Five Ways Blockchain Can Keep Mineral Supply Chains Conflict-Free

Jennifer Scholze

Conflict minerals – those extracted in conflict zones and sold to fund the conflict – are a challenge in the mining industry and throughout the world. Time Magazine reports that international efforts to stop the sales of “blood diamonds” and other conflict minerals are facing problems. Loopholes in the current system allow these products to get onto the market and reach consumers. Using a blockchain lowers the risk of conflict mineral purchases for individuals and businesses.

Verifying the origin of a mineral

Individuals selling a mineral in the blockchain must show proof of ownership. Since the information about the product is public, users in the chain can verify the data by tracing previous trades for the mineral. Verifying a mineral’s owner prevents the sale of stolen minerals through the online system.

Users must meet specific standards to use the online system or tool. They are required to provide appropriate documentation to confirm ownership, country of origin, and other details related to the mineral. A document of authenticity gives the buyer peace of mind and provides a greater level of comfort in purchasing the mineral.

According to The Economist, a blockchain’s key benefit is the updated and amended ledger of information. Since no individual controls the chain, everyone has access to information about the mineral’s origins. Users in the chain keep the information up to date. The peer to peer sharing with open transparency and clear standards of documentation limit the risk of obtaining conflict minerals.

Data transparency

Transparency is an important aspect of preventing the sale of conflict minerals. The World Economic Forum states that a full transaction history of an item sold through a blockchain allows for greater transparency. It limits the risk of purchasing from deceptive sellers. The public access element of a blockchain allows individuals to clarify and verify information. The individual purchasing an item can audit through the online tools and system.

Security for transactions

When a sale does not have proper security solutions, individuals and companies have a higher risk of purchasing conflict minerals. They may accidentally buy the mineral if a third party interferes with the sale. Alternatively, poor security may suggest an underlying problem with the system.

The Economist reports that a blockchain ledger prevents double-spending and creates a unique signature for different users within the system. While the system offers a reasonable level of anonymity in relation to an individual’s personal identity, the public nature of the transaction allows buyers to purchase with confidence. The ledger requires a digital signature, which limits the security risks when making or receiving a payment through a blockchain.

Reducing loopholes in the system

While international authorities, systems, and laws limit the sale of conflict minerals, loopholes allow these minerals to enter the market. A blockchain reduces the risk of conflict mineral purchases by verifying data and setting specific standards for a sale. The seller provides the proper documentation, which becomes a public record. Items must be supported by a document of authenticity, proof of origins, and proof of ownership. Third parties have access to the information and digital miners provide an online service to audit the data. The implementation of a blockchain is an effective method of preventing the purchase of conflict minerals.

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?


About Jennifer Scholze

Jennifer Scholze is the Global Lead for Industry Marketing for the Mill Products and Mining Industries at SAP. She has over 20 years of technology marketing, communications and venture capital experience and lives in the Boston area with her husband and two children.

Retail IoT: How to Streamline Inventory Supply Chains

Christoph Schroeder

Your inventory is the lifeblood of your retail store. Whether you are selling bath products or artisanal cheeses, you need to have a steady flow of products moving from the warehouse to the front room to please your customers. If most of your business relies on brick-and-mortar sales, a reliable inventory supply chain is necessary to ensure that you have products when you need them. Yet many retailers struggle with warehouse and front-store issues that can affect their sales line.

Inventory management is a challenge for most retailers. Merchandise tracking is never fully accurate once shipped products reach the warehouse floor and are placed onto shelves. Employees often rush to fill empty shelves without taking note of inventory numbers or updating depleted batch numbers in warehouse computer systems, and you end up unaware of product shortages until it is too late to get a shipment in on time.

On the opposite end of the spectrum, product overstock can lead to waste and high budget costs. Your inventory can be impacted by warehouse placement if you don’t have the proper backroom shelving system set up. Products may get lost among other items so you end up ordering too many products that can’t be moved in a timely fashion. If you have perishable items, you have to throw old products away.

Another issue is product quality. Merchandise such as food or beverages can be negatively affected by factors such as changes in temperature, pressure, or vibrations, and you may end up placing inferior products on shelves. If customers have a bad experience, it can damage your reputation and the future of your business.

Lastly, inventory management can be affected by employee theft. Without a  tracking and security system in place, you could end up ordering products to replace those that employees have taken.

Retail IoT: Stores focusing on technology for better inventory management

You cannot be at the front of the store helping customers while also managing the back of the store. You are busy with administrative tasks, talking with new suppliers or coming up with new marketing strategies to help your business grow. Yet finding a way to better manage your inventory must also be a top priority. The retail Internet of Things (IoT) may be one solution to improve product quality, reduce waste, and monitor stocked products.

The Internet of Things basically refers to the connectivity of “things” such as computers, networks, and equipment systems that can communicate and share data in real time without human interaction. In the retail industry, storefronts and warehouses are becoming equipped with innovative technologies that help keep track of your inventory and help you gain better visibility and traceability of merchandise.

Here are several technologies that you may be able to employ now or in the near future to enhance your inventory management processes.

Smart shelves

Smart shelf strategies can help you track inventory movement without impacting employee productivity. Sensors on smart shelves and RFID chips on merchandise link to warehouse computer systems. When customers purchase a product, updated inventory data is sent to the warehouse. Inventory alerts can inform workers when they need to restock shelves and order more products.

Store cameras

While cameras are not a new technology, enhancements have been made that allow you to better track inventory as it moves from the back to the front of the house. Smart displays show how much inventory is on the shelves. Cameras can also bring added security to your business to prevent product theft from employees or customers. Furthermore, this technology can be used to monitor store traffic, create heat maps, and gain insight on promotion effectiveness and other initiatives.

Track and trace sensors

The retail supply chain, or the time between when products leave the manufacturer and reach your store warehouse, can also benefit from IoT innovations. Sensors and beacons on products in trucks can monitor temperatures and vibrations during transport. This strategy is essential for food and beverage products to protect from sudden humidity, temperature changes, or vibrations that could adversely impact product shelf life. Once the products reach your loading dock, your warehouse staff can better evaluate for product loss. Furthermore, monitoring trucks with the help of geofencing can enable employees to spend time with customers instead of waiting for shipments.

Investing in retail IoT

According to Forbes, about 71 percent of retailers will be investing in IoT technologies by 2021. Business owners are recognizing how IoT tools can bring better visibility and traceability methods to their stores and warehouses. These technologies can streamline inventory management as well as make operations more efficient and cost-effective. As the retail market increasingly competes with online stores, these innovations can help enhance the shopping experience for customers.

In summary, IoT can seamlessly connect the front- and back-offices, boost process efficiency, and increase customer-centricity. This can help bring customers into the “sweet spot” of value, convenience, and experience.

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 Retail.


Christoph Schroeder

About Christoph Schroeder

Christoph Schroeder is Global Vice President of Vertical Merchandising and Supply Chain at SAP. He works with premier customers such as adidas, Luxottica, Giorgio Armani, and Tommy Hilfiger. Follow Christoph on Twitter @nightrain_x.

Visibility 3.0: Transforming From Basic Information Sharing To Supply Chain Alignment

Sudy Bharadwaj

In the late mid-to-late ’90s, the early days of collaboration brought visibility to organizations from their supply base. This level of visibility was usually in the form of basic information, such as quantity of components needed (high tech), amount of ingredients needed (consumer goods), feedstock required (chemicals), amount of active pharmaceutical ingredients needed (life sciences). Moving from fax machines to email was the first generation of supplier visibility. Remember “e-business”? The advent of email provided transactional efficiency and some level of process automation.

e-business circa 2000

Figure 1: E-business circa 2000. Business and supplier with (a) simple lines of communication, (b) disparate business processes, and (c) limited information exchanged.

What’s next?

A McKinsey study reveals organizations that collaborate on innovation with suppliers achieve higher growth than organizations that fail to do so. Innovation with suppliers can take many forms, including migrating from the buyer’s process efficiency to overall supply chain alignment – process efficiency between buyers and their supply base. If the figure above represents basic visibility, then the figure below represents increasing levels of supply base alignment between buyer/supplier (left side), or supply chain alignment among members of a supply chain (right side). Rather than a distinct line between buyer and supplier, there is a union between the buyer and supply base.

supply chain alignment

Figure 2: Supply chain alignment is a union of business processes and information within an ecosystem.

The metaphor? Reduce the lines and integrate processes using richer data. Examples of richer data include the sharing of capacities, manufacturing information, sales data, and quality data. Naturally, some data is confidential and cannot be shared, but more sharing results in greater efficiency, typically resulting in lower costs, faster response to customers, less waste, and a variety of other improved performance metrics.

In an earlier blog post, I discussed How Manufacturing Organizations Can Regain Control Of Their Supply Chain; the concepts reviewed in this post can be applied to supply chain alignment. Examples of transforming from simple communication to more of a union in the supply chain vary by industry:

  • Semiconductor: The chip in your smartphone is usually made by contract manufacturing organizations where quality and yield information are shared among members of the ecosystem in near real-time in order to make decisions about yield or quality in response to supply and demand.
  • Life sciences: Most pharmaceutical ingredients are developed for use in multiple countries, but associated documentation and other regulatory requirements vary across jurisdictions. Aligning the production of documentation with that of ingredients can reduce approval time, thus reducing time to market. Much the same holds true for medical device supply chains.
  • Automotive: Products recalls are a natural way of life in this industry. The ability to quickly trace components, sub-components, and sub-sub-components all the way down to commodities such as polycarbonate, aluminum, and other basic materials can help to assess which end products (automobiles) require a recall and whether a specific manufacturing line needs to switch one or more suppliers.

Summary

Digital transformations require consideration of the supply base and discussions about aligning supply chains by sharing more information than ever. Think about removing lines and providing more of a union of business to improve profitability, flexibility, and agility – for all members of the ecosystem.

To learn more about how digital transformation can impact your supply chain operation, link to our value calculator and analyze the impact and generate your own customized reports.


Sudy Bharadwaj

About Sudy Bharadwaj

Sudy Bharadwaj has over 25 years experience in helping businesses transform operations in a variety of areas and industries. Sudy's current focus for SAP is helping organizations transform direct spend leveraging supply chain and sourcing competencies.

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.