The Chinese character for “crisis” means danger and opportunity. The coronavirus, aka Covid-19, outbreak has already wreaked havoc in the global economy, curtailed international and even domestic travel, and caused roughly 7,146 fatalities to date around the world.¹ The reaction to this outbreak, as driven by corrective and preventive action (CAPA), may however save thousands of lives not only this year but in the future. It should also initiate serious thought as to the desirability of reshoring U.S. manufacturing capability that should have never been sent offshore in the first place, and generate new opportunities in distance networking technologies.

The Automotive Industry Action Group’s Effective Problem Solving manual (CQI-20) defines an outstanding nine-step CAPA process that is applicable to almost any problem a manufacturer might encounter. These are essentially:

A large portion of a digital project manager’s job is making sure the right parts of the project are being worked on. Projects need to be prioritized. Tasks within projects need to be prioritized, too.

Plan View’s Project and Portfolio Management Landscape Report found that prioritization was consistently the second biggest challenge that organizations face. Also, McKinsley surveyed 1,500 professionals and found that only 9 percent were happy with their time allocation.

Many famous writers, businesspeople, and global influencers have stressed the importance of getting your priorities in order.

Mark Twain famously said, “To change your life, you need to change your priorities.”

The same applies to project management.

Each article in this series presents new tools for increasing return on investment (ROI), enhancing customer satisfaction, creating process excellence, and driving risk from an ISO 9001:2015-based quality management system (QMS). They will help implementers evolve quality management to overall business management. In this article we look at demonstrating and establishing various subclauses of Clause 5—Leadership, to build organizational excellence and assess risk.

Clause 5—Leadership

Words have meaning. Throughout the history of ISO 9001, the terms “top management” or “senior management” have been used to describe an organization’s decision and policy makers. These individuals are ostensibly those who are accountable to themselves or to a board of directors for the company’s success. They have the power to hire and fire, and to establish the organization’s operational infrastructure.

In work with more than 700 companies, the term “management” was most often appropriate because those in charge were directors and benevolent dictators. Very few were true leaders of people who created an environment where everyone could achieve their highest level of success and excellence.

Each new year brings about a period of reflection, where one can think back on the path that the previous year took us on. 2020 represents an even larger opportunity for reflection as the world enters a new decade. Reflection provides an opportunity to learn and improve, and extends beyond just an individual to include industries and businesses. As a U.S. manufacturing enthusiast, I’m looking back over the past 10 years at how manufacturing has changed, evolved, and innovated so that I can continue to support that evolution.

The U.S. manufacturing industry is entering this new decade in a much different state than when it entered the last. The industry is no longer shaking off the aftereffects of the Great Recession, but it is still grappling with the economic uncertainty that comes with new trade deals, tariffs, and other global uncertainties. There is also the need to keep pace with the ever-increasing speed of technological change. Industry 4.0 and its adoption by U.S. manufacturers has begun to pick up steam, and manufacturing’s digitization is only going to increase. Things like 3D printing, advanced robotics, artificial intelligence, and smart factories are becoming more commonplace in the U.S. manufacturing industry, emphasizing a deepening need for stronger cybersecurity.

Analytical balance scales are a part of many laboratories. If you use them regularly, you need to keep the analytical scales well-maintained. They are extremely sensitive, and factors like dust, vibration, and air drafts will throw off the accuracy of the scales. This is why it is important to maintain and calibrate them regularly so that you get accurate weights every time you measure.

An analytical balance will work efficiently only if it is maintained properly. Follow the specific manufacturer’s recommendations that come with the balance.

Apart from that, follow these eight tips.

Keep it in the right environment
Keep the analytical balance in an area that is free of vibration and has controlled temperature and humidity.

Don’t place the balances next to doors or windows because opening or closing them will result in air drafts or temperature fluctuations that can lead to inaccurate measurements.

Keep it clean
Keep the analytical balance clean. Debris inside the weighing chamber can affect the weighing results.

Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur.

That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.

With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed. It may be only a matter of time before a wound or scratch becomes life-threatening. Yet few new antibiotics have entered the market of late, and even these are just minor variants of old antibiotics.

Although the prospects look bleak, the recent revolution in artificial intelligence (AI) offers new hope. In a study published in February 2020 in the journal Cell, scientists from MIT and Harvard used a type of AI called deep learning to discover new antibiotics.

We are currently living in the digital age and are drowning in an ocean of data. Organizations have a large number of data entities and data elements, and a large volume of data corresponding to the same, and they continue to amass more and more data with each passing day. With the large amount of data coming in, it’s important to know what is “quality” data, and what isn’t.

Data entities, elements, dimensions... oh my!

Before we continue, let me explain a little data terminology as it pertains to databases, or data storage. “Data entities” are the real-world objects, concepts, events, and phenomena about which we collect data. “Data elements” are the different attributes that describe the data entity. Thus, a data entity serves as the container that comprises all the data elements that describe it.

Consider a machine shop that has many types of machines: CNCs, lathes, presses, and the like. A “machine” would be the data entity representing a physical object sitting on the shop floor, and the data elements might be machine type (e.g., CNC), machine ID, machine name, machine make, machine location, machine uptime, and so forth, which store attribute values for the different machines.

Additive manufacturing (AM, aka 3D printing) is increasingly accepted as an end-product manufacturing method, rather than just for prototyping. However, ensuring the final quality of parts for use in critical applications such as medical, and particularly aerospace, can still be a labor- and cost-intensive process. AM machine makers have most recently been concentrating on improving the actual printing technology of their equipment, and monitoring build progress in situ, so that printed parts not only perform as expected, but also can meet the various standards set by different industries.

Such proof generally comes in two forms: certification of the final product and qualification of the equipment, software, and materials used to produce that product. Two industry experts—Laura Ely from the Barnes Group and Zack Murphree, from AM equipment and software provider VELO^3D—define and discuss these issues, and other important related ones, in the Q&A below.

What’s changed in additive manufacturing that makes certification more important these days?

When the 2019 Nobel Prize in Economics was announced, it was an exceptional moment because in addition to celebrating contributions to economic science, a methodology was also recognized. The laureates Abhijit Banerjee, Esther Duflo, and Michael Kremer are known for their work in randomized controlled trials (RCTs), which develop evidence-based recommendations for public policy and organizations in the fight against global poverty.

Work with RCTs has led to the identification and implementation of impactful poverty-reduction policies in areas such as skills training and agriculture. But as a methodology, RCTs are not only for the developing world. They have been used for decades in pharmaceutical companies’ clinical trials, and in this age of data, they hold enormous promise to identify business solutions and support evidence-based management.

A recent study showing that data entry is one the most redundant and hated workplace tasks raises questions about why, in the age of artificial intelligence, data mining, and smart technologies, this task is still being done manually.

Is there any way it could be less despised?

My ongoing fieldwork in a data-driven startup, referred to as Sage (a real company, but not its real name due to confidentiality requirements), suggests that technological solutions are not nearly as sophisticated as many assume—and are not going to replace human data entry any time soon.

For nearly two years, I’ve been studying the evolution of Sage’s hiring practices and jobs.