Digital Tools Can Close The Order Management Performance Gap

Nilly Essaides

Automation makes a big difference in the performance of the order-management process. Companies that manually input most of their orders take longer, spend more, and have more errors. The Hackett Group 2017 benchmarking research analysis shows that world-class companies (in the top quartile) automate 64% of their orders, or 1.5X more than typical companies. They complete the orders in a third of the time and have 50% fewer errors.

Source: The Hackett Group 2017 Benchmarking Study Analysis

For a time, there was nothing companies could do about orders that came in via phone, fax, or email. An agent had to input and validate them. But digital technologies are opening new connectivity paths; these new tools reduce the need for human intervention, dramatically speed up the process, and make it more cost-effective.

Three digital routes: a use case

One fast-growing, $2 billion distribution company gets 65% of its orders via its website. However, the rest come in via email or fax. Those orders used to take about 10 minutes each to process and almost always required a follow-up call to confirm an address or part number. To reduce manual labor, the company adopted three digital initiatives:

  1. It acquired an artificial-intelligence (AI)-enabled solution that reads incoming faxes and emails if they arrive in a standardized format. It then directly enters them into the order management system.
  2. It set up the capability to work directly with third-party portals like Ariba, where the company’s catalog is already stored.
  3. It develops APIs to enable more advanced customers to establish direct machine-to-machine connectivity.

The results have been dramatic. Using the first path for only a portion of its customers, the company cut cycle time on orders from ten to three minutes, leading to significant savings of 50 person-hours per day.

According an executive at the company, artificial intelligence is the biggest buzzword, and everyone is going digital. Between AI and APIs, this company is taking advantage of digital technologies to simplify, automate, and lower the cost of its order management process. That’s the way of the future.

Going forward

Using the latest in digital technology, order management can improve its performance in three ways:

  1. It can automate the process of emailed and faxed orders through AI-enhanced systems that read more standardized orders and directly enter them into the internal order-management systems.
  2. It can rely on robots to bridge gaps between multiple systems including external portals.
  3. It can build APIs to connect its systems with those of its customers for direct machine-to-machine interaction.

Using such approaches, finance can greatly reduce the cost and increase the accuracy of the order management process, closing the gap between world-class order management organizations and the peer or median group.

For more on how automation and AI is transforming business processes, see How Digitalization Drives Automation In Finance.


Nilly Essaides

About Nilly Essaides

Nilly Essaides is senior research director, Finance & EPM Advisory Practice at The Hackett Group. Nilly is a thought leader and frequent speaker and meeting facilitator at industry events, the author of multiple in-depth guides on financial planning & analysis topics, as well as monthly articles and numerous blogs. She was formerly director and practice lead of Financial Planning & Analysis at the Association for Financial Professionals, and managing director at the NeuGroup, where she co-led the company’s successful peer group business. Nilly also co-authored a book about knowledge management and how to transfer best practices with the American Productivity and Quality Center (APQC).

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.

Transforming Higher Education And Research With The Internet Of Things

Andy David

The vast number of connected things and the explosion of data generated by connected devices are changing the way businesses are run across sectors. Higher education and research is also being re-calibrated by possibilities offered by the Internet of Things (IoT), artificial intelligence (AI), and machine learning (ML).

What are some of the use cases? How can IoT transform education? Let’s take a look at the potential.

1. Immersive and connected educational spaces

Sophisticated facilities are crucial to attracting students and faculty. IoT and future-facing technologies can enable universities to build immersive educational spaces with mixed virtual-plus-reality environments for learning intelligently. By providing a sense of “being there,” AI, IoT, and ML can enrich both students’ learning experience and the faculty’s teaching experience, in part by detecting conditions when it makes sense to switch to different learning scenarios.

Imagine teaching a lesson on volcanoes while showing live, 3D information generated through sensors, live feeds, and other live data on Sakurajima in Japan, Mount Vesuvius in Italy, and Cotopaxi in Ecuador.

Now imagine if students in a classroom or at home could interact with other students, educators, and experts across the world studying the same topic. This type of information sharing can be of tremendous value for learning. 

2. Connected infrastructure: Safer, more-efficient use of space

With universities’ infrastructure connected to personal devices of educators, researchers, and students, every stakeholder can dynamically plan and more efficiently use university space. Students will know whether study pods are full and they should collaborate on projects online rather than meeting at the library. Researchers can determine in real time whether space in their favorite lab is available, or book a lab in sister resources if needed.

Entire buildings can be monitored and surveilled with empowered sensors, RFIDs, cameras, and connected devices to improve safety and security. If a building must be evacuated, the system will transmit the safest plan in real-time to anyone detected in the building.

3. Personalized learning

With smart things – such as cameras, health trackers, learning devices, and more – gathering information about students connected to an institution’s learning management system, universities can create personalized learning solutions with study plans and learning paths tailored to individual students.

Information can be automatically gathered about students and their use of learning resources, and AI and ML can be harnessed for the system to learn and adapt. For example, as a student demonstrates mastery by passing tests, the system can offer higher-level learning resources to the student. Conversely, supplementary materials can be provided to a student who is struggling to comprehend the material.

Smarter sensors can be harnessed to detect and determine changes, such as when students are distracted during learning, and generate alternate learning scenarios. Intelligent tutoring systems can also provide dynamic feedback about students’ current learning state and improve the ability of ML to learn and predict better.

4. Increased sustainability and cost savings

IoT is already making a considerable difference in reducing costs and improving productivity and safety in the energy sector. Remote monitoring of room utilization and equipment can generate analytics to help higher education and research institutions conserve valuable energy and save significant dollars. Facility managers can use energy data to assign equipment and rooms based on utilization to make sure resources are used in a sustainable manner.

Sophisticated sensors in research equipment and assets can trigger predictive and proactive service to decrease maintenance costs and downtime. Sensors can also collect data on access control, waste control, and other types of operations to highlight areas that need improvement – and ultimately save valuable manpower and countless hours.

5. AI-powered research

To be successful, researchers must collaborate across research projects while being acknowledged for their unique contributions. AI and ML can be harnessed to intelligently expand a researcher’s network to adjacent fields, connect across disciplines, or discover insights in previously unknown papers. It can also surface related problems where new research collaboration may be reciprocally beneficial.

An interesting example is Quartolio – an initiative launched by the MIT Global Entrepreneurship program working with the NYU StartEd Incubator, the New York Institute of Technology, and other universities. It claims to improve researchers’ workflow by automating research discovery and identifying connections across research on a productivity platform powered by AI. Quartolio also aggregates, curates, and facilitates research for student and professional researchers – learning how articles, datasets, and other media are connected so researchers can move one step closer to their next breakthrough.

Thrive into the future

To continue thriving into the future, universities and research institutions need to create a destination for brilliant minds.

IoT and future-facing technologies provide educational institutions and research powerhouses new possibilities to transform the very fabric of education and research. IoT and other innovations can strip away barriers in education such as geography, language, and economic status. The potential is simply too promising to be ignored.

Find out how other organizations including higher education institutions are driving innovating and transforming digitally. Download resources here.


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 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.