This is a challenging time for many companies. On the one hand, supply chain disruption, labor shortage, and raw material and energy price hikes are driving up business costs. On the other, demand is slack due to consumers’ reduced purchasing power and an uncertain global economic outlook. Together, these conditions squeeze companies’ profits and cast a shadow on their growth prospects. Executives everywhere in the world find it more urgent than ever to sharpen their organizations’ innovation strategy to create new markets for growth.

Economics has long taught us that R&D and technology advances, as measured by R&D spending and numbers of patents, are the central drivers of innovation and growth. But market creation for growth isn’t inevitably about technological innovation.

Consider how Comic Relief, a major and distinctive charity based in the United Kingdom, created a new market in the deep red ocean of the charity industry where competition was intense and growing, costs were up, and donations were down. It did so by redefining the industry problem and engaging everyone to do something funny for charitable giving.

The first step in using data is understanding what data analytics can and can’t do. Artificial intelligence (AI) systems are powerful but are best used for prediction models. The role of a leader is to use those predictions to inform decisions.

I’m a professor of organizational behavior at the Stanford Graduate School of Business, where I teach a class titled People Analytics. People analytics is the application of data analytics methods, especially machine learning algorithms, for the purpose of informing people-related decisions inside organizations. Here are five takeaways from that class.

1. Be an informed consumer of data analytics

Data are revolutionizing how people are managing inside organizations. Using data analytics doesn’t mean that you need to be a data analyst. What you need to be is someone who understands what data analytics can and can’t do. Most important, understand how to interpret data analytics in terms of what it means about what’s happening inside your organization.

In late 1999, Scott Paton, then editor-in-chief and publisher of Quality Digest, asked his readers to provide him with their definitions of quality. The feedback was impressive: More than 80 responses were received, ranging “from the sublime to the ridiculous,” in Napoleon's words.

In his editorial column, Paton shared some of the proposed definitions. The editor’s picks consisted of two categories: the “more light-hearted definitions” and “the more serious definitions.” It is within the latter category that an interesting twofold definition of quality by Karl Albrecht of Karl Albrecht International appeared:
1. Objective quality is the degree of compliance of a process or its outcome with a predetermined set of criteria that are presumed essential to the ultimate value it provides. Example: the proper formulation of medicine.
2. Subjective quality is the level of perceived value reported by the person who benefits from a process or its outcome. It may subsume various intermediate quality measures, both objective and subjective. Example: the pain relief provided by medication (Paton, 2000).

For years, organizations have taken a defensive “castle-and-moat” approach to cybersecurity, seeking to secure the perimeters of their networks to block out any malicious actors. Individuals with the right credentials were assumed to be trustworthy and allowed access to a network’s systems and data without having to reauthorize themselves at each access attempt.

However, organizations today increasingly store data in the cloud and allow employees to connect to the network remotely, both of which create vulnerabilities to this traditional approach. A more secure future may require a “zero-trust architecture,” in which users must prove their authenticity each time they access a network application or data.

The manufacturing and production industries have rapidly evolved during the past 10 years, faced with significant challenges both in finding resources to run production and in manufacturing under tighter tolerances for surface texture and 3D feature dimensions.

The strict tolerances required in advanced manufacturing increases the need to have very repeatable metrology systems as part of the manufacturing line. Often a robot is used to exchange parts between the production tool and metrology equipment, leading to a fully automated process that removes some of the variability found with human operators.

Through the same automation path, results from a metrology tool are transferred to a central server that keeps track of trends or abnormalities using statistical process control (SPC) or other methods. This evolution is often referred to as Industry 4.0, where manufacturing is highly automated and directly integrated with metrology.

With everything from our smartphones to our money going online, it’s hard to ignore the influence blockchain has on our society. By 2030, experts expect the technology to boost global economies by $1.76 trillion. But with exponentially increased demand for products in all industries, it’s hard to understand how.

All of our processes are going digital, and blockchain is behind it all. From manufacturing to healthcare to even our defense sector, we’re finding quicker ways to transfer information along the global supply chain. The technology is developing quickly, and there’s no better time to get informed about its future. Continue reading for a closer look at how blockchain is solving inherent supply chain issues.

What is blockchain technology?

With blockchain tech starting to assimilate into the fabric of our society, it’s imperative that we understand what it is and how it works.

Set up as a form of public ledger, blockchains replace intermediaries for transferring information. The digital ledger uses a variety of methods to streamline transfers and increase their securities.

Air gauge measuring devices have given highly reliable and precise measurements since the 1940s. As the name suggests, an air plug or air ring gauge is a gauging probe that uses metered air under controlled pressure to sense the back pressure generated as it impinges on the surface of the part under measurement.

The controlled air flows through one or more nozzles or jets, and the resulting change in the airflow or pressure is measured by a precise sensing device. This pressure change is compared to the same measurement of a part with precisely known dimensions. The comparative measurement is then converted to an absolute measurement value for the part being measured. 

To get the maximum benefit of this system of gauging, it’s important to understand air gauges and what factors contribute to their accuracy.

Air plug gauges are used for internal gauging, and air ring gauges are for external gauging. Usually, two jets face outward in an air plug gauge, and inward in an air ring gauge. They are symmetrically and precisely positioned on the diameter to balance the air flowing out. The amount of airflow under pressure depends on the particular readout system’s operating pressure and the size of the nozzles/jets.

It turns out that the triple threat of being locked away in quarantine, forbidden from visiting clients in person, and being under pressure to continue to deliver sales and profits has had a seismic impact on digital transformation. The Covid-19-led paradigm shift in how customers work and buy has given rise to a significant increase in the adoption of digital marketing tools, services, platforms, and strategies.

These shifting winds have led to an increased dependency on chief marketing officers (CMOs) to be the “meteorologist” of the times, helping to decipher changing customer behaviors, interpret the influence of digital platforms, and lead a recovery of normalcy from the pandemic.

With this increased dependency on CMOs comes added pressure to perform. CEOs expect their CMOs to bear more responsibility and ensure that growth happens faster than ever. Forget about the medium-term: Right now is what matters.