Metallized substrates such as two-piece metal packaging are expensive to produce, and they make print color-control challenging. Although many in the metal decorating sector are hesitant to embrace color measurement, it’s the fastest, most accurate, and cost-effective way to produce consistent color across processing plants to achieve sustainability initiatives.

In this article, we’ll discuss the shortcomings of visual evaluation in a color control program, the components of a digital color solution for metal packaging, and how to determine which color measurement device is best for measuring color on metallic substrates.

First, for those resistant to using color measurement, you can see the results of a beverage can gauge R&R study completed by X-Rite in 2020 that confirms the accuracy and repeatability of an X-Rite digital color solution in a beverage can manufacturing environment. In short, the study confirmed that there was no significant variation due to the instrument’s variability or the process of repositioning and remeasuring a can. Also, it showed that there is zero probability that a part would be misclassified as either good or bad.

Northwest Industrial Resource Center (NWIRC), one of the centers that make up the Pennsylvania Manufacturing Extension Partnership, is helping launch student-run enterprises throughout northwest and north-central Pennsylvania. These enterprises connect industry with high schools and career and technical centers for very real and purposeful work. In the process, students develop relationships with manufacturing leaders, and in some cases address capacity issues for these local companies.

The student-run enterprise model isn’t new, and it definitely isn’t a one-size-fits-all for high schools. The NWIRC has been effective at bringing together schools with local industry to explore and plan for an enterprise that fits each school’s current capabilities. The focus is kept on future growth and matching the needs of manufacturers in each community.

Soft skills, the behavioral and social traits that enable individuals to work harmoniously with one another, aren’t just nice to have. They’re essential for the growth of a nation.

In France, 60 percent of employers consider soft skills, such as the ability to organize, adapt, and work in teams, to be more important than technical competencies. However, France is far behind other developed economies when it comes to its stock of soft skills.

In our report for the Conseil d’Analyse Economique, an independent advisory group for the French prime minister, we estimated how much France could benefit by closing its soft skill gaps and enable the expansion of sectors that are projected to see total factor productivity growth in years to come.

Despite the important and ever-increasing role of artificial intelligence in many parts of modern society, there is very little policy or regulation governing the development and use of AI systems in the United States. Tech companies have largely been left to regulate themselves in this arena, potentially leading to decisions and situations that have garnered criticism.

Google fired an employee who publicly raised concerns over how a certain type of AI can contribute to environmental and social problems. Other AI companies have developed products that are used by organizations, such as the Los Angeles Police Department, where they have been shown to bolster existing racially biased policies.

Twenty years ago, digital tools were “a thing.” Now they flow through all things.

To some degree, every manufacturer is now a digital company. And with the right modern platform, any manufacturing organization can break down silos of ownership to focus on the intersection of people, processes, and technology. Here at Dozuki, our job is to help companies hold themselves to higher standards. Below, I’ll show you how to choose the right tools to suit your unique needs. You’ll learn how to navigate the balancing act between new technology and cultivating the people-centric processes that make businesses successful.

1. Standardization 4.0

In traditional manufacturing environments, standards are manually recorded and updated using either paper binders or clunky spreadsheets and PowerPoint presentations. By using digital tools, standards can be effortlessly and continually shared and updated.

In addition to free-flowing information, this new wave of standardization in Industry 4.0 allows for more precise control over revisions and information access.

The world of work will always revolve around people working with people. Meeting together is an important way to get things done, which is why meetings will always be a part of organizational life. But they’re also hated by most people—not because they get things done (which they do), but because they typically waste so much time. Since we can’t get rid of them, the answer is to get better at them by making them more efficient, more valuable, and more concise.

Take a look at your calendar. How many of your meetings are 60 minutes or longer? Imagine how much time you would have to get your work done (or even just have space to think) if they were 25-minute meetings. And imagine how appreciative your team members and colleagues will be for that time when they attend your meetings.

Twenty-five minutes is not an arbitrary number: There is both art and science involved. Put simply, 25 minutes is practical, easy, and achievable—and it has the smarts to back it up.

Action steps

There are three steps to the 25-minute meeting roadmap:

Miles had just been promoted to his first position at the director level after two short years of working for a large manufacturer in a complicated, heavily regulated industry. What surprised him most about the culture of the company is how project-oriented the firm had become during his tenure as a manager.

Common practice was to assign a project team to study the details to the nth degree. Miles felt the phenomenon of analysis-paralysis was slowing down decision making, and he wasn’t alone in this opinion. His boss had chosen him for the new role in part because he was decisive and able to weigh options quickly. Those capabilities had borne fruit over the last year, allowing the company to capitalize on opportunities while its competitors were still evaluating the options.

Miles was certain that the power of his position would allow him to influence future decisions, but he was concerned about the team (and process) he’d inherited. He questioned whether they’d be able to operate with the agility that Miles felt the organization sorely needed. 

The phrase “supply chain” became part of the everyday vernacular during the pandemic, as supply chain issues seemed to affect everyone's life—from toilet paper to automotive components. Supply chain problems are well known to cause disruptions with product delivery, particularly in the complicated world of global production. While manufacturers have always had to deal with unexpected changes in the supply chain, the past couple of years saw ramped-up problems.

Of course, vulnerabilities in supply chains can’t be completely prevented, but they can be addressed, whether they’re caused by a natural disaster, a lack of raw materials, or a labor shortfall.

Now there’s a sustainability imperative, as well. Purchasing patterns are changing significantly, which will affect B2B markets, even if only because B2C trends eventually influence B2B products. Customers are now actively choosing businesses based on the sustainability of their brands or products. Businesses that are the first to cater to the needs of these ecologically focused customers will benefit commercially from this trend.

How can manufacturers maintain a resilient and yet sustainable supply chain?

Ask a smart home device for the weather forecast, and it takes several seconds to respond. One reason for this latency is that connected devices don’t have enough memory or power to store and run the enormous machine-learning models needed for the device to understand what a user is asking of it. The model is stored in a data center that may be hundreds of miles away, where the answer is computed and sent to the device.

MIT researchers have created a new method for computing directly on these devices that drastically reduces this latency. Their technique shifts the memory-intensive steps of running a machine-learning model to a central server where components of the model are encoded onto light waves.

The waves are transmitted to a connected device using fiber optics, which enables tons of data to be sent lightning-fast through a network. The receiver then employs a simple optical device that rapidly performs computations using the parts of a model carried by those light waves.

This technique leads to more than a hundredfold improvement in energy efficiency compared to other methods. It could also improve security, since a user’s data don’t need to be transferred to a central location for computation.