During the early 2000s, I was a recent software engineering graduate. Along with a friend and fellow graduate, I landed some project work with a major pharmaceutical company. The CEO, who had just signed up to the U.N. Global Compact, needed to know how sustainable the company’s supply chain was. He tasked the chief procurement officer (CPO) to audit the company’s suppliers—some 150,000 of them.

Back then already, supplier data were a struggle. The CPO needed to know who all these suppliers were and whether they complied with a list of principles to which the company had committed. It was our job to develop the platform from which this could be determined. Long story short, the CPO was able to gain the visibility that he required. This meant he could demonstrate compliance at the board level, making the project a success.

It was exciting for us to witness the role that data played in making supply chains more visible. In the 20 years that have followed, we’ve had the opportunity to explore this topic with some of the world’s biggest brands. If there’s one thing I’ve learned from this experience, it’s that good supplier data are a function of good supplier engagement. And importantly, the reverse is also true.

Listening to customers is critical for healthcare organizations to ensure they’re delivering high-quality care to their patients. Sure, the traditional methodology of doing so via surveys can increase customer retention and profitability. But much like evolving from analog to digital, there’s a better way to listen to patients.

Surveys don’t work. The responses nearly always lack context, and asking an already frustrated patient to answer a few questions skews the results.

Conversational intelligence—that is, using speech analytics to listen at scale—tells more than any written survey and is a must for all patient-focused healthcare organizations. While conversations happen in many places throughout a healthcare organization, contact centers already record customer conversations, making them the perfect place to apply conversational intelligence.

Let’s dig deeper into three ways conversational intelligence enables healthcare organizations—especially their contact centers—to use patients’ voices in the boardroom to increase positive outcomes.

One of the key stakeholders in stakeholder capitalism is the employee. You could argue that the employee is the key stakeholder, because without employees you’d have no stakeholders at all. This is why employers need to stay aware of today’s health environment and its effect on their employees. Employee sickness, absenteeism, and poor morale related to illness harm the entire company.

A wave of sickness

The University of Washington’s Institute for Health Metrics and Evaluation projects a new wave of Covid this winter that could more than quadruple the current infection rate, which aligns with projections of a major winter wave by the U.S. Food and Drug Administration.

There’s a growing movement to increase competency and skills-based education and hiring practices in both the public and private sectors.

For example, the Executive Order on Modernizing and Reforming the Assessment and Hiring of Federal Job Candidates calls on the federal government to “ensure that the individuals most capable of performing the roles and responsibilities required of a specific position are those hired for that position.” This results in “merit-based reforms that will replace degree-based hiring with skills—and competency-based hiring.”

Similarly, the “Principles for Growing and Sustaining the Nation’s Cybersecurity Workforce” emphasizes the importance of expanding the candidates pool by discontinuing the use of degrees as a mandatory requirement for jobs and revising job postings to be more transparent about the skills needed to perform and thrive in the role.

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.