Consider these two pieces of recent industry data: (1) 75 percent of the workforce will be millennials by 2025. Thousands of experienced workers are retiring daily. The Silver Tsunami is real, and it’s rising fast. This unprecedented talent loss is draining industry of its ability to train and retain the incoming workforce. Manufacturers need to adopt proactive solutions to combat the effects of the shifting workforce of people hitting normal or early retirement.

The manufacturing industry (largely) welcomed artificial intelligence with open arms. Less of the dull, dirty, and dangerous? Say no more. Planning for mechanical assemblies still requires more than scratching out some sketches, of course—it’s a complex conundrum that means dealing with arbitrary 3D shapes and highly constrained motion required for real-world assemblies. 

Human engineers, understandably, need to jump in the ring and manually design assembly plans and instructions before sending the parts to assembly lines, and this manual nature translates to high labor costs and the potential for error. 

In a quest to ease some of said burdens, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Autodesk Research, and Texas A&M University came up with a method to automatically assemble products that’s accurate, efficient, and generalizable to a wide range of complex real-world assemblies. Their algorithm efficiently determines the order for multipart assembly, and then searches for a physically realistic motion path for each step.

It’s an adage heard time and time again: You can’t teach an old dog new tricks. Conversely, is it possible that you can’t train a new dog without using some new, exciting tricks? With technology changing at rapid rates, the newer generation is accustomed to different training styles and methods. In an interview with Stu Goose, vice president of partnerships at DeepHow, we discussed just how accommodations can be quickly and easily made to train new hires without wasting valuable time and resources.

“Try and imagine that you’re job shadowing with an apprentice, but instead, we’re recording this as a video,” says Goose, setting the stage for the video trainings that DeepHow creates. “We ask them to explain what they’re doing, why they’re doing it, the tools that they use. Maybe if something goes wrong, [we ask] how to recover from that.... Once the video is finished, it automatically creates a spoken transcription, [using] speech recognition [so that] if your target audience’s native language is different from the one that the expert recorded in, we have—automatically—the ability to translate that into different languages.”

This explanation is not hypothetical. It’s a very real description of DeepHow’s capabilities as an international AI training tool for companies.

Research shows that during times of economic uncertainty, companies that find a balance between reducing the resources they need to survive and investing in key areas for growth will fare better through a recession and beyond. It’s a nuanced approach that involves playing offense and defense at the same time.

Many small and medium-sized manufacturers have been significantly affected by the Covid-19 pandemic and find themselves with few apparent options. They have reduced their use of resources to the point that they feel there’s no time for anything beyond operations. When they do have time, it’s due to a decrease in business—and that means they don’t have money to invest.

As a result, they may become risk-averse, hesitant to upgrade machinery or hire new people before more business returns. But exploring and exploiting opportunities involves risk. Hunkering down to wait out economic uncertainty is typically not a path that leads to growth or positive change.

Getting the most out of your older capital equipment is a priority for any manufacturer. Luckily, the technology needed to bring legacy equipment into the internet of things (IoT) is readily available.

Let’s take a look at some specific products and technologies businesses can use to bring legacy machines into their digital manufacturing platform.

Smart sensors

One of the principal concerns with legacy machines is the lack of real-time awareness of and insight into their operations. One of the simplest ways to get that equipment to generate those data is to attach sensors to them. Flexible, low-cost IoT-enabled sensors can be attached to the legacy assets to generate data about their performance—speed, vibration, environmental data, and more.

Smart sensors can send the data they collect to on-location servers, edge devices, or the cloud; IoT software can then generate reports from the data, providing operators with information in real time that may have taken days or weeks to generate previously—and too late to fix a problem. A company could use those data to improve shop floor and business functions almost immediately.

Yes, I have a wicked dream. No, not that definition of wicked—I mean wicked in the sense meant by scientists when they discuss “wicked problems.” Wicked problems are those that typically involve a combination of technical, social, and economic challenges. Wicked problems are daunting. They’re complex with many interdependencies. Typically, their solution involves collaboration among people with different technical disciplines and different self-interests.

The realization that what I have had for years is a wicked dream hit me recently when I read an article in the July-August 2022 American Scientist, “The World Needs Wicked Scientists.” It’s the basis for much of the information presented below.

My wicked dream is about a world in which every geographic community is a community of excellence. Why is creating a true community of excellence like solving a wicked scientific problem? Let me share some of the characteristics of a wicked scientific problem, and you’ll see the parallels to achieving communities that are vigorous and resilient.

Producing quality work is imperative in every field, particularly in the construction industry. A well-built structure, whether it’s an educational facility, hospital, or a commercial establishment, provides shelter, safety, and stability, which is why quality should always be a top priority. As soon as a construction project begins, it’s every project team’s intention to provide quality services. An established quality plan in construction that uses lean principles leads to more reliable outcomes.

Various obstacles can occur at any time during a project, especially in the planning and construction phases, causing disruptions in plans. These types of interference can lead to unforeseen delays, expensive rework, and long-term ramifications for the client if quality issues compromise building integrity. In fact, a study conducted by Frontiers in Engineering and Built Environment discovered that poor quality can increase the cost of a building by more than 50 percent and can cause up to 50 percent of project delays. 

The computation for skewness does not fully describe everything that happens as a distribution becomes more skewed. Here we shall use some examples to visualize just what skewness does—and does not—involve.

The mean for a probability model describes the balance point. The standard deviation describes the dispersion about the mean. Yet a simple description of skewness is elusive. Depending on which book you read, skewness may be described as having something to do with the relative size of the two tails, or with the weight of the heavier tail of a probability model.

By far the easiest way to understand what increasing skewness does to a probability model is to compare models with different amounts of skewness. But before we can do this, we have to first standardize those models. This is because skewness is defined in terms of standardized variables; skewness is what happens after we have taken into account differences in location and dispersion. (If we compare two distributions that have not been standardized, differences in location and dispersion may obscure differences in skewness.) So here we will be working with standardized distributions where the mean is always zero and the standard deviation is always equal to one.

Engineers at MIT have developed ultralight fabric solar cells that can quickly and easily turn any surface into a power source.

These durable, flexible solar cells—thinner than a human hair—are glued to a strong, lightweight fabric, making them easy to install on a fixed surface. They can provide energy on the go as a wearable power fabric or be transported and rapidly deployed in remote locations for assistance in emergencies. They are one-hundredth the weight of conventional solar panels, generate 18 times more power per kilogram, and are made from semiconducting inks using printing processes that can be scaled in the future to large-area manufacturing.

Because they are so thin and lightweight, these solar cells can be laminated onto many different surfaces. For instance, they could be integrated in the sails of a boat to provide power while at sea, adhered to tents and tarps that are deployed in disaster recovery operations, or applied to the wings of drones to extend their flying range. This lightweight solar technology can be easily integrated into built environments with minimal installation needs.

In a press statement released on Jan. 6, 2023, the European Commission reported the adoption of a proposal to allow more time to certify medical devices to mitigate the risk of shortages. The proposal introduces a longer transition period to adapt to new rules, as foreseen under the Medical Devices Regulation (MDR).

According to the commission, the new deadlines depend on the medical devices’ risk class and will ensure continued access to medical devices for patients. It will also allow medical devices placed on the market in accordance with the current legal framework and that are still available to remain on the market (i.e., no “sell-off” date).

“Our rules on medical devices will always prioritize patient safety and support for innovation,” says Stella Kyriakides, EU Commissioner for Health and Food Safety. “A combination of factors has left healthcare systems across the EU facing a risk of shortages of life-saving medical devices for patients. Today, we propose a revised regulatory timeline to provide certainty to industry in order to continue producing essential medical devices, reducing any short-term risk of shortages, and safeguarding access for patients most in need.”