Open Banking: How To Avoid A Shipwreck

John Bertrand

In less than six months, open banking sets sail. We do not want open banking to be like Twelfth Night: a shipwreck. As Shakespeare’s extraordinary play unfolds, it slips into a environment similar to today’s. The production may be magical, but there is also the presence of a wise fool (Feste) and a whistling regulator (Malvolio).

Here are three rules to help you maintain smooth sailing with open banking:

1. Third parties can, with client approval, access data, and make payments

You may have noticed that some credit score companies or financial services providers are now offering free credit scores. Why would a service that used to cost consumers now be provided for free?

Starting in January, companies that provide loans can ask to see the actual amount of money coming into the bank account of the person or organisation requesting the loan. The regulatory question of affordability of any given loan will be clear and more importantly, visible to any regulatory review, as bank account balances will be actual figures, not estimates.

The third parties can be any supplier, not necessary another bank or even one with financial attachments to the bank that holds the bank account. They can be independent of the bank account provider.

2. Banks must ensure that third parties operate securely

The bank that holds the client’s bank account must make certain that all third parties operate in as secure an environment as the bank does, and that it will continue to do so going forward. The third party must meet security standards required by the banking industry and must link electronically to the banks through open APIs.

3. Clients must be informed of third-party data or transaction requests

The bank is responsible for ensuring that their client, if taking advantage of the third party’s offerings, is continuously informed of any requests, of the response to those requests, and of any data or transactions made between the bank and the third party. This puts even more pressure on banks to respond in real time to their clients.

Banking is changing

Hold on a minute, you might be saying, the bank must do all this to let in third parties for a share in their clients’ wallets? Yes, it is part of the ongoing regulations to create greater competition.

One of the items highlighted by regulators was the £1.2 billion per annum generated by the banks for fees on unapproved overdrafts on current accounts. Fees charged often exceed payday loan companies for similar amounts of credit. This recent FCA report on high-cost short-term credit shows that the average overdraft line is £120, whereas the average outstanding is £130; a mismatch in the banks’ favour. Malvolio would approve, as the computer software (which was probably written in Cobol in the 1990s) charges from a central fee file anyone who is overdrawn at the close of the day.

While banks can easily manage cash flows avoiding costly unauthorised overdrafts in the private banking, large corporate, and wealth sectors, few have tried this across current accounts in general. Third parties can now offer solutions across multiple banks that help people and companies avoid overdrafts. 

Doing nothing is not an option

Banks must be compliant with this, and the work must be done regardless, so let’s look at the opportunities. The key is to provide a secure data path to the cloud.

Once in the cloud, third parties (which can be the banks) can offer:

  • Active wealth money management services using AI/bot technology for all
  • Multi-bank services to better service our increasingly global footprint in both retail and commercial banking
  • Ongoing improvement of digital services for you or your business, including sharing foreign-exchange margins and same-day international payments

Who better to do this than the banks themselves?

The rules are set. The key question is, how will the retail and corporate banking markets react to third-party solutions? Shakespeare might offer this advice for banks planning to go beyond compliance and compete with third parties: “Be not afraid of greatness.”

For more on open banking, see Open Banking: The Fuzzy Period.


Digital Transformation And Five New Imperatives For The Paper And Packaging Industry

Jennifer Scholze

No industry has been more affected by digital disruption than paper and packaging. The challenge has been to survive this shift and leverage digital technologies in a world far less dependent on paper products than it was 10 years ago.

Uneven impact of digital disruption

The impact of digital disruption has not been felt evenly across the paper and packaging industry. Tissue, hygiene, and packaging are doing well and seeing healthy growth. The segments that have been impacted the most are newsprint, graphic, and printing paper. These segments must redefine how to create value in a digital world.

Leaders across all industries have found ways to leverage technology to solve digital challenges. Although it may seem counterintuitive to recommend that the print and packaging industry become more digital, they must to stay relevant.

Five strategies for paper and packaging success in a digital economy

  1. A healthy segment can still be improved. While healthy, packaging can grow by leveraging innovations such as artificial intelligence or augmented reality. Machine learning can help improve product quality and maintenance and is a game changer in terms of improving process automation. Augmented reality can support workers to maintain devices safely and more cost-effectively without needing to call skilled technicians.
  1. Customer collaboration can drive new business and margins for paper and packaging companies using digital tools. Using digitized information, paper and packaging companies can provide additional services based on customers’ individualized needs, such as co-development of new packaging materials or detailed tracking information for shipped goods. Technology can also be the answer to revamping channels to open up new businesses with higher margins. One example is Sappi. As part of the company’s digital transformation initiative, Sappi targeted the European markets as an opportunity for transformation of customer segments and the use of the merchant-retailer channel.
  1. Digital transformation improves logistics and operational efficiency. Paper and packaging companies have complex manufacturing and logistics challenges. These range from transportation and warehousing to process management and asset downtime. Capturing and analyzing data from machines, vehicles, or products allows better predictions, simulations, and decisions. Automation and connectivity across the plant floor reduce error rates, add speed, and cut operating costs. Analyzing sensor data from machines helps predict possible failures early and reduces unplanned downtimes.
  1. Digital tools like machine learning help make the most out of your workforce. Digital tools help employees spend less time on repetitive tasks and more time on strategic analysis and action by automating tasks. An added benefit of leveraging digital tools to create cost-effective, outcome-driven, human-machine partnership workflows across the organization is that smart automation also tends to reduce the number of errors and accidents that occur when automation isn’t built into the model.
  1. Key partnerships with technology providers are essential in the execution of this type of digital transformation: No company can be successful if it tries to tackle digital transformation alone. In the paper and packaging industry, strategic partnerships with technology providers who understand how to synthesize emerging technologies to address core business processes is a key ingredient to success. By partnering with technology solutions providers who can not only fulfill current technology needs but help them co-innovate on the business side to drive disruption (rather than just react and adapt to it), paper and packaging companies can more easily reinvent their industry for a digitally driven world where their products don’t have to be commoditized and devalued.

The paper and packaging industry was hit hard by the explosion of digital content in recent years, but new technologies open up a world of opportunities to improve operational efficiency, accelerate speed to market, become a hub of product innovation for retailers, and create more value to its customer ecosystem than ever before.

Learn how to bring new technologies and services together to power digital transformation by downloading The IoT Imperative for Energy and Natural Resource Companies. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?


Jennifer Scholze

About Jennifer Scholze

Jennifer Scholze is the Global Lead for Industry Marketing for the Mill Products and Mining Industries at SAP. She has over 20 years of technology marketing, communications and venture capital experience and lives in the Boston area with her husband and two children.

Using Machine Learning To Turbocharge Financial Services Innovation

Toni Tomic

When people talk about artificial intelligence (AI), the discussion commonly turns to flashy robotics – how they support manufacturing production lines, disable explosives, or even vacuum the floors.

Major steps in AI were made in the late 1950s and early ‘60s, with flagship examples like the ELIZA computer program that demonstrated the superficiality of communication between humans and machines. From then until the 1980s, there was great promise that AI could revolutionize businesses, but there was no major disruption.

Today it feels as if AI is born again, and it is much more than robots. Innovative new AI technologies are delivering benefits to a wide variety of industries, including financial services.

According to the “Worldwide Semiannual Cognitive Artificial Intelligence Systems Spending Guide” from International Data Corporation (IDC), worldwide revenues for cognitive and artificial intelligence (AI) systems will reach $12.0 billion in 2017, an increase of 59.1% over 2016. “Cognitive and artificial intelligence solutions continue to proliferate across all industries, resulting in significant growth opportunities,” said Marianne Daquila, research manager, Customer Insights and Analysis, at IDC. “Some of the use cases are very industry specific, such as diagnosis and treatment in healthcare, and in others, they are common across multiple industries such as automated customer service agents. The variety, application, and nature of cognitive/artificial intelligence use cases are resulting in ubiquitous spend over the forecast period.”

One of the most interesting disciplines is machine learning, a specific type of AI that allows computers to learn without being explicitly programmed to do so. Machine learning uses statistical theory and exponentially more powerful computer processing to help businesses quickly realize valuable insight from their data.

This is great news for banks and insurers. Not only are these service providers facing falling profit margins, rising customer expectations, and increasing competition from fintechs, but they also need to cut costs. With machine learning, they can extract value from huge volumes of data, cheaply and effectively.

Machine learning can help traditional global banks that operate accounts at 140 to 170 British pound sterling (GBP) to compete better with challenger banks that operate at 4 to 44 GBP.

Creating intelligent financial services

Machine learning is ideal for addressing three dominant financial services challenges (see figure), including:

  • Customer front office: Unsupervised machine learning techniques can help banks and insurers segment their customers and offer personalized, targeted products. These technologies can also improve speed and agility, helping companies compete with fintech firms through enhanced knowledge of their customers.
  • Regulation and compliance: Using automated reports, stress-testing solutions, and behavioral analysis of e-mails and phone records to identify suspicious customer or employee behavior, machine learning can boost regulatory compliance. It can also enhance fraud detection, improve anti-money laundering efforts, and more effectively detect credit risk.
  • Operational efficiency: By combining Big Data with machine learning, financial services companies can automate back-office operations, reduce errors, and accelerate process execution. Insurers can improve and automate claims handling by recognizing patterns in pictures or individuals involved in damages, for example. Machine learning algorithms can also elevate talent management and recruitment by evaluating the resumes of successful employees while searching for online candidates with similar traits and experience.

Financial services challenges addressed by machine learning

1. Automating front-office applications

One of the most interesting machine learning applications helps financial services companies segment customers and offer targeted products or services. Let’s look at how the technology works.

Cluster analysis discovers distinct groups within the customer base and identifies similarities over several dimensions. Because this process is unsupervised, banks or insurers do not need to define the characteristics. The technology discovers these on its own.

Once the customer base is segmented, the technology builds predictive models. Algorithms help identify the most suitable products for each customer. And because the algorithms learn as they go, they can recognize changes in customer preferences in real time and automatically adjust product recommendations and provide the right advice at the right time.

The benefits can be significant. Personalized offerings make customers feel understood, increasing satisfaction. Successful cross-sell and upsell efforts can increase revenues. Service speed increases when banks and insurers can automatically recognize a change in behavior and respond instantly, without human intervention.

2. Choosing the right AI functionality

Traditional database technologies and analytics tools are not powerful enough to support machine learning. Fortunately, a new generation of innovative solutions is coming to market.

Many vendors are seeking to capitalize on this burgeoning industry. As with any new technology, decision-makers must carefully assess how well the tools meet the needs of the business.

When choosing a machine learning solution for customer retention, for example, we advise financial services companies to select tools that automatically:

  • Manage dynamic data from a variety of customer channels and build an overview of the customer journey
  • Sort, classify, and route events, pinpointing critical changes and reliable customer churn indicators
  • Identify customers who are about to churn and take proactive steps to prevent customers from leaving

In the battle to provide better customer service, machine learning technology is becoming a differentiating technology for financial services institutions. There are two ways to get there. Companies can:

  • Use standard applications, where machine learning is embedded and shipped with the software
  • Develop their own applications based on available cloud platform solutions and toolsets

Preparing to address obstacles

Without a doubt, there is huge potential to bring efficiency and effectiveness to a new level by injecting AI and machine learning into the financial services business. So what is hindering financial services institutions from becoming more successful with AI and machine learning and gaining benefits from disruptive technology?

There are three major obstacles:

  • Full senior management buy-in that extends beyond funding a proof of concept
  • Shortage of specialist skills to operate and maintain the technology
  • Costs of the AI system

With these challenges, getting quick wins in a short time frame is becoming more and more crucial. And since vendors are embedding machine learning scenarios into existing software applications, this might be a good starting point for companies that want to simultaneously work on major disruptive use cases.

Is your company ready to embrace the competitive advantages offered by AI and machine learning? To learn more about how machine learning can help financial services companies innovate and compete, contact me in the comments below or on LinkedIn.


Toni Tomic

About Toni Tomic

Toni Tomic is the Vice President and Global Head of Transformation at SAP. He is responsible for the SAP innovation strategy for disruptive technology such as artificial intelligence, blockchain, and the Internet of Things. Toni also oversees FinTechs and drives financial services business development globally. Before joining SAP, Toni worked for 10 years in strategy and management consulting focused on business and IT strategy, post-merger integration, and restructuring programs in the financial services industry.

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.