Living The Live Supply Chain: Why You Need Data Scientists

Hans Thalbauer

In Part 1 of this series we explored the essentials of deploying a live supply chain. In Part 2 we look at why data scientists will be increasingly key to supply chain success.

When it’s completed in 2030, the Square Kilometer Array will be the largest telescope ever built, and will capture 35,000 DVDs of data every second. When astronomers showed off an early iteration in July 2016, they pointed it at a moon-size section of sky. What did they find? Nearly 1,300 previously unknown galaxies.

Supply chain operators can be forgiven for feeling like those astronomers. The trove of new data they’re capturing — from business systems, IoT devices, social media, and so on — has the potential to transform their views of customers, suppliers, manufacturing, logistics, and more. But making sense of all that data can be more than challenging. For that, they’ll increasingly need data scientists.

From business as usual to business-critical

Actively managing supply chain performance has never been more business-critical. Globalization, regulatory requirements, technology complexity, volatility of supply and demand, and greater dependence on suppliers have all increased business risk. The only way to make sure the supply chain operates in a way that meets customer needs and drives business success is by leveraging data in as close to real time as possible.

Increasingly, that data will be both structured and unstructured. Structured information from business systems includes traditional transactional data such as purchasing, production orders, and sales.

But you can’t operate a truly real-time, or “live,” supply chain without unstructured data. And that will come from a variety of sources. The rapidly falling cost of IoT technology means you can embed sensors in everything from production equipment to low-cost consumer goods. Social media can contribute customer sentiment about companies and products to help you sense demand, risk, and opportunities. Crowdsourcing apps can let you track everything from weather to traffic to holiday spending.

Data scientists to the rescue

In the meantime, logistics operators are grappling with an aging, shrinking talent pool. Logistics employs 6 million people in the United States, but it will need another 270,000 new workers per year to keep up with growth. At the same time, 60 million baby boomers will exit the workforce over the next nine years, but only 40 million younger workers will replace them, according to U.S. Census data.

It’s no wonder 79% of participants in the 2016 Third-Party Logistics Study feel unprepared for the impact of the labor shortage on their supply chains. And only 38% of executives are “extremely or very confident” their supply chain has the competencies it needs.

In particular, a live supply chain requires the data scientists — and technology — that can wring the most value from your data. That starts with identifying relevant data sources, figuring out how to capture the data streams, and understanding how to harmonize it at the most granular level. It continues with the ability to parse useful information from data noise, and to analyze the useful information to extract new insights.

Those insights then need to be placed in the proper context for each function. The same information holds different value — and needs to be delivered in different ways — for R&D, production planners, logistics managers, executive decision makers, and so on.

Perhaps most important, data scientists must empower the supply chain with predictive analytics that let you quickly and accurately forecast demand. That needs to happen before competitors make the same predictions — and before your customers realize they have desires your business isn’t meeting.

Thanks to sophisticated scientists and technology, researchers just determined that the universe holds 10 times more galaxies than previously thought. With the right talent and tools, what vast new opportunities will your supply chain discover?

Learn more about how running a live supply chain can help you thrive today and innovate for tomorrow, visit us at


Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is globally responsible for solution management and the go-to-market functions for SAP digital supply chain solutions and the SAP Leonardo portfolio of Internet of Things solutions. In this role, he is engaged in creative dialogues with businesses and operations worldwide, addressing customer needs and introducing innovative business processes, including the vision of creating a live business environment for everyone working in operations. Hans has more than 17 years with SAP and is based out of Palo Alto, CA, USA. He has held positions in development, product and solution management, and the go-to-market organization. Hans holds a degree in Business Information Systems from the University Vienna, Austria.

Air Cover For The Endangered

Rick Price

David Allen sits in a semi-dark trailer, eyes on a computer screen. He is scanning an image from an aerial drone skimming the treetops at the Dinokeng Game Reserve in South Africa. It is searching for elephants. A cloud of dust appears in the camera’s field of view – the plane banks toward it, and there! A big bull, ears flapping with each step, a tracking collar around his neck. The ERP Air Force has found its target, with the aim not to destroy, but to save the creature from his nemesis, the human poacher.

The link to poverty

The group behind this – a non-governmental organization called Elephants, Rhinos, & People or ERP, was founded in 2014 by to preserve and protect Southern Africa’s wild elephants and rhinos through alleviating rural poverty. A look at some numbers shows the link between poverty and threats to the elephants and rhinos.

According to, rhino horn is worth up to hundreds of times the per-capita income in poor rural areas of Southern Africa. On the global black market, it can fetch up to $80,000 per kilogram. Depending on the species, a rhino horn can weigh three to four kilos, so one horn can command more than $300,000 – an unimaginable fortune to most people there.

Grim statistics

The result is carnage. Wild African elephants are being killed at the rate of four an hour. At the end of 2016, the population was 352,000, according to Group Elephant. Rhinos are in far worse shape: The total population as of this writing is 19,682 Southern White Rhinos and 5,042 Black Rhinos for a total of 24,724 in all of Africa. The government of South Africa says 529 rhinos were poached in that nation alone in the first half of 2017. ERP says all in all, three rhinos are killed every day. Any effort to preserve this species must give people an alternative to poaching and a stake in the animals’ survival, through jobs and economic opportunities like tourism. Conservation initiatives also have to make the most of scarce resources, and that requires cutting-edge technology.

Covering a lot of ground

These are big animals, they can move quickly, and their stomping grounds are large. Dinokeng alone encompasses more than 45,000 acres. It would be impractical to hire enough people to keep tabs on every elephant and every rhino there every moment of every day. Inside the reserve, they are safer, but if they break out into the surrounding suburban area, just north of Pretoria, they face threats, including exposure to poachers who might not risk the reserve itself. ERP works with Dinokeng to patrol the fences and help keep the animals in, but there’s a limit to what they can accomplish. That’s where the ERP Air Force, the tracking collars, and Big Data come in.

Eyes in the sky

Back in the trailer, the GPS collar monitoring system has alerted drone pilot David Allen that the bull is getting too close to the fences. The drone follows the elephants as they approach the limits of the reserve. The operator can then direct rangers in SUVs or a helicopter to nudge the herd away from the boundary. That reduces the poachers’ ability to kill the animals – outside Dinokeng, there is less protection. The drones will also watch for poaching within the reserve, and ERP hopes to use them for other initiatives.

Big Data to protect big creatures

ERP uses cutting-edge digital technology to save these wild elephants and rhinos, both by keeping them in safe areas and by alleviating the poverty of the local population. All of that requires the ability to wrangle huge amounts of data quickly, from knowing where the elephants are, to routing the people to them. A group of technology companies, including SAP and its partner EPI-USE, have collaborated with ERP to build a system capable of collecting, organizing, storing, and retrieving that information.

Next on the roadmap is predictive analytics: As EPI-USE’s Jan van Rensburg said in May 2017, “We rely upon analytics in the background, a platform where we can predict where poaching will happen. And we use the data that we build up and store in our in-memory database so that we can more efficiently use the drones, and send them to the spots where poachers are more likely to be. And when a poacher is found by the drone, we can dispatch a ranger to deal with it.”

A weapon to save, not kill

So for ERP, Big Data and the ability to use it in real time have become a weapon in the fight to save endangered elephants and rhinos, while helping people in poverty create new ways to earn a living and turn away from poaching.

Want to know more about how ERP uses Big Data and drones to save endangered wild Elephants and Rhinos through alleviating rural poverty? Watch the video.


Rick Price

About Rick Price

Rick Price is an Emmy Award-winning journalist who now works at SAP, where he tells stories of customers' digital transformation.

How Artificial Intelligence Can Increase Your Business Productivity

Sayan Bose

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

Why AI is the future for discrete manufacturers

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

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

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

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

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

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

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

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

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

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


Sayan Bose

About Sayan Bose

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

The Blockchain Solution

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

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

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

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

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

The Spine of Digitalization

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

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

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

What Is Blockchain and How Does It Work?

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

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

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

Why does blockchain matter for the supply chain?

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

Hints of the Future

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

But we could tell that it had vast potential.

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

What’s the Magic Combination?

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Blockchain Is the Change Driver

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

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

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

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

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

Challenges on the Path to Adoption

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

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

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

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

Blockchain Blackouts

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

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

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

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

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

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

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

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

Customers Will Expect Change

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

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

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

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

About the Authors

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

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

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

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

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

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



The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

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

Machine learning

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

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

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

Business application of machine learning: employee satisfaction

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

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

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

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

Predictive analytics

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

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

All predictive analytics applications involve three fundamental components:

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

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

Business application of predictive analytics: marketing campaign optimization

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

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

How machine learning and predictive analytics are related

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

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

Explore machine learning applications and AI software with SAP Leonardo.


Shaily Kumar

About Shaily Kumar

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