Jobs Of The Future: The Collaborative Financial Officer

Conor Donohoe

Strength in financial core competencies is no longer enough for CFOs. In the future, the role of the CFO will be a fully collaborative one, with finance being just one part of a long list of skills that include leadership, decision-making, and a solid working knowledge of the latest technology trends affecting decision-making.

A global survey of finance executives conducted by SAP in partnership with Oxford Economics found that the high-performing minority (11.5%) stood out for their strong skills in collaboration. Most CFOs collaborate with other departments on governance, risk management, and compliance (GRC) out of necessity. But these leaders took it one step further, collaborating on marketing, customer service, and sales with their respective departments—breaking out of the traditional silo that finance occupies in so many businesses.

At the other end of the spectrum, almost half the companies surveyed that had experienced zero or negative revenue in the last 12 months cited isolation between departments as one of the core reasons behind their business’s poor performance.

Businesses and CFOs with poor collaboration are missing out on one of the greatest strengths of their finance department: the huge value provided by the data they collect. Data-driven decision-making is key to making the correct strategic choices, and businesses that constrict the benefits of financial insights to the finance department are at a significant competitive disadvantage.

Three ways finance leaders can act more collaboratively

Clearly, collaboration is becoming increasingly essential for CFOs, but how can finance leaders boost their ability to positively influence other areas of the business? Let’s take a look at three areas of opportunity:

1. Take advantage of the latest technological innovations

The next wave of technology will have a multiplicative effect on the ability of the finance function to impact other areas of the business. Technologies such as artificial intelligence, machine learning, and Big Data analysis will allow for better insights into financial data and free CFOs to focus on strategic analysis and adding value.

The aspiring collaborative financial officer should embrace new technologies, such as digital assistants, and the efficiency gains they provide. For example, Howdy, a Slack bot, can be trained to automatically collect status updates from team members and collate them in a report, saving a manager from having to perform this function manually. In the future, CFOs will rely on digital assistants to perform a wide range of administrative tasks, including managing employees, booking meetings, and doing data analysis.

2. Cultivate leadership and other soft skills

Technical financial expertise is essential, but the collaborative financial officer will require many other skills. Leadership, communication, and diplomacy are all needed to enable collaboration, and CFOs must improve themselves in these areas.

Additionally, CFOs must work with HR to train existing finance teams in these skills so they can work with other functions more effectively. These skills should also be highlighted when hiring new team members.

3. Empower the entire finance function to collaborate

A collaborative CFO is good, but what businesses need is a collaborative finance function. CFOs must empower their teams to support all other functions with their expertise, including areas that finance rarely gets involved with, such as marketing.

To get started, CFOs should calculate where finance can add the biggest value. When other departments see the performance boost collaboration can provide, you won’t need to seek them out. They’ll come to you.

Find out more about what the leading CFOs are doing to ensure that they and their business succeeds.

Learn how organizations are gaining instant financial insights and using them to make better decisions—both now and in the future. Register now for the 2017 Financial Excellence Forum, Oct. 10-11 in New York City.

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Conor Donohoe

About Conor Donohoe

Conor Donohoe is an ERP consultant for SAP S/4HANA Cloud at SAP. He is a qualified chartered accountant, with first-hand experience of how technology can drive business change. Trained in a Big Four accounting firm, he is experienced in advanced analytics and reporting within the professional services, banking, and pharmaceutical industries. Conor comes from the ERP user side, so understands the challenges ERP users can encounter with their systems – and where real gains can be made.

The Growing Importance Of Chatbots In The Customer Journey

Mark de Bruijn

“Hi, I’m Clippy, can I help you with anything?”

Many users of older Microsoft Office software will remember Clippy, the animated paperclip that often popped up and offered to help while you were working on a text. Love it or loathe it, everyone had an opinion about Clippy. And despite the wide range of user opinions, this helpful paperclip was a forerunner in the area of chatbots.

In subsequent years, this technology underwent major further development, and thanks to a combination of artificial intelligence and machine learning, it is well on its way to taking over an important part of the customer journey. According to Gartner, by 2020 chatbots will be handling no less than 85% of all customer service interactions.

One example of this is Smart Mirror, which provides clothing advice on the basis of your customer data. Charly, as we call this mirror, tells you exactly which sunglasses suit you, and which flip-flops match your glasses. As ironic as it sounds, chatbots offer a wide range of options for creating valuable and personal customer interactions, and you’ll see more and more chatbots in the customer journey in the future.

Many customer journeys, an infinite number of possibilities

The customer journey is more complex than ever. Quite simply, it is no longer possible to refer to a single customer journey. Consumers dash all over the place, online and offline, and expect a personalized approach from companies across the entire omnichannel journey.

Communicating with them in the right way, and at the right place and time, is quite a challenge for marketers. A good integration of chatbots in the customer journey can provide consumers with quality injections, by making efficient use of machine learning and intelligence. Although only 19% of consumers are currently using chatbots, 95% think they will make more use of chatbots in the coming years.

Smart mirror, smart mirror on the wall…

A talking mirror is no longer a fantasy from Snow White but is now a reality. In contrast with the mirror in the fairy tale, Charly does give an honest answer if its opinion is requested. By making use of intelligence and machine learning, this loyal assistant knows exactly what suits the person in the mirror.

The consumer links personal data (such as data from a fitness app) to his or her account, and along with historic data (such as previous purchases), this makes it possible to make a good determination of the consumer’s taste. The smart mirror also uses face recognition software, which is also linked to the personal account.

This is just one example of how chatbots can enrich the customer journey. It is therefore hardly surprising that 77% of companies indicate that they would like to implement chatbot technology in order to increase customer satisfaction and conversion. Chatbots can also initiate efficiency improvements in the areas of costs and HR. In short, the benefits of chatbots for the business are well known, but there are still a number of challenges for chatbot developers.

Chatbot challenges

Almost half of consumers still prefer human contact at the other end of the line, so chatbots are primarily used for answering simple, everyday questions. A third of consumers indicate that they would be happy to use chatbots for all interactions, as long as the option is also offered to easily contact a person when needed.

Looking at the challenges for chatbots, the lack of human finesse is seen as the biggest obstacle (66%). Other major challenges identified are development and maintenance (35%) and finding the right balance between human interactions and interactions with chatbots (33%).

Developers will have their hands full in the coming period with the further development of chatbots, so that, like Charly, they can do more than just simple interactions. For example, it is assumed that chatbots will be playing a significant role in e-commerce in the future. But one essential aspect for this is an integrated payment system. If that doesn’t arrive, the chatbot will never be able to serve as an effective sales tool.

The added value a chatbot can have for the customer journey has not yet reached its peak, by a long way. In the last 10 years, the chatbot has evolved from a paperclip in Microsoft Office to a mirror dispensing clothing advice. The meteoric development of artificial intelligence and machine learning will only further increase the power of the chatbot.

Learn how chatbots in the customer journey can enhance e-commerce. Download the free white paper here

This article originally appeared on The Future of Customer Engagement and Commerce.


Mark de Bruijn

About Mark de Bruijn

Mark is an energetic and positive marketer with a focus on creativity, teamwork, digital, data and technology. Responsible for SAP Hybris in the EMEA region (Europe, the Middle East and Africa). He is passionate about SAP Hybris solutions for marketing, sales, service, commerce and billing.

Robotic Process Automation Across Industries

Shaily Kumar

Robotic process automation (RPA) is the application of software and technology with the use of artificial intelligence to carry out repetitive tasks quickly, tirelessly, and accurately. It enables you to create software robots that mimic human interactions using a computer system’s user interface. It was introduced not to replace humans, but to complement their efforts and ease tedious tasks. When the technology was first introduced, it was not fully accepted by all individuals and industries, but its wide-ranging benefits are now becoming clear.

RPA has been a great development in many industries today as large and small size industries that perform a lot of tedious tasks use it to ease operations.

RPA enhancements in 2017

The concept of automation has been around for some time, but it is now seeing a significant technological evolution in the sense that emerging software platforms are reliable enough for use in large enterprises. In 2017, RPA saw tremendous improvement with the introduction of new software that complemented or replaced existing tools. Comprehensive product update enabled users to automate more with less effort. This intelligent automation enables processes that are faster, simpler, scalable, and more secure.

In 2017 RPA also attracted the interest of more enterprises starting their digital transformation journey. Development of modern software robots has improved productivity, work quality, and customer satisfaction.

All in all, 2017 has seen a major turnaround for robotic process automation as it improved in all sectors.

Expectations in 2018

With the fast growth and impact of RPA, especially in the last few years, many people wonder what to expect next. What should we expect in 2018? How will robotic process automation affect businesses going forward?

At the rate researchers are improving existing RPA software, we can expect to see the application of artificial intelligence to boost operational efficiency. Robotic process automation should develop to a level where robots will be able to accurately recognize images and speech and process simple tasks. RPA applications will enable robots to interact with users in innovative new ways; for example, mimicking human interaction with technology. Cognitive reasoning will be incorporated into systems by utilizing cloud, machine learning, and Big Data.

In these ways, RPA will enable organizations to reduce costs, boost efficiency, and improve the accuracy of complex data in the coming year.

Industry applications

Banks, financial institutions, and insurance companies process large numbers of operations daily. In sectors that require intensive and bulky operations, RPA can be used as a virtual worker, replacing humans in mundane and repetitive tasks. Robotic process automation allows modern banks to meet their high demands for audibility, security, and data quality, while also improving operational efficiency. In credit card applications, automated software is used to handle tasks such as issuing cards to users. RPA improves the speed and accuracy of tasks, which in return increases productivity.

The retail industry is one industry that benefits greatly from the use of RPA. Automated software has been designed to handle fraudulent accounts, for example, as well as to update orders and process shipping notifications, eliminating the need to manually track shipped goods.

In telecommunications, RPA is used to monitor CRM subscriber feeds, fraud management data, and customer data updates. RPA also acts as middleware to automate user information.

Robotic process automation is a form of digital machine labor that replicates human cognitive functions and performs tasks accurately and efficiently in many industries. It has experienced a great development in recent years, especially in 2017, with the convergence of cloud, analytics, and powerful machines.

In 2018, we can expect to see further improvements in RPA, including cognitive reasoning applications. The technology’s ability to enable fast, precise operations in industries such as retail, telecommunications, and finance will continue to drive growth going forward as organizations adopt RPA tools to boost operational efficiency and cost savings.

For more insight on AI and advanced technologies in the workplace, see The Human Angle.


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.

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.