Jody Muelaner’s picture

By: Jody Muelaner

Whether we like it or not, manufacturing is becoming digitized and connected. Industry increasingly connects production machinery with internet of things (IoT) devices, gathers multiple real-time sensor information into large datasets, and harnesses machine learning to make data-driven decisions. The advantages of this Fourth Industrial Revolution are expected to generate huge increases in profits during the next few years. However, these developments are not without risk.

I’m not going to discuss the existential risk of drifting into dependence on a system so complex that only machine intelligence can make any sense of it. Cybersecurity presents much more immediate risks. Industry 4.0 brings the possibility of both terrorists and state actors gaining the ability to remotely shut down and sabotage critical infrastructure and military assets.

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By: Rick Gould

Well over half the world’s population does not have access to safe sanitation. For many people, this means the indignity and risks that come of having no toilets. The answer, it seems, lies in new sustainable treatment plants. The International Organization for Standardization (ISO) and the Gates Foundation have joined forces to show how clean toilets and standards can change people’s lives forever.

In 2010, the United Nations formally declared that access to clean water and safe sanitation are fundamental human rights. Aligned to this, the United Nations’ Sustainable Development Goal Six (SDG 6), which states that everyone should have access to safe sanitation by 2030. This, in turn, would eliminate open defecation, which billions must still endure. According to the Joint Monitoring Program for Water Supply and Sanitation, the official United Nations mechanism tasked with monitoring progress toward SDG 6, 2.3 billion people lack any form of sanitation at all, and more than 200 million tons of human waste go untreated each year.

Matthew M. Lowe’s picture

By: Matthew M. Lowe

While most business sectors have welcomed the efficiencies and benefits that cloud technologies and software-as-a-service (SaaS) offerings bring, the life sciences industry has been slow to embrace external cloud networks. Merely a decade ago, in fact, an International Data Corp. survey showed that 75 percent of CIOs and IT executives in life sciences and healthcare fields surveyed said that security risks were their primary reason for opposing cloud technologies.

Cloud-averse attitudes are slow to change, and industry research shows that companies that manage health information continue to show major resistance to cloud technology.

David Dubois’s picture

By: David Dubois

Faced with a growing range of tech solutions in marketing, from AI to big data to blockchain, business-to-business (B2B) companies too often choose the status quo. Recent evidence suggests the divide between success and failure is not about how much companies spend, but how well they integrate technological solutions that create value.

In other words, a company’s digital investment does not necessarily translate into marketing return on investment (ROI). For that to happen the firm needs to build a digital marketing organization—data-driven marketing capabilities around the customer. 

A pivotal and enduring dimension of success in B2B markets lies in the relationship a company has with its clients. Thus, identifying the type of relationships that you have or would like to have with your customers is an excellent starting point to select and embed digital technology into your strategy. And this process is increasingly important for B2B companies if they are to maintain growth even as digital disruption accelerates the shift from B2BigB to B2SmallB.  

Jon Speer’s picture

By: Jon Speer

The European Medical Device Regulation (MDR) is a new set of regulations that governs the production and distribution of medical devices in Europe, and compliance with the regulation is mandatory for medical device companies that want to sell their products in the European marketplace.

If your company was already compliant with the Medical Devices Directive (MDD), don't be fooled into complacency: The MDR represents brand-new regulations with significant changes.

For those seeking to better understand why the regulations have changed, and what some of the major changes are, let’s take a look at some of the most common questions we hear from our users.

1. Why did the MDD need an update?

There were many reasons the MDD needed to be updated. For instance, when the MDD came into law in 1992, software as a medical device (SaaMD) did not yet exist. Software was something that controlled electric machines, and apps that patients could use to monitor their own health were still nearly 20 years away.

Jay Arthur—The KnowWare Man’s picture

By: Jay Arthur—The KnowWare Man

When I first learned quality improvement back in 1989 at Florida Power and Light, the consultants who trained us taught a very specific way to draw a Pareto chart. They’d been trained in Japan, the place where quality improvement first took root during the 1950s, so I took it for granted that the way they drew Pareto charts was the authentic and best way to do so.

A Pareto chart combines a bar graph with a cumulative line graph. Using the way we were taught to draw a Pareto chart (figure 1), the bars are touching, making it extremely easy to visually compare levels from one bar to the next. The bars span the entire available space along the x axis. The cumulative line graph springs from the bottom left corner of the first big bar, and each subsequent point is plotted from the corresponding top right corner of its bar.

James J. Kline’s picture

By: James J. Kline

The term “risk-based thinking” (RBT) is familiar to those in the quality profession. This familiarity comes in part from its inclusion in ISO 9001:2015, the International Organization for Standardization (ISO) quality management system standard. Although numerous articles and several books have been written on how to implement ISO 9001:2015 in the private sector, little has been done with regards to the public sector.

This reflects two facts. First, the idea of systematically managing the risks governments face is relatively new. Second, where risks are being managed by government organizations, there is no consistent approach. Some are using ISO 9001:2015 and others are using ISO 31000. ISO 31000, revised in 2018, is an enterprise risk management standard.

This article looks at what public-sector organizations are thinking about, and doing, to manage risks.

Nicole Radziwill’s picture

By: Nicole Radziwill

As early as 2015, McKinsey’s “Digital America” report projected that adoption of Industry 4.0 technologies in manufacturing alone was expected to increase domestic GDP by more than $2 trillion by 2025. This estimate, developed from expectations surrounding productivity enhancements, waste reduction using methods from lean manufacturing, and new business models enabled by technologies like 3D printing and practices such as remanufacturing, is on track to not only be metbut exceeded.

Manufacturing is being revitalized

All of these sea changes are happening because of dataand the software used to collect, manipulate, and understand them. While traditional manufacturing jobs have relied on physical and mechanical skills, new manufacturing jobs require additional cognitive skills. As a result, manufacturers are scrambling to identify and roll out technology training for workers that will best support these emerging needs. At the same time, organizations recognize that institutional memory remains critical. Job shadowing and mentorship will be required to bridge the gap, especially as the people who entered the workforce during the 1970s and 1980s begin to retire.

Isaac Maw’s picture

By: Isaac Maw

In manufacturing today, data analysis tools can give management the information it needs to make better decisions in areas such as maintenance and labor. Unfortunately, however, many data analytics systems require large sets of historical data to generate accurate and useful results.

According to Rebecca Grollman, a data scientist at Bsquare, anomaly detection is different. These algorithms can begin generating useful information without needing to be trained on historical data. Although simple, anomaly detection can be used for applications such as detecting machine stoppage, sensor malfunctions, tracking production output, and more. Engineering.com recently spoke with Grollman about this solution. 

How essential is historical data in typical data science applications?

Multiple Authors
By: Vip Vyas, Diego Nannicini

Is your enterprise dominated by passive thinking and prescribed routines? Or is it one that generates fresh thinking and unlocks insights into the future?

The viral popularity of TED Talks—with more than a billion views to date—highlights the innate hunger we have for discovering breakthrough ideas.

When it comes to making that high-stakes decision or tackling the most pressing challenges facing your firm, whose experience, inspiration, and insights do you seek? Just as important, why do you look up to those particular individuals or organizations? What do they possess that draws your attention?

What if this wisdom and intelligence resided in your own organization? What does it take to become a thought leader within one's firm?

One thing for sure is that thought leadership is not created by accident. It flows from intention and focus. The top thought leaders deal with cutting-edge issues in their field of expertise. They use high-visibility platforms, including keynote presentations and conferences, as well as relevant pro-bono work, as opportunities to amplify their ideas and develop a dedicated fan base eager to road-test their insights.

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