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Last Updated on January 30, 2018

One Question to Know If You Can Predict Your Future

One Question to Know If You Can Predict Your Future

The Boston “Big Dig” project — a central artery and tunnel through the city — is one of the biggest engineering fails of modern times.[1] While it did get completed, it took way longer than expected. It was eight years behind schedule when finished, and the cost got way out of control. It was supposed to be $2.6 billion and became $24 billion counting interest on the debt. Concrete was mixed wrong. A ceiling collapsed and killed a car passenger. The entire process was a mess.

    But how did this happen? How did a series of capable adults and city officials so drastically miss on the time and budget for a large project? And what can we learn from it?

    We are bad estimators

    We often want to assume projects and new initiatives will go according to a best-case scenario, i.e. no delays, no interruptions, etc. That’s usually not the case. Other priorities arise. Distractions happen.

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    You should be positive about the projects you undertake, yes. But you also need to prepare for the worst — and since most of us aren’t great predictors of the future, we definitely need those plans. Think of a basic, daily example: the supermarket. Oftentimes you’ll tell yourself “20-30 minutes for essentials.” Then you end up there 1 hour. We’re not good at estimating time, in general.

    When we were considering a new feature on Lifehack’s website, we initially thought we could finish it in two weeks. That didn’t seem like very long. But the concept of “two weeks” doesn’t help identify the time for each section of the project.

    So we asked ourselves a critical question: Can I break this down into smaller chunks?

    We decided to break down the work like this:

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    • 1.5 hours for research
    • 3 hours for building the tools foundation
    • 1 hour for testing
    • 1 hour for amendments etc.

      When we broke it down, it seemed like 2.5 weeks was a more reasonable time estimate.

      If you break a big project into smaller items, it’s easier to estimate. What if you thought of a 15-week project as 15 one-week projects? Wouldn’t that make the planning more successful?

      Further breaking it down

      Break big chunks into small, manageable tasks, then work through those one step at a time. Repeat the question: can I break this down still?

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        Whenever you break something down, think about going even further down — a 15-week project can get down to a 20-hour chunk, but that 20-hour chunk can become a series of 2-hour chunks too.

        The goal here is to make things easier for yourself — and make the estimation of time more realistic.

        Some projects, like The Big Dig, are huge in nature. That is true. But even The Big Dig could have been broken down into manageable chunks and probably come in closer to time and budget expectations.

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        In your personal life, you can definitely execute this strategy. Just make the big, overwhelming projects into small, manageable pieces. Work through those. Eventually the whole big project will be done!

        Featured photo credit: pixabay via pixabay.com

        Reference

        More by this author

        Leon Ho

        Founder & CEO of Lifehack

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        The Productivity Paradox: What Is It And How Can We Move Beyond It?

        The Productivity Paradox: What Is It And How Can We Move Beyond It?

        It’s a depressing adage we’ve all heard time and time again: An increase in technology does not necessarily translate to an increase in productivity.

        Put another way by Robert Solow, a Nobel laureate in economics,

        “You can see the computer age everywhere but in the productivity statistics.”

        In other words, just because our computers are getting faster, that doesn’t mean that that we will have an equivalent leap in productivity. In fact, the opposite may be true!

        New York Times writer Matt Richel wrote in an article for the paper back in 2008 that stated, “Statistical and anecdotal evidence mounts that the same technology tools that have led to improvements in productivity can be counterproductive if overused.”

        There’s a strange paradox when it comes to productivity. Rather than an exponential curve, our productivity will eventually reach a plateau, even with advances in technology.

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        So what does that mean for our personal levels of productivity? And what does this mean for our economy as a whole? Here’s what you should know about the productivity paradox, its causes, and what possible solutions we may have to combat it.

        What is the productivity paradox?

        There is a discrepancy between the investment in IT growth and the national level of productivity and productive output. The term “productivity paradox” became popularized after being used in the title of a 1993 paper by MIT’s Erik Brynjolfsson, a Professor of Management at the MIT Sloan School of Management, and the Director of the MIT Center for Digital Business.

        In his paper, Brynjolfsson argued that while there doesn’t seem to be a direct, measurable correlation between improvements in IT and improvements in output, this might be more of a reflection on how productive output is measured and tracked.[1]

        He wrote in his conclusion:

        “Intangibles such as better responsiveness to customers and increased coordination with suppliers do not always increase the amount or even intrinsic quality of output, but they do help make sure it arrives at the right time, at the right place, with the right attributes for each customer.

        Just as managers look beyond “productivity” for some of the benefits of IT, so must researchers be prepared to look beyond conventional productivity measurement techniques.”

        How do we measure productivity anyway?

        And this brings up a good point. How exactly is productivity measured?

        In the case of the US Bureau of Labor Statistics, productivity gain is measured as the percentage change in gross domestic product per hour of labor.

        But other publications such as US Today, argue that this is not the best way to track productivity, and instead use something called Total Factor Productivity (TFP). According to US Today, TFP “examines revenue per employee after subtracting productivity improvements that result from increases in capital assets, under the assumption that an investment in modern plants, equipment and technology automatically improves productivity.”[2]

        In other words, this method weighs productivity changes by how much improvement there is since the last time productivity stats were gathered.

        But if we can’t even agree on the best way to track productivity, then how can we know for certain if we’ve entered the productivity paradox?

        Possible causes of the productivity paradox

        Brynjolfsson argued that there are four probable causes for the paradox:

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        • Mis-measurement – The gains are real but our current measures miss them.
        • Redistribution – There are private gains, but they come at the expense of other firms and individuals, leaving little net gain.
        • Time lags – The gains take a long time to show up.
        • Mismanagement – There are no gains because of the unusual difficulties in managing IT or information itself.

        There seems to be some evidence to support the mis-measurement theory as shown above. Another promising candidate is the time lag, which is supported by the work of Paul David, an economist at Oxford University.

        According to an article in The Economist, his research has shown that productivity growth did not accelerate until 40 years after the introduction of electric power in the early 1880s.[3] This was partly because it took until 1920 for at least half of American industrial machinery to be powered by electricity.”

        Therefore, he argues, we won’t see major leaps in productivity until both the US and major global powers have all reached at least a 50% penetration rate for computer use. The US only hit that mark a decade ago, and many other countries are far behind that level of growth.

        The paradox and the recession

        The productivity paradox has another effect on the recession economy. According to Neil Irwin,[4]

        “Sky-high productivity has meant that business output has barely declined, making it less necessary to hire back laid-off workers…businesses are producing only 3 percent fewer goods and services than they were at the end of 2007, yet Americans are working nearly 10 percent fewer hours because of a mix of layoffs and cutbacks in the workweek.”

        This means that more and more companies are trying to do less with more, and that means squeezing two or three people’s worth of work from a single employee in some cases.

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        According to Irwin, “workers, frightened for their job security, squeezed more productivity out of every hour [in 2010].”

        Looking forward

        A recent article on Slate puts it all into perspective with one succinct observation:

        “Perhaps the Internet is just not as revolutionary as we think it is. Sure, people might derive endless pleasure from it—its tendency to improve people’s quality of life is undeniable. And sure, it might have revolutionized how we find, buy, and sell goods and services. But that still does not necessarily mean it is as transformative of an economy as, say, railroads were.”

        Still, Brynjolfsson argues that mismeasurement of productivity can really skew the results of people studying the paradox, perhaps more than any other factor.

        “Because you and I stopped buying CDs, the music industry has shrunk, according to revenues and GDP. But we’re not listening to less music. There’s more music consumed than before.

        On paper, the way GDP is calculated, the music industry is disappearing, but in reality it’s not disappearing. It is disappearing in revenue. It is not disappearing in terms of what you should care about, which is music.”

        Perhaps the paradox isn’t a death sentence for our productivity after all. Only time (and perhaps improved measuring techniques) will tell.

        Featured photo credit: Pexels via pexels.com

        Reference

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