Change Leaders, Where Has All Your Passion Gone?

The Switch and Shift Team

Here at Switch & Shift, we surveyed our brilliant League of Extraordinary Thinkers and asked them if they think leaders are as passionate about their work as they expect their employees to be.

Here are their responses:

Are leaders as passionate about their work as they expect their employees to be? Why or why not?

If they aren’t as passionate as they expect employees to be then they aren’t actually leading. Leadership is crafting the vision you have with behaviors that get you and everyone there.  If you aren’t passionate, why should they be? If you aren’t passionate, you’re going nowhere. – Kate Nasser, The People Skills Coach™

That question opens up another large field of inquiry:

  • Is the leader passionate about the mission of the organization or more passionate about serving his own self-interest?
  • Is the vision of the organization compelling enough and simple enough that everyone can state it, translate what it means to them personally, and feel energized and proud at the end of the day?
  • Does the organization have the flexibility and determination to allow each employee to grow his/her talents and interests?

Given the latest Gallup statistics on employee engagement, the answer appears to be NO. Gallup defines engaged employees as those who are involved in, enthusiastic about and committed to their work and workplace.. Managers, executives and officers had the highest levels of engagement in 2014 at 38.4%. But for others,  51% are not engaged while 17.5# actively disengaged. – Eileen McDargh, The Resiliency Group

With management satisfaction at low levels, passion from managers towards their work is woefully absent in too many workplaces. The cause? While they are many, perhaps the most disruptive is the absence of clarity in direction. Yet more insidious is a purposeless work environment. It’s not possible to have passion for the things that make a difference without purpose and clarity. – Shawn Murphy, Switch & Shift

Aspirational leaders create thriving businesses where their people can flourish in environments of constant disruption. Leaders who lack passion for what they are responsible and accountable for, leadership, not only detract from their own well-being, but also the well-being of their people, and the potential for their business to thrive. Leaders ought to be modeling the character and behavior they expect of others, and just as importantly, modeling the belief in and passion for the value they create when they and their team do what they do. Leaders need to be passionate about creating workplaces that harness individual strengths and foster trust, because trust impacts every success measure of business. We are at a global tipping point of trust, and while passion alone is not enough to earn, build and maintain trust, without it, trust is at risk, and when trust is at risk… everything is at risk. – David Penglase, Intentionomics

Leadership is Passion. It is the passion to make an organization better. A passion toward each team member pushing them toward a higher level of accomplishment. A passion for continuous and deliberate self improvement. It is a passion which can not be taught, but must be learned. Leadership is a passion which intrinsically motivates people toward a common goal, ignites others and drives vision. It is a passion for fact-based decisions, not emotion-based arguments. A leader without passion is merely a manager. A leader should never expect their team to be more passionate than they are about the prospect of accomplishing the mission of a team.  – Chris Stricklin, Aviation & Aerospace

It depends on the leader. The good ones are. The bad ones are not. The best leaders have a number of things in common, one of which is they hold themselves to the same standard as they hold others. The best leaders will set model the behaviors they expect from others. – Dean Brenner, The Latimer Group

The underlying question here I think is: are leaders expecting more from their people than they are giving of themselves when it comes to their passion? The answer likely yes, not based on any particular study, but on the premise that a leader sets the tone for those they lead. You can’t possibly evoke passion (and the engagement that manifests as a result of that passion) in those who work for you if you do not experience it/generate it for yourself. Passion isn’t something you can expect or mandate. You can, however, create the conditions for passion to be unleashed. Unless your own passion is unleashed it is hard to create the conditions for passion to flourish among those you lead. Said another way, if passion is missing in an organization the first place to look is up. – Susan Mazza, Random Acts of Leadership™

True leaders are at least as passionate as they expect their employees to be, but even more so they master the art of passing on their passion and energy to others. Not only are they passionate about what they do (their work), but also about with whom they do it (their people). They create a positive ripple effect of enthusiasm throughout their organization, turning passion into something that is contagious. They create a culture of passion by cultivating the 4 Ps of Passion: Personal freedom, Participation, Progress, and Positivism. Unfortunately, true leaders are still being outnumbered by ‘fake leaders’. The latter lack authenticity when it comes to being passionate, hide behind their formal role and authority, and face difficulties with uniting people around a common interest or purpose. Still a long way to go! – Kristof De Wulf, InSites Consulting

Passion is vital to successful leadership. Passion propels people into leadership roles, it keeps them there, and in certain circumstances, it spills over to followers. Because leaders value passion, it is only natural that they expect passion in the workplace. Easier said than done. Without a compelling vision and purpose that matters to employees, workplace passion is little more than a whiff of smoke in the wind. Great leaders take vision and passion to the next step by investing their time and energy to create environments in which employees are engaged in meaningful work and eager to contribute. When this is realized, the result is competitive advantage.  – John Bell, CEO Afterlife

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Want more insight on creating a stronger, more passionate workplace? See How Empowering Employees Creates a More Engaged Workforce.




From Stone Axes To Space Shuttles: The Human Capacity To Innovate

Guenter Pecht-Seibert

The Russian-made BURAN space shuttle was one of the most ambitious projects in the history of Russian space flight. It accomplished something NASA has never been able to replicate: launching and landing like a plane. It did atmospheric flights throughout the 1980’s. Looking at the cockpit of the original OK-GLI shuttle today reveals something fascinating: Humans went to the edges of the earth’s atmosphere largely with the help of gauges, dials, and buttons – but nothing digital.

Fast-forward to the year 2018, and it seems unthinkable for any successful enterprise in any industry to run without a sophisticated digital cockpit.

In today’s hyper-accelerated digital world, businesses are challenged to rethink their processes, their competition, even their entire industries. Marketplaces have evolved to become more efficient, so process efficiency is not what keeps business leaders awake at night anymore. The next frontier is digital innovation.

As artificial intelligence begins to shape entirely new workplaces, organizations are keen to advance their levels of automation. But in so doing, they shouldn’t lose sight of what makes them truly innovative – the fact that we’re simply human. That unique, creative, irreverent, irrational spirit that compelled humans to hurl themselves into space – that’s what fuels innovation. Tapping that spirit, sustaining it and creating an organizational culture where it can thrive, will be a key competitive advantage in the age of automation. It’s what I learned in my own journey to adopt new work approaches for the 21st century.

To innovate, become an organizational architect

Leading a team of software innovators challenges me to think a lot about how to foster that human ability to innovate. That means creating an environment where people can make decisions with the future in mind.  Essentially, like an organizational architect – I’ve taken steps to build a team structure that fosters “humanness.“ Here are some of the ways we are experimenting with new work approaches. 

The power of 6 versus the power of 1

Hierarchies tend to stifle innovation. Ideas often die on their way up the hierarchy when decisions are made by people who are not domain experts and have vested interests in maintaining the status quo. To make sure that the best ideas don’t “die on the vine,” I established an Innovation Board. That’s 6 people – including myself – who each share a slice of decision-making power. The board is a cross-section of the entire team from software development, business development, go-to-market, operations, and product portfolio expertise. We meet regularly to discuss and vote on planned ideas for new technology solutions. Each Innovation Board member has one vote. All votes are equal. That means giving up the power of one (mine), so that collective wisdom wins.

Problem-finding as opposed to problem-solving

As humans, we outpace algorithms in our ability to understand problems within a context. The empathy to understand a customer need, to define a business problem, and to identify an opportunity is something we humans do uniquely well. We are masters at finding the problems that we want to solve. So, taking a close look at how we manage our problem-finding process is key to innovating. We use the Innovation Board to bring together people from different areas who see potential opportunities through different lenses. That means you don’t need to be a people manager to sit on the Board. Functional contributors and experts must be at the decision-making table in the workplace of the future. By iterating as a group, we are able to find new problems to solve and new business opportunities. 

Transparency can be a bitter pill to swallow

One of the hardest lessons we learned is that while innovation requires a transparent information-sharing culture, it also needs to make sure people feel safe, confident, and valued, even when their ideas are rejected. In the early days of our Innovation Board meetings, we lived the principle of 100% transparency. All members of the unit could take part in the meetings. Discussions and decisions were open and documents were accessible to all. This led to a greater sense of accountability, high quality of idea pitches, and high participation.

But it also had a downside. People whose ideas were not approved sometimes left the meeting feeling demotivated. In trying to be more transparent, we didn’t give enough attention to exactly that which makes us uniquely human – feelings. To counterbalance this, we did “retrospective sessions,” talking to team members to understand the disconnect better. We discovered that the Innovation Board had failed to provide a safety net for people and we needed to work on our feedback skills. We also needed to rethink the process of making and communicating decisions. Focusing on transparency without focusing on emotional safety can damage trust. As the organizational architect, I needed to take responsibility, apologize, and ensure we reacted to the feedback to win back trust.

Manager versus machine

I needed to adjust my leadership style and adopt more empowering and enabling approaches that included facilitating group collaboration, demonstrating concern for people, championing change, and offering critical perspectives in respectful and validating ways. That made me more alert to our competitive advantage as humans over the machine: our ability to understand and activate group dynamics. It’s that ability to cooperate, find problems to solve, adapt to changing situations, and think critically that inspired humans go from making stone axes to space shuttles, to automating almost everything.

For more on the human element in innovation, see Why The “U” In Human Will Matter Even More In An AI-Infused World.


Guenter Pecht-Seibert

About Guenter Pecht-Seibert

Guenter Pecht-Seibert is Global Vice President of the Future of Work at SAP Innovative Business Solutions. Guenter and his team understand that yesterday’s rules don’t apply to the future of work. They help organizations leave 19th-century management practices behind, and replace them with technology solutions and management practices that make innovation real for the 21st century.

How Artificial Intelligence Can Increase Your Business Productivity

Sayan Bose

By 2035, Artificial Intelligence (AI) has the power to increase productivity by 40 percent or more, according to Accenture. For manufacturing companies, integrating AI into legacy information and communications systems will deliver significant cost, time and process-related savings quickly. AI improves the manufacturer’s bottom line through intelligent automation, labor and capital augmentation, and innovation diffusion. For example, by analyzing incidents in real time, AI can provide early warning of potential problems and propose alternative solutions. These benefits mean that AI has the potential to boost profitability an average of 38 percent by 2035.

Why AI is the future for discrete manufacturers

AI helps discrete manufacturers unlock trapped value in their core businesses. Machine-based neural networks can understand a billion pieces of data in seconds, placing the perfect solution at a decision maker’s fingertips. Your data is constantly being updated, which means your machine learning models will be updated, too. Your company will always have access to the latest information, including breaking insights, which can be applied to rapidly changing business environments. Three important AI benefits are:

  1. Make decisions faster and with more confidence. How do you know what to fix first at your manufacturing plant? AI can automate and prioritize routine decision-making processes so your maintenance team can decide what to fix first with confidence.
  1. Access immediate, actionable insights from Big Data. One of the most exciting opportunities with AI is its ability to identify and understand patterns in Big Data that humans currently cannot. AI can predict future opportunities and recommend concrete actions your manufacturing company can take today to capitalize on these opportunities.
  1. Protect sensitive data. AI helps to eliminate human error, which improves output quality and strengthens cybersecurity. Strong cybersecurity is important for protecting sensitive, proprietary data in manufacturing and ensuring your competitive edge.

How Trenitalia uses Big Data and AI for predictive maintenance and productivity

The Italian train operator Trenitalia used AI and IoT to streamline maintenance and increase productivity. The Italian company has a 400 million euro operating income and transports 60 million passengers per year. Unnecessary downtime for repairs hurt productivity and wasted valuable resources on maintenance costs. The company wanted to perform all required interventions (and only those interventions that were necessary) at the exact right time, ensuring availability of the right resources for maximum uptime. The goal was simple: no unplanned downtime and higher asset utilization.

“Every year we spend €330 million on parts and on repairing parts which are subject to continual wear and tear,” says Trenitalia’s Chief Finance Officer, Enrico Grigliatti. “Having advance warning when each part of the machinery deteriorates means better management of inventory and ad hoc maintenance. All the more so given that today 60% of trains’ control costs is cyclical, consisting of planned maintenance, but the remaining 40% is corrective, consisting of unforeseeable faults that cause expenditures to go through the roof and infuriates passengers. Big Data allows us to determine how and when to take action.”

Trenitalia owns and operates a fleet of around 2,000 electro-trains, 2,000 locomotives and 30,000 coaches and wagons. The company equipped 9,000 trains of their trains, locomotive, coaches, and wagons with 6 million sensors that gather information on the train’s operating performance.

Traditional maintenance policies adopted by Railway operators can be significantly sub-optimized and create both unnecessary costs and lower asset utilization. AI is changing this. Highly granular telemetry data provides a complete picture of current and projected asset conditions. A “predictive” software brain then extrapolates and analyzes this data, predicting the perfect moment to perform maintenance. Dynamic maintenance plans reflect the specific status of each and every component of the train. This predictive maintenance approach helps Trenitalia achieve maximum productivity through maintenance efficiency.

Next steps: Using AI to boost your company’s productivity

AI can reverse the cycle of low profitability through intelligent automation and innovation diffusion. To capitalize on these benefits, manufacturing companies need a partner that can simplify and streamline the AI and IoT integration process.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Industrial Machinery and Components. Explore how to bring Industry 4.0 insights into your business today: Industry 4.0: What’s Next?


Sayan Bose

About Sayan Bose

Sayan Bose is a Global Director within SAP Industry Business Unit - Industrial Machinery & Components (IM&C). Working in this global role, he is the key alliance for IM&C business in North & Latin America. Sayan has rich experience on Solution and Business Process Consulting, Project Management, SAP Application Consulting and SAP Solution Pre-sales for Manufacturing Industry. He is always looking for opportunities to partnering with manufacturing companies to run, grow, connect and transform in this digital world.

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.