigger payment on completion – all without added human intervention, in a secure framework.

That’s the concept, at any rate.  As Leising notes, “Once you can create contracts – which in essence are just operating procedures – you can use them to manage almost any kind of enterprise or organization.”

A variant on the technology would see companies participate in an Ethereum platform on a closed invitation basis, given that a public platform tends to increase security risks, whereas a semi-private network among aligned business partners might provide an effective alternative with the same end result.

A variety of companies are exploring their options today.  John Hancock is experimenting with compliance tracking and anti-money-laundering regulations in its wealth management unit.  Airbus is exploring ways to move its entire supply chain to a Blockchain.

If all this sounds a lot like the future of enterprise business models, one shouldn’t be surprised.  There are security and logistical wrinkles to be worked out, to be sure, but the idea of a self-regulating supply chain of integrated enterprise systems that embrace project management, verifiable currency transfers and contract fulfillment has a lot of companies paying attention to the Blockchain ledger technology.

Right now, Ethereum is helping to lead the way.  It’s a name worth remembering.

 

 

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Over a decade ago, the late Peter Drucker (for my money, the wisest business professor and most clear-headed business writer ever) wrote in the Harvard Business Review that the best executives follow 8 similar practices:

  • They think about what needs to be done
  • Consider what is best for the enterprise
  • Develop action plans
  • Take responsibility for business decisions
  • Encourage communication
  • Focus on opportunities instead of problems
  • Run productive meetings
  • And embody a team mentality

Recently, HBR added a ninth trait, noting in a survey of 35,000 employees that people are happiest at their jobs when they are led by executives who have deep knowledge of the core operations of the business.  In the U.S., supervisor competency had a stronger influence on employee job satisfaction than salary.

In today’s increasingly complex, digital work environment, it becomes increasingly critical to understand the core technical concepts underlying the tools and solutions we bring to our jobs every day, while ensuring that executives are balancing that with the necessary core business vision.  We often emphasize to clients that while we may be a group of tech-focused individuals, we are first and foremost “all about the business.”

It’s harder than ever to successfully straddle the biz-tech line today, yet more important than ever, too.  The only difference between today and when our firm started in the late 80s is that the pace of change is increasing.  And along with it, so is the challenge we all face, in simply keeping up.

You’re not alone.

 

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As software providers, we focus largely on manufacturing (and distribution) clients, and so as a service to all we like to periodically report on trends and developments in the manufacturing sector, such as today’s post about a jobs program that helps Americans get back into manufacturing in a practical, no-nonsense kind of way.

A good start can be found at a workforce initiative in Louisville, Kentucky, called KentuckianaWorks, funded by several entities including local and federal agencies, along with JPMorgan Chase.  Recognizing that “manufacturing jobs are here and growing in numbers” but that unskilled assembly-line work has been replaced by advanced manufacturing jobs, KentuckianaWorks made a large commitment to support training for manufacturing (and other) jobs by designing a five-week “Certified Production Technician” training program, along with a two-week variation, for displaced workers aged 18-60.  Only a bit over half stick around to completion, but those who do find jobs quickly.  The program has already placed nearly a thousand graduates at an average of about $13 per hour.

The skills programs focus on computing and technical skills, as well as basic math and problem-solving – in other words, just want manufacturing managers say they are looking for today.  Meanwhile, over 80% of U.S. executives said in a recent survey that the skills gap will affect their ability to meet customer demand, and nearly as many claimed it would “make it more difficult for them to use new technologies and increase productivity,” according to a recent article in Bloomberg Businessweek.

Over the next decade, well over 3 million manufacturing jobs are expected to become available as boomers retire and economic growth spurs work opportunities.  Those figures come from the Manufacturing Institute in Washington D.C. Yet a “skills gap” means that about 2 million of those jobs could go unfilled.

The KentuckyianaWorks program works with local manufacturers to help design their two courses.  Companies who have worked with KW graduates say that their basic training “sets them apart from other entry-level candidates,” so that once hired, employers can help employees expand their knowledge and increase the likelihood of continued employment and promotions.  Recruiters say that while not every hire works out, the success rate with these trainees is higher than with other hires.

In an era of increasing and often unrealistic clamor and hype about bringing back jobs, it’s programs like these that are helping to make manufacturing hiring a reality, and closing the gap between needed hires and the skills gaps too often found in potential hires.

 

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Annette Franz is a noted blogger who writes often (here) on “improving both the customer experience and the employee experience by utilizing their software platforms to facilitate listening to and operationalizing the voice of the constituent.”  That’s a mouthful, but in short, she is all about empowering people and consulting on improving customer and employee experiences.

In a recent article in the March/April issue of APICS Magazine, she reminds all of us who manage businesses of a few key tips for empowering our employees so that they become more productive – both for us and to our customers.  Following are a few of Ms. Franz’s suggestions:

  • Define what empowerment means at your company. Think ahead and set expectations and boundaries.
  • Outline what doing right means and what it looks like.
  • Describe and reinforce with your team what great customer experience is, and what it means for the customer and to the business.
  • Ensure employees have the knowledge and skills to do what you’re expecting of them. Train, communicate and provide a framework.  Then, let them do their jobs.
  • Make sure workers know how they affect business outcomes.
  • Confirm that your people have a clear line of sight to the customer. Let them lose the script – empowered employees don’t need one.  Trust them to make the right choices and decisions for the customer.
  • Remind employees that going the extra mile doesn’t have to cost a dime. Customers just want them to listen and act.  Allow for common sense, but don’t necessarily rely on it.
  • Evaluate progress and the business environment. If necessary, eliminate any vagueness and refine goals.
  • Provide feedback and coaching so people know if they’re on the right track.
  • When employees comprehend the vision and are allowed to execute on it, businesses realize numerous meaningful productivity enhancements in teamwork, creativity and overall satisfaction.

We’ve been in consulting long enough ourselves to have seen these principles put in play by smart companies with forward thinking managers.  Sadly, we’ve also witnessed the cold vibe of the highly secretive, micro-managed, non-empowered company.  Which would you rather work for?

 

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