Introducing The Insider’s Guide To Improving Payments And Cash Flow

Alan Cohen and Scott Pezza

This is the introductory blog in our Payments and Cash Flow Series, explaining why you should act now to improve your payments and cash flow.

Lack of defined goals, organizational will, and alignment across stakeholders: that’s the reality for most companies when it comes to payment processing and cash flow management. But it doesn’t need to stay that way. You just need guidance on best-practice approaches, and that’s what you will get in this blog series.

Through 12 blogs published over the next few months, we will share our more than 40 years of experience in business payments and cash flow management to help you plan, develop, and execute a successful program. Whether you are just starting your payment and/or cash flow initiative, in the middle of implementing one, or growing your program, this blog series will help you along the way.

Why do you need better payments and cash flow solutions?

Payments: Payment processes have been largely unchanged for 30 years. Electronic payments continue to increase. So does fraud. Identifying better methods to simplify the process and shifting responsibilities (for example, “Know Your Customer” checking, supplier bank account maintenance, and so on) is key to mitigate risk, maintain or lower costs, and improve the supplier experience.

Cash flow: Improving cash flow creates enormous value that is hard to replicate in other functions or via sales or margin growth.

  • Every $1 billion in targeted spend extended by 15 days (payment terms) results in more than $40 million of cash flow improvement.

What about suppliers that prefer early payment?

  • There is opportunity to earn $1-$2 million per $1 billion in targeted spend in early-payment discounts, at high annual percentage rates (APRs), with low risk, as funds have already been committed via the procurement process.

Why now?

Your competitors are reading information just like this, and are either planning or executing their own strategies right now. Who can blame them? By paying earlier than necessary—and earlier than your peers—you’re effectively financing their business. Delaying efforts to optimize your payment process and timing of payment gives competitors an advantage.

The numbers shared above are valuable, but they are “only” metrics. What is truly important is what these metrics mean to your company. For example, could one extra day of reduced days payable outstanding (DPO) mean:

  • Opening a new store or manufacturing plant
  • Increasing research and development
  • Raising dividends for your shareholders

That’s how you align this initiative to your corporate strategy. Improving cash flow and discount savings results is a competitive advantage, providing less reliance on banks for credit, and creating shareholder value. Moving forward now unlocks the same value that would likely require the equivalent of 15%–25%+ revenue growth. What are you waiting for?

What makes this series different?

There are three main answers to this question:

  • Each blog will be brief and direct, leveraging the advice of two experts with decades of payments and cash flow experience.
  • There will be a clear call to action with defined next steps, to help you plan, execute, and succeed with your payment and/or cash flow initiative.
  • We will share additional resources upon request to help you achieve your desired outcome. This can be worksheets, project plans, presentations, and other tools that can help you implement a recommended call to action.

To kick off the series, our next post will examine the importance of organizational will and alignment in achieving success. You will learn about the steps you need to take for organizational alignment, and a call to action to make it happen.

To learn about best-practice approaches to managing cash and optimizing working capital, read this complimentary e-book from the specialized treasury consulting firm Strategic Treasurer: Leading Practices for Treasury in the Financial Supply Chain.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube


Alan Cohen

About Alan Cohen

Alan Cohen is VP Payments & Financing Strategy, SAP Ariba. Alan has over 20 years of payments and working capital experience as a practitioner, consultant, and banker. In his current role, he leads the payments and financing strategy for SAP Ariba to help clients achieve improved business outcomes. Previously, at Coca-Cola Enterprises, Alan led the procure-to-pay transformation that encompassed sourcing, procurement, and payables automation, and the company became one of the first to benefit from dynamic discounting. Alan holds a supply chain management degree from Arizona State University. In 2015, he was part of a team that won SAP’s Hasso Plattner Founders Award for an innovative approach to B2B payments. Alan lives in Atlanta with his wife and 2 daughters. He has served on the board of the Weinstein School since 2007 and actively participates in 2nd Helpings, a nonprofit to rescue and deliver surplus food.

Scott Pezza

About Scott Pezza

As part of SAP Ariba's Nework Value Organization Center of Excellence, Scott researches, compiles, and shares best-practice information to help customers get the most out of their investments. With a focus on the financial supply chain (invoice management, payments, discounting, and supply chain finance), his research helps inform strategic planning, performance measurement, and program execution. He has spent the past 15 years in the B2B technology space, in roles ranging from software development and support to research and consulting. Scott earned his BA in English and Philosophy from Clark University, his MBA from Boston University Graduate School of Management, and his JD from Boston University School of Law, where he served on the Executive Board of the Annual Review of Banking and Financial Law.

Lessons On GRC Platforms From The Forrester Wave: Keep Your Eyes On The Road

Bruce McCuaig

On February 15, Forrester released The Forrester Wave: Governance, Risk, and Compliance Platforms, Q1 2018. SAP continues to be rated a leader. But the market for governance, risk, and compliance (GRC) platforms is maturing, and at SAP, we believe the criteria for evaluating and selecting a vendor are evolving.

Should you choose the GRC vendor with the strongest current offering or the GRC vendor with the strongest strategy?

SAP occupies neither position. But is there a third and better option?

Keep your eyes on the road, not the vendor

Anyone buying a new car has access to consumer reports comparing and rating vehicle manufacturers and models against a variety of criteria. Vehicle entertainment systems are among the criteria rated.

However, for most vehicles made in the last 5-10 years, the supplier of the original equipment vehicle entertainment system is not displayed. In some cases, the supplier is not even mentioned in the owner’s manual. There may be a lesson here for customers looking for GRC solutions.

In automobiles, the entertainment system is an outcome, not a separate process. The outcome is evaluated, not the vendor. Customers evaluate and purchase the complete vehicle, not an individual component.

In my recent vehicle purchase, the UI of the OEM entertainment system was rated poorly in several analyst reports.

The real issue, however, isn’t whether the vehicle entertainment system’s UI is easy to learn. That may be a useful criterion for an in-home entertainment system. But the critical criterion for the UI in a vehicle entertainment system is this: Can you use it while keeping your eyes on the road? That is the essential outcome. Acoustic performance is a given. But can the system add value and synergies from other vehicle systems through native integration and continuous monitoring?

Does the entertainment system provide continuous monitoring of the vehicle navigation system, integration with the alarm system, compatibility with the mobile communication and vehicle maintenance systems? Does it provide audio alerts for maintenance and safety issues? Will it warn you of hazards ahead? Does it connect to the cloud?

Does it make the vehicle better?

The specific stand-alone features and UI of the entertainment system in your vehicle are important but secondary to the main goal. Integration of the system into the vehicle’s overall performance is the key criterion. Criteria that help you select a home entertainment system are not useful for evaluating a system for your car. And beyond the core capabilities that are important for your home entertainment system, are there other features that add value and influence the car-buying decision?

The path for ERP providers in the GRC market

The goal is balance and integration. GRC performance without strategy is short-lived. GRC strategy without strong capability is not effective. Growing both simultaneously is difficult, but in the end, is the only sustainable option.

I’d suggest that is exactly where an ERP provider should be positioned and exactly the trajectory to follow.

Eventually, GRC systems, like car entertainment systems, will be subsumed into the ERP landscape of your business. The value of the GRC systems will lie in their integration into the underlying ERP system and the cloud and their contribution to performance, not in their stand-alone virtues.

Download and read The Forrester Wave: Governance, Risk, and Compliance Platforms, Q1 2018.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube


Bruce McCuaig

About Bruce McCuaig

Bruce McCuaig is director Product Marketing at SAP GRC solutions. He is responsible for development and execution of the product marketing strategy for SAP Risk Management, SAP Audit Management and SAP solutions for three lines of defense. Bruce has extensive experience in industry as a finance professional, as a chief risk officer, and as a chief audit executive. He has written and spoken extensively on GRC topics and has worked with clients around the world implementing GRC solutions and technology.

The CFO’s Strategy Playbook: Seven Key Elements

Johannes Vogel

Part 1 of the 4-part CFO Strategy Playbook series

In my first blog, “Seven Finance Strategy Questions That Can Start the Paradigm Shift in the CFO Domain,” I discussed the reasons why many CFOs are starting to look at their finance team’s functional strategy. They revisit the way the finance teams are set up, what values and vision the team stands for, how to better serve the “customers” of the CFO team, and what changes are needed to implement this transformation.

In this article, I will explore which key content elements should be part of a functional finance strategy. The article can be used as a finance strategy playbook.

The elements provide a framework for the CFO team’s strategic organizational development. The focus of this post will be on strategic content and less on approach. In working with my customers’ finance teams, I do sense a growing interest in reviewing or fine-tuning their functional finance strategies, combined with a willingness toward more experimentation and smaller initiatives to try out innovation options. This frame of mind provides a good starting point for starting any finance transformation. The finance strategy creates the foundation and framework for transformation of the finance function.

Let’s explore what questions should be addressed in developing a company’s finance strategy:

  • How does the overall company strategy inform and impact the CFO team’s functional strategy?
  • Which megatrends and innovations are relevant for the company at large and for the finance team in particular?
  • What are the requirements of the business and of capital market stakeholders or investors, which the finance team needs to address in three to five years?
  • Which finance activities provide the most value and need to be strengthened—for example, decision support, capital allocation, active business partnering?
  • What should be the vision and mission for the finance team looking at the needs of the rest of the company and the finance team for the next three to five years?
  • Which are the topics of strategic focus for the CFO team, what are “strategic thrust areas”?
  • How does finance define its role relative to the business, and what operating model and governance are needed to sustain that role?
  • What people and cultural aspects should be considered, and what changes for job profiles are required?
  • What is the status quo of the finance team and what initiatives are needed?
  • How can we track and measure success?
  • What is the roadmap and implementation plan to operationalize and execute the strategy?

Finance strategy – a definition

On a more abstract level, functional finance strategies deal with external and internal requirements for the finance team. They consider external megatrends and innovation and make specific what the vision and mission should be and what strategic thrust areas are relevant. The strategic thrust areas make transparent what areas the finance team will create value for the business. They also address how business insight is generated and made available to the operative business. In addition, there is typically an organizational update of roles and responsibilities.

Ideally, the finance strategy also covers the operating model (team structures and in- and outsourcing decisions), at team governance, and at ways of working and required skill profiles and cultural aspects. Finally, the finance strategy should include actionable initiatives and tasks, showing what is needed to master the transformation and how the change will be implemented.

The seven finance strategy elements

Let’s assume that a CFO together with a team wants to start the definition of their functional finance strategy. The structure might look like this:

  1. The context
  1. The objectives
  1. Governance
    • Design principles
    • Strategy development process
  1. Status quo and requirements
    • Megatrends and innovation relevant for finance
    • Status quo of the CFO team (as-is state)
    • Requirements for the finance team
  1. Finance strategy dimensions*
    • Finance strategic thrust areas
    • Finance strategy vision and mission statements
    • Finance strategy details (by strategic thrust area)
    • Impact on the Finance operating model and organizational structure
    • Summary to-be state
  1. The finance strategy gap (as-is state vs. to-be state)
  1. Implementation road gap
    • Initiative summaries
    • Finance initiative portfolio and prioritization
    • Roadmap and implementation plan

In upcoming posts, I will delve into more detail.

*Please note that the various aspects of a company’s financing strategy, which frequently is also viewed as part of the overall finance strategy, warrant a separate article.

For more insight on financial leadership, see Why Complacency Is An Expensive Mindset For The CFO.


Johannes Vogel

About Johannes Vogel

Johannes Vogel is director of Finance & Regulatory for Finance Strategy & Digital CFO Services at BearingPoint. Johannes is an experienced professional in management consulting in the areas of finance strategy, finance process improvement, digital CFO services, and process management. He has worked with national and international clients in creating functional strategies for CFO teams preparing to support the business by creating value and business insights, while running a cost-efficient yet technologically modern organization. Other projects Johannes conducted with his clients included digital CFO assessments and benchmarking, as well as ERP and finance process implementations. Prior to consulting, Johannes worked in various finance functions of an Atlanta-based international media group. Johannes is a lecturer for a Master Program at the Universität der Künste in Berlin and likes to post on finance topics. In his time off, he enjoys playing guitar with friends and tries to spend as much time as possible with his sailing buddies somewhere off the coast of Croatia or elsewhere.

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.