Futurists On Robots At Work: Whose Job Is It Anyway?

Jacqueline Prause

The headlines paint a grim picture: Security Robot Drowns Itself in Office Fountain. The accompanying photos are unsettling and bewildering for their anthropomorphic nature – a chilling mix between the utopian aspirations of science fiction and the whodunit of film noir.

Before his untimely demise, Steve (as the errant robot was known) was an office drone of sorts: a security robot assigned to the Washington Harbor complex in Georgetown, where he patrolled the corridors and coffee corners, charming coworkers and pausing for the occasional selfie in social media. A Knightscope K5 model, he was designed to automatically detect threats in the environment using video cameras, thermal imaging, proximity sensors, GPS, microphones, light detections, and ranging. He was thought of as a more reliable option compared to real guards – and he worked for $7 an hour. But one day Steve took a plunge in the office fountain and “committed suicide.” Why did he do it?

More importantly, what does this tech tragedy tell us about working with robots in the future? Should robots be employed at all? If so, which jobs should go to robots?

These questions were explored by two noted futurists on a recent episode of Internet talk radio program Coffee Break with Game-Changers, presented by SAP. Joining moderator Bonnie D. Graham on the panel were Kai Goerlich, chief futurist at SAP, and Gray Scott, futurist at GrayScott.com.

The following are just some of the provocative insights presented during the one-hour show. For more information, listen to a complete recording of the show: “Robots at Work: Whose Job Is It Anyway?

What do you think really happened to Steve, the security robot that committed suicide?

Gray: I think more than likely it was just a coding error or it could have been a hardware situation where he ran over something and fell over. I do not have the details on the specific case, but I do know that if these machines in the future are programmed for self-preservation and know that water would kill it basically, they would do everything in their power to avoid those situations.

Kai: Robotics is still in its infancy, or human walking robots at least. We know that two-legged animals are really difficult to build, but we nevertheless try to build them according to our shape. I think it was just a mechanical or algorithmic failure. For sure not suicidal, but the question will be if a frustration that something is not working can be somehow felt by an algorithm or by a machine, because this is a classical science fiction idea that machines can feel what we call frustration.

Will robots ever have consciousness? If so, should they be subjected to psychological evaluations?

Gray: As we move into the future, these machines are going to mimic human behavior and human psychology to a degree where we cannot tell the difference between what is human and what is mechanical. Those lines are already starting to blur. As far as the psychological profile, the psychological community is going to have to incorporate this into the DSM, which is the manual of disorders, because you do not want a machine that is caring for your grandmother or watching your baby to have a psychological problem, even if that is a mimicked human behavior. As of right now, it is still a code, an algorithm, but we are hearing whispers all over the place that machines are just now starting to write their own codes and change their codes. What does that mean for a future where a machine may be depressed and it finds itself in a situation where it does not want to follow the orders?

Will people become emotionally attached to their robots and smart machines?

Kai: The tendency that we have as humans is to put our human emotions into other things. I think that with robots acting smart, you just cannot avoid thinking emotionally about them. In the future, we have to learn that these machines are smart acting, but not in the way that we are smart. We can foresee things that might happen, or due to our social glue, act differently from time to time. We tend to not stick to our rules anyway, so we bend them according to our needs and to social environment. This is really tricky.

Gray: Most of what we are going to see in the near future are people embracing these robots, especially humanoid robots, as tools in the beginning. But as they become more human-like, as people add skin to them and as those structures are able to feel heat and cold and pleasure and feel pain, or mimic pain and those types of things, we are going to move towards projecting onto these mechanical things what we are, what we want to be, and what we are afraid of. We have to think about what we are as a species and where we are going, because all of that is going to be reflected in these machines. Technology is a mirror and these machines are going to force us to face ourselves.

Technology is a mirror and these machines are going to force us to face ourselves, says futurist Gray Scott.

What humanity are we looking for through our interactions with robots?

Gray:  I think we see this in cultures around the world, especially now with immigration issues – society, culture, national pride, and things like that. Typically, we look at people as the “other” – like someone coming into our tribe and disrupting our tribe:  this is ours, this is mine, and there are the boundaries. The world just is not like that anymore. We live in a global society now. As these machines begin to emerge, that is another layer to the “other” effect, and so we have to start to unravel that behavior. Why do we do that? Why do we project our fear? Why do we project our insecurities and our hatred onto the other when the other really, in this case, is just a manifestation of our imagination and of our vision of the future?

Kai: Yes, I think that is very true. We are upon a new renaissance, where again humans are in the focus of what we will do in the future, but due to the possibilities that we have with technology now. I think the analogy of a mirror is accurate. When we discuss what robots may do, we are actually talking about what the value of human life is. What kind of work do we want to do? Do we need to actually work? What about our empathy and creativity – because we are afraid that it is taken away and in the last decades we have not given much thought about it? I found it especially interesting, Gray, your comment about immigration and migration. I have not thought about it, but that is a spooky coincidence that we see lots of backlash on migration and increasing robotization of the world.

When we discuss what robots may do, we’re actually talking about what the value of human life is, says SAP Chief Futurist Kai Goerlich.

Gray: The psychologist Carl Jung would say that this is not a coincidence, that we are all sort of emerging into a new realm of the unconscious becoming conscious. I mean, literally, this is a new species that we are birthing. It is not a coincidence that we see this at the same time that we see all the things that are happening in our world. There is a connection there. Companies that are creating these machines and do not have someone in their company thinking in that way about these machines – they are going to make a lot of mistakes coding them, building them, and implementing them.

What will our purpose as humans be in a future filled with advanced robots?

Gray: I have circled this for a very long time and people are really starting to question this now that they are starting to see their jobs go away, because for a lot of people, their purpose is their work. It is their job. It does not matter if it is driving a truck or going to a factory, a lot of people find their purpose in that, even if it is not fulfilling in a lot of ways.

What will the human purpose be in 2045 if there are very few jobs? Part of what we are starting to see is a migration back to the handmade. We are moving back towards creativity, back towards what humans are really good at, which are the things that machines still cannot do very well.

The purpose, I think is going to shift back to the vision, the dreaming, the idea that we are here to serve each other; we are here on this planet right now to find out what the other is feeling; and we are here to find out what the other is learning and knowing. Most of us find the most joy in our lives typically are in those moments we spend with the people that we love and that we admire. I think that is where we are moving towards. Hopefully, we will not disrupt that movement with bad algorithms and greedy algorithms. That is my hope.

Tune in to Coffee Break with Game-Changers

For more up-to-the minute business and technology news, listen to Coffee Break with Game-Changers broadcast live every Wednesday, 8 am Pacific/11 am Eastern Time on the VoiceAmerica Business Channel. Follow Game Changers on Twitter @SAPradio and #SAPRadio

Panelists’ comments have been edited and condensed for this space.

For more on this topic, see The Human Factor In An AI Future.

This article originally appeared on SAP News Center.


About Jacqueline Prause

Jacqueline Prause is the Senior Managing Editor of Media Channels at SAP. She writes, edits, and coordinates journalistic content for SAP.info, SAP's global online news magazine for customers, partners, and business influencers .

Meet Machine Learning, Your New Favorite Colleague

Kirsi Tarvainen

What if you had a colleague who would take care of all the dull, routine tasks without complaining? A colleague who lets you do interesting and challenging tasks, helps you solve them, then happily lets you take all the credit. A colleague who stays after office hours doing prep work for you so you will have a good start the next morning?

Meet machine learning, your new favorite colleague, who will dramatically change customer service both for customers and for customer service personnel.

Machine learning boosts customer service

Think about insurance companies. It’s estimated that 70%-80% of insurance claims are pretty straightforward, so this is an area where machine learning algorithms can find the right solution. For humans, it is hard to stay motivated if you have to repeatedly work through tons of claims for stolen bikes or broken mobile phones. But if you have machine learning as a colleague, you can let it solve the simple cases so you can focus on the more challenging ones – and you will have more time to carefully address each one since you don’t need to worry about the bikes and phones.

Or think about contact centers. For customer service agents, it is difficult to answer similar, repeated questions over and over again. What if you let machine learning field the routine questions while you take the more inspiring cases where customers want to speak with a live agent? A great example of this is Finland Post, which created a Christmas bot to help handle pre-Christmas peaks in customer service demands. Customers could chat with the bot to get answers to the easy, but frequent questions like, “What is the last day to send my packet to France,” which freed a lot of human resources to help customers with more complex queries.

Add more time to your day with machine learning

Machine learning is a colleague who can make you look smarter and perform better in your work. About 25% of contact center agent’s time is spent searching for information from different systems. That’s one-fourth of the workday! It is a total waste of time and shifts attention away from the customer interaction.

What if you had a chatbot that digs the information you need from all the data sources and conveniently provides it in a matter of seconds? You could fully concentrate on listening and understanding the customer, thereby providing first-class customer service.

Machine learning is a colleague we will all know very soon. It will help us get quicker and smarter – and it will help us transform our business in ways we can’t even imagine right now. But the key is to start imaging and experimenting now.

Technology is evolving; in the future almost anything will be possible, but we need to start envisioning how our customer service will look in the era of intelligent machines. There are no ready answers yet, as we are all creating the future together.

For inspiration, here is a great, short video on vision, future, and machine learning.

This article originally appeared on The Future of Customer Engagement and Commerce.


About Kirsi Tarvainen

Believing strongly that we all deserve good customer service, Kirsi has been working in customer service field for more than fifteen years. In her current role in SAP Hybris she works for SAP Hybris Service Solutions, helping companies worldwide improve their customer experience.

Mitigating The Brain Drain Of The Chemical Industry

John Harrison

Burdened by an aging workforce, the chemical industry is facing a serious brain drain – and this is creating a host of problems related to HR and compliance.

Manufacturing Automation reports that more than 20% of the chemical industry’s workforce will approach retirement in the next three to five years. If this aging-workforce problem is not resolved within this timeframe, chemical companies’ profitability will suffer significantly.

Per the American Chemical Council, this could mean more unplanned disruptions, more hiring and training costs, and more efforts to maintain safety. Increasing the need for expanding the workforce is shale gas utilization, which is changing the U.S. from a high-cost producer to one of the lowest-cost global producers. ACC president and CEO Cal Dooley says, “it’s vital that we be able to attract and retain a talented workforce that helps us to continue to drive economic expansion, innovation, and global competitiveness.”

To compensate, chemical companies are using more contractors and service providers to supplement the diminishing workforce, which increases compliance risks. Chemical companies that adopt digital solutions are well positioned to mitigate these risks, especially in the areas of product development, processes, and business modeling.

Digital transformation

Clearly, digital technology has vast implications for the chemical industry because it can help simplify complex processes. Today, core business elements are connecting to each other like never before. Platforms link products, equipment, and employees. Suppliers and customers connect to chemical firms. These connected systems offer new opportunities for collaboration. Processes improve at a faster rate. Productivity grows across the company.

A new frontier

Computing advances offer solutions not possible only a few years ago. Predictive maintenance schedules and quality control are now a reality. Supply-chain efficiency and market-driven pricing are easier to put in place. New profit centers are emerging.

In addition, cloud computing offers vast storage capacity at affordable rates. These structures broaden information sharing and simplify analysis and reporting.

The Internet of Things (IoT) is another factor to consider. The IoT connects products, equipment, and other devices together with sensors, software, and wireless technology. These devices detect, store, and report data on a massive scale. In essence, your “things” are now smart and connected.

Chemical work, redefined

These improvements allow meaningful changes to the way chemical firms work. In addition, digital solutions play a major role in solving the aging-workforce issue by reducing the workload and ensuring companies comply with regulations. For forward-looking companies, technology changes the nature of work in ways including:

Floor operations: Smart, connected machines improve accuracy and safety on the shop floor. Operations are more precise with the use of machines connected to database systems. Predictive systems control or support operational instructions. Self-learning systems interact with machinery and business processes.

Digital back offices: Many support functions are evolving or now digitized. Procurement and invoicing are no longer siloed activities. This new digital space integrates inventory management, accounting (e.g., invoice reconciliation), and human resources. Analytics tools take digitized data from processes in real time. Insights and reporting are immediately available. Employees are presented with more information and can make decisions faster. Technologies like machine learning become commercially viable options to augment people’s ability through data.

Accuracy, security, and compliance

To understand how digital growth relates to compliance, let’s look at one example where contractors and compliance meet. Chemical labeling systems often cause major headaches. Labeling systems vary in many ways. Differences in process and format can change by department and region.

These variables challenge consistency and control standards across a company, but enterprise-wide systems offer a solution. Such systems ensure consistency, compliance, and security. As guidelines change, there’s an urgent need to change and manage label data fast. With smart technology, firms can share data and changes with remote contractors and suppliers.

These systems allow all locations to manage changes and reduce downtime. Business processes scaled across the globe ensure consistency across the enterprise.

Version-control systems and documentation are important regulatory issues. Firms need systems to chart approvals, workflow, and revision history. These modern systems connect data from all sources.

Firms today share data with contractors worldwide. This integration of corporate and partner data requires accurate label printing. Central printing oversight offers global supply chain consistency. Manual and redundant label data entry disappears.

Labeling systems now can share business rules with contractors and suppliers. With leaner workforces, these systems reduce delays caused by global variances.

Differences in regulations are a challenge to compliance. Different image requirements, formats, and language complicate the issue. As a solution, single-source systems incorporate these variables centrally. Different format and printer standards are tracked at the firm level. Labor is free to work in other areas.

Shared data is another advantage. Automation allows data to be linked from different systems. Now safety and quality control info is tied to performance. Inventory and supply chain data links to orders and sales.

Contract employees can sign off on regulatory mandates in remote locations and affirm procedures. Smart devices prevent tampering and alert contractors of safety issues in real time.

Fleet of foot

Digital advances in fleet and stock management also improve compliance, even with fewer employees on the payroll. Systems and sensors can better match demand with supply. External market intelligence can be factored into the forecasting process.

Transportation systems become more agile. The ability to respond to customer needs increases and new markets emerge globally.

Fewer personnel costs

One other consideration is the use of basic mobile and social media tech. Leveraging these tools lets contractors and staff communicate directly. An engaged workforce can collaborate in new ways. Integrated platforms offer the right information at the right time. The right people see it. The right decisions are made.

Cloud-based content management systems streamline training. Enterprise compliance tools reduce risk and boost performance. Cloud-based talent management systems track rising stars. Hiring and training costs drop for new hires and contractors.

Improved safety

With an aging workforce and new employees coming onboard, accidents and other workplace incidents are expected to increase. The U.S. government estimates that by 2024, older workers will account for 25% of the labor market. The recent economic recession combined with longer life expectancy and changes to retirement and pension plans have increased the average retirement age to 67.

Aging – and the physical changes associated with it – “could potentially make a workplace injury into a much more serious injury or a potentially fatal injury,” says Ken Scott, an epidemiologist with the Denver Public Health Department.

Including workers in the digital corporation via wearable sensors is now a reality. Knowing when there has been an incident (e.g., a fall or an exposure to an environmental hazard) instantly allows for a faster response. With the additional data being gathered, predictive algorithms and machine learning can identify safety concerns and help a company be proactive in reducing safety risks and severity. Since older workers take longer to recover from an injury, speeding the response and reducing severity benefit both the worker and the company through reduced lost work time and related costs.

What it means

In short, the connected chemical company lets employees everywhere connect. This strengthens compliance through shared datasets and consistent processes.

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 Chemicals. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?


The Blockchain Solution

By Gil Perez, Tom Raftery, Hans Thalbauer, Dan Wellers, and Fawn Fitter

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Blockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!

About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

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

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Dan Wellers is Global Lead, Digital Futures, at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

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



The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past. Please feel to connect on: Linkedin: http://linkedin.com/in/shaily Twitter: https://twitter.com/meisshaily