Metals Industry Braces For A Technological Disruption

Stefan Koch

You could be forgiven for assuming that innovation only happens in young or fast-moving sectors. Mature, commodity-based industries like metals hardly carry the sexy allure of, say, information tech or even automobiles. But that doesn’t mean you’d be right.

To the contrary, staying competitive in this industry requires innovation. You only have to consider the demands placed on metal companies to realize that innovation is happening all the time. On the topic of innovation for the products themselves, thousands of new patents for metal alloys are requested every decade. From spaceflight to automobiles to house and office building, these alloys provide an unparalleled strength.

Then consider innovation from a business perspective to support the changing marketing demands. Metals companies need to find solutions to be profitable in oversupplied, competitive, and constantly changing markets. Proven business models and business plans based on enormous capital investments in plants and assets will face significant challenges.

Leading companies are embracing the new paradigm shifts in technology and reimagining their processes, asset management frameworks, and customer relationships. This reimagination requires a digital metals business framework that overlays the production and distribution value chains of metal products. All participants – miners, primary metals producers, fabricators, and distributors – will use digital innovation to anticipate real-time demand and supply, enhance process excellence for operational efficiency, operate resilient supply chains, and innovate the customer experience.

Do companies like ArcelorMittal, Severstal, Kloeckner, NLMK, or Tata Steel really need to embrace the digital age? In one word: Yes. Here’s how they can – and are – doing so.

Better customer relationships

Leading metals companies are quickly embracing digital technology. This helps drive customer relationships, business process efficiency, innovation, and growth.

One company that has put a new emphasis on customer relationships is steel giant Severstal.

In order to secure value in the future, Severstal is reassessing and expanding its role as a cornerstone of the metals ecosystem.

With respect to Industry 4.0 and digital transformation, the company sees that a culture of accelerated innovations is incompatible with traditional ways of thinking, and it demands major cultural transformation from the organization – especially in how planning and execution impact customers. Severstal has been leading discussions with its customers, suppliers, and partners to make conscious decisions on where to follow change initiated by others, versus where to take a leadership role and drive change. Digital technology is giving Severstal more opportunities to understand its customers through their behavior and communicate with them through personalized experiences and online portals, with the goal being to manage the supply chain in order to best meet customer timelines and changing demand.

Here’s a specific use case for how technology can improve customer relationships in the metals industry: Customers expect metals producers to deliver on time as expected. But production schedules and quality outcomes fluctuate and can affect delivery schedules, which can cause problems for customers. So what’s the solution? Having a digital innovation system that allows metals producers to provide information about the products to help their customers react to changes. It also helps them understand how to better use, handle, or deploy a product. As a result, customers can adjust their own production plans. Or they can use quality data to change the welding power or decrease the heat level to save energy. Being able to provide the correct data to the customer about the exact location of the products could be an up-sell or value-added service, as well. The customer can plan more precisely because the expected arrival date is more reliable.

Responding to change – and being more sustainable

As metals companies begin reimagining their entire business, they need an IT architecture that provides stability for the core enterprise processes. They also need flexibility in areas where constant change happens, such as customer demand for innovation (e.g., products made from recycled material or high-strength steel). Efforts to reduce carbon footprint of metals production will drive new products that will also be easier to reuse, collect, and recycle. For example, companies will start offering metal rental as an alternative to purchase. Steel producers will offer new light-steel grades for cars and are already developing innovative ways to apply the new materials to avoid aluminum or carbon fiber becoming the material of choice.

As products become more intelligent and interconnected, we see big opportunities for producers. They can use sensors to both reduce costs and make sure they are producing exactly what customers order. Analyzing sensor data from machines helps predict possible failures early. The data also reduces unplanned downtime. Plus, it helps ensure product quality fits within expected parameters. For the mill products and mining industries, digital technology is helping by connecting customer information, assets, products and processes.

Versatile and revolutionary

From a consumer’s standpoint, technology is helping make transactions within the metals industry smoother and easier, not to mention producing products that are far superior.

As seen with Kloeckner, an international steel distribution company, innovations usher in product advances, software development, online marketing, and improved customer service. This promises more efficient processes and optimized data exchanges.

“Based on our digital solutions, we are redesigning all supplier and particularly customer-related processes to be simpler and more efficient,” says Gisbert Ruehl, CEO of Kloeckner & Co.

Quantifying precisely how much consumers or companies will benefit from technological advances is an exercise in futility. However, one thing is clear: The metals industry is undergoing a rapid disruption courtesy of innovations and technology. To read more about this transformation and how it impacts you, check out “Mill and Mining – Metals Industry in the Digital Transformation.”

A move to the cloud

Of course, any move towards digitization involves running in the cloud. For the metals industry, speed, flexibility, and cost reduction are the biggest drivers for moving to the cloud. Moving HR to the cloud is already common, and procurement is not far behind. Metals companies are also interested in integrated business planning functionality. They also want to leverage the cloud and make it simpler to access many sources for consolidation. They want to benchmark and compare different sites for their energy use.

Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Energy and Natural Resource Companies. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?


Stefan Koch

About Stefan Koch

Stefan Koch is responsible for SAP solutions for the metal industry globally. In this role, he looks closely at all aspects of how technology can be applied to drive efficiency, innovation and growth across the metals industry. He is in frequent discussions with leading metals companies, industry user groups, technology implementation partners and independent software vendors. Presently Stefan is guiding a number of ongoing discussion with metals companies on how to drive Digital Transformation in Metals and to identify the role of Industry 4.0 and IoT in this context.

Higher Education Is A Business – Is That So Bad?

James Krouse

There is little question that education is one of the most important services to ensure we evolve and grow as a society. Education can be considered a service, and services require resources. Within the modern, free-market, capitalist world, “resources” means money. The higher-education mission remains the purpose, but do not confuse it with the practicality of survival or the ability of the institution to continue to deliver that service to the student (customer).

Yet, the economic challenges facing institutions of higher education are significant, and the divide between revenue and expenditures continues to separate. These challenges increasingly demand creative management to ensure viability and sustainability, just like a business.

Taxes across many jurisdictions are pronounced, and donations, grants, and charity are largely insufficient to support the costs and maintenance of higher education institutions and teaching staffs. So, the primary means to pay for college lie with continual escalations of tuition and fees borne by the student body. The student body is finding it difficult to absorb those increasing costs, especially in the face of questions on the return on investment and preparation for the job market.

So, what are institutions to do? They must think creatively and adapt to meet changing economic and environmental factors and students’ expectations. That means more focus on costs and expenditure, just like business. It also means exploring alternate revenue streams and creative finances, just like business. Increasingly, advanced technology provides the key to creative business thinking. Institutions are utilizing new, aggressive technologies to improve efficiency and reduce resource waste. They are utilizing technology to improve the student (customer) experience by embedding analytics to manage systems and support in real time, all focused toward maximizing successful outcomes.

Finally, the economic squeeze for economic resources in higher education is breeding competition for those customers across the institutional landscape. Institutions are increasingly leveraging advanced marketing and social media technologies to maximize outreach to prospective students. The new costs are reviewed as a necessary investment, or a cost of doing business. Without a solid and broad enrolled student foundation, the institution could not exist. It may have been unheard of 20 years ago for a university or college to go out of business, but institutions are failing, and in increasing numbers, today.

Institutions that are creative, open to the advances that technology can provide in maximizing economics, and seeking to manage their operations with an eye toward efficiency will survive and thrive and continue their purpose-driven mission to educate. Other institutions may be lost as a necessary corrective market action.

Learn more, download this higher-education whitepaper to get a more in-depth understanding of how your institution can embark on your digital transformation journey.

Join us at SAPPHIRENOW to hear from leading experts on how they are shaping their journey enabled by SAP.


James Krouse

About James Krouse

James Krouse is the director of Global Solutions Marketing at SAP. He is the global strategic marketing lead for the healthcare and higher education industry groups and is responsible for tailoring GTM strategies, analyst relations, government relations, positioning, and messaging.

As Machine Learning Remakes Industries, Leaders Must Transform Enterprise IT

Jim McHugh

From cars that autonomously navigate dark and icy roads, to MRI scanners trained to spot brain abnormalities, to warehouses managed by sensors, drones, and robots, machine learning is already transforming industries in profound ways.

These applications are emerging amid a faltering Moore’s Law, which has run up against the laws of semiconductor physics. For four decades, we could count on the doubling of computational power every two years. Now, traditional semiconductors can only deliver about 10% performance gains in this timeframe. That means the performance gains that sustained advancements in the use of information technology through the PC, mobile, and cloud eras can no longer be relied upon to propel the promise of machine learning.

Instead, graphics processing units (GPUs) – chips evolved from those that power image-intensive video games and professional visualization applications – will provide the computational power needed to drive the machine learning revolution. A new computing model, called accelerated computing, takes advantage of the GPU’s faster processing speeds to train the complex algorithms used in machine learning software.

However, most companies’ data centers, where the algorithm training must take place, run on servers with traditional processors. This is hardly surprising, given that machine learning has only recently verged on mainstream business operations. An enterprise that intends to transform itself using machine learning will need to invest in the necessary combination of hardware and software to tap the vast promise of AI.

The power behind the algorithms

Machine learning is poised to change the way business is done across a range of industries. Consider the following examples.

Transportation. Automakers, at the forefront of AI’s transformation of the $10 trillion transportation industry, are racing to show how AI can differentiate their brands. Enhancing safety will be high on the list, as each year there are tens of millions of accidents worldwide and over a million fatalities. Companies worldwide are using a compact, GPU-powered supercomputer in the vehicle that is capable of guiding autonomous cars.

The same holds true for truck manufacturers and logistics businesses. GPU-powered servers in the data center are being used to train, virtually, autonomous trucks and other vehicles how to drive on millions of miles of high-definition mapped roads in a broad range of weather, road, and traffic conditions. Through such simulated driving efforts, the algorithms that run autonomous vehicles will be able to learn continuously from data collected from actual driving situations to make real-time decisions.

Healthcare. Medical imaging alone is estimated to become a $49 billion market worldwide by 2020, making it the biggest source of data in healthcare. Radiology, a prime area for machine learning advances, accounts for a large portion of medical images. According to Academic Radiology, the average radiologist must interpret a CT or MRI examination every three to four seconds to meet workload demands. In an eight-hour workday, that adds up to 8,000 images per radiologist a day.

AI algorithms can be trained to spot abnormalities using real and simulated medical images. This makes devices such as MRI scanners the first line of defense in spotting disease. These and similar devices can speed diagnosis, greatly improve accuracy, and allow doctors to concentrate their energies on the most difficult cases.

Manufacturing and agriculture. Advances in image recognition are creating a range of industrial Internet of Things opportunities. For example, IoT is becoming central to warehouses and fulfillment centers. Machine learning – fueled by image recognition, data, and sensors – steers robots among humans in warehouses.

Manufacturing companies are using connected machines such as drones and robots to inspect industrial equipment, which offers companies potential savings of tens of millions of dollars annually. Industrial farming won’t be left behind. Images taken from drones and satellites will be treated with machine learning to boost crop yields. Farming companies can use images and algorithms to process all the data captured by satellites to monitor the soil conditions and overall crop health. Analytics can track and predict weather changes that could impact crop yields.

An infrastructure for machine learning

All told, the nascent business opportunities enabled by massive data collection and the implementation of algorithms will require rethinking the data center. Without investments in enterprise IT infrastructure, machine learning can’t deliver what it promises.

A critical step toward business transformation is to make sure an organization’s data center can support compute-intensive workloads. GPU-accelerated computing redefines the economics of data center computing, replacing racks of CPU-based servers with far less hardware installation, power, and cost. For example, a company could potentially replace 300 CPU-based servers with one or two GPU-based servers, for a cost savings of more than 85%.

Those managing a company’s data center infrastructure need to ensure they have enough accelerated computing power and storage to handle all the data needed. This involves evaluating the whole picture to understand the incredible savings that can come from modernizing your architecture for the AI world.

Business leaders who perform due diligence to ensure their hardware is a match for their company’s machine learning ambitions will quickly understand the value of GPU computing.

To learn more about the technology requirements for deep learning, check out this webcast on May 24, 2018 and this white paper.


Jim McHugh

About Jim McHugh

Jim McHugh is vice president and general manager at NVIDIA with over 25 years of experience as a marketing and business executive with startup, mid-sized, and high-profile companies. He currently leads NVIDIA Deep Learning Systems – NVIDIA DGX Systems and GPU Cloud. Jim focuses on building a vision of organizational success and executing strategies to deliver computing solutions that benefit from GPUs in the data center. He has a deep knowledge and understanding of business drivers, market/customer dynamics, technology-centered products, and accelerated solutions.

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.