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.


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.

How Is Digital Economy Growth Driving Utility Demand?

James McClelland

Part 10 of the “Intelligent ERP-Driven Industries” series

The United Nations projects that the world’s population will reach 9.8 billion by the year 2050 and 11.2 billion by 2100. Though our world is far from a humanitarian utopia, much of the Third World is developing. The digital economy is growing in ways that were never imagined. One area that impact is being felt is in utility demand.

Countries develop and economies improve. Some experts believe utility demand will double or triple by 2050. Vehicles and mass transit add to this load as electric transportation becomes more popular. How do utilities keep up with this level of demand in a way that is sustainable for our rapidly rising population?

Digital economy growth drives utility demand

As population and income rise, the digital economy grows. When households have more money to spend, utility demand increases. These two concepts are basic to anyone who has studied developing economies. But the increase in demand doesn’t mean a return to smog and pollution.

Deregulation, carbon sequestration, and decentralized production are changing how power is produced. Clean water is finally being seen as a finite resource to be protected. A stronger global focus on sustainability is changing the utility industry. This is happening even as demand increases.

The impact of disruption

Disruption is changing how people work. According to one estimate, telecommuting increased by 79% from 2005 to 2012. New business models are contributing to that change. It’s now possible to hire a virtual assistant from Malaysia and a researcher from South Africa from the comfort of a home office. The future office building may be one that doesn’t exist. How will that impact traffic and transit patterns? What will it do to the traditional urban utility grid?

Disruption is making new companies big and traditional companies obsolete. Failing to adapt to this new reality can have a serious impact on industry leaders. The focus on sustainable development is changing whether customers use utilities at all. New wind, solar, water, and power storage technologies are driving much of this change.

How do existing utilities meet these challenges? New business models can help existing companies find new revenue sources. Smart building services, load balancing, and supply innovations can be part of this process. Combining operations and information technology allow businesses to optimize business processes.

Industry and global collaboration is key

Collaboration is another aspect of digitization that is driving new energy trends. A new utility business can be both a competitor and a partner. Previously unrelated industries are collaborating to improve energy production, delivery, and efficiency. A great example of this is the collaboration of telecoms with utility-meter manufacturers to develop smart meters.

How have these meters impacted the industry? Water meters can now detect and measure low-flow levels that were unheard of in the past. Home and business owners can monitor electricity usage in real time and adjust utilization accordingly. Sudden changes catch water leaks before they become serious problems. Utility workers can focus on improving other areas of infrastructure instead of reading meters.

Automotive newcomer Tesla made waves with its Powerwall home energy storage system. Paired with solar panels or another charging source, it provides the option of going completely off-grid. It’s gained enough attention for existing manufacturers to get into the game. Mercedes, Nissan, and BMW have now launched their own home battery systems.

Focus on the customer

Another aspect of digitization is the shift to a customer-centric approach. As demand increases, customers still want fully functional appliances, tech, and amenities in their homes and places of business. But at the same time, they want environmentally sound development of products, water, and power. How can those demands be met in a sustainable fashion?

Imagine this scenario: A family lives in a high-rise development. Natural lighting through fiberoptics and windows with coatings that reflect heat help reduce their daily energy usage. Telecommuting means they may not own a car or deal with public transportation very often.

To meet their daily needs, they may have groceries delivered or shop online for clothing. This requires strong communications and delivery structures. Their appliances and electronics use cutting-edge technology. Smart-energy design helps reduce the electrical load on the grid.

Solar window frames and parking shades generate power. Smart-energy networks offer better rates during times of peak production, lowering the impact on the grid when these production assets go offline as the sun sets. New power storage options allow vehicle owners to provide stored vehicle power to the grid overnight while charging quickly in the morning.

Focusing on changing trends in population and demand growth enables existing utilities and manufacturers to compete in the digital market. Although most executives recognize the importance of digitization, most companies don’t yet have a solid strategy to get there.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading “Accelerating Digital Transformation in Utilities.”


Billion-Dollar Business: Driving Profits From Purpose

Shannon Lester

Why does your business exist?

In the past, the answer was to generate financial returns for shareholders.

Today, it’s about so much more. It’s about finding purpose. It’s about making the world a better place by supporting noble causes. It’s about inspiring customers, employees, and the world at large by committing your company to important social issues that affect us all.

In Billion Dollar Business: Driving Profit from Purpose, the latest episode of Game-Changing Conversations, presented by SAP, our panel of experts discussed how your enterprise can become purpose-driven – and how embracing purpose can drive profits.

Framed by several inspiring quotes, these thought leaders offered listeners three key pieces of advice for getting started:

1. Adopt a can-do attitude: Everything seems impossible until it’s done

Addressing complex societal issues through business can feel overwhelming.

In fact, you’re probably asking yourself how your company can prioritize things like sustainability, human rights, and climate change while driving profits and keeping consumers, employees, shareholders, and stakeholders happy.

The first step is to shift your perspective. Stop viewing purpose as a box to check off your to-do list. Instead, see it as an opportunity to improve the world, inspire consumer loyalty, and drive profits.

As panelist Freya Williams, CEO of Futerra North America, explained, the purpose-led organization is the “business model of the future,” and what seems to be impossible today will be “business as usual tomorrow.”

“[There is a] huge opportunity to monetize this big pivot to sustainable and purpose-led business in society,” she said. “There are many companies out there that have a billion dollars or more in annual revenue from products and services that align with sustainability or have social good at their core.”

Today, building a billion-dollar business doesn’t require compromising on your beliefs. You can drive profitable growth while proudly standing up for and supporting meaningful issues.

2. Take a long-term view: Intend to live forever

A major challenge of investing in purpose is longevity. Judging your business by next week’s or next quarter’s projected performance doesn’t capture everything. You need to take a long-term view of purpose – and that will lead to business results.

Panelist Jim Sullivan, vice president of Sustainability Management & Strategy at SAP, cited a 2017 McKinsey study that proved this point.

McKinsey found that firms with more long-term views exhibited stronger financial performance. These organizations increased their market capitalization by an average of $7 billion more than companies with short-term approaches to purpose.

On top of that, long-term firms added 12,000 more jobs, on average, than their short-term peers. If all 615 companies McKinsey studied had a more long-term view of purpose, the U.S. economy would have benefited from the addition of over 5 million jobs between 2001 and 2015.

Rome wasn’t built in a day. Building a billion-dollar business takes time. So, practice patience. Take a long-term view and assume you’ll live forever so you can better weather changes and stay the course in your pursuit of building a purpose-driven, billion-dollar business.

3. Focus on conservation, not consumption: The world owes you nothing

“As we take from the world,” said panelist Padmini Ranganathan, global vice president of SAP Ariba, “we have to give back to it.”

Purpose-driven companies are increasingly putting nature at the core of their decision-making processes. Issues like preservation and sustainability are growing in importance to businesses, consumers, and employees alike.

In the past, organizations were driven by bottom lines. They aimed to reduce production costs to increase profitability. Today, purpose drives profitability, and companies take great care to ensure their products are ethically sourced and produced.

Consumers naturally respond to this. Buyers are proud to support brands that avoid chopping down rainforests or give back to communities where they operate.

This devotion to purpose also attracts staff. Prospective employees are more likely to join your company and current workers are more likely to be passionate about their jobs when they identify with your organization’s values.

While millennial consumers and employees may have fueled this interest in purpose-led organizations, they’re not the only generation that recognizes the importance of purpose.

Baby boomers and Generation X grew up in different worlds. But they understand the value of supporting noble causes – and they’ve embraced this shift toward a better, more sustainable future.

Unilever, Tesla, Chipotle, and Everlane are just a few examples of purpose-driven businesses. Some are “born good,” as Freya Williams explains, meaning they were founded with a purpose built into their philosophy. Others are “born again,” meaning they reinvented themselves by introducing sustainability into their businesses.

However your purpose originates, it’s crucial to have one.

The final word

No commentary on purpose-driven businesses would be complete without an outlook of the future. As the Game-Changing Conversations show wound down, host Bonnie D. Graham asked her panelists for predictions on what lies ahead with purpose in the business world.

Freya Williams hopes that by 2030 all companies will have met their sustainable development goals, and purpose-driven businesses will be viewed as the new normal.

Jim Sullivan believes new companies that fail to embrace a purpose won’t last long – and they’ll never stand a chance of becoming billion-dollar businesses.

Padmini Ranganathan expects companies to focus on eradicating human rights violations or cutting ties with suppliers that are guilty of them. She also predicts that businesses will begin emphasizing data and technology to increase supply chain transparency.

Whatever the future holds, one thing is clear: Purpose deserves a place in your business. In fact, it should be one of the core reasons your company exists in the first place.

Game-Changing Conversations, presented by SAP, is an online radio series focused on co-innovation, purpose, partnership, and collaboration. To learn more, listen to the episode.


Shannon Lester

About Shannon Lester

Shannon Lester is the Global Marketing Leader for SAP’s Strategic Customer Program. She has over 20 years of marketing experience in technology and professional services, with a strong background in executive stakeholder management and demand generation. Shannon lives in Canada with her husband and two children.

The Human Angle

By Jenny Dearborn, David Judge, Tom Raftery, and Neal Ungerleider

In a future teeming with robots and artificial intelligence, humans seem to be on the verge of being crowded out. But in reality the opposite is true.

To be successful, organizations need to become more human than ever.

Organizations that focus only on automation will automate away their competitive edge. The most successful will focus instead on skills that set them apart and that can’t be duplicated by AI or machine learning. Those skills can be summed up in one word: humanness.

You can see it in the numbers. According to David J. Deming of the Harvard Kennedy School, demand for jobs that require social skills has risen nearly 12 percentage points since 1980, while less-social jobs, such as computer coding, have declined by a little over 3 percentage points.

AI is in its infancy, which means that it cannot yet come close to duplicating our most human skills. Stefan van Duin and Naser Bakhshi, consultants at professional services company Deloitte, break down artificial intelligence into two types: narrow and general. Narrow AI is good at specific tasks, such as playing chess or identifying facial expressions. General AI, which can learn and solve complex, multifaceted problems the way a human being does, exists today only in the minds of futurists.

The only thing narrow artificial intelligence can do is automate. It can’t empathize. It can’t collaborate. It can’t innovate. Those abilities, if they ever come, are still a long way off. In the meantime, AI’s biggest value is in augmentation. When human beings work with AI tools, the process results in a sort of augmented intelligence. This augmented intelligence outperforms the work of either human beings or AI software tools on their own.

AI-powered tools will be the partners that free employees and management to tackle higher-level challenges.

Those challenges will, by default, be more human and social in nature because many rote, repetitive tasks will be automated away. Companies will find that developing fundamental human skills, such as critical thinking and problem solving, within the organization will take on a new importance. These skills can’t be automated and they won’t become process steps for algorithms anytime soon.

In a world where technology change is constant and unpredictable, those organizations that make the fullest use of uniquely human skills will win. These skills will be used in collaboration with both other humans and AI-fueled software and hardware tools. The degree of humanness an organization possesses will become a competitive advantage.

This means that today’s companies must think about hiring, training, and leading differently. Most of today’s corporate training programs focus on imparting specific knowledge that will likely become obsolete over time.

Instead of hiring for portfolios of specific subject knowledge, organizations should instead hire—and train—for more foundational skills, whose value can’t erode away as easily.

Recently, educational consulting firm Hanover Research looked at high-growth occupations identified by the U.S. Bureau of Labor Statistics and determined the core skills required in each of them based on a database that it had developed. The most valuable skills were active listening, speaking, and critical thinking—giving lie to the dismissive term soft skills. They’re not soft; they’re human.


This doesn’t mean that STEM skills won’t be important in the future. But organizations will find that their most valuable employees are those with both math and social skills.

That’s because technical skills will become more perishable as AI shifts the pace of technology change from linear to exponential. Employees will require constant retraining over time. For example, roughly half of the subject knowledge acquired during the first year of a four-year technical degree, such as computer science, is already outdated by the time students graduate, according to The Future of Jobs, a report from the World Economic Forum (WEF).

The WEF’s report further notes that “65% of children entering primary school today will ultimately end up working in jobs that don’t yet exist.” By contrast, human skills such as interpersonal communication and project management will remain consistent over the years.

For example, organizations already report that they are having difficulty finding people equipped for the Big Data era’s hot job: data scientist. That’s because data scientists need a combination of hard and soft skills. Data scientists can’t just be good programmers and statisticians; they also need to be intuitive and inquisitive and have good communication skills. We don’t expect all these qualities from our engineering graduates, nor from most of our employees.

But we need to start.

From Self-Help to Self-Skills

Even if most schools and employers have yet to see it, employees are starting to understand that their future viability depends on improving their innately human qualities. One of the most popular courses on Coursera, an online learning platform, is called Learning How to Learn. Created by the University of California, San Diego, the course is essentially a master class in human skills: students learn everything from memory techniques to dealing with procrastination and communicating complicated ideas, according to an article in The New York Times.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing.

Although there is a longstanding assumption that social skills are innate, nothing is further from the truth. As the popularity of Learning How to Learn attests, human skills—everything from learning skills to communication skills to empathy—can, and indeed must, be taught.

These human skills are integral for training workers for a workplace where artificial intelligence and automation are part of the daily routine. According to the WEF’s New Vision for Education report, the skills that employees will need in the future fall into three primary categories:

  • Foundational literacies: These core skills needed for the coming age of robotics and AI include understanding the basics of math, science, computing, finance, civics, and culture. While mastery of every topic isn’t required, workers who have a basic comprehension of many different areas will be richly rewarded in the coming economy.
  • Competencies: Developing competencies requires mastering very human skills, such as active listening, critical thinking, problem solving, creativity, communication, and collaboration.
  • Character qualities: Over the next decade, employees will need to master the skills that will help them grasp changing job duties and responsibilities. This means learning the skills that help employees acquire curiosity, initiative, persistence, grit, adaptability, leadership, and social and cultural awareness.


The good news is that learning human skills is not completely divorced from how work is structured today. Yonatan Zunger, a Google engineer with a background working with AI, argues that there is a considerable need for human skills in the workplace already—especially in the tech world. Many employees are simply unaware that when they are working on complicated software or hardware projects, they are using empathy, strategic problem solving, intuition, and interpersonal communication.

The unconscious deployment of human skills takes place even more frequently when employees climb the corporate ladder into management. “This is closely tied to the deeper difference between junior and senior roles: a junior person’s job is to find answers to questions; a senior person’s job is to find the right questions to ask,” says Zunger.

Human skills will be crucial to navigating the AI-infused workplace. There will be no shortage of need for the right questions to ask.

One of the biggest changes narrow AI tools will bring to the workplace is an evolution in how work is performed. AI-based tools will automate repetitive tasks across a wide swath of industries, which means that the day-to-day work for many white-collar workers will become far more focused on tasks requiring problem solving and critical thinking. These tasks will present challenges centered on interpersonal collaboration, clear communication, and autonomous decision-making—all human skills.

Being More Human Is Hard

However, the human skills that are essential for tomorrow’s AI-ified workplace, such as interpersonal communication, project planning, and conflict management, require a different approach from traditional learning. Often, these skills don’t just require people to learn new facts and techniques; they also call for basic changes in the ways individuals behave on—and off—the job.

Attempting to teach employees how to make behavioral changes has always seemed off-limits to organizations—the province of private therapists, not corporate trainers. But that outlook is changing. As science gains a better understanding of how the human brain works, many behaviors that affect employees on the job are understood to be universal and natural rather than individual (see “Human Skills 101”).

Human Skills 101

As neuroscience has improved our understanding of the brain, human skills have become increasingly quantifiable—and teachable.

Though the term soft skills has managed to hang on in the popular lexicon, our understanding of these human skills has increased to the point where they aren’t soft at all: they are a clearly definable set of skills that are crucial for organizations in the AI era.

Active listening: Paying close attention when receiving information and drawing out more information than received in normal discourse

Critical thinking: Gathering, analyzing, and evaluating issues and information to come to an unbiased conclusion

Problem solving: Finding solutions to problems and understanding the steps used to solve the problem

Decision-making: Weighing the evidence and options at hand to determine a specific course of action

Monitoring: Paying close attention to an issue, topic, or interaction in order to retain information for the future

Coordination: Working with individuals and other groups to achieve common goals

Social perceptiveness: Inferring what others are thinking by observing them

Time management: Budgeting and allocating time for projects and goals and structuring schedules to minimize conflicts and maximize productivity

Creativity: Generating ideas, concepts, or inferences that can be used to create new things

Curiosity: Desiring to learn and understand new or unfamiliar concepts

Imagination: Conceiving and thinking about new ideas, concepts, or images

Storytelling: Building narratives and concepts out of both new and existing ideas

Experimentation: Trying out new ideas, theories, and activities

Ethics: Practicing rules and standards that guide conduct and guarantee rights and fairness

Empathy: Identifying and understanding the emotional states of others

Collaboration: Working with others, coordinating efforts, and sharing resources to accomplish a common project

Resiliency: Withstanding setbacks, avoiding discouragement, and persisting toward a larger goal

Resistance to change, for example, is now known to result from an involuntary chemical reaction in the brain known as the fight-or-flight response, not from a weakness of character. Scientists and psychologists have developed objective ways of identifying these kinds of behaviors and have come up with universally applicable ways for employees to learn how to deal with them.

Organizations that emphasize such individual behavioral traits as active listening, social perceptiveness, and experimentation will have both an easier transition to a workplace that uses AI tools and more success operating in it.

Framing behavioral training in ways that emphasize its practical application at work and in advancing career goals helps employees feel more comfortable confronting behavioral roadblocks without feeling bad about themselves or stigmatized by others. It also helps organizations see the potential ROI of investing in what has traditionally been dismissed as touchy-feely stuff.

In fact, offering objective means for examining inner behaviors and tools for modifying them is more beneficial than just leaving the job to employees. For example, according to research by psychologist Tasha Eurich, introspection, which is how most of us try to understand our behaviors, can actually be counterproductive.

Human beings are complex creatures. There is generally way too much going on inside our minds to be able to pinpoint the conscious and unconscious behaviors that drive us to act the way we do. We wind up inventing explanations—usually negative—for our behaviors, which can lead to anxiety and depression, according to Eurich’s research.

Structured, objective training can help employees improve their human skills without the negative side effects. At SAP, for example, we offer employees a course on conflict resolution that uses objective research techniques for determining what happens when people get into conflicts. Employees learn about the different conflict styles that researchers have identified and take an assessment to determine their own style of dealing with conflict. Then employees work in teams to discuss their different styles and work together to resolve a specific conflict that one of the group members is currently experiencing.

How Knowing One’s Self Helps the Organization

Courses like this are helpful not just for reducing conflicts between individuals and among teams (and improving organizational productivity); they also contribute to greater self-awareness, which is the basis for enabling people to take fullest advantage of their human skills.

Self-awareness is a powerful tool for improving performance at both the individual and organizational levels. Self-aware people are more confident and creative, make better decisions, build stronger relationships, and communicate more effectively. They are also less likely to lie, cheat, and steal, according to Eurich.

It naturally follows that such people make better employees and are more likely to be promoted. They also make more effective leaders with happier employees, which makes the organization more profitable, according to research by Atuma Okpara and Agwu M. Edwin.

There are two types of self-awareness, writes Eurich. One is having a clear view inside of one’s self: one’s own thoughts, feelings, behaviors, strengths, and weaknesses. The second type is understanding how others view us in terms of these same categories.

Interestingly, while we often assume that those who possess one type of awareness also possess the other, there is no direct correlation between the two. In fact, just 10% to 15% of people have both, according to a survey by Eurich. That means that the vast majority of us must learn one or the other—or both.

Gaining self-awareness is a process that can take many years. But training that gives employees the opportunity to examine their own behaviors against objective standards and gain feedback from expert instructors and peers can help speed up the journey. Just like the conflict management course, there are many ways to do this in a practical context that benefits employees and the organization alike.

For example, SAP also offers courses on building self-confidence, increasing trust with peers, creating connections with others, solving complex problems, and increasing resiliency in the face of difficult situations—all of which increase self-awareness in constructive ways. These human-skills courses are as popular with our employees as the hard-skill courses in new technologies or new programming techniques.

Depending on an organization’s size, budget, and goals, learning programs like these can include small group training, large lectures, online courses, licensing of third-party online content, reimbursement for students to attain certification, and many other models.

Human Skills Are the Constant

Automation and artificial intelligence will change the workplace in unpredictable ways. One thing we can predict, however, is that human skills will be needed more than ever.

The connection between conflict resolution skills, critical thinking courses, and the rise of AI-aided technology might not be immediately obvious. But these new AI tools are leading us down the path to a much more human workplace.

Employees will interact with their computers through voice conversations and image recognition. Machine learning will find unexpected correlations in massive amounts of data but empathy and creativity will be required for data scientists to figure out the right questions to ask. Interpersonal communication will become even more important as teams coordinate between offices, remote workplaces, and AI aides.

While the future might be filled with artificial intelligence, deep learning, and untold amounts of data, uniquely human capabilities will be the ones that matter. Machines can’t write a symphony, design a building, teach a college course, or manage a department. The future belongs to humans working with machines, and for that, you need human skills. D!


About the Authors

Jenny Dearborn is Chief Learning Officer at SAP.

David Judge is Vice President, SAP Leonardo, at SAP.

Tom Raftery is Global Vice President and Internet of Things Evangelist at SAP.

Neal Ungerleider is a Los Angeles-based technology journalist and consultant.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.

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Machine Learning In The Real World

Paul Taylor

Over the past few decades, machine learning has emerged as the real-world face of what is often mistakenly called “artificial intelligence.” It is establishing itself as a mainstream technology tool for companies, enabling them to improve productivity, planning, and ultimately, profits.

Michael Jordan, professor of Computer Science and Statistics at the University of California, Berkeley, noted in a recent Medium post: “Most of what is being called ‘AI’ today, particularly in the public sphere, is what has been called ‘machine learning’ for the past several decades.”

Jordan argues that unlike much that is mislabeled “artificial intelligence,” ML is the real thing. He maintains that it was already clear in the early 1990s that ML would grow to have massive industrial relevance. He notes that by the turn of the century, forward-looking companies such as Amazon were already using ML throughout their business, solving mission-critical back-end problems in fraud detection and logistics-chain prediction and building innovative consumer-facing services such as recommendation systems.

“Although not visible to the general public, research and systems-building in areas such as document retrieval, text classification, fraud detection, recommendation systems, personalized search, social network analysis, planning, diagnostics, and A/B testing have been a major success — these are the advances that have powered companies such as Google, Netflix, Facebook, and Amazon,” Jordan says.

Amazon, which has been investing deeply in artificial intelligence for over 20 years, acknowledges, “ML algorithms drive many of our internal systems. It’s also core to the capabilities our customers’ experience – from the path optimization in our fulfillment centers and Amazon’s recommendations engine o Echo powered by Alexa, our drone initiative Prime Air, and our new retail experience, Amazon Go. “

The fact that tech industry leaders like Google, Netflix, Facebook, and Amazon have used ML to help fuel their growth is not news. For example, it has been widely reported that sites with recommendation engines, including Netflix, use ML algorithms to generate user-specific suggestions. Most dynamic map/routing apps, including Google Maps, also use ML to suggest route changes in real time based upon traffic speed and other data gleaned from multiple users’ smartphones.

In a recent article detailing real-world examples of ML in action, Kelly McNulty, a senior content writer at Salt Lake City-based Prowess Consulting, notes: “ML isn’t just something that will happen in the future. It’s happening now, and it will only get more advanced and pervasive in the future.”

However, the broader uptake of ML by enterprises – big and small – is less much less known. A recently published study prepared for SAP by the Economist Intelligence Unit and based on a survey of 360 organizations revealed that 68 percent of respondents are already using ML, at least to some extent, to enhance their business processes.

The report adds: “Some are aiming even higher: to use ML to change their business models and offer entirely new value propositions to customers…… ML is not just a technology.” The report’s authors continue, “It is core to the business strategies that have led to the surging value of organizations that incorporate it into their operating models – think Amazon, Uber, and Airbnb.”

McNulty notes that there are both internal and external uses for ML. Among the internal uses, she cites Thomson Reuters, the news and data services group, which, after its merger in 2008, used ML to prepare large quantities of data with Tamr, an enterprise data-unification company. She says the two partners used ML to unify more than three million data points with an accuracy of 95 percent, reducing the time needed to manually unify the data by several months and cutting the manual labor required by an estimated 40 percent.

In another example of enterprise use of ML, she notes that GlaxoSmithKline, the pharmaceuticals group, used the technology to develop information aimed at allaying concerns about vaccines. The ML algorithms were used to sift through parents’ comments about vaccines in forums and messaging boards, enabling GSK to develop content specifically designed to address these concerns.

In the financial sector, ML has been widely used for some time to help detect fraudulent transactions and assess risk. PayPal uses the technology to “distinguish the good customers from the bad customers,” according to Vadim Kutsyy, a data scientist at the online payments company.

PayPal’s deep learning system is also able to filter out deceptive merchants and crack down on sales of illegal products. Additionally, the models are optimizing operations. Kutsyy explained the machines can identify “why transactions fail, monitoring businesses more efficiently,” avoiding the need to buy more hardware for problem-solving.

ML algorithms also underpin many of the corporate chatbots and virtual assistants being deployed by enterprise customers and others. For Example, Allstate partnered with technology consultancy Earley Information Science to develop a virtual assistant called ABIe (the Allstate Business Insurance Expert). ABIe was designed to assist Allstate’s 12,000 agents to understand and sell the company’s commercial insurance products, reportedly handling 25,000 inquires a month.

Other big U.S. insurance companies, including Progressive, are applying ML algorithms to interpret driver data and identify new business opportunities.

Meanwhile, four years ago, Royal Dutch Shell became the first company in the lubricants sector to use ML to help develop the Shell Virtual Assistant. The virtual assistant enables customers and distributors to ask common lubricant-related questions.

As the company noted at the time, “customers and distributors type in their question via an online message window, and avatars Emma and Ethan reply back with an appropriate answer within seconds.” The tool was initially launched in the U.S. and UK but has since expanded to other countries and reportedly can now understand and respond to queries in multiple languages, including Chinese and Russian.

In the retail sector, Walmart, which already uses ML to optimize home delivery routes, also uses it to help reduce theft and improve customer service. The retail giant has reportedly developed facial recognition software that automatically detects frustration in the faces of shoppers at checkout, prompting customer service representatives to intervene.

Among SAP’s own customers, a growing number are implementing ML tools, including those built into SAP’s own platforms and applications. As SAP notes, “Many different industries and lines of business are ripe for machine learning—particularly the ones that amass large volumes of data.”

The manufacturing, finance, and healthcare sectors are leading the way. For example, a large European chemicals company has improved the efficiency and effectiveness of its customer service process by using ML algorithms to automatically categorize and send responses to customer inquiries.

In the mining sector, Vale, the Brazilian mining group, is using ML to optimize maintenance processes and reduce the number of purchase requisitions that were being rejected causing maintenance and operational delays in its mines. Before implementation, between 25 percent and 40 percent of purchase requisitions were being rejected by procurement because of errors. Since implementation, 86 percent of these rejections have been eliminated.

Elsewhere a large consumer goods company, the Austrian-based consumer good company, is using ML and computer vision to identify images of broken products submitted by customers from the over 40,000 products in the company’s catalog. The application enables the company to speed up repairs and replacements, thereby improving customer service and the customer experience.

Similarly, a global automotive manufacturer is using image recognition to help consumers learn more about vehicles and direct them to local dealer showrooms, and a major French telecommunications firm reduced the length of customer service conversations by 50 percent using chatbots that now manage 20 percent of all calls.

But not every enterprise ML deployment has worked out so well. In a highly publicized case, Target hired a ML expert to analyze shopper data and create a model that could predict which female customers were most likely to be pregnant and when they were expected to give birth. (If a woman started buying a lot of supplements, for example, she was probably in her first 20 weeks of pregnancy, whereas buying a lot of unscented lotion indicated the start of the second trimester.)

Target used this information to provide pregnancy- and parenting-related coupons to women who matched the profile. But Target was forced to modify its strategy after some customers said they felt uncomfortable with this level of personalization. A New York Times story reported that a Minneapolis parent learned of their 16-year-old daughter’s unplanned pregnancy when the Target coupons arrived in the mail.

Target’s experience notwithstanding, most enterprise ML projects generate significant benefits for customers, employees, and investors while putting the huge volumes of data generated in our digital era to real use.

For more insight on the implications of machine learning technology, download the study Making the Most of Machine Learning: 5 Lessons from Fast Learners.