5 Business Processes Machine Learning Is Revolutionizing

Conor Donohoe

Part 2 in a 3-part series. Read Part 1 and Part 3.

The complexity facing business has never been greater. Information pours into databases at unprecedented speed and from sources unimaginable even just a few years ago.

Information on customer sentiment, employee performance, market movements, work-in-progress status, financial positions, project completion, and countless other sources is about to be dwarfed by the data generated by the Internet of Things (IoT). There’s gold in this data, but most businesses don’t have the tools to extract it.

In their 2016 Big Data Dilemma report, members of the U.K. House of Commons Science and Technology Committee wrote: “Despite data-driven companies being 10% more productive than those that do not operationalize their data, most companies estimate they are analyzing just 12% of their data.”

So, the question is not “should we operationalize our data?” Rather, it about “how can we reduce complexity to uncover the insight and advantage locked in the data?”

The machines have the answer

The answer is to leverage machine learning. It’s available now and allowing all functions to evolve processes, continuously innovate, and adjust planning and delivery so businesses can meet market changes and customer expectations.

So, how can you use the real-time context available to you via intelligent cloud ERP to deliver value? Here are five examples of how machine learning can do just that.

1. Finance: Accruals

Machine learning could help cut through the myriad factors finance teams consider when determining bonus accruals. Current headcount, salaries, and bonus plans are the starting point, and CFO teams try to forecast all key performance indicators in compensation plans. From there, they try to calculate the most accurate accrual (likely adding a buffer, to be safe). However, accuracy often ends up being a matter of luck more than anything else.

By applying machine learning to these calculations, predictive analytics could serve as a valuable tool to generate unbiased accrual figures, leaving finance teams more time during closing periods for other activities that require human review and judgment.

2. Procurement: Contract negotiation

Strategic procurement is a complex process involving a wide range of information and continuous supplier communication. Its costs go directly to the bottom line, so anything that improves efficiencies and reduces inventory will make a material difference.

Machine learning can mine historical data held in your system to predict contract lifecycles. It will forecast the point in time when a purchasing contract is expected to be consumed 100%. With this new innovation, you can make sure that you renegotiate purchasing contracts to suit actual needs – not take a best-guess approach – and adjust your business as required.

3. Sales: Project bidding

Assessing whether to bid for commercial projects means evaluating each opportunity individually based on the project characteristics – size, complexity, skills available, potential for overrun, and so on. To qualify this assessment, businesses depend on managers who have previously worked on similar projects. That can limit decisions to the individual perspective of those managers.

Machine learning could give sales and project teams the power to access decades worth of projects from around the world at the touch of a button. In leveraging these insights, teams could then develop a better-informed assessment, mapping the project against a much larger database of historical projects. Ultimately, machine learning can help firms decide whether to bid, what level to bid, and how to plan projects to ensure profitability.

4. Marketing: Getting closer to the customer

TDWI research indicates that marketing is often one of the first groups in a business to make use of more advanced technology to better understand customers. In marketing, machine learning is often used for customer segmentation and to provide customers with the “next best” offer.

A learning model can be trained on how customers with similar characteristics responded historically to an offer. Other use cases include up-selling, cross-selling, and operationalizing machine learning in recommendation engines.

5. Manufacturing: Increase production yields

Increasing production yields by the optimization of team, machine, supplier, and customer requirements is already happening, thanks to machine learning.

Using machine learning to connect the data from thousands of automated parameters all reporting in real time is a game changer. Ensuring that the quality of products meets ever-increasing customer expectations can increase sales as well as eliminate waste. At the same time, issues that could halt production can be anticipated and maintenance scheduled to fix problems before they arise.

The technology to rebuild processes across all functions already exists, so there’s every reason to leverage machine learning to your advantage today. Find out how SAP S/4HANA Cloud can help you.


Conor Donohoe

About Conor Donohoe

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

Techpreneurs Take Healthcare To The Masses

Devika Rao and Ankita Sahni

SigTuple and Forus Health are leveraging technology—cloud, artificial intelligence, machine learning—to reimagine the healthcare system. The two startups are creating devices and systems that bring down the cost of diagnosis and treatment and extend the reach of healthcare.

The Indian healthcare industry is a plethora of opportunity seeking solutions. Ours is a country of too many people and too few healthcare professionals. There are only 20,000 ophthalmologists, for instance, for India’s 1.3 billion residents. Most of the Indian population lives in remote, hard to access areas. Many of them are too poor to pay for health care. Only 17% of Indians have health insurance. Public hospitals may be free but they are overcrowded, poorly maintained and cash strapped to provide drugs and equipment. Private ones are prohibitively expensive for most.

Yet this is precisely a situation crying out for “frugal innovation”—creating devices and systems that bring down the cost of diagnosis and treatment and extend healthcare’s reach. Both the startups featured in this section use technology to do just that.

Indeed, both are the brainchildren of techies, not doctors—techies who have thought long and hard about India’s healthcare problems. K. Chandrasekhar, co-founder and CEO of Forus Health, spent 24 years in the semiconductor and embedded software business. A tech conference, which he attended while working with Philips revealed that the India has the highest population of blind in the world, but that 80 per cent of the blindness was preventable if detected in time. This proved to be his road to Damascus moment and led to the setting up of Forus Health. The founders of SigTuple, Rohit Kumar Pandey, Apurv Anand and Tathagato Rai Dastidar, met up while working at the American Express Big Data lab in Bangalore.

Forus health preprogrammed

What it is: Forus (“for us”) Health is a Bangalore-based technology company, set up in 2011, with a mission to eradicate preventable blindness. Forus manufactures “3nethra.” three allied devices that diagnose eye disorders. They are called 3nethra Classic, 3nethra Flora, and 3nethra Neo.

What it does: 3nethra Classic takes pictures of the retina and the cornea. Images of the retina assist the doctors in diagnosing diabetic retinopathy, glaucoma, retinal detachment, etc., while images of the cornea assist in diagnosis of cataract and other corneal disorders. 3nethra Flora carries out a process called fluorescein angiography by which a dye is injected in the eye to highlight abnormalities. 3nethra Neo detects the presence of a condition called Retinopathy of Prematurity (RoP) which is the detachment of the retina which can potentially lead to blindness in premature newborns. All three devices were developed entirely in-house. From common problems like Diabetic Retinopathy to corneal disorders and major eye complications, all can be detected using 3nethra Classic and Flora. 3nethra Neo provides invaluable service for newborns, as RoP, if left untreated, soon develops into permanent blindness.

Main advantages: 3nethra’s USP is that it can be operated by technicians with minimal training. It is portable and can be easily carried to remote areas by a technician, who can then transmit images of the affected eye back to the ophthalmologist. The devices are integrated with a unique cloud-hosted telemedicine application called Foruscare. “We must have done over 300,000 remote diagnoses using the cloud so far,” says Chandrasekhar. The portability, ruggedness and compact design of the 3nethra devices allow their use in all kinds of places and circumstances – optical store kiosks, mass screening programs in slums, and mobile eye clinics in remote, non-motorable areas. 3nethra is also much cheaper than its market alternatives. Before 3nethra Neo was developed, for instance, detecting RoP, which requires wide angle imaging of the eye, required a US-manufactured device which cost $100,000-120,000. 3nethra Neo is available at one-fourth the price. 3nethra Classic, the flagship device of Forus Health, diagnoses retina related problems, such as diabetic retinopathy or macular edema.

We must have done over 300,000 remote diagnosis using the cloud so far. The devices are integrated with a unique cloud-hosted telemedicine application called Foruscare. — K. Chandrasekhar, founder, and CEO, Forus Health

Impact: In just six years, 3nethra has touched the lives of around 2 million people across 26 countries and averted blindness in about 300,000. “It is operational in countries like Philippines, Mexico, Thailand and Brazil,” says Chandrasekhar. In India, only 40 per cent of Forus’s customers are from Tier 1 and 2 towns; the rest are equally divided between Tier 3 and 4 towns and villages.

Future: Forus will continue innovating in ophthalmology to create solutions that benefit the less-affluent sections. “The retina is considered a marker for many other physical problems too,” says Chandrasekhar. “I strongly believe that at some point in time it will be possible to identify the health of other organs of the body by studying the retina. We want to start working on that.”

Team and backers: Forus was set up by K. Chandrasekhar and Shyam Vasudev who together defined the products and set the technology roadmap. Currently Chandrasekhar is CEO and Vasudev, director. Other key members are Arun Krishnan (CTO, head of R&D and engineering) and Srikumar Venugopalan (CFO). Among the marquee investors who have supported Forus Health are Accel Partners, IDG Ventures, and The Asian Health Fund.

Takeaway: In just six years, 3nethra has touched the lives of around 2 million people across 26 countries and averted blindness in about 300,000. “It is operational in countries like Philippines, Mexico, Thailand and Brazil,” says Chandrasekhar.

With the data already stored, a trained pathologist has taught Shonit to read the blood smear images, break them down into red blood cells, white blood cells and platelets, recognize abnormalities and draw conclusions from them.

Nearly 60% of Forus’ customers are equally divided between Tier 3 and 4 towns and villages.

SigTuple Technologies

What it is: SigTuple, set up in 2015 and also headquartered in Bangalore, has built an artificial intelligence (AI) platform as a base for creating robots to carry out diagnostic tests. Its first solution is out, a robotic pathologist’s assistant called Shonit.

What it does: Diagnostic tests are presently carried out by technicians in pathology labs. SigTuple has built an AI platform called Manthana, that aims to increase the efficiency of medical experts in five areas– blood, urine and semen anal- ysis, taking of chest X-rays and eyescans.

Takeaway: In just six years, 3nethra has touched the lives of around 2 million people across 26 countries and averted blindness in about 300,000. “It is operational in countries like Philippines, Mexico, Thailand and Brazil,” says Chandrasekhar.

Manthana has three layers: Kurma, Mandara, and Vasuki. (The names are taken from Hindu mythology and relate to the well-known tale of the churning of the oceans by the gods and the demons; here, it is the ocean of data that is churned for insights.) Kurma is the back-end engine that enables training and execution of multiple AI models. Mandara is the reporting engine and Vasuki is the interface used to capture different kinds of data.

Using the three layers of Manthana, SigTuple has created Shonit, whose eyes are a smart digital scanner and brain is in the cloud. So far, Shonit has been confined only to blood testing. Teaming up with diagnostic labs and hospitals in Bangalore, and using smart microscopes, SigTuple founders have digitized the images of thousands of blood smear tests conducted in the labs and fed them into Shonit’s brain. SigTuple has a panel of 24 medical experts helping it to annotate the data accurately. With the data already stored, a trained pathologist has taught Shonit to read the blood smear images, break them down into red blood cells, white blood cells and platelets, recognize abnormalities, and draw conclusions from them.

Main advantages: The likes of Shonit can make up for the shortage of pathologists in the country. It has speed on its side – while a human being would take around 30 minutes over an abnormal blood sample, Shonit does the job in less than 10. While 35 human analysis always has an element of subjectivity, Shonit’s analysis is much more objective. This is because every blood diagnosis stored in Shonit’s brain requires the consent of at least three different experienced pathologists.

Blood smear images generated by Shonit, along with their interpretation, can reach pathologists anywhere across the globe in less than three minutes on the cloud platform, enabling them to make their own diagnoses as well, without ever having seen the original blood sample. And Shonit is forever ready to learn more – to give it more features, no new hardware is needed, only upgrades in the cloud. As the cloud gets better, so will Shonit. “Advancements in the field of cloud computing will improve the efficiency of the platform and in turn reduce the cost of the solution, especially on recurring usage,” says Pandey of SigTuple.

Shonit costs less than a tenth of the international products providing comparable services. In India, no product of this kind is being made anywhere.

Impact: It is early days yet, but Shonit has undergone three clinical validations and is in pilot mode in a few labs.

Our ultimate vision is to revolutionize healthcare delivery by bringing data-driven solutions that will completely digitize the healthcare industry and bridge the abysmal doctor-patient ratio by improving the efficiency of medical experts.

— Rohit Kumar Pandey, co-founder, and CEO, SigTuple Technologies

Future: Shonit will be followed by the robot Shrava, which will conduct urine analysis; Aadi, which will do the same with semen; Dhristi, which will focus on the eye, carrying out fundus photography and optical coherence tomography (OCT) scans; and Vaksha, which will do chest X-rays. While Shrava and Aadi are nearing the clinical validation stage, Dhristi is still in the product development stage, while research on Vaksha has yet to be completed.

Team and Backers: Rohit Kumar Pandey (co-founder and CEO), Apurv Anand (co-founder and CTO), Tathagato Rai Dastidar (co-founder and chief scientific officer). Investors include Accel Partners, IDG Ventures India, Endiya Partners, Pi Ventures, Venture Highway Axilor, Sachin and Binny Bansal of Flipkart and Amit Singhal, formerly with Uber and Google.

Takeaway: The likes of Shonit can make up for the shortage of pathologists in the country. It has speed on its side – while a human being would take around 30 minutes over an abnormal blood sample, Shonit does the job in less than 10.

Though Forus Health bestrides 26 countries and SigTuple has barely stepped out of the lab, both have vital features in common. They are both expanding the reach of healthcare, ensuring that health testing is available to – and affordable for – more and more people. Both also depend crucially on the cloud for their innovations. Indian pharma’s contribution to combating the global AIDS outbreak is well known. At the turn of the century, when antiretroviral drugs, all manufactured by global pharma behemoths, cost patients US$40 day, Cipla sold generic versions of the same drugs charging a dollar a day. It sidestepped the patent violation charge by using processes different from those of the multinationals and thereby saved millions of lives, espe-cially in Africa. Forus Health and SigTuple have shown how techpreneurs too can identify such opportunities in healthcare and devise low-cost solutions for them.

For more on healthcare technology, see A (Big) Data-Driven Approach To Healthcare Innovation.


Devika Rao

About Devika Rao

Devika Rao is an entrepreneur and writer based in Bangalore. She has over 15 years of work experience in research, marketing and communications.

Ankita Sahni

About Ankita Sahni

Ankita Sahni works on branded content and strategic initiatives at FactorDaily. Prior to this she worked with mainstream television channels as a content producer and anchor.

Making Business With A Chatbot

Drew Bates

Chatbots are not a new concept. They’re not even from this century. Alan Turing’s 1950 paper “Computing Machinery and Intelligence” laid the foundations of computer conversation via the Turing Test. Almost 30 years ago (1979, the same year Sir Tim Berners-Lee invented the World Wide Web), Moviefone began its interactive telephone service. The first real and widely used “chatterbot,” named Smarterchild, was powered on in 2000.

These fundamental and literal milestones along the chabot revolution make us question why it has taken so long. The answer is quite straightforward and grounded in reality. Chatbots, despite their on-paper potential to represent computer intelligence, have really just been an alternative way to search for information or complete simple, routine operations.

Voice and text commands add value by using their infinite patience to channel your path across a known set of choices with less deviation and more speed. These principles have been adapting quite favorably into customer service. Gartner predicts that by 2020, 85% of all customer interactions will be handled by chatbots.

Most companies say their top three customer questions represent the lion’s share of request volume. One of China’s largest food delivery companies, Eleme, receives 200,000 support requests between 11 a.m. and 2 p.m. on weekdays. Ninety percent are “My food hasn’t arrived yet” and “My food fell out of the container.” These questions are becoming robot-answered with some version of “We’re sorry. Nothing can be done right now. Here’s a discount code for your next order.”

It is no surprise, then, that the upward creep of chatbots has primarily been absorbed by this form of direct questions-and-answers. What we have recently reached could be bluntly described as a glorified FAQ around a tight decision tree. Now, thanks to rising network speeds, edge computing, and service integration, as well as humans’ near-readiness to talk to computers, the real revolution is beginning.

Enter the conversational user interface (UI). This is a more general term for natural-language based interaction with computers. Chatbots are a key part of the interface. The bigger picture includes text, voice, visual patterns (like cards and buttons), cross-platform activities, and a new way of getting complex things done.

The power here is to make interaction natural. In our early years, humans experience implicit cognitive development as they learn to communicate with other people. Everything else is explicitly learned; using a mouse, typing on a keyboard, browsing an OS, or putting multi-currency partial refunds into the booking system for an old sales order. Some are more natural than others. And many require specific training.

But what if this wasn’t the case? What if we could make our everyday actions happen naturally – without training, errors, irrelevance, or mashing buttons – in order to reach the desired outcome with the least immediate pain.

This is a significant dimension beyond looking up movie times or complaining about late food delivery. When it comes to getting things done, we are playing on a new dimension of business solutions.

8 things to keep in mind when building a conversational business UI

We’ve been spending time here in the SAP Innovation Lab understanding what conversational UI means to small and midsize businesses (SMBs) as they conduct day-to-day business. What we’ve found has been very revealing. With the right mindset comes the potential to redefine chat-based interactions. Here are some of the key points to keep in mind when building solutions designed to help businesses operate:

  1. Business vocabulary is already outside the natural sphere of conversation. Buzzword and industry-specific jargon are interspersed among seemingly general sentences, making them particularly difficult to extract. Common, abstract nouns like “opportunity” or “refund” often become concrete or proper nouns to the user. They refer to a specific type of business object and perhaps a specific instance of an object. Expect to define a dictionary of business actions and build well-defined intents across them to accurately detect sentences like, “I have an opportunity.”
  1. Once the user’s intent is understood, magic can happen. Typical business interfaces involve lists, forms, mandatory fields, default values, and dependencies. With a conversational interface, all of this must be maintained entirely behind the scenes. Assume that missing information can be requested as part of the rolling interaction. In this way, users are not overwhelmed with clutter and are instead getting precisely their intended job done.
  1. Businesses are complex. Trying to handle all possibilities in a single thread can be both difficult to implement and potentially dogmatic to use. In the Lab we are building a family of ERP bots that are experts on their own domain and act as concierges to their service; creating opportunities, handling customer records, or summarizing information. By openly talking to each other within the UI, they provide handover in a more natural-feeling way than typical interfaces. As humans, we are actually quite comfortable with talking with different people about different topics.
  1. In business, interactions are rarely discreet. A huge milestone in chatbot development is nested conversation threads. A prompt to the user such as: “Tell me the customer’s name” may very likely be answered with a question: “Who are my customers?” As tasks become complex and potentially lengthy, define a strategy for dealing with interruptions. Do this either by visibly parking the existing thread, handing it over to a new agent, or indicating that a delay has happened. Messages in the log like “cancel,” “get me out,” and “forget it” are common indicators that users are getting lost and want to start over.
  1. Success is a very specific outcome and failure is not an option. This is business. Users have to get their job done, and inputting a sales order has no margin of error. They don’t have consumer-style flexibility to try a different provider or come back later. Business interfaces are particularly downward-sticky. Once an action fails, trust is broken and spreadsheets are opened. Ensure that the interface provides commitment, confirmation, and feedback when performing actions like creating and modifying records.
  1. Extensibility and underlying complexity cannot be ignored. As a general rule, the conversational UI should be able to handle the same degree of customization as the standard UI, whether that means user-specific metadata or client-specific dependencies such as mandatory fields or tax calculations. It is okay to narrow the scope and push/refer to existing methods if they are too complex to handle right now. It is not okay to ignore them and essentially throttle back your business software to a rudimentary version for the sake of adding a chatbot.
  1. Start with zero UI. The most common denominator for integration is pure text. While platforms may build on top of this with UI elements and widgets, there is no guarantee they can be directly transposed between platforms. Thinking text-only also ensures the basics of conversation are covered. The final solution may never be 100% text/voice (we believe it is more likely that smart speakers will converge with displays before too long), but starting this way ensures the fundamentals of conversation are covered.
  1. Natural language is synonymous with intelligent interactions. If the interface is human-like, then the brain should be human-like, too. This is one of the biggest areas where conventional chatbots fail to satisfy because we have such high expectations going in. In business, this becomes a prerequisite of business intelligence, whether that be smart-handling stock replenishment, identifying churn-risk support tickets, or simply automatically applying discounts. It may mean rethinking the underlying business-logic in your solution.

If it works, conversational business UI will become a seamless part of day-to-day life without even acknowledging the Turing Test. Users will become closer to your products than ever, benefiting from being better understood on their own terms. This is a new realm of chatbots which, if done right, will trigger many other questions and solutions which might just redefine the way business happens.

For more on the positives and pitfalls of AI in business, see Teaching Machines Right from Wrong.


Drew Bates

About Drew Bates

Drew Bates is responsible for SMB innovation. He writes from SAP Labs in Shanghai China on the topic of lessons learned whilst being on the forefront of modern technology.

Diving Deep Into Digital Experiences

Kai Goerlich


Google Cardboard VR goggles cost US$8
By 2019, immersive solutions
will be adopted in 20% of enterprise businesses
By 2025, the market for immersive hardware and software technology could be $182 billion
In 2017, Lowe’s launched
Holoroom How To VR DIY clinics

Link to Sources

From Dipping a Toe to Fully Immersed

The first wave of virtual reality (VR) and augmented reality (AR) is here,

using smartphones, glasses, and goggles to place us in the middle of 360-degree digital environments or overlay digital artifacts on the physical world. Prototypes, pilot projects, and first movers have already emerged:

  • Guiding warehouse pickers, cargo loaders, and truck drivers with AR
  • Overlaying constantly updated blueprints, measurements, and other construction data on building sites in real time with AR
  • Building 3D machine prototypes in VR for virtual testing and maintenance planning
  • Exhibiting new appliances and fixtures in a VR mockup of the customer’s home
  • Teaching medicine with AR tools that overlay diagnostics and instructions on patients’ bodies

A Vast Sea of Possibilities

Immersive technologies leapt forward in spring 2017 with the introduction of three new products:

  • Nvidia’s Project Holodeck, which generates shared photorealistic VR environments
  • A cloud-based platform for industrial AR from Lenovo New Vision AR and Wikitude
  • A workspace and headset from Meta that lets users use their hands to interact with AR artifacts

The Truly Digital Workplace

New immersive experiences won’t simply be new tools for existing tasks. They promise to create entirely new ways of working.

VR avatars that look and sound like their owners will soon be able to meet in realistic virtual meeting spaces without requiring users to leave their desks or even their homes. With enough computing power and a smart-enough AI, we could soon let VR avatars act as our proxies while we’re doing other things—and (theoretically) do it well enough that no one can tell the difference.

We’ll need a way to signal when an avatar is being human driven in real time, when it’s on autopilot, and when it’s owned by a bot.

What Is Immersion?

A completely immersive experience that’s indistinguishable from real life is impossible given the current constraints on power, throughput, and battery life.

To make current digital experiences more convincing, we’ll need interactive sensors in objects and materials, more powerful infrastructure to create realistic images, and smarter interfaces to interpret and interact with data.

When everything around us is intelligent and interactive, every environment could have an AR overlay or VR presence, with use cases ranging from gaming to firefighting.

We could see a backlash touting the superiority of the unmediated physical world—but multisensory immersive experiences that we can navigate in 360-degree space will change what we consider “real.”

Download the executive brief Diving Deep Into Digital Experiences.

Read the full article Swimming in the Immersive Digital Experience.


Kai Goerlich

About Kai Goerlich

Kai Goerlich is the Chief Futurist at SAP Innovation Center network His specialties include Competitive Intelligence, Market Intelligence, Corporate Foresight, Trends, Futuring and ideation. Share your thoughts with Kai on Twitter @KaiGoe.heif Futu


Blockchain: Much Ado About Nothing? How Very Wrong!

Juergen Roehricht

Let me start with a quote from McKinsey, that in my view hits the nail right on the head:

“No matter what the context, there’s a strong possibility that blockchain will affect your business. The very big question is when.”

Now, in the industries that I cover in my role as general manager and innovation lead for travel and transportation/cargo, engineering, construction and operations, professional services, and media, I engage with many different digital leaders on a regular basis. We are having visionary conversations about the impact of digital technologies and digital transformation on business models and business processes and the way companies address them. Many topics are at different stages of the hype cycle, but the one that definitely stands out is blockchain as a new enabling technology in the enterprise space.

Just a few weeks ago, a customer said to me: “My board is all about blockchain, but I don’t get what the excitement is about – isn’t this just about Bitcoin and a cryptocurrency?”

I can totally understand his confusion. I’ve been talking to many blockchain experts who know that it will have a big impact on many industries and the related business communities. But even they are uncertain about the where, how, and when, and about the strategy on how to deal with it. The reason is that we often look at it from a technology point of view. This is a common mistake, as the starting point should be the business problem and the business issue or process that you want to solve or create.

In my many interactions with Torsten Zube, vice president and blockchain lead at the SAP Innovation Center Network (ICN) in Potsdam, Germany, he has made it very clear that it’s mandatory to “start by identifying the real business problem and then … figure out how blockchain can add value.” This is the right approach.

What we really need to do is provide guidance for our customers to enable them to bring this into the context of their business in order to understand and define valuable use cases for blockchain. We need to use design thinking or other creative strategies to identify the relevant fields for a particular company. We must work with our customers and review their processes and business models to determine which key blockchain aspects, such as provenance and trust, are crucial elements in their industry. This way, we can identify use cases in which blockchain will benefit their business and make their company more successful.

My highly regarded colleague Ulrich Scholl, who is responsible for externalizing the latest industry innovations, especially blockchain, in our SAP Industries organization, recently said: “These kinds of use cases are often not evident, as blockchain capabilities sometimes provide minor but crucial elements when used in combination with other enabling technologies such as IoT and machine learning.” In one recent and very interesting customer case from the autonomous province of South Tyrol, Italy, blockchain was one of various cloud platform services required to make this scenario happen.

How to identify “blockchainable” processes and business topics (value drivers)

To understand the true value and impact of blockchain, we need to keep in mind that a verified transaction can involve any kind of digital asset such as cryptocurrency, contracts, and records (for instance, assets can be tangible equipment or digital media). While blockchain can be used for many different scenarios, some don’t need blockchain technology because they could be handled by a simple ledger, managed and owned by the company, or have such a large volume of data that a distributed ledger cannot support it. Blockchain would not the right solution for these scenarios.

Here are some common factors that can help identify potential blockchain use cases:

  • Multiparty collaboration: Are many different parties, and not just one, involved in the process or scenario, but one party dominates everything? For example, a company with many parties in the ecosystem that are all connected to it but not in a network or more decentralized structure.
  • Process optimization: Will blockchain massively improve a process that today is performed manually, involves multiple parties, needs to be digitized, and is very cumbersome to manage or be part of?
  • Transparency and auditability: Is it important to offer each party transparency (e.g., on the origin, delivery, geolocation, and hand-overs) and auditable steps? (e.g., How can I be sure that the wine in my bottle really is from Bordeaux?)
  • Risk and fraud minimization: Does it help (or is there a need) to minimize risk and fraud for each party, or at least for most of them in the chain? (e.g., A company might want to know if its goods have suffered any shocks in transit or whether the predefined route was not followed.)

Connecting blockchain with the Internet of Things

This is where blockchain’s value can be increased and automated. Just think about a blockchain that is not just maintained or simply added by a human, but automatically acquires different signals from sensors, such as geolocation, temperature, shock, usage hours, alerts, etc. One that knows when a payment or any kind of money transfer has been made, a delivery has been received or arrived at its destination, or a digital asset has been downloaded from the Internet. The relevant automated actions or signals are then recorded in the distributed ledger/blockchain.

Of course, given the massive amount of data that is created by those sensors, automated signals, and data streams, it is imperative that only the very few pieces of data coming from a signal that are relevant for a specific business process or transaction be stored in a blockchain. By recording non-relevant data in a blockchain, we would soon hit data size and performance issues.

Ideas to ignite thinking in specific industries

  • The digital, “blockchained” physical asset (asset lifecycle management): No matter whether you build, use, or maintain an asset, such as a machine, a piece of equipment, a turbine, or a whole aircraft, a blockchain transaction (genesis block) can be created when the asset is created. The blockchain will contain all the contracts and information for the asset as a whole and its parts. In this scenario, an entry is made in the blockchain every time an asset is: sold; maintained by the producer or owner’s maintenance team; audited by a third-party auditor; has malfunctioning parts; sends or receives information from sensors; meets specific thresholds; has spare parts built in; requires a change to the purpose or the capability of the assets due to age or usage duration; receives (or doesn’t receive) payments; etc.
  • The delivery chain, bill of lading: In today’s world, shipping freight from A to B involves lots of manual steps. For example, a carrier receives a booking from a shipper or forwarder, confirms it, and, before the document cut-off time, receives the shipping instructions describing the content and how the master bill of lading should be created. The carrier creates the original bill of lading and hands it over to the ordering party (the current owner of the cargo). Today, that original paper-based bill of lading is required for the freight (the container) to be picked up at the destination (the port of discharge). Imagine if we could do this as a blockchain transaction and by forwarding a PDF by email. There would be one transaction at the beginning, when the shipping carrier creates the bill of lading. Then there would be look-ups, e.g., by the import and release processing clerk of the shipper at the port of discharge and the new owner of the cargo at the destination. Then another transaction could document that the container had been handed over.

The future

I personally believe in the massive transformative power of blockchain, even though we are just at the very beginning. This transformation will be achieved by looking at larger networks with many participants that all have a nearly equal part in a process. Today, many blockchain ideas still have a more centralistic approach, in which one company has a more prominent role than the (many) others and often is “managing” this blockchain/distributed ledger-supported process/approach.

But think about the delivery scenario today, where goods are shipped from one door or company to another door or company, across many parties in the delivery chain: from the shipper/producer via the third-party logistics service provider and/or freight forwarder; to the companies doing the actual transport, like vessels, trucks, aircraft, trains, cars, ferries, and so on; to the final destination/receiver. And all of this happens across many countries, many borders, many handovers, customs, etc., and involves a lot of paperwork, across all constituents.

“Blockchaining” this will be truly transformational. But it will need all constituents in the process or network to participate, even if they have different interests, and to agree on basic principles and an approach.

As Torsten Zube put it, I am not a “blockchain extremist” nor a denier that believes this is just a hype, but a realist open to embracing a new technology in order to change our processes for our collective benefit.

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Juergen Roehricht

About Juergen Roehricht

Juergen Roehricht is General Manager of Services Industries and Innovation Lead of the Middle and Eastern Europe region for SAP. The industries he covers include travel and transportation; professional services; media; and engineering, construction and operations. Besides managing the business in those segments, Juergen is focused on supporting innovation and digital transformation strategies of SAP customers. With more than 20 years of experience in IT, he stays up to date on the leading edge of innovation, pioneering and bringing new technologies to market and providing thought leadership. He has published several articles and books, including Collaborative Business and The Multi-Channel Company.