Innovation Article

Pavel Kireyev’s picture

By: Pavel Kireyev

Good salespersonship is a species of street smarts. It’s about quickly sizing up your customers and pitching your wares in terms that reverberate with their unspoken needs and desires. As artificial intelligence (AI) and machine learning increasingly intersect with e-commerce, these priceless human skills are finding algorithmic analogues—not just at point of sale, but throughout the customer journey.

The results will be familiar to online shoppers everywhere. Netflix’s and Amazon’s algorithms leverage the data from each customer click to fine-tune their recommendations and drive consumption. The tech behemoths also deploy consumer activity data to sharpen their email and social media marketing. This is likely only the beginning because the technology’s predictive prowess is improving all the time.

Multiple Authors
By: Ian Chipman, Knowable Magazine

Imagine yourself in your local supermarket, doing your usual grocery run. In aisle after aisle, countless brands vie for your dollars and attention. Colgate, Crest, and Aquafresh toothpaste. Smucker’s, Welch’s, and Dickinson’s strawberry jam. Coke, Pepsi, and RC Cola soft drinks.

Like most consumers, you probably prefer one over another in dozens of categories that are collectively called consumer packaged goods. This class of goods includes things with a relatively short life cycle such as food, drinks, cosmetics, and cleaning products. These items are used and replaced quickly, compared with durable goods like appliances and cars. True product innovations in this realm are rare, and actual distinctions between brands are typically slim. Yet reaching for your go-tos is almost automatic, and economists have long tried to determine why.

It’s especially curious because, as research has repeatedly shown, consumers in blind taste tests routinely fail to pick out their preferred brands. And they will happily purchase name-brand products such as Advil even when identical generic products sit right next to them on the shelf for a fraction of the cost.

Alberto Castiglioni’s picture

By: Alberto Castiglioni

Ensuring the quality of a car’s performance and design, FARO 3D measurement technology solutions provide simple yet accurate ways of taking contact and noncontact measurements for quality control in automobile manufacturing and assembly.

Portable CMMs such as articulated arms can be used for rapid prototyping, analyzing car body panels, or inspecting a body-in-white, while large-volume laser trackers can be implemented for part inspection, alignment, machine installation, robot calibration, or reverse engineering tasks.

FARO has recently developed and introduced new portable metrology solutions that add measurement features and capabilities to its FaroArm product family: the FARO 8-Axis system and the FARO PRIZM Laser Line Probe.


The FARO 8-Axis system delivers innovative, real-time part rotation to streamline quality inspection processes.

The FARO 8-Axis system combines the portable FARO Quantum FaroArm or Quantum ScanArm portfolio products with a functionally integrated, yet physically separate, eighth axis.

Scott Cowen’s picture

By: Scott Cowen

It was a year ago that our country lost one of its most well-known and respected mavericks in recent political history. After John McCain passed away, many felt that his death left a void that would be hard to fill and wondered whether nonconformist leaders like him, who usually worry more about what’s right than about what’s popular, still exist. The McCain Institute for International Leadership even launched a nonpartisan campaign called #MavericksNeeded, reminding us all of the need to uphold principles of freedom and democracy, encourage moral reasoning, and bring progress.

One year later, we get our pick of politicians on the national stage who are willing to go it alone and ruffle some feathers. The four freshman Congresswomen known as the “squad” come to mind, and so might the four Republicans who crossed party lines when they voted with all Democrats in the House to condemn President Trump’s recent “go home” comments. Texas Democratic Representative Al Green has been called a maverick for trying to get Trump impeached three times without the necessary support from his party.

Jody Muelaner’s picture

By: Jody Muelaner

How would you like to go hands-free, maintain visual focus, and save time? These are just some of the benefits of using voice command to control machinery. Increasingly sophisticated natural language processing, based on artificial intelligence, also means that it is becoming possible to issue complex commands by simply telling a machine what you want it to do. This removes the need to memorize specific command words or learn complex menu systems and sequences of instructions.

During the past few years, voice-assisted technology has become very popular in the consumer market. Products such as Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft’s Cortana have become household names—quite literally, as people use them to turn on the lights and control the heating. This technology is also appearing in the industrial market, where things can be considerably more challenging.

Matt Minner’s picture

By: Matt Minner

Across the United States, small and medium-sized manufacturers are contemplating integrating industrial robots into their facilities. There is a growing awareness that increasingly flexible and affordable robotics systems can help existing workers in a variety of different ways, taking on repetitive tasks and freeing up staff for higher level work and increasing productivity overall.

To serve this growing need, dozens of robotics systems integrators have come online and promise complete packages to guide manufacturers, from initial assessment to fully realized industrial automation. But deferring to these experts can feel a little imposing to manufacturers that rely on established processes they’ve developed internally.

So how does a manufacturer contemplating a first robot-integration project participate fully so that the project is a success on their terms? Here are four suggestions to guide you during the process.

Anthony D. Burns’s picture

By: Anthony D. Burns

Not long ago, we had a client inquire about virtual reality (VR) and quality training. VR and its close relative, augmented reality (AR), are hot technologies right now, not just in entertainment, but also in industry, including their use in training. So it’s no surprise that clients inquire about them. However, as with any technology, you must pick the right tool for the right job, and VR and AR are not always the right choice.

VR explained

VR is a technology that allows a person to feel immersed in a 3D virtual world. This is usually achieved using a headset with a separate display for each eye. Unlike a single-screen view of a 3D scene, the slight parallax in views for each eye gives the viewer an immersive, 3D effect. Headsets range from $10 for Google Cardboard with your mobile phone, to well over $1,000 for self-contained headsets.

Movement of the headset wearer’s head is detected by the accelerometer and/or gyroscope in the mobile phone (Google Cardboard) or the proprietary headset. This movement is then used to adjust the view of the two cameras, one for each eye, in the 3D virtual scene. As you move your head, the scene moves.

Dylan Walsh’s picture

By: Dylan Walsh

In principle, the mountaineer’s work is simple: “To win the game he has first to reach the mountain’s summit,” said George Mallory, who took part in Britain’s first three attempts on Everest during the 1920s. “But, further, he has to descend in safety.”

The tension between these two goals—summiting while also surviving—makes the Himalayas context especially interesting and relevant for companies also balancing multiple goals, says Lindred Leura Greer, an associate professor of organizational behavior at Stanford Graduate School of Business.

“Mountaineering provides an interesting setting, and an extreme one, in which you’re trying to win while also trying to mitigate loss,” Greer says. “This looks a lot like, say, a startup, where you’re trying to maximize to become a unicorn while at the same time trying to make sure the small details don’t pull you under.”

Given this analogue, Greer and other researchers used mountain climbing as a lens to explore longstanding assumptions about group performance. For decades, academics have suggested a straightforward link between a group’s solidarity and its success: The more a group operates with a single mind, the better its execution.

Bill Kraus’s picture

By: Bill Kraus

Continuous improvement is generally considered to be a journey in pursuit of perfection and is regularly associated with the concept of lean manufacturing. In early 1990, reflecting on the Toyota Production System, the National Institute of Standards and Technology Manufacturing Extension Partnership (NIST MEP) created a six-part definition regarding the components of lean manufacturing:
• A systematic approach
• To identifying and eliminating waste
• Through continuous improvement
• By flowing the product
• At the pull of the customer
• In pursuit of perfection

In late 1990, our Arkansas Manufacturing Solutions (AMS) MEP team embraced lean and began promoting it to our manufacturing clients. However, in our enthusiasm, we inadvertently set about “doing lean to our people rather than with our people.” Considerable time and effort was expended in the teaching of “tools” (e.g., value stream mapping, 5S, quick changeover, kanban). Then, like hammers looking for nails, we descended upon the factory floor.

David Mitchell’s picture

By: David Mitchell

Using a novel capability to reason about shape, function, and attachment of unrelated parts, researchers have for the first time successfully trained an intelligent agent to create basic tools by combining objects.

The breakthrough comes from Georgia Tech’s Robot Autonomy and Interactive Learning (RAIL) research lab and is a significant step toward enabling intelligent agents to devise more advanced tools that could prove useful in hazardous—and potentially life-threatening—environments.

The concept may sound familiar. It’s called “MacGyvering,” based off the name of a 1980s—and recently rebooted—television series. In the series, the title character is known for his unconventional problem-solving ability using differing resources available to him.

For years, computer scientists and others have been working to provide robots with similar capabilities. In their new robot-MacGyvering work, RAIL lab researchers led by associate professor Sonia Chernova used as a starting point a robotics technique previously developed by former Georgia Tech professor Mike Stilman.

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