Brad Jobe’s picture

By: Brad Jobe

Artificial intelligence (AI) has the potential to reshape the healthcare industry. There is a massive amount of healthcare data available for AI to process. Nearly one-third of the world’s data volume is generated by the healthcare industry, and the volume of big data is projected to increase faster in healthcare than in any other field.

As health systems continue to deal with razor-thin margins, and consumers demand high-quality, cost-effective patient care, leveraging large datasets with AI can drive cost efficiencies across the industry, including within the healthcare technology management (HTM) field.

Although AI doesn’t replace human oversight and management, it allows biomedical equipment technicians (BMETs) and other HTM professionals to work more efficiently and focus on their core skill set and responsibilities. The HTM field should embrace AI opportunities while being mindful of the potential risks and concerns that come with implementing these emerging technologies.

Zach Winn’s picture

By: Zach Winn

For professor Elsa Olivetti, tackling a problem as large and complex as climate change requires not only lab research but also understanding the systems of production that power the global economy.

Her career path reflects a quest to investigate materials at scales ranging from the microscopic to the mass-manufactured.

“I’ve always known what questions I wanted to ask, and then set out to build the tools to help me ask those questions,” says Olivetti, the Jerry McAfee Professor in Engineering at MIT.

“Climate change mitigation and resilience is such a complex problem, and at MIT we have practice in working together across disciplines on many challenges,” says Elsa Olivetti. “It’s been exciting to lean on that culture and unlock ways to move forward more effectively.” Photo by Jared Charney.

Creaform’s picture

By: Creaform

As manufacturers transition toward Industry 4.0 to speed up production cycles and accelerate their time to market, they nevertheless continue to face many challenges, particularly with respect to automating quality control.

Reducing costs drives the need for automated quality control

Automating quality control is indeed a compelling strategy for manufacturers to not only improve product quality, drive innovation, and address the manufacturing skills gap, but also to reduce direct quality control costs and the expensive ripple effect of poor quality or lack of conformance.

In fact, according to the American Society for Quality, quality-related issues cost manufacturers 15%–20% of sales revenue—with some going as high as 40%. Just think of all the quality control costs that can be incurred because of noncompliance:
• Rework and scrapping
• Production line downtimes
• Recalls and repairs
• Purchasing new materials
• Changes to the production planning to accommodate a new run
• Retesting

James Barai’s picture

By: James Barai

Environmental consciousness is a priority for both consumers and businesses, now more than ever. Sustainable business practices continue to gain popularity across various industries, including the nutrition and food industry. In this realm, scientific laboratories are a resource-intensive space as they generate chemical and plastic waste, pose risks from hazardous chemicals, use copious amounts of water, and consume more energy than comparably sized commercial functions.

As advocates for the environment, it’s not just about us recognizing these challenges but also addressing them. While the ethos of green initiatives is clear, practical strategies for transitioning labs to greener operations have historically been elusive. Sometimes, they are even economically challenging. To make such shifts effective, a change in approach is essential, ensuring that costs and the quality of results aren’t compromised.

Erin Vogen’s picture

By: Erin Vogen

One large concern when maintaining a business’ facilities and assets is cost. Managing the costs of repairs, new parts, and personnel can present a challenge.

Although maintenance can be costly, it’s important to see it as an investment that prolongs equipment life span, enhances productivity, and improves employee safety. Plus, when innovative maintenance technologies enter the mix, the result can be significant cost savings for any maintenance program. Let’s explore how.

Understanding maintenance technologies

Historically, companies often relied on manual approaches to organize their maintenance. Spreadsheets and handwritten work orders were the norm. Many companies still use these methods. Unfortunately, manual tracking and coordination often leads to mistakes, and can be difficult and time-consuming to keep organized.

Maintenance technologies bring new efficiencies to maintenance programs. They help companies schedule, automate, and access the information they need from anywhere about regular maintenance tasks.

Let’s discuss some technologies that are revolutionizing maintenance strategies for companies and industries of all kinds.

Mike Figliuolo’s picture

By: Mike Figliuolo

Anyone who has ever seen a crew team rowing down the river has likely wondered why one person is a passenger and everyone else in the boat is rowing like mad. It would seem the coxswain has the easiest job in the boat.

It’s actually incredibly difficult, and my 16-year-old daughter (who is a coxswain) has taught me a few great leadership lessons as I’ve watched her cox over the past few seasons.

The gold-medal winning U.S. rowing team (coxswain at lower left) at the 2008 Olympic Games in Beijing. Credit: Danny Moloschok/Pool/Getty Images

As you run your organizations, there are a few lessons you can take from her and apply to be better leaders of your teams as well. They’re counterintuitive approaches to leadership. But if you apply them well, I think you’ll be pleased with the results.

Those lessons are:
• Know your team’s needs at any given moment
• Small tweaks can have a big impact
• Being small doesn’t mean being weak
• Demand a lot from your team—they’re up to it

Nicolas Lachaud-Bandres’s picture

By: Nicolas Lachaud-Bandres

Imagine a factory where quality assurance actually increases the production speed. Advanced metrology equipment is well on the way to making this a reality by introducing new levels of connectivity with other pieces of equipment and software throughout the factory.

Communication between different elements of the product life cycle enables lightning-fast feedback loops, allowing you to spot problems and intervene before a component falls out of tolerance.

In such a factory, integrating metrology equipment with other systems revolutionizes the production process. Every decision and action, from design to delivery, is informed by real-time data provided by these interconnected systems. An autonomous exchange of information enables the factory to optimize productivity, quality, and efficiency.

That would be a very smart factory indeed. If that sounds like a distant vision, think again: Metrology is shedding its reputation as a bottleneck. Here we discuss PRESTO and the power of robotic metrology cells, one of the most compelling examples of this technology in action, and explore how it can help us to reimagine quality for smart manufacturing.

Master Gage and Tool Co.’s picture

By: Master Gage and Tool Co.

Calibration is essential in almost every facet of industrial processes. The calibration process verifies test instrument accuracy by comparison with recognized standards, and measurement validity hinges on one crucial concept: traceability.

Traceability adherence ensures a continuous link between your unit under test (UUT) and that standard. Here, we examine traceability and explore why it’s necessary for achieving accurate measurement results.

What is traceability?

Traceability is documented evidence that confirms measurement accuracy through an unbroken chain of calibration events. When calibrating an instrument, accurate measurement results rely on this definitive link between the UUT’s indication and those established standards. For most industrial customers, an accredited calibration laboratory typically maintains these reference standards, possibly linking all the way to the National Institute of Standards and Technology (NIST). An accredited lab uses its standards, which are traceable to a higher-echelon lab, to calibrate field-level instruments. This traceable path ensures that the calibrated instrument measurements are dependable.

Ian Wright’s picture

By: Ian Wright

Curing time is the Achilles heel of multimaterial 3D printing. Typically, a multimaterial 3D printer uses thousands of nozzles to deposit resins, which are then smoothed with a scraper or roller before being cured with ultraviolet (UV) light. As a result, this process is constrained by how quickly the resins cure. This limits the types of materials that can be 3D printed.

Now, engineers from MIT, ETH Zurich, and the startup Inkbit have developed a new system that uses computer vision to monitor the printing process and adjust deposition rates to ensure material consistency across each layer of a build. Because the system replaces the need for smoothing or scraping, it can work with materials that cure more slowly than the acrylates most commonly used in 3D printing. These include thiol-based materials, which cure more slowly than acrylates but are also more elastic, more stable over a wider range of temperatures, and don’t degrade as quickly when exposed to sunlight.

Moreover, the automatic adjustments make 3D printing on the new system faster than comparable production-grade systems that need to pause or slow down to adjust for curing times. How much faster? Approximately 660 times, according to the researchers.

Multiple Authors
By: Scott A. Hindle, Douglas C. Fair

Parts 1, 2, and 3 of our series on statistical process control (SPC) have shown how data can be thoughtfully used to enable learning and improvement—and consequently, better product quality and lower production costs. Another area of SPC to tap into is that of measurement methods. How do we ensure that the measurement data we get provide us with accurate process and product insight? Read on to see how SPC techniques play this key role as well as generating actionable and profitable insights from your data.

Measurement consistency

Standard samples are routinely measured as part of measurement monitoring programs, and an essential property of such samples is that they don’t change over time. (If the standard sample is of value 10 on Day 1, it retains the value 10 on days 2, 3, 4... and so on.) A commonly used standard is a reference weight, because reference weights don’t change unless they are damaged or poorly handled.

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