Smart Products And Innovative Services Help Airlines Reimagine The Future

Eva Roe

Printed tickets and plastic boarding passes are as outdated for air travel as accordion-folded road maps for a road trip. Savvy passengers arrive at the airport with only a digital confirmation and a cell phone boarding pass.  Checking arrival and departure times is just as painless by mobile app.

The digitalization of the airline industry, as much as it has changed operations and passenger flying habits over the past two decades, has only just begun.

NBC Evening News recently reported Delta Airlines’ system-wide launch of chip-embedded baggage tags, after testing them at 84 U.S. airports. To reduce the staggering number of lost bags, other airlines will install their own digital tracking systems in coming months. According to, more than 24 million bags were lost or mishandled in 2014.

The rollout of the Delta trackable tag is just one solution. The digital snapshot reduces lost baggage and faulty routing. Many other fresh ideas are coming, including programmable luggage with integral identification.

Experimental mobile apps allow passengers to keep a virtual eye on their belongings throughout the entire trip. In the future it may even be possible to order your bags delivered at a specific time to a specific destination address. Tomorrow’s travelers may have no need to keep their bags always in sight!

Build traveler engagement

Nowhere is the need for connectivity and interactive involvement more clear than for the traveling public. Today’s journey generally begins with a digital search, as travelers learn about weather conditions, departure delays, and airport gate changes.

Sabre, the industry giant that revolutionized airline booking and scheduling five decades ago, has a prototype mobile phone app that stores past bookings and compares travel options based on previous patterns. This and other application prototypes will alter the face of business and leisure travel worldwide. Digital personalization, even personalized pricing models, are the way of the future.

The demand for connectivity has also changed the look and the feel of airports. Modern travel is interactive. Passengers tap into data streams to track flight progress and chart arrival and departure times with precision. Airlines notify passengers of scheduling changes and gate assignments via text or email.

Onboard connectivity and increased passenger comfort are the next great frontiers for airline travel, and some airlines have addressed those needs in novel ways.

Digitalization flies high

Aeromexico’s new 787 offers passengers a “cruise ship” ambience with lounge areas, boarding lobbies, self-service bars, and snack kitchens. A new interior configuration for select long-haul flights includes spacious seating, aisle access for every passenger, and touch screens with television access for every seat.

The future of taking trips around the world may be forever changed. Travelers are always interested in destinations, but the journey has not always been fun. Now, though, the actual “getting there” part of the trip may become more than half the fun.

Technology has brought flight management software to the point where data on specific aircraft performance and other air traffic can be “crunched” to determine optimal flight plans, allowing controllers to predict arrival times within 10 seconds and to hold an airplane to a charted course that doesn’t deviate more than a wingspace.

Target customer concerns

Precise control of flight navigation helps save fuel and reduces pollution. This “green approach” to flying, already in use in Brisbane, Australia, means fewer delays and reduces noise in neighborhoods around the airport.

Dozens of European airports are exploring the technology. Steve Fulton of Naverus, a GE subsidiary that designs and installs navigation data systems, says that adopting the technology throughout Europe could save fuel and reduce pollution by more than 8 percent.

That, in turn, is another way to tap into passengers’ sensibilities, build passenger loyalty, and keep travelers happy.

Connectivity encourages loyalty

The future of airline travel hinges, as it always has, on customer loyalty management. Frequent flyers appreciate points and rewards, but they want more than that. With increasing access to digital technology, passengers are more discerning and much more demanding. Price may be less important than service and performance.

Passengers now have the ability to make quick and easy changes, thanks to the connected world. Customer satisfaction is critical, and there are exciting new ways to promote that satisfaction. Travelers today are seeking a total experience, so airlines must offer options for engagement in meaningful ways. Customers respond well to the availability of “one-stop shopping” and online booking not only for flights, but also for hotels, rental cars, and sightseeing packages.

Relevant, up-to-date traveler profiles allow for increased interaction. Targeted promotions based on personal profiles and social media relationships are vital. It’s not only ticketing and boarding that have gone paperless and high-tech; the entire travel experience has been transformed by the digital age.

For more insight on this digital age of airlines, see Build a Better Customer Journey for the Digital Traveler.


Eva Roe

About Eva Roe

Eva Roe is the Head of Airlines Solutions at SAP. She is responsible for developing a strategy for the industry working closely with customers and translating their requirements to marketable solutions. One focus area is defining how technology innovations become enablers for new business models and processes in the airline industry.

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.

How Technology And Data-Driven Insight Can Boost Employee Engagement

Andre Smith

Human resources professionals know that happier employees are more productive. In today’s hyper-competitive global economy, employee productivity can mean the difference between business success and failure. That means employee happiness should be a top priority for any company that aims to succeed.

The difference between a happy, engaged workforce and a dissatisfied one is vast. Surveys indicate that companies with highly engaged employees outperform others by as much as 202%. Still, according to Gallup polling data, only about 32% of employees report that they feel motivated and engaged at work. To fix the problem, we must first understand what causes it. Here are the most common roadblocks to employee engagement, and how technology and data-driven management can help overcome them.

Rigid schedules

In a recent survey, as many as 70% of employees reported that having flexible work hours was very important to them. In today’s modern interconnected world, it’s easier than ever to create flexible work arrangements for employees. The key to making it work is to utilize comprehensive workforce management software to analyze business needs before beginning a flexible work initiative.

Gary Corcoran, of Advance Systems, says, “Gaining a full picture of necessary staffing levels throughout the workweek is the first step to creating a flexible schedule. In reality, the flexibility allowed should extend as far as business needs will allow. That way, employees stay happy and the business won’t find itself short-staffed at busy times.”

Lack of work/life balance

In 2016, American workers failed to use a combined 662 million earned vacation days. A big reason for this is anxiety about requesting vacation time. The results are profoundly damaging for both employees and companies. Once again, a data-driven solution is in order. By keeping careful track of which employees aren’t taking vacation time, human resource managers can intervene to make sure they take a break.

This goes a long way towards changing the office culture surrounding vacations. Depending on the business structure, making vacations mandatory can create a positive change. Companies can even offer automated paycheck deductions into vacation savings accounts using services like 401Play to encourage health employee vacation habits.

Poor internal communication

One of the most common employee complaints is lack of transparency and communication within their organization. Not seeing clear reasons for their work or understanding their place within the bigger picture is dispiriting and counterproductive. To combat this, a variety of technological tools may be employed. First and foremost, the internal communication system should include an all-in-one platform like Slack, which all employees can use to remain connected with one another. At the team level, project-based communication tools like i done this make task management a snap and help employees to see how their work dovetails with that of their co-workers.

Engagement begins at the top

There are many other factors that can contribute to employee engagement than mentioned here. There is, however, an easy, but often overlooked, way for managers to boost employee engagement: They must embody their own engagement policies. For instance, if the business allows flexible scheduling, managers should use it as well, always striving to stay productive and set an example.

The same concept goes for things like vacation time and communications: It’s all about setting a tone. The level of commitment to creating a healthy and motivated workplace at the management level is the true determining factor in the success of any engagement policy. Efforts by managers let employees know that they are valued. That creates the kind of engagement that cannot be bought or quantified.

Learn more about how data analytics in HR can get your business Moving From Gut Instinct to Data Insight.


Andre Smith

About Andre Smith

Andre Smith is an Internet, marketing, and e-commerce specialist with several years of experience in the industry. He has watched as the world of online business has grown and adapted to new technologies, and he has made it his mission to help keep businesses informed and up to date.

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.