Unravel Complexity To Realise Massive Savings In Higher Education

Andy David

Complexity exists in education. And it costs.

A typical university supports dozens of email systems, multiple LMS platforms, BYOD, and perhaps even cloud environments, as well as a staggering number of university software applications. This places negative pressure on the university enterprise, especially in today’s digital era, that is compelling digital transformation. Complexity stifles innovation and hampers collaboration among faculty, staff, and students. It impedes agile response to changing conditions and new opportunities.

And the cost of complexity is significant. Bain & Co. estimates that University of California, Berkeley, can save US$25 million by cutting organizational redundancy[1]. And it approximates that University of North Carolina, Chapel Hill can save US$12 million by reducing organizational complexity[2].

Clearly, cutting out complexity is a pressing issue. It significantly limits an institution’s ability to benefit from the increasingly digital world. But how can universities map the path to a simplified world?

Simplification: Starting where the savings are

We often talk about how simplification should begin: First, by reimagining the institution’s academic business models; second, the institution’s business processes; and third, the way the workforce works. But it is useful to bring this discussion to where institutions can save the most as it often piques the most interest.

In my earlier blog, “The Digital Future of Higher Education: Part II”, I shared how massive savings can be realized with a digitization strategy on the enterprise side of the institution, especially on the finance side. And I shared an example from University of Colorado saving hundreds of thousands by just automating expense management from faculty and others associated with faculty.

Now, I want to highlight another area of focus that can amount to significant cost savings: strategic sourcing and procurement. Sure, it might not be the sexiest subject. But it certainly is one where massive savings can be reaped. Every year, universities and other higher education and research institutions spend a colossal sum purchasing goods and services. An illustration: In the United Kingdom higher education sector, this amounts to approximately £10 billion annually[3]. The ability to discipline this area by digitizing the supplier experience can result not merely in impressive budgetary savings, but also manpower cutback—allowing universities to redirect precious human capital to where it is required the most.

Ohio State University Medical Center: US$1M savings with a single new supplier

Let’s take a look at a fine example demonstrated by The Ohio State University Medical Center—an example I briefly touched on before. The OSU College of Medicine is consistently ranked among the top 40 medical schools in the United States, and among the top 15 public universities. Its medical center is home to more than 20 research centers and institutes, as well as 25 core research laboratories that promote collaboration among experts from virtually all departments, divisions, and branch campuses of the OSU.

The organization wanted to maximize cost savings and time in sourcing so that it can remain focused on its core mission of improving lives through innovation in research, education and patient care. It also wanted to improve sourcing visibility and validation, and centralize all contract management to ensure contract compliance.

Prior to implementing a digital solution, sourcing projects lacked collaboration owing to lack of visibility. Many elements of sourcing and contract management were manual—such as the manual tracking of changes contract terms and conditions, or contract expirations—which was tedious and risky. There was no central repository for contracts between OSU Medical Center and the university which impeded cooperation. To deal with the challenges, the organization decided to choose a software-as-a-service (SaaS) solution to avoid spending money or IT resources on integration. It implemented Ariba solutions to standardize and streamline its strategic sourcing process.

The result? OSU Medical Center gained improved visibility, validation, and cost savings. A quick win was the identification of a new supplier through the Ariba network, resulting in US$1 million in savings. And that’s just savings attained through a single new supplier! The organization also achieved a 60% time reduction in quantifying sourcing event results and managing project pipeline—time that can be redirected to its advancing core mission. Furthermore, the implementation enabled increased collaboration and visibility between the medical center and university at large. And the best thing about its investment is that it gets automatic upgrades and enhancements as a SaaS solutions.

Start simple. Start now.

Universities need to tackle complexity to move forward into the future. This can start simple with a simple step in tackling digitization on its enterprise side. The vast savings achieved can go a long way to help your institution reimagine its future, and the effects of simplification on the organization can be transformative.

Still wondering why 97 of the top 100 world’s leading universities and research institutions run SAP? Download these resources to find out more.


[1] http://www.bain.com/publications/articles/financially-sustainable-university.aspx

[2] http://universityrelations.unc.edu/budget/documents/2009/UNC%20Efficiency%20and%20Effectiveness%20Options_FINAL.pdf

[3] https://www.theguardian.com/higher-education-network/blog/2013/feb/18/university-procurement-consortia-savings-investment


Andy David

About Andy David

Andy David is the Director of Healthcare, Life Sciences, and Postal Industry for the Asia-Pacific and Japan region at SAP. He has more than 14 years of professional experience in IT applications to government, healthcare, and manufacturing industries. Andy has been working with public sector organizations for over 12 years and plays a pivotal role in determining the strategy across the region, covering market analysis, business development, customer reference, and building up the SAP brand in the public sector.

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.

What Aioli Can Teach You About Taking Your Business Global

John Ward

There’s no doubt that going global can offer a company significant business opportunities.

A piece in BBC News puts it simply: “Despite the difficulties, international expansion is often the only way for firms to increase sales.” As the article goes on to explain, other countries can provide a company with fresh customers, less competition, and an elevated demand for its product or service – especially when domestic growth is beginning to flatline.

But any business with globalization on its mind needs to put together a good go-to-market strategy and make the right preparations to be successful.

Grupo Choví – a Spanish producer of traditional sauces and other condiments – wants to spread these regional favorites quickly around the world.

And they have a plan to make it happen.

Ancient recipe leads to domestic success

“It all started back in 1950 with our aioli sauce,” explains Ximo Marco, IT director at CHOVÍ S.L.U. (Grupo Choví). “Now Choví is the taste of Spain.”

Aioli is ancient stuff. Choví’s classic aioli is made with garlic, oil, eggs, lemon, and salt. According to the company’s website, Mediterranean aioli recipes date back to the early 10th century. And Spain inherited the sauce from the Romans – who learned how to make it from the Egyptians!

Today the company makes a number of products, including mayonnaise and salad dressing. Grupo Choví’s domestic success in Spain has been impressive. It produces more than 18 million kilograms of condiments annually and has seen 15% sales growth year over year.

But the company’s current vision extends far beyond the Iberian Peninsula.

Now it’s aioli time for the world

“We want to bring that taste to people all over the world – and we need the right technology to do it,” Marco continues. “We’re taking this tradition and making it Choví 4.0.”

To support its plans for major global expansion, Grupo Choví recently replaced its outdated systems with new ERP software and revamped many of its manual paper-based processes. The goal was to increase control and gain greater visibility company-wide, helping the company adapt quickly to market changes around the world. Unified and centralized data help ensure accuracy in cost and revenue numbers. Plus, instant anytime, anywhere access to that data means sales teams no longer need to contact IT to run reports on customers and accounts.

“It gives us the real-time data access and agility we need to move into global markets,” Marco says.

Some final advice

Marco’s “high-tech” comment about Choví 4.0 reflects a fascinating mindset. This decades-old company makes traditional family foods, but he describes the business as if it were a Silicon Valley newcomer. Perhaps at the heart of the Grupo Choví story is a bit of business advice that applies to even well-established companies.

Want to expand globally? Think and run like a startup.

Why not join Grupo Choví at the upcoming Intelligent ERP Industry Virtual Summit? You will get insights, ideas, and inspiration from companies and experts in your industry. Click here to learn more and register.

This article originally appeared on Forbes SAPVoice.



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.



Why Blockchain Is Crucial For FP&A: Part 1

Brian Kalish

Part 17 in the Dynamic Planning Series

In these times of almost continuous technological change, there is a natural tendency to be suspect of whatever is being heralded as the “flavor of the month” or the “next best bet.” In early 2017, I was graciously given the opportunity to speak on what I believed to be the technologies that were transforming finance and specifically, the FP&A function. The talk I ended up giving covered five areas:

  • Advanced analytics and forecasting
  • Robotic process automation
  • Cloud and Software-as-a-Service
  • Artificial intelligence
  • Blockchain

While all these topics deserve further investigation, for this article, I want to focus on blockchain. Part of the reason for diving deeper into blockchain is the lack of understanding of what it actually is and the great amount of time people in the finance function are currently spending talking about it. This has greatly changed in the past nine months.

Last March, while hosting an FP&A Roundtable in Boston, I ask a group of 25 senior FP&A professionals how familiar they were with the concept of blockchain. Out of this august group, there was only one participant who felt truly comfortable with the concept. I still get asked on a regular basis, all over the world, “Blockchain. What is it?”

Blockchain: What is it?

By allowing digital information to be distributed but not copied, blockchain technology has created the spine of a new type of Internet. Picture a spreadsheet that is duplicated thousands of times across a network of computers. Now imagine that this network is designed to regularly update this spreadsheet, and you have a basic understanding of blockchain.

Information held on a blockchain exists as a shared and continually reconciled database. This is a way of using the network that has obvious benefits. The blockchain database isn’t stored in any single location, meaning the records it keeps are truly transparent and easily verifiable. No centralized version of this information exists for someone to corrupt. Hosted by many computers simultaneously, its data is accessible to any authorized user.

Blockchain technology is like the Internet in that it has a built-in robustness. By storing blocks of information that are identical across its network, the blockchain 1) cannot be controlled by any single entity and 2) has no single point of failure. The Internet itself has proven to be durable for almost 30 years. It’s a track record that bodes well for blockchain technology as it continues to be developed.

A self-auditing ecosystem

The blockchain network lives in a state of consensus, one that automatically checks in with itself on a regular basis. A kind of self-auditing ecosystem of a digital value, the network reconciles every transaction that happens at regular intervals. Each group of these transactions is referred to as a “block.” Two important properties result from this:

Transparency. Data is embedded within the network as a whole, and by definition, is available to all authorized users.

Incorruptibility. Altering any unit of information on the blockchain would mean using a huge amount of computing power to override the entire network. In theory, it is possible; however, in practice, it’s unlikely to happen.

A decentralized technology

By design, the blockchain is a decentralized technology, so anything that happens on it is a function of the network as a whole. Some important implications stem from this. By creating a new way to verify transactions, aspects of traditional commerce may become unnecessary.

Today’s Internet has security problems that are familiar to everyone. However, by storing data across its network, the blockchain eliminates the risks that come with data held centrally. There are no centralized points of vulnerability that can be exploited. In addition, while we all currently rely on the “username/password” system to protect our identity and assets online, blockchain security methods use encryption technology.

I hope this little tutorial helps describe what blockchain is. In my next article, I’ll discuss the value of blockchain to the FP&A profession.

For more on this topic, read the two-part “Blockchain and the CFO” series and “When Blockchain Fulfills CFOs’ Paperless Vision.”

2018 will be a busy year with FP&A Roundtables in St. Louis, Charlotte, Atlanta, San Diego, Las Vegas, London, Boston, Minneapolis, DFW, San Francisco, Hong Kong, Jeddah, and many other locations around the world to support the global FP&A community.

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Brian Kalish

About Brian Kalish

Brian Kalish is founder and principal at Kalish Consulting. As a public speaker and writer addressing many of the most topical issues facing treasury and FP&A professionals today, he is passionately committed to building and connecting the global FP&A community. He hosts FP&A Roundtable meetings in North America, Europe, Asia, and South America. Brian is former executive director of the global FP&A Practice at AFP. He has over 20 years experience in finance, FP&A, treasury, and investor relations. Before joining AFP, he held a number of treasury and finance positions with the FHLB, Washington Mutual/JP Morgan, NRUCFC, Fifth Third Bank, and Fannie Mae. Brian attended Georgia Tech in Atlanta, GA for his undergraduate studies and the Pamplin College of Business at Virginia Tech for his graduate work. In 2014, Brian was awarded the Global Certified Corporate FP&A Professional designation.