Juggling balls: Improving products, watching the world

Barb ClearyIn a 1995 interview, tech guru Steve Jobs posited that empires could crash and burn if the emphasis is on sales rather than on product. “Companies forget what it means to make great products,” he said.1 Instead, they direct resources to selling, rather than improving and innovating.

If empires can crash and burn with this approach, what about organizations? A classic story tells of a company with a great innovation that customers clamor for; but the company, intent on advertising and promoting its product, loses sight of possibilities to improve it, and eventually sales drop in spite of increased advertising budgets, and the company goes bankrupt.

Improvement comes not only with a consistent focus on quality, but also with an eye on what else is happening in the world. This demands consistent contact with customers and competitors, as well as a professional interest in economic forces that may have an impact on businesses.

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Missing the point: Gage variability and operational definitions

Barb ClearyThe process of analyzing gage variability is often highly structured, involving an examination of the gages themselves for sensitivity to temperature changes, magnetic fields, and other factors. These are the easy ones. The second area of variability has its source in gage operators themselves, who may have different levels of training, experience, fatigue, and even attitude.

Collecting data offers clues to sources of variability. But when this disciplined analysis fails to uncover real reasons for variability, it may be time for the Sherlock Holmes of variability to look at operational definitions—often the most overlooked consideration when evaluating variation among measurement devices.

Elementary, my dear Watson? Perhaps, but nonetheless these definitions can lead to levels of variation in gage output if they are vague or nonexistent. “In the opinion of many people in industry, there is nothing more important for transaction of business than use of operational definitions. It could also be said that no requirement of industry is so much neglected” (Deming, 276).

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Driverless analysis? Accurate predictions demand more than a chart

Barb ClearyIf you get off the highway and take an alternate route when traffic slows to one lane, you are making a prediction. Likewise, if you decide to invite someone to dinner, that too is a prediction. The scientific method? Predictive in nature. Every time you make a decision, you are making a prediction of an outcome, and choosing one over another based on this prediction.

Prediction skills become second nature because of this daily application. These predictions may not be based on data or evidence, but involve some subjective guess about a preferred outcome. In the case of choosing a traffic route or a dinner date, it’s clear that not much data is involved. The decision involves subjective interpretations, intuitive hunches, and guesses about potential outcomes.

Will data analysis really enhance prediction accuracy? There are no guarantees, without adding a certain amount of understanding of data, of variation, and of process performance.

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Data in everyday life: Grilled or broiled, fried or boiled–hot dogs are a July phenomenon

Barb Cleary“A hot dog at the ball park is better than steak at the Ritz.”

At least that’s what Humphrey Bogart is said to have commented. With the summer season underway and ball parks in full swing, hot dogs at the ball park, on the grill, and in the lunchbox will help to celebrate National Hot Dog Month in July. And many agree that there’s nothing like a hot dog with mustard. Or relish or ketchup or smeared with chili.

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The perennial question: which chart is best?

Barb ClearyThe perennial answer is, of course, “It depends.”

For decades of users, Shewhart control charts have provided information about process stability. Like all Shewhart charts, data is plotted over time and in order from oldest to most current. The traditional control chart, an old standby, is not the only possibility when it comes to garnering information from process data. In monitoring processes with small drifts or changes, for example, the exponentially weighted moving average (EWMA) chart may offer an improvement over traditional Shewhart control charts.

But again, that depends. Certain processes—for example, in the chemical industry—benefit from understanding small shifts or drifts in a process. For other industries, Shewhart control charts do the job quite effectively.

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Trend prediction: Connections with customers matter

Barb ClearyAmong the “Ten top business trends that will drive success in 2016,” reported in an end-of-2015 Forbes article by author and consultant Ian Altman, was the point that “Top performing companies will focus on connecting customers.”

Citing examples that include Uber, Airbnb, Kickstarter, and others, Altman notes that these companies may own no real estate and have no funds to invest, and yet they are among highly successful firms in 2016. He attributes their success, in part, to the fact that in the case of Uber, for example, “they excel at connecting riders with drivers.” Baker predicts that “The most valuable companies will connect buyer to seller, or consumer to content.”

Does this signal a return to customer service as a priority?

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