16 Smart And Effective Uses For Digital Twin Technology

A digital twin is precisely what its name suggests: A digital copy of a physical object or system—even a human being. It may be a simple concept, but the potential applications are anything but. Through the ongoing collection and exchange of data, a digital twin can simulate and even predict the behaviors and reactions of its physical twin in a variety of conditions, providing invaluable insights to industries ranging from manufacturing to healthcare.

Digital twin technology allows businesses and organizations to test products and processes, study and predict how real-world conditions can affect physical objects and beings, and make well-informed, big-impact decisions with minimized financial and human safety risks. Below, 16 members of Forbes Technology Council share some of the fascinating ways industries and organizations are leveraging digital twin technology.

1. Minimizing Manufacturing Waste

We at Cuby use digital twin technology to make sure we produce 1-to-1 kits of the parts needed in our prefab construction process. It’s been estimated that up to 40% of the solid waste in the U.S. is construction and demolition waste. Manufacturing all the parts in advance allows us to reduce waste by up to 90%. – Aleksandr Gampel, Cuby Technologies, Inc.

2. Building Resilient Supply Chains

Businesses are increasingly using digital twin technology to build resilient and responsive supply chains. The digitization of supply chain processes provides businesses the opportunity to increase organizational efficiency by predicting serious problems and deceleration. In fact, it is estimated that by 2025, 80% of participants in industry ecosystems will rely on digital twin technology. – Radhika Krishnan, Hitachi Vantara

3. Mitigating Disruptions Due To Weather And Climate

Businesses are using digital twin technology to mitigate climate-related disruption. By combining data from public sources, such as weather data, with data from suppliers and partners, leaders can see how an unplanned weather event might impact the flow of goods across their supply chain, then use this insight to quickly pivot orders, routes or suppliers to limit waste and meet demand. – Rohit Shrivastava, Anaplan

4. Studying And Refining Processes

Process mining combined with simulation gives reliable visibility into as-executed processes (versus relying on what somebody thinks is happening) and the ability to do “what-if” analyses. This is effective because it allows one to see what’s really happening and simulate changes before making them. Often, changes do nothing or create a bigger problem elsewhere. Simulation and mining prevent that. – Michael Nyman, iGrafx

5. Making Data-Driven Manufacturing Decisions

The utilization of digital twins in the manufacturing industry has seen large growth. Digital twins increase productivity and reduce costs by combining the physical and digital worlds to make data-driven decisions, prolong asset life cycles and minimize unexpected maintenance disruption across assets. This modernizes the sector by moving from the “break and fix” approach to proactive maintenance. – Cindy Jaudon, IFS

6. Testing Health Intervention And Engagement Strategies

Health outcomes improve when patients are confident, connected and engaged. Digital twin technologies provide healthcare organizations the option to test drive new interventions and engagement strategies. This lowers the risks of rolling out new programs by testing hypotheses through a simulated pilot while also enabling cost-conscious innovation. – Trisha Swift, PricewaterhouseCoopers

7. Improving Patient Outcomes

A digital twin enables accurate and continuous monitoring. That data flow can inform data-driven decisions. For example, doctors are using an individual’s genetic makeup to model new organs for transplant. Because these processes can leverage a populationwide data set of digital twins, they can replicate an individual human body’s internal system to improve treatment outcomes for all. – Nicholas Domnisch, EES Health

8. Expanding Professional Services Capabilities

Knowledge-rich professional services firms are building digital twins of accountants, advisors and auditors using graph-based intelligent automation. These are distinct from previous technologies, because the decisions these professionals make are complex, contextual and nonlinear. In a world where there are big skills shortages and raging inflation, this form of IA is closing the gap. – James Duez, Rainbird Technologies

9. Onboarding And Knowledge Sharing

A digital twin use case that is an easy entry point and can provide quick ROI is training or onboarding. In areas where experienced employees are preparing for retirement, where there is high turnover or where there are general labor shortages, having a prerecorded “virtual” expert that can walk you through the instructions in real time can be a game changer and is much more effective than a giant paper manual. – Samantha Williams, Sonoco

10. Providing Safe Training

Today, manufacturing organizations are leveraging digital twin technologies to replicate machinery that would typically put employees in harm’s way. Here, a virtualized version of the original piece of machinery can be used to give employees training experience with the virtual machinery without also putting the employee’s health and safety in peril. – Marc Fischer, Dogtown Media LLC

11. Understanding Multidimensional Problems

Digital twin technology shines the brightest when it helps companies better understand a multidimensional problem—one that is too complex to easily solve. Because it is a way to visualize and make better decisions, the technology has become extremely effective for everything from product design to diagnosing medical issues to better understanding variables that affect business expenses. – Josh Dunham, Reveel

12. Improving Manufacturing Efficiencies

A very interesting field of application is manufacturing. Thanks to a digital twin of a production plant, with all its different lines and machines, we can launch simulations to generate greater efficiency or to detect potential bottlenecks. Simulating the manufacture of new products or variants of existing products is also a very useful application. – Miguel Llorca, Torrent Group

13. Developing And Training Self-Driving Vehicles

Without digital twin technology, it would be impossible to develop self-driving vehicles at the scale and with the reliability we are witnessing nowadays. Billions of simulations on a “virtual road” by a “virtual car” allow for training machine learning models to forecast accidents and plan not just the fastest, but also the safest, routes so that drivers can entrust the actual driving to robots. – Aleks Farseev, SoMin.ai

14. Budgeting And Financial Planning

Financial and operational data is the lifeblood of a company, but it’s difficult to “see all of it” and understand it in real time. Digital twins offer real-time, big-data-enabled simulation modeling that can be particularly useful for budget and financial planning. The technology can streamline tasks such as procurement, case management and capital resiliency and deliver powerful insights for finance leaders. – Nicola Morini Bianzino, EY

15. Managing Traffic

Digital twins are already effectively used by urban planning councils in many U.S. cities for efficient traffic management. They help in simulating real-world congestion at junctions, predicting what may get worse when and where, and they can be used to test multiple mitigation techniques by leveraging the best mix of ML and city know-how. Dashcam-backed digital twins are explored alongside junction twins. – Pramod Konandur Prabhakar, Pelatro PLC

16. Simulating Real-World Conditions

One way businesses are leveraging digital twin technology is by using it to simulate the physical world. For example, a company can use a digital twin to simulate a real-life situation so that they can predict how their product or service will behave in that environment. Another way is by using it to understand how their customers use their services and products. – Leon Gordon, Pomerol Partners

Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.

Source: Council Post: 16 Smart And Effective Uses For Digital Twin Technology

Critics By Carlos Miskinis Digital twin research expert

To explain what Digital Twin means in simple words, it is a digital replica or a representation of a physical object (e.g. aircraft engine, person, vehicle) or an intangible system (e.g. marketing funnel, fulfillment process) that can be examined, altered and tested without interacting with it in the real world and avoiding negative consequences.

Think of it as an online platform for testing, creating and altering objects that are based in reality, without engaging with them in the real world itself. Technologies similar to this one have been used in various industries long before this concept was created, however, this new definition of the technology has much more potential, power, and scalability that can replicate, monitor and test virtually anything you can think of.

The rise of the Internet of Things (IoT) has complimented the adoption of this new technology, as IoT has resulted in its cost-effective implementation. Virtual twins have become imperative to business today, consistently named as a strategic technology trend in recent years. The complexity of technology has led to many questions within the industry. One of the most important ones is how is it changing the way design, planning, manufacturing, operation, simulation, and forecasting is traditionally functioning?

A physical twin that was replicated on a virtual platform is a near real-time digitized copy of a physical object. It is a bridge between the digital world and the physical world. Its core use is to optimize business performance, through the analysis of data and the monitoring of systems to prevent issues before they occur and prevent downtime. The simulations that are produced will help to develop and plan future opportunities and updates within the process or product.

 The benefits of virtual twin technology are astronomical, with industries such as agriculture, government, transportation and retail experiencing rewards from the technology and benefits going forward. Companies must find methods to prevent the risk of potential product defects among their assets and future products. This piece of tech allows production costs to be minimized, as companies will save expenses when products are right the first time.

There is no need for expensive physical tests or updates to the products or process. Research with manufacturers has found that this concept will enable the reduction of development costs of the next generation of machines by well over 50%. The features of the tech also provide added confidence to boost product performance and aid complex decisions, preventing costly downtime to robotics and machinery.

The core benefit of why most companies started twinning their processes, products, and services via simulations is due to their efficiency. Businesses are racing to market their product faster than their competition, and having the ability to virtually simulate scenarios where a product is tested for failure via multiple angles helps the situation immensely. Not mentioning the fact that the development and testing costs are usually reduced hundreds of times.

This technology will be able to anticipate how the product and process will perform through digital simulations and analysis. The accessibility of reliable and consistently updated information provides the assurance needed to make faster decisions and increase the speed of production to overtake competitors. Here is a use case infographic we have presented in our workshop about understanding virtual twins – find event information here.

We can see how a virtual twin simulation is used to replicate and optimize machinery that regulates water flow in a factory. By doing this, developers can see every moving detail on the screen and then make the calculated decision to upgrade, optimize or make positive changes accordingly. Offices that adopt this technology early will attract innovative and leading talent. Offices will be able to incorporate interactive features to improve employee satisfaction and productivity using data-driven simulations.

Employees who will use digital twin technology will be able to expand their engagement with online tools, such as interactive maps to locate colleagues on the floor, book meetings and complete tasks with more diligence and accuracyManagers will also be able to supervise remotely with the tool that will be similar to a 3D map that will be created using virtual online platforms that are based on simulations.

 It is evident that the digital twin concept will benefit many people within the supply chain. Combining this disruptive concept with IoT technology is an incredible opportunity for businesses to improve. Ultimately, it will also allow stakeholders to improve the overall efficiency and cost of their business, and improve many aspects of work for employees….

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Intelligent Business Practices are a Smart Path To Unleashing Growth

Earlier this year, I predicted the rise of intelligent business practices as a trend shaping Australian small-to-medium businesses.

Barely two months on, things have already progressed to the point that it’s worth revisiting and expanding on what this all means.Intelligent business practices are a transformational, once-in-a-decade type technology shift that promises huge business effectiveness gains. These practices resolve points of friction, improve productivity and efficiency, unlock growth and help organizations scale.The existence of intelligent business practices suggests that unintelligent or under-intelligent practices also exist.

Naturally, no practice starts out as such, but they may degrade in quality or efficiency over time, slowly becoming out of step with best practice despite everyone’s best intentions.Small-to-medium business owners or leaders are generally acutely aware of the problems their organizations face, but almost universally haven’t landed on a way to solve them. Business leaders not only don’t have all the answers but also aren’t often able to quantify the cost of leaving these known problems unaddressed.

This has a dollar value. Research, for example, pegs the loss of productivity from poor internal communications and dealing with unnecessary email volumes at thousands of dollars per employee per year. Another study by Forrester found employees “waste an average of 12.5 hours per week on manual and repetitive tasks because of outdated solutions and practices”, costing an organization of 100 employees $2.7 million a year.

More organizations would act faster if they could quantify the costs of their inaction and the costs of what they were missing out on. To this end, we’re seeing more organizations cost their business-as-usual practices to establish a baseline that can then be lowered by approaching business practices more intelligently.

Starting the change

The first step towards doing that is to be in the right mindset: to be open to opportunities for continuous business practice improvement.

Then it’s a matter of identifying the change opportunities and prioritizing them in order of potential efficiency and cost-out impact.This should be a relatively straightforward exercise that looks at the organization and identifies the three biggest challenges, friction points, growth inhibitors or opportunities that, if removed, could unleash the most growth and revenue.

This is an incredibly healthy exercise for any business to undertake at any point of its existence, but as many of us emerge from a challenging two years, the timing has never been better.It’s worth also putting some limits around this. This exercise should be approached with no preconceived notion of solutions, technology stacks or vendors. This isn’t the right place to run experiments. This should be nothing more than a straightforward examination of how efficiently the business operates.

A truism for every business, including our own, is that if you’ve been operating for any number of years, you’ll reach a point where you do things a certain way that becomes the ‘known’ or the common way of running a transaction or other process internally. That way might or mightn’t be well-documented. It might be intimately known only by one person or a handful of people. It may also not be considered optimal or best practice today. But it’s *your* process and a part of the business DNA that has allowed you to achieve success to date.

Even within the same industry, businesses solve problems in different ways. There may, of course, be some commonality between approaches, but often they reflect and run according to the unique personality of the business and how it has evolved. The point of running a discovery exercise on these processes and practices is to determine how successful internally they’ve allowed you to be, but then how much more successful you could be if the processes were optimized and operated in a more intelligent way.

If hundreds of thousands of dollars in potential benefits are tied up in a sub-optimal business process or practice, but a pathway exists to make that process or practice more intelligent – without incurring high cost – this should be considered a threshold or trigger point to drive towards more intelligent business practices.The impact of adding intelligence to one business practice or process should quickly become apparent to the people that work with it, and to customers that see and experience the output of how smoothly it now operates.

This creates internal excitement for change. People want more, and it often leads organizations to drive intelligence deeper, and make more far-reaching changes to the way they operate. Confidence builds and is converted into a cycle of continuous improvement. Given the nature of the business environment, all leaders would be well encouraged to take a fresh look at their businesses, either by themselves or with the aid of a specialist process consultancy like Tecala.

But, it all starts with a simple, straightforward and fresh look at the way you do business. Against the current backdrop of economic recovery and opportunity, SMEs and their leaders would be well advised to start now.

Source: Intelligent business practices are a smart path to unleashing growth

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Reducing Risk When Migrating Mission-Critical Applications To The Cloud

Over the last decade, significant strides have been made in cloud computing, and today’s enterprises have substantial data and application footprints in the cloud. Many organizations are moving toward implementing cloud-based operations for their most crucial business applications.

A cloud-first mindset is usually a given for new companies and continues to gain traction for established enterprises. Still, existing legacy infrastructures and large on-premise footprints that don’t map easily to cloud architectures are slowing and even blocking faster adoption.

Organizations are poised to prioritize cloud investments over the next five years, according to the results of IDC’s Future of Operations research. The appeal includes the potential for an improved experience for customers, employees and suppliers/partners, better development agility, improved time to market and increased operational efficiency across organizations.

Although a pivot to the cloud could complete the evolution of the business from an operational and capital perspective, significant barriers to broader adoption still exist.

Cloud spending is on track to surpass $1 trillion by 2024, partly due to urgent changes to business operations driven by the pandemic, which accelerated cloud adoption timelines for many companies. And the results of recent research find that optimizing cloud costs tops companies’ 2021 priorities for the fifth year in a row.

Increasingly ambitious migration timelines are driving important decisions about moving critical applications without fully understanding the risks. Organizations are addressing application and data migration with largely ineffective one-size-fits-all solutions that don’t always meet expectations, often causing more problems than they promise to solve.

Others are moving with extreme caution when deciding which applications to keep on-prem and which to move, migrate or refactor. Mission-critical apps remain on the legacy infrastructure to assure control over the foundational data and safely maintain business as usual.

Moving from massive, on-prem data centers to the cloud presents a future filled with possibilities but also a level of risk due to the various unknowns within this significant paradigm shift. After all, a mission-critical app can be essential to the immediate viability of an organization and fundamentally necessary for success.

Although moving to the cloud is the way forward for many modern companies, migration can prove time-consuming and highly challenging, often with incomplete or unacceptable results. Successful migration can further business opportunities, but the risk of failure is considerable, and the high visibility that accompanies these major initiatives increases the level of exposure and consequences of said failure.

Mission-critical initiatives often cross the length and breadth of organizations, across low-level operational groups up to the C-suite and beyond. But just as all data is not created equal, neither are clouds or migration strategies.

Data Mobility Matters

With increasing cloud investments comes a growing need for more accessible data mobility. As more data moves to the cloud and strategies expand to occasionally include multicloud environments, there’s an expectation that underlying cloud resources deliver about the same level of performance as on-prem. But, often, the required type and volume of cloud resources are not available and deployment is difficult or impossible.

Performance is instrumental in determining where a mission-critical application should live and drives myriad scaling considerations and challenges. Sometimes, particular features, functionality and capabilities are lacking.

Perhaps the data primarily resides in a private or hybrid cloud to engage in cloud bursting on the public cloud when capacity needs to balloon. Longtime legacy challenges of architecting for the peak versus the average persist. Still, cloud decisions have forced IT leaders to relinquish a level of control over the physical infrastructure, significantly increasing risk.

Managing data mobility is challenging. To increase success, plan an approach that minimizes workflow disruptions of critical processes while ensuring sufficient capacity to support expected workloads and providing enough scalability to handle unexpected workloads. Managing random workload fluctuations requires a solid plan and a scalable, flexible and agile architecture to avoid those black swan events that are all too threatening.

Cloud Migration Considerations

Successful migration is not easy, but for many applications, it’s pretty simple to migrate to a platform as a service (PaaS) or managed service and be up and running fairly quickly. But for those performance-sensitive vertical stack monolithic applications running on the most expensive hardware for decades, moving can prove challenging and even impossible.

Ideally, refactoring enhances an application without negatively modifying external behavior and improving the internal architecture, as well as perhaps gaining cost efficiencies, maintenance or performance. But not all mission-critical applications are a fit for a refactor. Complexity, cost and the risk of disrupting a mission-critical app that’s performing as expected are valid reasons to leave some apps on-prem.

Others are constrained by performance requirements that aren’t achievable in the cloud with current offerings. There are fundamental limitations to the types of applications and databases that can quickly move to the cloud, and overhauling those solutions introduces significant risk, possibly resulting in critical delays and higher costs.

Solving Migration Problems

The best plan to mitigate the risks and improve the odds for cloud migration is to eliminate silos between multiple clouds and on-prem — regardless of type or location — facilitating a free flow of information in a simple, resilient, well-understood fashion. The next truth can’t be overstated:

Data is the new oil and should be treated as such. Just as trained specialists are leveraged to find and extract oil, specialized experts should be utilized when performing high-risk, business-changing moves regarding mission-critical data and the application stacks that access it. Ideally, the team migrating mission-critical applications should be proficient in enabling data mobility across environments without refactoring to reduce risk.

The question of cloud migration in 2021 is often no longer “if” but “when and how.” The material risk of maintaining the status quo can be significant, and avoiding moving mission-critical applications to the cloud is often no longer an option. A wise man once said, “What’s dangerous is not to evolve,” and this truism fully applies to an organization’s journey to the cloud.

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Source: Reducing Risk When Migrating Mission-Critical Applications To The Cloud

Get Bigger, Go Faster: How Venture Capital Markets Won By Tossing Out Their Rulebooks In 2021

Mark Goldberg knows how much the venture capital market has changed in recent years. A partner at Index Ventures, Goldberg remembers the days when investors would meet up on Monday mornings to sip cappuccinos, pore over pitch decks and dig into due diligence. But in today’s record-breaking market, things look a little different.

At his firm, lengthy investor meetings have been replaced with blocked calendar slots in case a partner has to sign a last-minute term sheet quickly. “The whole way that we have organized has changed to adapt to the market, ” Goldberg says. “Now capital is on demand. You can get a round done in 24 hours from a traditional VC fund and a host of new entrants.”

The due diligence process for a potential startup investment is extensive and entails gathering market data, rifling through documents of financial data and getting to know a company’s founders and customers through interviews.

This exercise used to take months on average, but in today’s market, venture capitalists have a week or two for the process — if they are lucky. Most are left to cram months of diligence into a weekend or in some cases, a single day. VCs have adapted streamlined strategies built on efficiency — all while attempting to avoid sacrificing quality. 

For many that has meant making the process more fluid than formal. VCs aren’t waiting for companies to come pitch to them, instead they are constantly tracking and gathering insight on startups that could be potential portfolio companies in the future.

Multi-stage firm Felicis Ventures even hired a head of research in 2021 to assist with this. Frontloading the work gives VCs a “prepared mind” — as multiple investors put it — allowing them to move quickly when presented with a term sheet. “We’ve done months and months of work that is invisible to the founder,” Goldberg says. “The diligence is more intensive now. It’s just you have to pick and choose your battles and be ready on a minute’s notice to say yes. You need to make a decision three months ahead.”

Seed-focused micro fund Bowery Capital says its small team has managed to fit the process into two to three weeks but can push really hard to get things done faster if needed. Bowery general partner Mike Brown tells Forbes that operating on an expedited timeline has pushed the firm to give more weight to new areas of due diligence.

“We really over-index on the team and clear their prior execution ability,” Bowery says, noting that as a seed investor these decisions surround a potential 10-year plus relationship. “If there is one thing we can’t get wrong with this stuff, it’s picking the wrong founders.”

As a solo general partner, Nisha Desai, the founder and managing partner at Andav Capital, says last year forced her to be incredibly disciplined with her time to make sure she gives herself enough time to properly research and prepare. Thankfully though, she thinks founders have also leaned into the dynamic.

“I will say founders have gotten smarter about due diligence. I rarely get deep or even do a first call, until I have enough information,” she says. “That’s something founders should recognize for solo GPs, our biggest asset and most valued asset is our time. We are only going to spend time with you if we think something is there.”

The compression of due diligence comes in lockstep with a huge new influx of capital into the venture world. On the seed level, the number of firms has grown from about 120 in 2013, when Pejman Nozad started his firm Pear, to “thousands” today, he says. Solo shops like Desai’s Andav Capital are also picking up steam. In some cases, angel investors have “morphed into more of an institutionalized firm,” says Defy Partners founder Neil Sequeria.

The abundance of capital has, in turn, allowed companies to raise money at an unprecedented rate, says IVP general partner Jules Maltz. Hopin, a virtual events startup in which Maltz’s firm invested, has raised four funding rounds since June 2020, in the process growing its valuation to $7.8 billion from $245 million. “Historically, 18 months was a good time period between one round and the next round,” Maltz says.

Although the most active investors in 2020 were blue chip venture firms, the first half of 2021 saw crossover funds Tiger Global and Insight Partners take pole position, according to data from Crunchbase. “The hedge funds and public investors who’ve come into the private markets have pushed the existing private investors like us to start upping our game on how we do diligence,” Maltz says.quintex-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1-1-1-2-2-1-1-1-1-1-1-1-1-768x114-1-1-2-1-1-4-1-2-2-1-2-1-1-1-1-1-1-1-1-1-1-1-1-1-1-1-2-1-1

But while venture shops have been forced to adjust to the hedge fund playbook in some ways, they are also changing the industry on their own terms. “One thing I think Andreessen Horowitz started, which isn’t often attributed to them, is this investing strategy where you put massive amounts of money into a seed deal which has nothing substantial yet to validate the valuation.

Yanev Suissa, managing partner of SineWave Ventures, a firm which specializes in public sector guidance for startups. The logic for these heavyweights, with whom SineWave often co-invests, is that one huge success in the portfolio is enough to validate all the high-risk bets.

“I think the other traditional bigger venture funds are going to be forced to follow that trend,” Suissa says. “They’re already starting to, and they’re going to keep doing it even if there isn’t a ton of financial discipline associated with it. That will be a problem going forward that every venture fund is going to have to navigate.”

Forbes reported in December that data connector startup Airbyte raised funds at a $1.5 billion valuation despite its annual recurring revenue not yet reaching $1 million. Another example in Andreessen Horowitz’s own portfolio is the data infrastructure startup Anyscale, which raised at a $1 billion valuation in December despite reports that its annual recurring revenue was below $5 million.

Cofounder Ion Stoica contends the company’s open source origins justified its valuation markup because it created a market even before sales commenced. He points to successful open source companies like Databricks and Confluent as precedent; even still, both firms were valued at half the price of Anyscale at the same funding stage.

In the case of Databricks, the company had $12 million in revenue ahead of its raise. To Ben Horowitz, who has invested in both Databricks and Anyscale, the influx of unicorns is simply a sign that VC is finally starting to reflect the market reality.

“I do think people get confused by the numbers when you look at them versus historical valuations,” Horowitz says. “In some ways, everything was undervalued in venture capital by a lot in that we were doing deals for very cheap for things that could be worth $100 billion. Pricing is catching up to what’s actually going on in the world.”

I’m a reporter covering venture capital, startups and investors out of New York. I was previously a reporter at the Venture Capital Journal and Private Debt Investor. I graduated from Emerson College in 2017 with a degree in journalism. Follow me on twitter at @rebecca_szkutak or send me an email at rszkutak@forbes.com. 

I am a senior reporter for technology, covering venture capital and startups with a focus on Silicon Valley and the greater West Coast. I am based out of Forbes’ San Francisco bureau, where I previously covered tech billionaires as a wealth reporter, and wrote about artificial intelligence as an assistant editor for technology. I graduated from Duke University, where I spent time as news editor for The Chronicle, the university’s independent news organization. Follow me on Twitter at @kenrickcai and email me at kcai [at] forbes.com.

Source: Get Bigger, Go Faster: How VCs Won By Tossing Out Their Rulebooks In 2021

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Effects of Inadequate Sleep and Poor Sleep Quality In Athletes

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Athletes are always looking for ways to improve performance and take goals to the next level. Efforts for doing just that are often limited to waking hours: nutrition, hydration, recovery protocols, supplement routine and, of course, training itself. And despite all this, research shows that, on average, athletes neglect a critical performance tool: sleep. So how does inadequate sleep affect athletic performance? Interestingly, the oversight of sleep can impact performance, both directly and indirectly, and the effects largely differ by sport. 

 
The impact of sleep quality on overall health
 

Before moving into the impact of sleep on performance, it is important to understand how sleep affects overall health and wellness. Both the amount and quality of sleep impacts our mood and energy levels, our metabolism, and immune system health. Inadequate quality sleep can be linked to a variety of serious health problems, including an increased risk of depression, obesity, type II diabetes, and cardiovascular disease. It can even increase an individual’s risk for illness and infection.

Athletes as a population do not get adequate sleep, contributing to overtraining syndrome

Adequate rest and recovery are considered key components of improving athletic performance and preventing sleep disturbances commonly reported in overtraining syndrome. Sleep provides the body with an opportunity to rest from both the physiological and cognitive stressors many athletes face throughout the day. However, despite the body of evidence on the benefits of sleep in athletes (and the potential for sleep to alleviate fatigue), sleep duration and quality are often neglected by athletes.

It is well-reported that, on average, athletes do in fact get less than seven hours of sleep per night, often of poor quality. This falls below the recommended eight hours to combat the negative effects of sleep deprivation. Despite some research limitations, the British Journal of Sports Medicine consensus statement on the topic states that sleep deprivation does affect recovery, training, and performance in elite athletes and that these athletes as a population do not get enough sleep.

Athletes are, in general, a highly motivated group—the type of people who may willingly restrict sleep to fit more activities into waking hours. But even if you’re someone who ‘gets by just fine’ on a restricted sleep schedule, such a lifestyle can have immediate detrimental effects; evidence shows that restricting sleep to six hours per night for just four consecutive nights can impair cognitive performance and mood, glucose metabolism, appetite regulation, and immune function.

Effects of sleep deprivation on different types of athletes

Before we jump into the research of the effects of sleep deprivation in athletes, a disclaimer: Despite the recognized importance of sleep in athletes’ routines, the research on sleep in athletic populations is sparse at this time. The available research on this topic has specific limitations, including the underrepresentation of female subjects, inconsistent research methods across studies, and small sample size.

Now, the science. Current research does show a number of potential performance implications of poor sleep that should be considered in both endurance and power sport athletes. Among the subjects that have been studied, individual sport athletes appear to be more susceptible sleep deficiency and had poorer sleep efficiency than their team sport counterparts.

Two main detrimental effects of sleep deprivation on performance in all sport types are cognitive impairments and mood disturbances. Blumert et al. looked at the effects of just 24 hours of sleep deprivation in collegiate weightlifters (so, for a single night’s sleep). While they saw no difference in performance tasks, training load or intensity, there was a significant difference in mood state including fatigue and confusion in the sleep deprived athletes.

There are also observed direct effects of sleep deprivation on physical performance. Oliver et al. studied endurance running performance in a 24 hour sleep deprived state and found that that subjects who were sleep deprived ran fewer miles in the same amount of time as well-rested athletes but with the same perception of effort.[8] Athletes should also be mindful of the non-direct consequences of sleep deprivation on their performance including but not limited to metabolism, hormone regulation, immune health, and limiting recovery.

Much like everything related to health, wellness, and performance, each individual will have different sleep requirements. These requirements may also vary depending on phase or training season, sex, training volume, intensity, and type of sport.

Biomarkers related to sleep and performance in athletes

Adequate sleep helps to regulate cortisol levels, and inadequate sleep can cause cortisol levels to rise above optimized levels. Cortisol is a catabolic steroid hormone that breaks down muscle, so chronically-elevated cortisol can directly combat progress to become stronger or faster in our athletic performance. 

Sleep also helps to regulate testosterone levels. This hormone is anabolic, meaning it helps build muscle (the opposite of cortisol). But, as you might have guessed, insufficient sleep can reduce testosterone levels.

Research shows that sleep deprivation can also cause chronic inflammation, as indicated by high hsCRP levels. As athletes, inflammation and muscle damage are to be expected with any sort of training—after all, we need to cause slight damage to our muscles to make them stronger. But chronic inflammation, the kind that’s caused by overtraining or insufficient rest, can leave an athlete prone to poor performance, illness, and injury. 

Actions for athletes to take to improve sleep

While the benefits of adequate sleep are well-documented in healthy individuals, the research specific to athletes and different athlete types continues to emerge. That being said, there are well-established actions you can take right now to improve your sleep. Here are some actions to optimize your sleep habits:

. If you have trouble getting the recommended amount of sleep at night, consider taking regular naps.

. Begin tracking your sleep with a wearable activity tracker. While research has displayed varying accuracy of these devices for sleep management, they can help you establish a healthy and regular bedtime routine.

. Work on implementing good sleep habits or a bedtime routine that reduces stress and promotes a good sleeping environment.

. Consider adjusting your exercise routine and incorporate more rest and active recovery in times of sleep deprivation or high life stress to help support your overall health and prevent injury or illness.

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Source: Effects of inadequate Sleep and Poor sleep Quality in Athletes.

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Kim, K; Uchiyama, M; Okawa, M; Liu, X; Ogihara, R (1 February 2000). “An epidemiological study of insomnia among the Japanese general population”. Sleep. 23 (1): 41–7. doi:10.1093/sleep/23.1.1a. PMID10678464. “Dyssomnias” (PDF). WHO. pp. 7–11. Archived (PDF) from the original on 18 March 2009. Retrieved 25 January 2009. Buysse, Daniel J. (2008). “Chronic Insomnia”. Am. J. Psychiatry. 165 (6): 678–86. doi:10.1176/appi.ajp.2008.08010129. PMC2859710. PMID18519533. For this reason, the NIH conference [of 2005] commended the term “comorbid insomnia” as a preferable alternative to the term “secondary insomnia.” Erman, Milton K. (2007). “Insomnia: Comorbidities and Consequences”. Primary Psychiatry. 14 (6): 31–35. Archived from the original on 15 July 2011. Two general categories of insomnia exist, primary insomnia and comorbid insomnia. World Health Organization (2007). “Quantifying burden of disease from environmental noise” (PDF). p. 20. 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