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The (R)evolution of PLM, Part 3: Using Digital Twins Throughout The Product Lifecycle

John McNiff

In Part 1 of this series we explored why manufacturers must embrace “live” PLM. In Part 2 we examined the new dimensions of a product-centric enterprise. In Part 3 we look at the role of digital twins.

It’s time to start using digital twins throughout the product lifecycle. In fact, to compete in the digital economy, manufacturers will need to achieve a truly product-centric enterprise in which digital twins guide not only engineering and maintenance, but every business-critical function, from procurement to HR.

Why is this necessary? Because product lifecycles are shrinking. Companies are managing ever-growing streams of data. And customers are demanding product individualization. The only way for manufacturers to respond is to use digital twins to place the product – the highly configurable, endlessly customizable, increasingly connected product – at the center of their operations.

Double the insight

Digital twins are virtual representations of a real-world products or assets. They’re a Top 10 strategic trend for 2017, according to Gartner. And they’re part of a broader digital transformation in which IDC says companies will invest $2.1 trillion a year by 2019.

Digital twins aren’t a new concept, but their application throughout the product lifecycle is. Here are key ways smart manufacturers will leverage digital twins – and achieve a product-centric and model-based enterprise – across operations:

Design and engineering: Traditionally, digital twins have been used by design and engineering to create virtual representations for designing and enhancing products. In this application, the digital twin actually exists before its physical counterpart does, essentially starting out as a vision of what the product should be. But you can also capture data on in-the-field product use and apply that to the digital twin for continuous product improvement.

Maintenance and service: Today, the most common use case for digital twins is maintenance and service. By creating a virtual representation of an asset in the field using lightweight model visualization, and then capturing data from smart sensors embedded in the asset, you can gain a complete picture of real-world performance and operating conditions. You can also simulate that real-world environment for predictive maintenance. Let’s say you manufacture wind turbines. You can capture data on rotor speed, wind speed, operating temperature, ambient temperature, humidity, and so on to understand and predict product performance. By doing so, you can schedule maintenance before a crucial part breaks – optimizing uptime and saving time and cost for a repair.

Quality control: Just as digital twins can help with maintenance and service, they can predictively improve quality during manufacturing. You can also use digital twins to compare quality data across multiple products to better understand global quality issues and quickly visualize issues against the model. And you can apply data collected by maintenance and service to achieve ongoing quality improvements.

Customization: As products become more customizable, digital twins will allow design and engineering to model the various permutations. But digital twins can also incorporate customer demand and usage data to enhance customization options. That sounds obvious, but in the past it was very difficult to incorporate customer input into the manufacturing process. Let’s say you sell high-end custom bikes. You might allow customers to choose different colors, wheels, and other details. By capturing customer preferences in the digital twin, you can get a picture of customer demand. And by capturing customer usage data, you can understand how custom configurations affect product performance. So you can offer the most reliable options or allow customers to configure your products based on performance attributes. You can also visualize lightweight representations of the twin without the burden of heavyweight design systems and parameters.

Finance and procurement: In our custom-configured bike example, different configurations involve different costs. And those different costs involve not only the cost of the various components, but also the cost for assembling the various configurations. By capturing sales data in the digital twin, you can understand which configurations are being ordered and how configuration-specific revenues compare to the cost to build each configuration. What’s more, you can link that data with supplier information. That will help you understand which suppliers contribute to product configurations that perform well in the field. It also can help you identify opportunities to cost-effectively rid yourself of excess supply.

Sales and marketing: The digital twin can also inform sales and marketing. For instance, you can use the digital twin to populate an online product configurator and e-commerce website. That way you can be sure what you’re selling is always tied directly to what you’re engineering in the design studio and what you’re servicing in the field.

Human resources: The digital twin can even extend into HR. For example, you can use the digital twin to understand training and certification needs and be sure the right people are trained on the right product features.

One twin, many views

Digital twins should underlie all manufacturing operations. Ideally you should have a single set of digital twin master data that resides in a central location. That will give you one version of the truth, and with “in-memory” computing-based networks plus a lightweight, change-controlled model capability, you’ll be able to analyze and visualize that data rapidly.

But not all business functions care about the entire data set. You need to deliver the right data to the right people at the right time. Design and engineering requires one set of data, with every specification and tolerance needed to create and continuously improve the product. Sales and marketing requires another set of data, with the features and functions customers can select. And so on.

Ultimately, as the digital product innovation platform extends the dimensions of traditional PLM, at the heart of PLM is an extended version of the digital twin. In future blogs we’ll talk about how you can leverage the latest-generation platform from SAP, based on SAP S/4HANA and SAP’s platform for the Internet of Everything, to achieve a live, visual, and intelligent product-centric enterprise.

Learn how a live supply chain can help your business, visit us at SAP.com.

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John McNiff

About John McNiff

John McNiff is the Vice President of Solution Management for the R&D/Engineering line-of-business business unit at SAP. John has held a number of sales and business development roles at SAP, focused on the manufacturing and engineering topics.

If The Financial Close Ain’t Broke, Don’t Fix It

Zach Deming

Part 5 in the continuous accounting blog series. Read Part 1, Part 2, Part 3, and Part 4.

But here’s the thing: The financial close is broken.

In the blog Why Did The Accountant Cross the Road, the idea of doing things the way they’ve always be done was put out to pasture. “Let’s not cross the road just because we did it last year.” Or you might say, let’s build new roads, new ways to improve the timeliness and accuracy of the close.

Well, building new roads is hard. And the best path forward starts with fixing the potholes.

That’s the idea behind Ventana Research’s advice to adopt a continuous improvement approach: Don’t think of a fictitious end state as the goal. The goal itself is continuous improvement. Success means always adapting, innovating, and improving.

Continuous accounting is not the destination, but a journey yielding continuous improvement in the quality, accuracy, and efficiency of accounting operations. But, beginning is always the hardest, so here are six steps to get you started.

Step 1 – Ask your staff accountants

What are your biggest challenges and most painful bottlenecks? Do you even know?

Ask your staff accountants. Those deepest in the weeds of manual effort are often the ones with the best ideas about how to streamline existing processes.

In fact, many of the procedures best suited for the first steps of your journey are often the most manual and risky – and the easiest to improve and automate.

Step 2 – Pick the low-hanging fruit

You should definitely imagine your ideal close process by playing the “what if” game. But quick wins and time to value come from finding and picking the low-hanging fruit that consume too much time for no good reason (see Step 1).

How many zero-balance accounts are you reconciling every month? Are you manually matching and reconciling bank account transactions to find exceptions? Automate this so you can focus only on the exceptions.

Step 3 – Optimize before automating

Split batch processes into smaller components, then schedule those components more often and embed them within daily activities. When your processes are improved and standardized, automate wherever possible.

One example is a prepaid account. Do you book a transaction on the first day of the month every period but save the associated account reconciliation for the end of the month? When are you doing your flux analysis?

Automate analysis weekly and monthly – during the close – instead of next month, quarter, or year.

Step 4 – Monitor results frequently

Leverage continuous activity to constantly review output, and investigate alerts from flux analysis, exceptions, and anomalies.

By moving away from manual spreadsheet analysis of transactions, you can focus solely on exceptions and anomalies and make adjustments as needed in real time.

By closely monitoring your close and managing it by exceptions, you will catch and resolve issues before they cause problems. This will naturally spread month-end close work earlier across the period.

Step 5 – Review outcomes

On a monthly or quarterly basis, review the outcomes of your continuous accounting journey. Discover macro trends and identify process and controls gaps. Compare successes vs. original objectives – what worked, and what didn’t?

In The Value of Continuous Accounting for Business, Ventana Research found “a correlation between the frequency of process reviews and achievement of better results: 67% of companies that review their close process monthly said they were able to shorten their close, compared to 50% that review quarterly and just 10% annually.”

Step 6 – Rinse and repeat

Combining the knowledge gleaned from the review stage, rinse and repeat. Return to Step 1 and focus on a more challenging tier of improvements and the more risky and critical gaps. Are there new problems uncovered in light of the freed time? There’s the beginning of your next round of improvements.

Adapt or…

You’ve heard it a thousand times, and you feel it every day: You need to do more with less. Beyond the risks associated with out-of-date accounting and finance practices, the increasingly complex nature of global business cycles means that companies that are slow to modernize their accounting operations are at a competitive disadvantage.

“The one constant in today’s business world is relentless change,” said Mario Spanicciati, BlackLine’s chief strategy officer. “Survival is determined by an organization’s ability to adapt and evolve, and success is granted to those who define the competitive advantage.”

You can make things better by embracing a culture of continuous improvement. The best-performing finance teams know that success means always adapting, innovating, and improving. This shouldn’t be discouraging. It’s inspiring. Every day can be better than the last, and you’ll hit a lot fewer bumps in the road to closing the books.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | FacebookYouTube

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Zach Deming

About Zach Deming

Zach Deming is the director of Product Marketing at BlackLine. Zach partners with accounting and finance leaders to help them realize how their spreadsheet-driven processes hiding in the shadows simply can’t cope with the demands of always-on modern business, and the expectation of real-time analysis. After a decade in the industry, he thinks the benefits of cloud software can free accountants from the chains of transactions, liberating them to focus on strategy and analysis. Zach is skeptical of the robot uprising, and as a champion of Continuous Accounting, he believes the future of accounting does not lie in new technology alone, but also with the nuanced intelligence of skilled accountants. Connect with Zach on LinkedIn and Twitter (@ZachDeming).

The CIO’s Starting Block

Eric Piscini , Gys Hyman and Wendy Henry

Do your customers trust you? And do you trust them? The emerging trust economy depends on each transacting party’s reputation and digital identity—and that’s where blockchain comes in. The technology behind digital contracts transforms reputation into a useful, manageable attribute.

Part 5 of a 5-part series. Read Part 1, Part 2, Part 3, and Part 4

You can also read the full article or download a copy at Deloitte University Press.

The hype surrounding blockchain is reaching a fever pitch. While this technology’s long-term impact may indeed be formidable, its immediate adoption path will most likely be defined by focused experimentation and a collection of moderately interesting incremental advances. As with any transformative technology, expertise will have to be earned, experience will be invaluable, and the more ambitious deployment scenarios will likely emerge over time. The good news? It’s still early in the game, and numerous opportunities await.

Here are some suggestions for getting started on your blockchain journey:

1. Come all ye faithful

The financial services industry is currently at the vanguard of blockchain experimentation, and the eventual impact of its pioneering efforts will likely be far-reaching. Yet blockchain’s disruptive potential extends far beyond financial services: Every sector in every geography should be developing a blockchain strategy, complete with immediate tactical opportunities for efficiency gains and cost savings within the organization. Strategies should include more ambitious scenarios for pushing trust zones to customers, business partners, and other third parties. Finally, sectors should envision ways blockchain could eventually be deployed to challenge core business models and industry dynamics. While it often pays to think big, with blockchain you should probably start small given that the technology’s maturity—like that of the regulations governing blockchain’s use—is still relatively low.

2. Wayfinding

Startups and established players are aggressively pushing product into every level of the blockchain stack. Part of your adoption journey should be understanding the fundamental mechanics of blockchain, what pieces are absolutely necessary for your initial exploration, and the maturity of the offerings needed for the specific scope being considered.

3. The nays have it

Ask your blockchain gurus to define scenarios and applications that are not a good fit for blockchain. This is not reverse psychology: It’s simply asking advocates to keep a balanced perspective, and thoughtfully casting a light on this emerging technology’s current limitations and implications. Sure, expect challenges and prescribed roadblocks to yield to future advances in the field. But until then, challenge your most enthusiastic blockchain apostles to remain objective about the technology’s potential upside and downside.

4. You gotta have friends

Blockchain offers little value to individual users. To maximize its potential—particularly for applications and use cases involving digital identity—explore opportunities to develop a consortium or utility for blockchain use.

5. Stay on target

Far-reaching potential can lead to distracting rhetoric and perpetual prognostication. As you explore blockchain, focus your brainstorming and your efforts on actionable, bounded scenarios with realistic scope that can lead to concrete results and—hopefully—better value. Wild-eyed aspirations are not necessarily bad. But they are best served by grounded progress that leads to hands-on proof and an earned understanding of what is needed to realize the stuff of dreams.

What you should know: Insight from Matthew Roszak, co-founder of Bloq

I’ve been in the venture capital business for over 20 years, co-founding six enterprise software companies along the way. I began hearing about Bitcoin in 2011, while serving as chairman of one of the largest social gaming companies in Southeast Asia. In that business, cross-border payments and payment processing quickly becomes a core competency. As the buzz around Bitcoin grew, I initially discounted this technology as “silly Internet money,” but by 2012, a number of people I trusted told me to take a harder look. So I did what I still tell people to do today: lock your door, turn off your phone, and study this new technology frontier for a day. I realized that this ecosystem will likely have incredibly profound effects on enterprise, government, and society—and is a generational opportunity for entrepreneurs and investors.

I began investing in a wide range of companies across the blockchain ecosystem, including digital wallets, payment processors, exchanges, and miners. This helped me develop a heat-map of the ecosystem, and more importantly, a network of technologists and entrepreneurs who were building the scaffolding for this new industry. It also led to my friendship and, later, partnership with Jeff Garzik, with whom I co-founded Bloq.

Enterprise demand for blockchain is real, but there are many questions to be answered. What type of software infrastructure do you need? What can we learn from enterprise adoption patterns of other transformative technology?

To the first question, the emergence of an open source, enterprise-grade blockchain software suite is developing quickly, and we’re investing an enormous amount of time and energy helping companies develop an infrastructure that, in many ways, defines the basic anatomy of a blockchain:

    Blockchain platform as the base communication and management layer of the network

    Nodes to connect to a blockchain network, which behave much like routers

    Wallets to securely manage and store digital assets

    Smart contracts to automate and streamline business processes

    Analytics to drive better decisions and detect network anomalies

The second question revolves around adoption curves. I see a story unfolding that is similar to those of the Internet and cloud computing. Right now, organizations are implementing blockchain technology internally to reduce costs by moving value and data in a more secure, more efficient manner. We are also beginning to see some activity in core operations and business processes that utilize blockchain’s encrypted workflow features. These are important stepstones helping drive an architectural step change in blockchain adoption.

Next, companies deploying blockchain networks should consider extending those platforms to their customers, suppliers, and partners. This is where network effects should start to blossom, and will likely lay the foundation for pursuing new economic opportunities—measured in trillions of dollars—think central banks issuing digital currencies, land title registries, a secure digital identity, and more. Yet organizations don’t strive just to be better—they want to operate at a different level. With blockchain, moving money should be as easy as email. In 10 years, banks may look more like Apple, Amazon, and Tencent, coupled with access to tons of products and services within those ecosystems. The discussion won’t be about whether to use blockchain—it will be about the economics of the platform and how to develop strong network effects.

The blockchain genie is out of the bottle, although the adoption curve remains unclear—will it be three to seven years? A decade, or longer? These networks for money’s new railroad will take time to adopt.

In the late 1990s, CEOs wondered if they should risk their careers by investing in and innovating with the Internet; today CEOs are in the same boat evaluating blockchain. Like any great technology evolution, the blockchain transformation requires passion and investment, dynamics that drive innovation. Right now, neither appears to be in short supply.

Bottom line

In a historic break from the past, the foundational concept of trust is being tailored to meet the demands of the digital age, with blockchain cast in the role of gatekeeper of reputation and identity. While the broader implications of this trend may not be fully understood for years to come, business and government are beginning to explore opportunities to selectively share composite digital identities with others not only to help establish trust but to exchange assets safely and efficiently, and—perhaps most promisingly—to proffer digital contracts.

Copyright © 2017 Deloitte Development LLC. All rights reserved. Reprinted by permission.

Read Part 1, Part 2, Part 3, and Part 4, or read the full article or download a copy at Deloitte University Press.

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Eric Piscini

About Eric Piscini

Eric is a Deloitte Consulting LLP principal serving the technology and banking practices with 20 years of experience defining IT strategies including M&A, technology infrastructure, IT operations, post-merger integrations, echannel strategies, payment, and digital transformations. In addition to serving financial institutions and banking regulators in core aspects of their technology environment, he also leads the Deloitte global cryptocurrency center serving financial institutions and retailers.

Gys Hyman

About Gys Hyman

Gys is a principal in Deloitte Consulting LLP’s Deloitte Digital practice, the world’s first creative digital consultancy. He is currently focused on the banking industry and has helped a number of organizations with large scale digital transformation efforts, ranging from designing, building, and implementing green field’s digital banking capabilities to large scale core banking systems transformation efforts.

Wendy Henry

About Wendy Henry

Wendy is a specialist leader in Deloitte Consulting LLP’s Federal Technology practice and works with clients to distill emerging technologies into simple business value discussions. An ever-curious individual, she thrives on understanding how emerging technologies can drive her clients’ business towards newly created value. She is a hands-on technologist with 30 years of large-scale, complex system integration experience across a wide variety of technologies, including blockchain, cloud, digital innovation, and location-based technologies.

Teaching Machines Right from Wrong

Dan Wellers

 

By 2018, smart machines will supervise over 3 million workers worldwide.
21% of consumers in an FTC study had confirmed errors on their credit reports.
2014: the first annual Fairness, Accountability, and Transparency in Machine Learning conference.
A private university encouraged 20-25 students to drop out based on AI predictions of
poor grades.

Real-world examples of misused AI algorithms abound. These are just a few:

  • Women who weren’t pregnant — or weren’t ready to reveal it — received special offers of baby products and “congratulatory” messages.
  • People with minority ethnic names received a disproportionate number of ads implying they had criminal records.
  • Guests at a party learned a ride-hailing company kept track of customers who stayed out all night and went home in the wee hours.

Ethical-Edge Cases

Credit scoring algorithms designed to evaluate lending risk are now commonly used to gauge reliability and trustworthiness, determining whether someone should get a job or apartment.

Insurance underwriting algorithms determine the extent, price, and type of coverage someone can get, with little room for disagreement.

Healthcare algorithms could be used to penalize the currently healthy for their probability of future illness.

Algorithms often use zip codes as proxy for (illegal) racial profiling in major decisions, such as employment and law enforcement.

Self-driving cars will have to learn how to react in an accident situation when every possible outcome is bad.


What Should We Do About It?

All machine learning contains assumptions and biases of the humans who create it — unconscious or otherwise. To ensure fairness, business leaders must insist that AI be built on a strong ethical foundation.

We can:

  • Monitor algorithms for neutrality and positive outcomes.
  • Support academic research into making AI-driven decisions more fair, accountable, and transparent.
  • Create human-driven overrides, grievance procedures, and anti-bias laws.
  • Include ethics education in all employee training and development.

Above all, we must consider this a human issue, not a technological one. AI is only as unbiased a tool as we make it. It’s our responsibility to keep it on the ethical straight and narrow.


Download the executive brief Teaching Machines Right from Wrong.


Read the full article AI and Ethics: We Will Live What Machines Learn

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Dan Wellers

About Dan Wellers

Dan Wellers is the Global Lead of Digital Futures at SAP, which explores how organizations can anticipate the future impact of exponential technologies. Dan has extensive experience in technology marketing and business strategy, plus management, consulting, and sales.

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Why Millennials Quit: Understanding A New Workforce

Shelly Kramer

Millennials are like mobile devices: they’re everywhere. You can’t visit a coffee shop without encountering both in large numbers. But after all, who doesn’t like a little caffeine with their connectivity? The point is that you should be paying attention to millennials now more than ever because they have surpassed Boomers and Gen-Xers as the largest generation.

Unfortunately for the workforce, they’re also the generation most likely to quit. Let’s examine a new report that sheds some light on exactly why that is—and what you can do to keep millennial employees working for you longer.

New workforce, new values

Deloitte found that two out of three millennials are expected to leave their current jobs by 2020. The survey also found that a staggering one in four would probably move on in the next year alone.

If you’re a business owner, consider putting four of your millennial employees in a room. Take a look around—one of them will be gone next year. Besides their skills and contributions, you’ve also lost time and resources spent by onboarding and training those employees—a very costly process. According to a new report from XYZ University, turnover costs U.S. companies a whopping $30.5 billion annually.

Let’s take a step back and look at this new workforce with new priorities and values.

Everything about millennials is different, from how to market to them as consumers to how you treat them as employees. The catalyst for this shift is the difference in what they value most. Millennials grew up with technology at their fingertips and are the most highly educated generation to date. Many have delayed marriage and/or parenthood in favor of pursuing their careers, which aren’t always about having a great paycheck (although that helps). Instead, it may be more that the core values of your business (like sustainability, for example) or its mission are the reasons that millennials stick around at the same job or look for opportunities elsewhere. Consider this: How invested are they in their work? Are they bored? What does their work/life balance look like? Do they have advancement opportunities?

Ping-pong tables and bringing your dog to work might be trendy, but they aren’t the solution to retaining a millennial workforce. So why exactly are they quitting? Let’s take a look at the data.

Millennials’ common reasons for quitting

In order to gain more insight into the problem of millennial turnover, XYZ University surveyed more than 500 respondents between the ages of 21 and 34 years old. There was a good mix of men and women, college grads versus high school grads, and entry-level employees versus managers. We’re all dying to know: Why did they quit? Here are the most popular reasons, some in their own words:

  • Millennials are risk-takers. XYZ University attributes this affection for risk taking with the fact that millennials essentially came of age during the recession. Surveyed millennials reported this experience made them wary of spending decades working at one company only to be potentially laid off.
  • They are focused on education. More than one-third of millennials hold college degrees. Those seeking advanced degrees can find themselves struggling to finish school while holding down a job, necessitating odd hours or more than one part-time gig. As a whole, this generation is entering the job market later, with higher degrees and higher debt.
  • They don’t want just any job—they want one that fits. In an age where both startups and seasoned companies are enjoying success, there is no shortage of job opportunities. As such, they’re often looking for one that suits their identity and their goals, not just the one that comes up first in an online search. Interestingly, job fit is often prioritized over job pay for millennials. Don’t forget, if they have to start their own company, they will—the average age for millennial entrepreneurs is 27.
  • They want skills that make them competitive. Many millennials enjoy the challenge that accompanies competition, so wearing many hats at a position is actually a good thing. One millennial journalist who used to work at Forbes reported that millennials want to learn by “being in the trenches, and doing it alongside the people who do it best.”
  • They want to do something that matters. Millennials have grown up with change, both good and bad, so they’re unafraid of making changes in their own lives to pursue careers that align with their desire to make a difference.
  • They prefer flexibility. Technology today means it’s possible to work from essentially anywhere that has an Internet connection, so many millennials expect at least some level of flexibility when it comes to their employer. Working remotely all of the time isn’t feasible for every situation, of course, but millennials expect companies to be flexible enough to allow them to occasionally dictate their own schedules. If they have no say in their workday, that’s a red flag.
  • They’ve got skills—and they want to use them. In the words of a 24-year-old designer, millennials “don’t need to print copies all day.” Many have paid (or are in the midst of paying) for their own education, and they’re ready and willing to put it to work. Most would prefer you leave the smaller tasks to the interns.
  • They got a better offer. Thirty-five percent of respondents to XYZ’s survey said they quit a previous job because they received a better opportunity. That makes sense, especially as recruiting is made simpler by technology. (Hello, LinkedIn.)
  • They seek mentors. Millennials are used to being supervised, as many were raised by what have been dubbed as “helicopter parents.” Receiving support from those in charge is the norm, not the anomaly, for this generation, and they expect that in the workplace, too.

Note that it’s not just XYZ University making this final point about the importance of mentoring. Consider Figures 1 and 2 from Deloitte, proving that millennials with worthwhile mentors report high satisfaction rates in other areas, such as personal development. As you can see, this can trickle down into employee satisfaction and ultimately result in higher retention numbers.

Millennials and Mentors
Figure 1. Source: Deloitte


Figure 2. Source: Deloitte

Failure to . . .

No, not communicate—I would say “engage.” On second thought, communication plays a role in that, too. (Who would have thought “Cool Hand Luke” would be applicable to this conversation?)

Data from a recent Gallup poll reiterates that millennials are “job-hoppers,” also pointing out that most of them—71 percent, to be exact—are either not engaged in or are actively disengaged from the workplace. That’s a striking number, but businesses aren’t without hope. That same Gallup poll found that millennials who reported they are engaged at work were 26 percent less likely than their disengaged counterparts to consider switching jobs, even with a raise of up to 20 percent. That’s huge. Furthermore, if the market improves in the next year, those engaged millennial employees are 64 percent less likely to job-hop than those who report feeling actively disengaged.

What’s next?

I’ve covered a lot in this discussion, but here’s what I hope you will take away: Millennials comprise a majority of the workforce, but they’re changing how you should look at hiring, recruiting, and retention as a whole. What matters to millennials matters to your other generations of employees, too. Mentoring, compensation, flexibility, and engagement have always been important, but thanks to the vocal millennial generation, we’re just now learning exactly how much.

What has been your experience with millennials and turnover? Are you a millennial who has recently left a job or are currently looking for a new position? If so, what are you missing from your current employer, and what are you looking for in a prospective one? Alternatively, if you’re reading this from a company perspective, how do you think your organization stacks up in the hearts and minds of your millennial employees? Do you have plans to do anything differently? I’d love to hear your thoughts.

For more insight on millennials and the workforce, see Multigenerational Workforce? Collaboration Tech Is The Key To Success.

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