How iFuture Robotics Is Revolutionizing Smart Logistics

Sunaina Patnaik

Shooting was an early passion for Rajesh Manpat. As a student, he won several medals for air rifle shooting, including at the National Games, where he equaled a national record. But technological innovation and entrepreneurship were passions as well.

No surprise then that his first business foray was a device he invented called Elite Scorer – a device that helps in shooting practice. It uses infrared laser technology to score targets in real-time and provides a digital display of shot locations, transmitting them over WiFi. This was the beginning of iFuture Systems, in 2008. Starting with just US$90, Manpat obtained a patent for the Elite Scorer and began marketing it in 2013. It has since acquired customers, including defense personnel and shooting enthusiasts, across 26 countries.

But Manpat aspired for more. Having understood the nuances of building a service business in industrial automation, he handed over the charge of iFuture Systems to his family and launched iFuture Robotics. iFuture Robotics is an original equipment manufacturer that builds innovative robots to address warehouse automation needs.

Autonomous robot

In 2015, after carefully observing warehouse operations, Manpat devised ARK, which stands for Autonomous Robot for the Known environment. This robot can navigate a warehouse it has been familiarized with – the “known environment” – independently, to stack inventory or retrieve it as required, thereby reducing manpower costs and offering unlimited opportunity for scaling. “It was our first structural robot,” says Manpat. “It automated a lot of backend processes of e-commerce companies.” ARK was a winner at Qualcomm’s “Design in India” challenge, leading to Qualcomm becoming an early investor in the project, providing advice on technical, strategic, and intellectual property-related aspects and giving Manpat access to all its products built for Internet of Things (IoT) applications.

The ARK robot will soon be available in three models of ascending size:

  • Mark1 can carry a payload of 20 kg and operate for six hours at a stretch
  • Mark2 can carry a payload of 50 kg and operate for 12 hours at a stretch
  • MINI can carry a payload of 500 kg and operate for eight hours at a stretch

Mark1 has been launched commercially, whereas the other two models will be available in 2018 along with another variant of ARK robot designed for factory floor automation. This variant can scan the environment and navigate its way to deliver parts and spares to different workers on the shop floor leading to lean and efficient manufacturing.

The customer base of iFuture Robotics is rapidly expanding in domestic and overseas markets. Among early users of Mark1 is one of India’s leading e-commerce retailers. “We have started a proof-of-concept with a large online retailer, distributing its products to its delivery vans,” says Manpat. iFuture Robotics has recently forayed into the European market by signing up a leading Swiss retailer in November 2017, and will deploy robots for sorting goods for retail stores in central warehouses. This makes iFuture Robotics the first Indian company to deploy robots abroad.

The robots are programmed to save both time and costs in innovative ways. “While loading the vans, for instance, the robots arrange the packages in such a way that no time is wasted when the vans reach the houses where deliveries are to be made,” says Manpat. “The process is structured to ensure that the least possible time is spent in the field. Our study shows 30%–40% increased efficiency in terms of both time and cost thanks to the robots because they can be used on multiple shifts in a single day, unlike human beings. Thus companies can run two or three shifts without any increase in manpower.”

Customization is key

Beyond the three basic robot models, Manpat takes pride in the customization that he, along with his team, has brought to the robots, enabling them to perform different tasks on the warehouse floor. “We have filed 10 patent applications in India and the United States,” he says. Manpat relies heavily on artificial intelligence (AI) tools. “The AI engine decides the best kind of robot for a job, the best product to pick at a particular point of time, the best path to take to reach each of the innumerable items in the warehouse, and more,” he adds. “AI technologies have been deployed to build models using large volumes of available data from which the system derives the best option.”

A unique feature of Manpat’s robots is the motion detection sensor they are equipped with, enabling them to maneuver their way even amid heavy traffic in any part of the warehouse. Such a safety measure is rare in mobile robotics, and Manpat acknowledges that he is still working on improvements. Sensor data is used to create better algorithms, leading to more reliable performance and data records captured in real time, which allows for preventive maintenance. For example, using the data helps predict the battery level of each robot at any stage so batteries can be changed in time to avoid disturbing warehouse operations. Manpat even hopes to use the data to capture real-time changes in the diameter of the robot’s wheels due to wear and tear and make adjustments so that the robot does not deviate from its path. This will lead to further savings on maintenance.

Manpat and his team have extensively deployed embedded hardware and computer-aided design in their robots. “We’ve used a lot of network and communications technology since we have multiple robots working in a single warehouse, which are connected to a single server,” he says. “A whole lot of different technologies have gone into building our solutions.”


Building the ARK was a challenge at every step. “The lack of local suppliers and vendors whose products met quality standards was a big hurdle in the early days,” says Manpat. “We had to import, which led to higher input costs and longer lead time. We had to plan production cycles well in advance. It was also difficult to raise capital from the market to acquire the hardware.” But he knows that to make his robots affordable, there is no alternative to procuring their parts locally. He also strives to find solutions that are scalable, so they can be applied in large numbers without incurring excessive costs.

Competition is another challenge – there are at least 10 companies manufacturing warehouse robots – but Manpat’s robots work out to be more economical because of their versatility. With labor costs low in India, a robot has to be able to perform the tasks of a number of workers to be worth a company’s investment. “I designed our products in such a way that each robot does more relevant tasks than other robots,” he says. “When my robots go around, they can handle six different SKUs at a time. In the United States or Europe, a robot handling a single SKU is enough to justify acquiring it, but not here.”

Since different Indian companies deploy different enterprise resource planning (ERP) software, another challenge for iFuture Robotics is creating robots whose application programming interface (API) is flexible enough to be compatible with all of them.

Another challenge is that building or acquiring a warehouse is itself a substantial investment, and companies are often reluctant to spend more on robots. Besides, most warehouses are designed for purely manual operations. “Our products suite is designed to work even in warehouses designed for manual operations only,” says Manpat. “But Indian companies need to factor in the future needs of automation and design warehouses accordingly.”

On the question of whether robots replacing people will lead to unemployment, Manpat remarked “Robots replace traditionally less value-added jobs with newer and more efficient jobs” He notes that robots have by no means increased unemployment in China, despite that country being the largest industrial robot manufacturing country in the world and the hub of cost-effective manufacturing solutions.

Looking ahead

In coming years, Manpat wants his company to grow beyond servicing only Indian needs. His team is working on technologies that will help make the robots function effectively across warehouses catering to different sectors, including opportunities in the West. “We want to build a large product portfolio addressing different segments of the logistics industry,” says Manpat. He has plans of moving beyond the warehouse. “We could develop underlying technologies outside the warehouse, perhaps creating autonomous driving capabilities in the logistics sector,” he adds. “We have a five-year product road map and are currently working on products we hope to launch in 2019. We aim to be global leaders in robotics for smart logistics by 2022.”

To learn more about the future evolution of the human-machine relationship, read the in-depth report Bring Your Robot to Work.


Sunaina Patnaik

About Sunaina Patnaik

Sunaina Patnaik writes features and editorials with focus on business and technology. She is also the author of the published book Warm Delinquencies.  

Meet Machine Learning, Your New Favorite Colleague

Kirsi Tarvainen

What if you had a colleague who would take care of all the dull, routine tasks without complaining? A colleague who lets you do interesting and challenging tasks, helps you solve them, then happily lets you take all the credit. A colleague who stays after office hours doing prep work for you so you will have a good start the next morning?

Meet machine learning, your new favorite colleague, who will dramatically change customer service both for customers and for customer service personnel.

Machine learning boosts customer service

Think about insurance companies. It’s estimated that 70%-80% of insurance claims are pretty straightforward, so this is an area where machine learning algorithms can find the right solution. For humans, it is hard to stay motivated if you have to repeatedly work through tons of claims for stolen bikes or broken mobile phones. But if you have machine learning as a colleague, you can let it solve the simple cases so you can focus on the more challenging ones – and you will have more time to carefully address each one since you don’t need to worry about the bikes and phones.

Or think about contact centers. For customer service agents, it is difficult to answer similar, repeated questions over and over again. What if you let machine learning field the routine questions while you take the more inspiring cases where customers want to speak with a live agent? A great example of this is Finland Post, which created a Christmas bot to help handle pre-Christmas peaks in customer service demands. Customers could chat with the bot to get answers to the easy, but frequent questions like, “What is the last day to send my packet to France,” which freed a lot of human resources to help customers with more complex queries.

Add more time to your day with machine learning

Machine learning is a colleague who can make you look smarter and perform better in your work. About 25% of contact center agent’s time is spent searching for information from different systems. That’s one-fourth of the workday! It is a total waste of time and shifts attention away from the customer interaction.

What if you had a chatbot that digs the information you need from all the data sources and conveniently provides it in a matter of seconds? You could fully concentrate on listening and understanding the customer, thereby providing first-class customer service.

Machine learning is a colleague we will all know very soon. It will help us get quicker and smarter – and it will help us transform our business in ways we can’t even imagine right now. But the key is to start imaging and experimenting now.

Technology is evolving; in the future almost anything will be possible, but we need to start envisioning how our customer service will look in the era of intelligent machines. There are no ready answers yet, as we are all creating the future together.

For inspiration, here is a great, short video on vision, future, and machine learning.

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


Kirsi Tarvainen

About Kirsi Tarvainen

Believing strongly that we all deserve good customer service, Kirsi has been working in customer service field for more than fifteen years. In her current role in SAP Hybris she works for SAP Hybris Service Solutions, helping companies worldwide improve their customer experience.

Mitigating The Brain Drain Of The Chemical Industry

John Harrison

Burdened by an aging workforce, the chemical industry is facing a serious brain drain – and this is creating a host of problems related to HR and compliance.

Manufacturing Automation reports that more than 20% of the chemical industry’s workforce will approach retirement in the next three to five years. If this aging-workforce problem is not resolved within this timeframe, chemical companies’ profitability will suffer significantly.

Per the American Chemical Council, this could mean more unplanned disruptions, more hiring and training costs, and more efforts to maintain safety. Increasing the need for expanding the workforce is shale gas utilization, which is changing the U.S. from a high-cost producer to one of the lowest-cost global producers. ACC president and CEO Cal Dooley says, “it’s vital that we be able to attract and retain a talented workforce that helps us to continue to drive economic expansion, innovation, and global competitiveness.”

To compensate, chemical companies are using more contractors and service providers to supplement the diminishing workforce, which increases compliance risks. Chemical companies that adopt digital solutions are well positioned to mitigate these risks, especially in the areas of product development, processes, and business modeling.

Digital transformation

Clearly, digital technology has vast implications for the chemical industry because it can help simplify complex processes. Today, core business elements are connecting to each other like never before. Platforms link products, equipment, and employees. Suppliers and customers connect to chemical firms. These connected systems offer new opportunities for collaboration. Processes improve at a faster rate. Productivity grows across the company.

A new frontier

Computing advances offer solutions not possible only a few years ago. Predictive maintenance schedules and quality control are now a reality. Supply-chain efficiency and market-driven pricing are easier to put in place. New profit centers are emerging.

In addition, cloud computing offers vast storage capacity at affordable rates. These structures broaden information sharing and simplify analysis and reporting.

The Internet of Things (IoT) is another factor to consider. The IoT connects products, equipment, and other devices together with sensors, software, and wireless technology. These devices detect, store, and report data on a massive scale. In essence, your “things” are now smart and connected.

Chemical work, redefined

These improvements allow meaningful changes to the way chemical firms work. In addition, digital solutions play a major role in solving the aging-workforce issue by reducing the workload and ensuring companies comply with regulations. For forward-looking companies, technology changes the nature of work in ways including:

Floor operations: Smart, connected machines improve accuracy and safety on the shop floor. Operations are more precise with the use of machines connected to database systems. Predictive systems control or support operational instructions. Self-learning systems interact with machinery and business processes.

Digital back offices: Many support functions are evolving or now digitized. Procurement and invoicing are no longer siloed activities. This new digital space integrates inventory management, accounting (e.g., invoice reconciliation), and human resources. Analytics tools take digitized data from processes in real time. Insights and reporting are immediately available. Employees are presented with more information and can make decisions faster. Technologies like machine learning become commercially viable options to augment people’s ability through data.

Accuracy, security, and compliance

To understand how digital growth relates to compliance, let’s look at one example where contractors and compliance meet. Chemical labeling systems often cause major headaches. Labeling systems vary in many ways. Differences in process and format can change by department and region.

These variables challenge consistency and control standards across a company, but enterprise-wide systems offer a solution. Such systems ensure consistency, compliance, and security. As guidelines change, there’s an urgent need to change and manage label data fast. With smart technology, firms can share data and changes with remote contractors and suppliers.

These systems allow all locations to manage changes and reduce downtime. Business processes scaled across the globe ensure consistency across the enterprise.

Version-control systems and documentation are important regulatory issues. Firms need systems to chart approvals, workflow, and revision history. These modern systems connect data from all sources.

Firms today share data with contractors worldwide. This integration of corporate and partner data requires accurate label printing. Central printing oversight offers global supply chain consistency. Manual and redundant label data entry disappears.

Labeling systems now can share business rules with contractors and suppliers. With leaner workforces, these systems reduce delays caused by global variances.

Differences in regulations are a challenge to compliance. Different image requirements, formats, and language complicate the issue. As a solution, single-source systems incorporate these variables centrally. Different format and printer standards are tracked at the firm level. Labor is free to work in other areas.

Shared data is another advantage. Automation allows data to be linked from different systems. Now safety and quality control info is tied to performance. Inventory and supply chain data links to orders and sales.

Contract employees can sign off on regulatory mandates in remote locations and affirm procedures. Smart devices prevent tampering and alert contractors of safety issues in real time.

Fleet of foot

Digital advances in fleet and stock management also improve compliance, even with fewer employees on the payroll. Systems and sensors can better match demand with supply. External market intelligence can be factored into the forecasting process.

Transportation systems become more agile. The ability to respond to customer needs increases and new markets emerge globally.

Fewer personnel costs

One other consideration is the use of basic mobile and social media tech. Leveraging these tools lets contractors and staff communicate directly. An engaged workforce can collaborate in new ways. Integrated platforms offer the right information at the right time. The right people see it. The right decisions are made.

Cloud-based content management systems streamline training. Enterprise compliance tools reduce risk and boost performance. Cloud-based talent management systems track rising stars. Hiring and training costs drop for new hires and contractors.

Improved safety

With an aging workforce and new employees coming onboard, accidents and other workplace incidents are expected to increase. The U.S. government estimates that by 2024, older workers will account for 25% of the labor market. The recent economic recession combined with longer life expectancy and changes to retirement and pension plans have increased the average retirement age to 67.

Aging – and the physical changes associated with it – “could potentially make a workplace injury into a much more serious injury or a potentially fatal injury,” says Ken Scott, an epidemiologist with the Denver Public Health Department.

Including workers in the digital corporation via wearable sensors is now a reality. Knowing when there has been an incident (e.g., a fall or an exposure to an environmental hazard) instantly allows for a faster response. With the additional data being gathered, predictive algorithms and machine learning can identify safety concerns and help a company be proactive in reducing safety risks and severity. Since older workers take longer to recover from an injury, speeding the response and reducing severity benefit both the worker and the company through reduced lost work time and related costs.

What it means

In short, the connected chemical company lets employees everywhere connect. This strengthens compliance through shared datasets and consistent processes.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value by reading Accelerating Digital Transformation in Chemicals. Explore how to bring Industry 4.0 insights into your business today by reading Industry 4.0: What’s Next?


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.

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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.