Harish Jose’s picture

By: Harish Jose

he dictum, “purpose of a system is what it does” (POSWID) is famous in cybernetics, attributed to the management cybernetician Stafford Beer.

Beer notes: “A good observer will impute the purpose of the system from its actions and thus from the resultant state.”

Hence the key aphorism and acronym. There is, after all, no point in claiming that the purpose of a system is to do what it consistently fails to do. I’ve written about this before here and here. In cybernetics, the emphasis is on what a “system” does, and not especially on what it is or what the designer or management of the “system” claims it’s doing. Thus, we can see that POSIWID has a special place in every cybernetician’s mind.

A “system” is a collection of variables that observers purposefully select to make sense of the world around them. The boundaries and parts of the system vary according to who is doing the observing. The purpose also is assigned by the observer.

Paul Laughlin’s picture

By: Paul Laughlin

As I started reading The Book of Why: The New Science of Cause and Effect, by Judea Pearl and Dana Mackenzie (Basic Books, 2018), I was reminded how often analysts trot out the bromide “correlation is not causation. It’s a well-known warning. Indeed, I often encourage those learning data visualization to ensure their designs don’t imply causation. But this book helped me think more deeply about it.

This is not a light read—but it is an important one. As Pearl and Mackenzie make so clear in case studies, causation is central to how we think as human beings. Most nonstatisticians are not really interested in patterns and correlations in the data. They want to know what causes an effect so they can take appropriate action.

Donald J. Wheeler’s picture

By: Donald J. Wheeler

Students are told that they need to check their data for normality before doing virtually any data analysis. And today’s software encourages this by automatically providing normal probability plots and lack-of-fit statistics as part of the output. So it’s not surprising that many think this is the first step in data analysis.

The practice of “checking for normality” has become so widespread that I have even found it listed as a prerequisite for using a distribution-free nonparametric technique! Yet there is little consensus about what to do if your data are found to “not be normally distributed.” If you switch to some other analysis, you are likely to find it too is hidden behind the “check for normality” obstacle. So you are left needing to customize your analysis by fitting some probability model to your data before you can proceed—and this opens the door to all kinds of complexities.

The histogram in Figure 1 represents 20 days of production for one product. It is clearly not going to pass any test for normality. But is there some other probability model that might be used?


Figure 1: Production data for June

Bruce Hamilton’s picture

By: Bruce Hamilton

In 1985, about the time I was discovering there was a better way to produce products, The Natural, a film about an aging baseball player with extraordinary talent, was garnering multiple Academy Awards. This archetype concerning natural “God-given” abilities is common in Western culture—in sports and the arts and even in business.

Early in my journey as a student of the Toyota Production System (TPS), I observed the same archetype on the factory floor, this time applied to specific lean tools. In a very natural way, certain employees revealed uncanny, focused abilities to reduce waste. Although there was broad interest in continuous improvement, leaders self-selected themselves to excel in specific lean tools. 

Gleb Tsipursky’s picture

By: Gleb Tsipursky

The biggest falsehood in business leadership and career advice may also be the most repeated: “Go with your gut.” Surely you’ve heard this advice often as a decision-maker, as well as some variations of that phrase, such as, “Trust your instincts,” “Be authentic,” “Listen to your heart,” or “Follow your intuition.”

I’m deeply frustrated, saddened, and angered when I see highly profitable companies, top-notch careers, and great business relationships devastated because someone bought into the toxic advice of going with their gut. When someone returns to work after some guru’s fire-walking seminar and starts to behave like their “authentic self,” they are simply shooting themselves—and their business—in the foot.

Our authentic selves are adapted for the ancient savanna, not the modern business world. Following our intuitions can lead to terrible decisions in today’s professional environment. For the sake of our bottom lines, we need to avoid following our primitive instincts and instead be civilized about how we address the inherently flawed nature of our minds.

Kate Zabriskie’s picture

By: Kate Zabriskie

‘Kendra, I think you’re going to do wonderfully at this next task. You have a good eye for detail, and that’s exactly what’s required here.”

“Tom, you have a real knack with people, and I’d like you to take on a temporary role in account management. I think you will thrive based on what I’ve seen you do with our internal customers.”

“When I was asked to recommend someone to head the new department, I immediately thought of you. You learn quickly, you work hard, and you’re good at bringing a team together. These next few months are going to be a heavy lift, and I can’t think of anyone else better suited to the task.”

Like gardeners planting seeds, people who spot potential can help others produce results they may never have imagined for themselves. By following a few steps, anyone can learn to see the future success in others.

Step One: Start with strengths

Pay attention to what’s special. Everyone has talents, and great potential-spotters zero in on those gifts. Is someone organized, great with people, quick to pick on new activities, or mechanically inclined?

Jonathan Gilpin’s picture

By: Jonathan Gilpin

Regardless of how much we, as a society, are able to implement and use technologies in business, global supply chains will always be dependent on the ways in which people interact with one another.

Even local supply chains can be problematic, but it’s predominantly global ones that can pose challenges for buyers and suppliers looking to do business ethically and work with companies with a strong code of ethics. Some of the problems that can arise include poor working conditions, poor wages, forced labor, and other forms of modern slavery, gender discrimination, and failure to mitigate against climate change.

Why are ethical supply chains important?

Did you know that 83 percent of supply chain professionals say that ethics are extremely or very important to their organizations?

Ethical supply chain management is being fueled by consumer demand, profitability, and visibility. The concept of ethical sourcing has risen in popularity during the past four years. Terms relating to the topic generated an average of 6,530 average monthly Google searches in January 2022, compared with just 2,640 monthly searches worldwide in February 2018.

William A. Levinson’s picture

By: William A. Levinson

Ryan Day1 describes how the rise of independent auto dealers is a “gray swan” event for the automobile industry. This was not only bound to happen, as observed by the author, but also long overdue. The article states, “...current state laws prohibit OEMs from selling new vehicles directly to consumers (D2C). Selling directly would cut out the dealership franchise—the middleman—and all the associated price markup fees. This could theoretically save car buyers an alleged 30 percent of the cost of a car.”

This problem has been known for decades, and it is not something the supply chain’s value-adding stakeholders should continue to tolerate. Dealerships do not add value to the transaction, but if the 30 percent figure is correct, then $7,500 of the price tag of a $25,000 vehicle constitutes pure waste. My recommendation to consumers, as an immediate recourse in the absence of changes of the laws in question, is to game the system by waiting until the end of the model year to buy a new car. The car is still new but, as it is now last year’s model, the dealership must offer a substantial discount to get it off the lot to make room for more inventory.

Bryan Christiansen’s picture

By: Bryan Christiansen

Top management often struggles to approve large sums required for annual maintenance because the expense is seen as a necessary evil. As a result, if a business encounters short-term financial constraints, the first place it looks for savings is maintenance.

This is why your maintenance budget can’t be an arbitrary number that is devised based on revenue. Instead, you must link maintenance expenditure to desired business outcomes.

Smart businesses understand that maintenance is an enabler of business success and growth. By understanding the trade-off between asset life and revenue, a business can target maintenance investments to optimize equipment uptime and longevity, maximize revenue, and delay capital expenditure.

In other words, maintenance budgeting is a balancing act. Let’s see how to walk that fine line.

Mastering maintenance budgeting

Budgeting is a fundamental requirement in business. Although never a perfect exercise, it allows business departments to identify their annual goals, allocate expenditure to achieve those goals, and keep evaluating performance against the plan. The maintenance department is no exception.

Anthony Tarantino’s picture

By: Anthony Tarantino

In 2007, Nassim Taleb described black swans as highly improbable events that had dramatic or even catastrophic effects on markets and economies. Until recently, it seemed that such events were indeed rare.1 There’s now a major rethinking with the world entering the third year of the Covid-19 pandemic, reshoring and near-shoring of supply chains, and the first major land war in Europe since World War II.

The disruptions to fragile global supply chains are becoming more the norm than the rare exception. Such dramatic and sudden changes call for faster and more-accurate decision-making, i.e., a consensus around one data-driven source of truth.

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