Three Ways To Grow A Culture Of Innovation At Work

Liz Brenner

When I was 10 years old, all I wanted was a skateboard. Because my parents refused to buy me one, I did what any resourceful 10-year-old would do – I asked my grandparents for one.

My grandpop was a frugal guy; there was no way he’d run out to buy me one. What he did was offer to build one for me.

It wasn’t fancy. It was essentially a piece of plywood with some wheels screwed into the bottom, but after a few modifications, it worked and I learned to ride.

Growing up, there were always magic solutions that came from my grandfather’s workshop in the basement. He could fix anything and build anything.

When I think about innovation, I think about him – always ready with a creative idea or solution, not afraid to make a mistake, and never giving up.

Innovation is not

  • a ping pong table in the office
  • a nap room
  • a special program launched by the HR team

Innovation is a culture—a culture of risk takers, idea generators, and simplifiers. People who don’t give up, and who speak up in an environment built on trust.

Cultures aren’t born; they’re made. And with anything we make, it takes time and focus.

Here are three small ways that you can grow a culture of innovation. And you don’t have to be the CEO to do so.

1. Find your leaders.

Culture starts with leadership, and leaders set the tone. They tell us the vision and priorities; they tell us what’s important and what to focus on. They also make or break whether or not the work environment is open and built on trust, through their transparency and authenticity. This is so important if we want innovation to grow.

On my first day at SAP, more than 11 years ago, I had the privilege of seeing our (at that time) CEO of SAP America, Bill McDermott, making his rounds in the office. He’d stop at each floor and a crowd of employees would gather to hear Bill’s thoughts and ask questions. Bill was inspiring – not only setting a vision, but also calling people out by name and being authentic. What he said to me and other employees, through actions not words, was that he was open and eager for us to have an impact…laying the groundwork for innovation.

What you can do:  Not every organization has a leader as dynamic as Bill. Find those great leaders – the ones who listen, champion ideas, and inspire their teams. They will likely be a few layers down from the top. Give them a platform to reach other employees outside of their teams. Have a Q&A session with them; it will go a long way in building trust in your culture.

2. Speak up!

The most innovative companies know that the best ideas start with diverse teams that bring together people from many backgrounds, with different experiences, to make amazing things happen.

But the magic really happens when those diverse teams feel empowered–to be themselves, to speak up, to share ideas.

A culture of innovation can’t just be top-down. Our leaders may set the tone, but it’s the people who bring a culture to life within an organization.

What you can do: Have you ever been in a meeting with someone who sits quietly and doesn’t say much? Call them out and gently ask for their view. Sometimes the best ideas and thoughts are hidden inside people who might not feel comfortable sharing. And don’t be afraid to share your own ideas. Get feedback from people who are different from you. If you talk only to people who are just like you, your solutions are only as good as your strongest idea.

3. Be fearless.

If we want innovation, we must take risks. If we take risks, there will be failures. We must remove the fear that exists about failure and see it instead as a lesson and a gift.

Employees must not only be willing to be themselves and share their ideas, they must also be comfortable taking risks and (at times) failing.

What you can do: Recognize and role model the behaviors you want to encourage in your organization. Ask your leaders to share a story about a personal failure and what they learned from it. On a team call, recognize someone who took a risk that might not have worked out as planned. Get backup if you fail. Force yourself to recognize the learning rather than the result.

One more thing…

Don’t let your years of work experience or your last project define your value and worth. Everyone has something to share and something to learn, so be bold and be yourself.

And perhaps you will inspire someone with your innovation, just like my grandfather did for me.

This blog is based on a speech for the Villanova University Graduate Human Resources Development Program.

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Photo credit: Back to the Future


Liz Brenner

About Liz Brenner

Liz Brenner is a Vice President of Human Resources at SAP focused on Employee Engagement and Organizational Design in the Talent and Leadership organization. Her specialties include Talent Strategy Development, Global Marketing Strategy Development, Relationship and Project Management.

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 To Measure The Business Impact Of Employee Collaboration

Daisy Hernandez

How many businesses measure the impact of collaboration on their enterprise? Heidi Gardner, Harvard Law School Distinguished Fellow and Lecturer, and author of “Smart Collaboration: How Professionals and Their Firms Succeed by Breaking Down Silos,” explores the importance of recognizing the role collaboration plays in order to help companies understand the true value of teamwork in the workplace.

Daisy: Why is collaboration important in today’s business environment?

Heidi: Collaboration is prominent across many business environments and sectors, in part because of a rise in “expertise specialization.” The rapid pace of knowledge change creates an incentive for executives to specialize, or become experts in, a specific subject matter across all knowledge-based industries.

At the same time, problems are increasingly complex and multi-disciplinary, no matter the subject area you are examining, from the business environment to the political climate. Therefore, narrowly specialized experts who integrate the knowledge with others’—that is, foster collaboration with their peers—are increasingly important across the landscape.

What are the imperatives for organizations or individuals to think about when developing collaboration incentives? What kinds of problems are these parties trying to address?

First and foremost, there is no “silver bullet” to collaboration implementation. Companies must realize that they need to transform their employee mindsets from individualized to team-oriented. Therefore, they must tackle this process with a multi-pronged approach that promotes individual and collective adoption of collaborative behavior.

How do you incentivize your employees to embrace a collaborative mindset? Find the pockets of collaboration excellence that already exist within the organization, and promote the upside of engaging in these ways. From financial profits to strategic company advantage, growth and reputation, organizational leaders must demonstrate how colleague-to-colleague collaboration strengthens the core business.

When working to develop new initiatives in a corporate atmosphere, there is a certain level of resistance stemming from the idea that “that couldn’t happen here.” Therefore, leaders must take a look at the atmosphere already in place.

What performance measurement practices does the company have? Are employee management and corporate communication systems running effectively? Are there physical arrangements in place that provide the opportunity for employees to have spontaneous interactions throughout the day, which sociologists have historically noted as important for community development? By taking a closer eye to these key components, businesses can more effectively establish collaborative practices and technologies that remove the common perception that it is too difficult to get people engaged, keep them updated, and avoid duplicated performance efforts.

How can employers use collaboration tools and strategies to knock down barriers and provide more flexibilities in employee work?

As mentioned earlier, while technology isn’t a “silver bullet,” it is a crucial tool to facilitate collaboration.

First, collaboration technology assists with corporate growth and development. For instance, when companies are experiencing global growth, possibly through a merger and acquisition, this evolution can make it difficult for individuals to realize their own company service offerings. With the right technologies, companies can provide access on an “as-needed” basis for people to explore their company and connect back with the individual who is best suited to address their questions and needs.

Secondly, these tools help to make work “come alive,” by replicating interpersonal relationships. Many companies lack the ability to foster familiarity and trust among employees. Collaborative technologies provide rich environments where colleagues can move past the immediate questions that come to mind when working with peers (such as others’ intentions or willingness to share credit) and participate in projects in a “functionally equivalent” manner.

Relationships can not only be fostered, but advanced, through the use of collaboration technologies. Developers have enabled these tools to humanize experiences and functionalities as much as possible through the creation of virtual workspaces. Employees are able to share emotions with each other and coordinate ideas through a respectful rapport.

Third, collaborating across time zones is never easy. With collaboration technology, however, bridging time zones is possible, as employees no longer have to wait to catch up on conference calls, but can now work productivity in a “round-the-clock” fashion. Additionally, with virtual workspaces, users can input doodles and quick notes that capture direct emotions more effectively than a typical font face would.

Have your clients used collaboration technologies in specific lines-of-businesses?

Yes. Across my research and client base, I have seen collaboration technologies support retention of top talent, improve onboarding of new hires, and enhance employee development. In fact, one of my financial services clients is utilizing collaboration strategies to bring people with different expertise, who may have otherwise not collaborated, together to create a more holistic, customer-centric business approach.

In doing so, this business is acknowledging that office environments are not always conducive to direct cross-department partnership. Just because people are working in the same office environment does not mean that they are regularly conversing and bouncing ideas off of one another. With collaborative technologies, employees who have highly complementary knowledge bases have a platform to connect and share.

This Q&A originally appeared on the SAP Community.

For more insight on building a culture of collaboration, see Developing Strong Virtual Team Culture Through Communication Tools.


Daisy Hernandez

About Daisy Hernandez

Daisy Hernandez is Global VP of Product Management at SAP. Under her executive leadership she leads product management and enablement for SAP Jam Collaboration. She is an experience professional who has held several leadership positions with specialties in social software, collaboration applications, cloud computing, enterprise software, agile methodologies and management.

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.

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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.