Mark Schmit’s picture

By: Mark Schmit

The Covid-19 pandemic has asked much of manufacturing executives. They’ve had to make decisions about staffing and operations in the face of tremendous health and economic uncertainty—and then adjust or even change decisions based on myriad shifting and evolving factors.

They’ve had to retool to produce new items for a new market to generate needed revenue while helping address an urgent demand for personal protective equipment, or PPE. They’ve had to master new skills and new tools to communicate with workers and customers, and foster community in a period of necessary isolation. Oh, and they’ve had to do all of these at the same time and very quickly.

It’s been a heavy lift, as manufacturing executives who took part part in a Sept. 30, 2020, virtual conversation on the near-term and longer-term impacts of the twin public health and economic crises made clear. The discussion was one in a series of 11 listening sessions hosted by the National Institute of Standards and Technology’s Hollings Manufacturing Extension Partnership (NIST MEP) called the “National Conversation with Manufacturers.”

Anne Trafton’s picture

By: Anne Trafton

In the era of social distancing, using robots for some healthcare interactions is a promising way to reduce in-person contact between healthcare workers and sick patients. However, a key question that needs to be answered is how patients will react to a robot entering the exam room.

Researchers from MIT and Brigham and Women’s Hospital recently set out to answer that question. In a study performed in the emergency department at Brigham and Women’s, the team found that a large majority of patients reported that interacting with a healthcare provider via a video screen mounted on a robot was similar to an in-person interaction with a healthcare worker.

“We’re actively working on robots that can help provide care to maximize the safety of both the patient and the healthcare workforce,” says Giovanni Traverso, an MIT assistant professor of mechanical engineering, a gastroenterologist at Brigham and Women’s Hospital, and the senior author of the study. “The results of this study give us some confidence that people are ready and willing to engage with us on those fronts.”

In a larger online survey conducted nationwide, the researchers also found that a majority of respondents were open to having robots not only assist with patient triage but also perform minor procedures such as taking a nose swab.

Johns Hopkins University’s picture

By: Johns Hopkins University

Since they came into use in 1938, electron microscopes have played a pivotal role in a host of scientific advances, including the discovery of new proteins and therapeutics as well as contributions made to the electronics revolution. But the field of electron microscopy must incorporate the latest advances in data science and artificial intelligence to realize its full potential in the years ahead, according to a global research team co-led by Mitra Taheri, professor of materials science and engineering at Johns Hopkins University’s Whiting School of Engineering.

In a commentary in Nature Materials, Taheri and the team discuss a model for an open, highly integrated, and data-driven microscopy architecture needed to address future challenges in the field such as energy storage, quantum information science, and materials design. They recommend an approach that integrates artificial intelligence and machine learning into each step of the microscopy workflow, enabling experiments and discoveries not possible with today’s microscopy technology alone.

Yves Doz’s picture

By: Yves Doz

There is no getting around the hype surrounding agile, the organizational concept originally codified by software developers in 2001. Powered by the demands of a fast-changing consumer landscape in recent years, agile’s reach has stretched beyond software development and now extends to customer relations as well as product and service development.

The agile school of thought holds that reorganizing business activities around cross-functional, self-managed teams, each with a clear purpose and focused on specific customer needs, leads to improved performance outcomes and customer-centric innovation.

Quality Digest’s picture

By: Quality Digest

Digital transformation is the integration of technology into all areas of a business, which fundamentally changes how organizations operate and deliver value to their customers. But what does success look like in a digital transformation? Project is on time and budget? Stakeholders are engaged early and often? Business objectives are met by implementing a digital solution? Stakeholders and end users are able to function in their jobs after go-live? Answer: All of the above!

All too often, change management is limited to communications or training. Although these activities are vital to a successful transformation, they are only components of a change management strategy that is focused on creating awareness, surfacing barriers to change, and achieving and sustaining end user adoption. The transformation is not complete at the time a digital solution is implemented. In many respects, the change is just beginning. A comprehensive change management program will continue to measure user adoption by monitoring quantitative and qualitative success metrics defined in the strategy.

Theodore Kinni’s picture

By: Theodore Kinni

There is no shortage of advice regarding the art and craft of business strategy. Yet, in 2019, when the consulting firm Strategy& surveyed 6,000 executives, only 37 percent said their companies had well-defined strategies, and only 35 percent believed that their strategies would lead to success.

Stanford Graduate School of Business professors Jesper Sørensen and Glenn Carroll peg this lack of confidence in the ability to make sound strategy to a dearth of critical analytical thinking. They find that the strategies that have driven the long-term success of companies such as Apple, Disney, Honda, Southwest Airlines, and Walmart are typically—and insufficiently—attributed to either an innovative vision or the fortuitous discovery of emerging opportunities. In their new book, Making Great Strategy: Arguing for Organizational Advantage (Columbia Business School Publishing, 2021), they assert that neither explanation tells the whole story.

Clare Naden’s picture

By: Clare Naden

It’s been about a year since the Covid-19 pandemic turned our world upside down, and that includes the world in which we work. Certainty has hung up its hat, normality looks unlikely to return, and unpredictability is here to stay for the long term. How can organizations manage in this context, and how can employees keep themselves safe while fulfilling their obligations?

Agility and flexibility are the hot new recruits, according to Sally Swingewood and Martin Cottam, manager and chair of ISO/TC 283, ISO’s expert committee on occupational health and safety (OH&S).

The committee recently published ISO/PAS 45005—“Occupational health and safety management—General guidelines for safe working during the Covid-19 pandemic,” a publicly available specification designed to help employers and employees in all areas of work, from one-man bands to multinationals.

Sébastien Breteau’s picture

By: Sébastien Breteau

It’s been about one year since the Covid-19 impact intensified from a seemingly isolated health scare to a worldwide, ubiquitous tragedy that has upended daily life as we know it. Ever since consumers first faced widespread product shortages of essential items during the early days of the pandemic, ranging from toilet paper to medical supplies and PPE, there has been an unprecedented spotlight on supply chain management.

Although much of the conversation has focused on responding to waves in supply and demand, supply chain data suggest that the pandemic is triggering long-lasting supply chain trends that present both pros and cons for quality measures in supply chains.

Knowledge at Wharton’s picture

By: Knowledge at Wharton

It’s a commonly held belief, one that gets played out daily in organizations around the world: Employees who receive performance feedback are much more likely to improve their performance than those who don’t get feedback. But research tells us that it’s simply not true.

Typically, performance after feedback improves only modestly—and more than one-third of the time, it actually gets worse. People who receive positive feedback often see no need for change, and those who receive negative feedback often react with skepticism, discouragement, and anger, dismissing the evaluation as inaccurate, unhelpful, or unfair.

But if feedback doesn’t always and easily improve performance, what should managers do? Research suggests that “pulling” is a better idea than “pushing.” Pulling entails teaching, coaching, and developing employees rather than pushing—or correcting—them. Pulling says, “Here’s how to get ahead in this company; we’ll provide you with guidelines and coaching to help you master these skills and behaviors.” Pushing says, “You’re not doing very well.” In employees’ eyes, it’s likely to be the difference between challenge or inspiration and criticism.

William A. Levinson’s picture

By: William A. Levinson

Traditional statistical methods for computing the process performance index (Ppk) and control limits for process-control purposes assume that measurements are available for all items or parts. If, however, the critical-to-quality (CTQ) characteristic is something undesirable, such as a trace impurity, trace contaminant, or pollutant, the instrument or gauge may have a lower detection limit (LDL) below which it cannot measure the characteristic. When this is the case, a measurement of zero or “not detected” does not mean zero; it means that the measurement is somewhere between zero and the LDL.

If the statistical distribution is known and is unlikely to be a normal (i.e., bell curve) distribution, we can nonetheless fit the distribution’s parameters by means of maximum likelihood estimation (MLE). This is how Statgraphics handles censored data, i.e., data sets for which all the measurements are not available, and the process can even be done with Microsoft Excel’s Solver feature. Goodness-of-fit tests can be performed to test the distributional fit, whereupon we can calculate the process performance index Ppk and set up a control chart for the characteristic in question.

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