The New Rules Of Engagement

Bernd Leukert

Recently I was asked if I ever get tired of discussing “digitalization.” While there is a simple answer to that question, there are no simple answers to what “digital” means for companies or us as individuals—be it in our private or professional lives.

Digitalization has moved from simply improving existing business models to profoundly disrupting them, resulting in a redefinition of roles and boundaries. We see the lines between industries blurring: Car manufacturers integrate solutions traditionally reserved for finance and telecommunications, sports apparel companies move into high-tech digital fitness. The rise of configurable, personalized products and e-commerce means that producers no longer simply produce: Manufacturers now also enjoy direct relationships with the end consumers.

As the rules of engagement between buyers, suppliers, and partners change, so do the ways we interact and collaborate—as both individuals and organizations. A strong, open ecosystem is now more important than ever, but equally important are the models of interaction. Increasingly in today’s world, it’s all about partnering to effectively deal with the challenges—and, of course, benefits—of digitalization.

Rethinking traditional collaboration models

The traditional role of software providers—and the clue is in the name—is also affected by this change. Simply developing, selling, and shipping the latest solutions—regardless of how cutting-edge they may be—is not enough anymore. Because despite realizing the importance of digitalization, many organizations still struggle to identify where and how emerging platforms, technologies, and applications could offer value. So it is not the question of why transform that often remains unanswered, but rather the what, the when, and the how. Companies want more than just a provider of software as their partner, they want a partner to help them digitalize products, processes, services—or in short, their business.

But digital transformation is obviously not a one-size-fits-all process: every company is different and there is no blueprint to apply that can cover all scenarios. What is possible, however, is to combine forces—the expertise of the provider and the customer—to uncover exciting new opportunities together. This shift sees the software provider move beyond software provision and even co-innovation to a business risk-and-benefit-sharing co-engineering model.

Sharing the risks and the benefits

Co-innovation has long played a crucial role in our success at SAP. Working hand-in-hand with our customers through the development process has led to many groundbreaking solutions. But in the co-innovation model, the relationship with the customer only goes so far. Once the product is on the market, the collaboration is scaled back significantly.

In our co-engineering projects, on the other hand, we take our relationship with the customer to the next level. We work together to understand their digital potential and then scale it out, with both parties sharing the risks and benefits—from the initial development through to its market launch and beyond. It’s about a long-term approach that lays the best possible foundation for sustained growth.

One great example of such a project is the “LANDLOG” collaboration with Japanese multinational Komatsu. The company, which specializes in manufacturing, selling construction, mining equipment, and industrial machinery, invested in the creation of a new cloud-based Internet of Things (IoT) platform. This “smart construction platform” is powered by a highly innovative IoT portfolio and aims to centrally manage and optimize construction processes, significantly improving resource utilization and safety standards. Here our partnership extends beyond simply delivering the co-innovated product: With a double-digit equity stake in the project, we are both taking on a share of the risks involved in such a venture, but at the same time, we will have a stake in its financial success. It other words, the success of this project is also the success of its stakeholders.

So, to return to the question of whether I am getting bored of discussing digitalization, the answer is obviously a clear no: There are still many challenges and opportunities ahead—something that I am regularly reminded of in discussions with our customers and partners. Remaining relevant, innovative, and competitive in rapidly changing markets requires us all to reexamine the ways we work, from what we provide to how we provide it. It’s a very exciting time for all organizations, and I am looking forward to working together with our customers and partners to shape the future.

For another perspective on digital transformation, see Why Digital Transformation Is A Nothingburger—And What To Do About It.


Bernd Leukert

About Bernd Leukert

Bernd Leukert is a member of the Executive Board of SAP SE with global responsibility for development and delivery of all products across SAP’s product portfolio.

How Is IoT Driving Growth In Equipment-as-a-Service Options?

Dietmar Bohn

The Internet of Things (IoT) is poised to deliver significant growth to many industries over the next few years. Within three years, it’s expected that companies selling IoT solutions will see revenues of over $450 billion. By 2025, it’s expected that there will be 75.4 billion connected devices worldwide. This provides a strong market for growth in many industries. The manufacturing industry is no different, with opportunities to improve uptime for customers and reduce high-dollar repairs.

At the same time, digitalization and disruption are providing the opportunity for companies with revolutionary new business models to enter the market. One new business model that shows great promise is integrating IoT technology and equipment with aspects of software-as-a-service models. But how will this model work in real life, what impact will it have on the companies that use it, and what benefits will it offer across a wide range of industries? Here’s a quick look.

How is the IoT driving growth in equipment-as-a-service options?

With the advent of cloud computing, software-as-a-service became popular. Essentially, it provided users with software access for a subscription fee, with the software-providing company handling maintenance, upgrades, and security issues. This concept has grown into a wide range of IT and other areas. As an example, from another industry, Netflix provides video services as a service through a monthly subscription fee.

Now the as-a-service model is being applied to a wide range of other industries. Equipment for many industries has often used the service contract or lease model. However, these models have had their own problems. Clients often don’t catch early warning signs that the equipment is having issues. The maintenance schedule may not be appropriate to the client’s site conditions. The equipment may be more than the end user needs. For whatever reason, service contracts can be expensive on both sides.

Equipment-as-a-service that implements IoT technology benefits both sides. Let’s take a look at how it might work in a business. ABC Manufacturing is an electronics manufacturing firm that uses automated MIG welders (metal inert gas welders) to produce part of its electronics components. It has service contracts for these welders, but it is not happy with the downtime and unexpected machine failures, which cost the company money. They’re also not quite sure that the equipment is right for their needs, with the limited-axis welders making somewhat sloppy welds when they reach particular angles.

XYZ  Equipment provides the welding machines but is not happy with the number of failures that could be prevented. These failures cost a lot of time and parts to fix. The unpredictable nature of the failures means sometimes they’re paying repair technicians to sit around while paying overtime when a machine breaks down at odd hours. At the same time, they’re also losing profitability from refunds to ABC Manufacturing for downtime on their lines. They know the customer isn’t quite sure about the machinery, but they’re not quite sure what they want to be changed.

After attending an equipment conference, XYZ’s CTO comes back to the office very excited about new IoT technology and business models. He convinces XYZ’s CEO to try an experiment with ABC’s service contract. XYZ’s CTO sets up an appointment with ABC’s production director and CEO to discuss options.

At the meeting, they talk about the issues with the welders. ABC doesn’t want to invest in any significant money in machinery it isn’t sure will work for their issues, so XYZ offers to set them up with a few 7-axis welders on an equipment-as-a-service option. ABC will pay a monthly fee for the use of the machinery, based on the outcome of the machinery. If they’re not happy with the equipment, ABC can end the subscription at the end of that subscription period without any penalty. XYZ will install sensors that use IoT technology to allow them to remotely monitor the equipment. This allows XYZ to determine when preventative maintenance is needed. The advanced notice lets XYZ schedule maintenance when it makes sense for both companies. XYZ makes fewer repairs and saves money. ABC avoids risk on the equipment. Everyone is happy.

Equipment-as-a-service provides great options for both equipment manufacturers and businesses. By integrating IoT technology with equipment contracts, many companies are gaining better uptime without the heavy investment. Equipment companies are also profiting from the lower failure rate as equipment is being serviced before problems get out of control. IoT technology is expected to add between $10 and $15 trillion to the worldwide GDP by 2030. Where does your company fall with these new possibilities?

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?


Dietmar Bohn

About Dietmar Bohn

Dietmar Bohn is the Vice President of Industry Cloud at SAP. He brings more than 15 years of CRM experience from both outside and inside SAP and more than 25 years of industry experience. Bohn has held different executive roles spanning CRM strategy projects, CRM implementation projects, CRM development and CRM product management. He holds degrees in Electrical Engineering and in Telecommunications.

Global Findings On IoT For Consumer Products

Don Gordon

The massive impact of Internet of Things (IoT) on consumer products is now beyond question, with more than 75 billion connected devices expected by 2025. That’s a significant change from 2017’s 8.4 billion IoT devices in use, which already represents an increase of 31% from 2016.

From iPhones to smart home devices like Nest and the Amazon Echo, IoT-enabled devices have captured people’s imaginations and wallets. But our brand-new study reveals that the IoT presents massive opportunities to consumer products companies in ways that are often unseen to consumers.

Currently, 60% of surveyed global manufacturers are implementing analytics on IoT data to optimize processes and production. But, as the study shows, this is just a small part of the picture. You can get a copy of our study here. However, these numbers have been mostly conjecture with regards to consumer products IoT.

Earlier this year, SAP conducted an in-depth international survey to create a more accurate overall picture. Our professionals received data from respondents in five countries in varying states of development. The individuals surveyed represent a wide range of consumer products industries and many different professional positions. Research demographics are available in the full study document. Here’s a short look at the results of this survey.

A common set of business challenges point to IoT

The top business challenge faced by these manufacturers were raw material cost fluctuation at 35%. This was followed by high logistics costs and shrinking operational margins, at 32% and 31%, respectively. After the top three, high lead time for products and inventory and the slow pace of innovation followed, at 28% each.

The top actions put in place to deal with these concerns were led by faster reaction to demand and capacity changes, at 32%. After this was improving product lead time and product quality and compliance, at 27% and 24%. Following this was focusing on more product innovation and increasing transport efficiencies, at 23% each. The compelling trend here is that many of these challenges are most hindering consumer products companies are areas where IoT has high potential to drive strong benefits.

Understanding and applying IoT technology

But why are they investing in IoT? The survey found that 41% of respondents have a clear picture of what IoT is and what it can do for their company. That still leaves a large number of respondents who know it is important but don’t have a strong grasp of IoT and the benefits it can provide. For overall reasons, having areas of applicability to the business and IoT’s potential value to their business top the list. But what areas are the companies planning on implementing first?

There are several key areas where implementation of IoT technology has been planned: Quality control came in at a high 61%. This suggests that this is the main driver for many businesses. After this: logistics management, distribution center management, inventory movement control, and transportation management.

The companies are taking several different approaches to implementing IoT into their operations. Key initiatives used include creating processes for managing IoT. Following that is training or acquiring IoT-capable staff, learning from early adopters’ actions, increasing budget, and building an organizational consensus.

Leaders and laggards

Leaders and laggards in this group tend to divide on several aspects. One aspect includes strategic drivers for companies implementing IoT into their supply chain. Leaders focus on improving product lead time, reacting to demand and capacity changes more quickly, and improving product quality and compliance. These aspects take a forward-focused approach. Laggards focus on decreasing out-of-stocks, improving cost-to-serve initiatives, and increasing product innovation. This shows a focus on catching up to market changes.

There was also a difference in implementing initiatives to improve IoT use. Leaders focus on creating IoT management processes, learning from early adopters and allocating more funds for the process. Though laggards also focus on creating IoT management processes, it is third on their list, after establishing partnerships to exploit IoT and building an organizational consensus of IoT technology implementation.

This survey helps to prove the potential of consumer products IoT. The results help us gain a stronger understanding of key issues with IoT implementation. We discovered that improving understanding of IoT capabilities needs to be addressed. Understanding allows companies to take advantage of IoT’s benefits. There were also many differences between countries, company sizes, and positions. More detailed information and many insights can be found in our original study report than can fit in this article. To take advantage of this information, download the full report.

By undertaking this survey, SAP has been able to assemble a large number of statistics that are specific to the consumer products IoT industry. As a leader in that industry, we believe it’s important to have accurate numbers to help consumer products companies move forward.  If you need help developing a comprehensive plan to bring IoT technology into your product line or company, please feel free to contact us today for more information.

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


Don Gordon

About Don Gordon

Don Gordon leads global Consumer Products industry marketing for SAP. Previously he led global Retail industry marketing for IBM. He lives in Philadelphia, considered by many to be the finest city on earth.

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.