How Can Monitoring Connected Assets On The Grid Improve Uptime?

James McClelland

In 2003, a power outage caused by out-of-phase system conditions impacted over 50 million people in the northeastern United States. Overgrown trees impacting power lines began a chain reaction of failures that led to the huge outage. Some customers in areas including upstate New York took up to three months to have power returned to their homes. Better monitoring of connected assets could be key to avoiding situations like this in the future.

How can monitoring connected assets on the grid improve uptime?

One strong concern for electrical utilities is the condition and longevity of transformers. Other than overhead-line failures, transformers have the highest rate of major failure out of all grid components. During peak loads, it’s very common for transformers to be overloaded.

The Onitsha Electricity Distribution Network was studied to determine transformer failure rates. It was found that following insulation failures, overloading was the second-highest cause of failure. Overloads caused 22.5% of all transformer failures in its grid.

Overloads can cause the transformer to fail earlier than would be otherwise expected. When the transformer fails unexpectedly, it can create serious issues for your utility. You could simply replace your transformers prior to failure. Unfortunately, that’s very expensive. Being able to figure out exactly when the transformer is most likely to fail allows you to get the longest use out of it without leaving customers without power. But how do you predict transformer failures?

The level of analytics needed to predict transformer failures was virtually impossible when the northeast blackout occurred. Today’s digitization technology makes it possible right now. But what can it really do for your power utility, production, or distribution organization? Let’s take a look at one scenario.

A study of metropolitan transformer overloads and failures

A major metropolitan area is having significant issues with transformer failures. The budget does not allow it to replace transformers without solid evidence that the transformer will fail shortly. Predicting end-of-life issues with a transformer is very difficult. With digitization on the horizon, the utility decided to see what solutions might be available to solve the problem.

The utility installed a solid digital core and analytics system. Internet of Things (IoT) technology allows load levels to be measured once a minute on over 100 transformers scattered across the metropolitan region. This enables the utility to collect over 200 million data points over the course of the year. A few years ago, analyzing that sheer volume of data would have been virtually impossible, especially for a utility.

With that level of detail, the utility can observe the conditions of the monitored transformers. They can see which transformers had what level of load over particular time periods – a single day, a week, a month, or the entire year. Represented by pinpoints, the transformers show different shades to represent their highest level of load. This gives the utility a good overall view of the condition of its connected assets.

The insights don’t stop there. The utility can limit the results to show only the transformers that remain in excess of 100% of their engineered capacity. The search can be completed in a quarter of a second. That’s a fraction of the time that was required for that level of analysis just a few years ago. They can also limit results to show transformers with an average load over 100%, which puts the transformer in priority for replacement.

Beyond that, the analytics available from the upgrade allow the utility to discern additional information on the transformers. Statistics are available on the transformer manufacturer and model to anticipate longevity and failure rates. With this data, the analytics program can calculate how much lifespan the transformer loses due to being overloaded. By having this information available, the utility can determine the expected remaining useful lifespan of each transformer.

With a few clicks of a button, it can change the view of each transformer to heat maps. This allows the utility to see where it has problem areas. Having this information allows the utility to plan for maintenance and replacements. It can stay ahead of unexpected failures and related outages. This allows it to maximize their budget to areas where it is most needed.

Having this capability drastically changed how the utility operates. Its customer service department sees significantly fewer complaints because of this change. Its crews can act in a proactive manner, staying ahead of many repair or replacement issues with the transformers. The utility can better spend its budget because there is a better expectation of what is most likely to happen in the future.

This scenario is not only completely possible with today’s technology, it’s already happened. SAP created a proof of concept test to help a utility in the greater New York City metropolitan area get a grip on its transformer loads and failure potential. SAP’s S/4HANA is a leading digital core technology that makes it possible. We can help your organization reduce your downtime through better analytics. Please feel free to contact us for more information.

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 Utilities.


IoT And The Evolution From Forecasting To Accurate Consumption Planning

E.J. Kenney

In a typical consumer products supply chain, planning is a collaborative activity that can involve everyone from raw materials suppliers to manufacturers to distributors. The one group that is not directly engaged, however, are the end consumers who will actually purchase and use the products.

With the advent of IoT, that’s about to change drastically. IoT facilitates access to usage and buying data, giving consumer products companies the ability to evolve from traditional forecasting to planning based on actual consumption.

The Marketing Science Institute quoted Intel Corporation’s Peter Levin: “With cheap sensors, democratized analytics, and new platform tools, we are moving from a ‘models’ to a ‘measures’ world.” This is a good way to see exactly how things are changing. Information will be readily available for manufacturers and suppliers to use to make key decisions regarding products. No longer are we focused on forecasting, but instead on real-time consumption.

IoT is already a part of many supply chains

Inventory and warehouse management, supply chain upper management, and even fleet management are already embracing the use of IoT technology within their operations. McKinsey reports that IoT will have an incredible economic impact of $2.7 to $6.2 trillion by the year 2025. Imagine the amount of data and insight that will come from the projected 50 billion connected devices by the year 2020. Being able to make better supply chain decisions based on analyzing massive quantities of IoT-based data will be a true differentiator. All of this is made possible because IoT creates a more direct connection between production and consumption.

What real-time data means for consumption

Currently, many consumer products manufacturers and distributors are using antiquated forecasting methods to create their “game plan” for the upcoming buying season. Forecasting has long been thought of as the only option. Projecting consumer usage and buying of a product is a highly subjective activity. When companies get forecasting wrong, it creates huge problems.

Forecasting is critical to brands that want to sell as much of a product as possible without oversaturating the market. Recently, the Puma X Sophia Webster Sneakers, a unique look that many consider a “must-have” item, sold out in just one day. With five styles available across the market, the company could have profited more with better production and planning of the product. Not only did this limit sales, but it also impacted the company’s reputation with consumers, encouraging them to turn to competitors’ products that were readily available in stock.

Another example is Kylie Cosmetics, a line of makeup products from Kylie Kardashian-Jenner. The product line sold out within three hours, much like her sister Kim Kardashian’s line of clothing did. With better predicting and analytics, the brand could likely have seen a better initial launch.

The advent of IoT changes all that. It empowers companies by providing a way to capture real data, in real time, directly from consumers. You know when products are being used, you know the demographics of who is using them, and you know the rate of use. All of this translates into a highly effective way of planning.

The question is, then, how is the data captured? Consider, for example, a shoe. A consumer buys a connected sneaker. That sneaker collects data about usage, wear and tear, and overall performance. That information can then be sent back to the company. The company can use this information to make adjustments to its product line—perhaps to boost overall quality—or it can send information about new models to encourage consumers to come back for a replacement.

The company understands who is using the product, where it is being used, and how often it is being used. All of this translates into highly usable data that can transform the company’s further production, new product deployment, and even design.

How can consumption planning improve business processes?

Today’s consumers are digital. They connect through phones, smart homes, and dozens of apps. Consumers benefit from digital services designed to work seamlessly with products that have IoT sensor technology built in.

For consumer products companies, there are substantial benefits in terms of better supply chain visibility. Real-time data gathering and analysis becomes the norm. In this way, advanced supply chains are able to manage product replenishment based on actual consumption rather than forecasts.

With IoT-driven planning, CP companies can operate in a more agile fashion, redirecting products to another location quickly based on actual demand. They know when to produce more products and when to send more – and even when to replenish them directly to a consumer’s home.

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


E.J. Kenney

About E.J. Kenney

E.J. Kenney is the Senior Vice President of Consumer Products Industry Business Unit at SAP. His responsibilities include managing industry business units, business planning and strategy, go to market and investment portfolio for Consumer Products and Wholesale distribution.

Machine Learning: The Hot Technology Keeping Products Cool

Lori Mitchell-Keller

With retail undergoing a massive transformation, machine learning has become an innovative asset by changing the way organizations deliver customer experiences.

The impacts of artificial intelligence and machine learning on the food and beverage industry beg the question: Could humans be replaced?

As companies adopt advanced technologies that are easily implemented and show worthwhile return on investment, there is a massive opportunity to help customers take and complete this digital journey – not just to innovate, but to scale as a digital business. With artificial intelligence (AI) and machine learning platforms, organizations’ front and back-office processes are evolving.

Many food and beverage companies are using AI, machine learning, and automation to revolutionize critical aspects of their businesses. Food and beverage retailers that stand out are leveraging a suite of machine-learning platforms to guarantee products are maintained properly. With this technology, retailers can ensure their beverages, refrigerated food, and frozen food products are always stored at the right temperature and are safe for consumption.

In addition, the technology allows retailers to accurately track inventory, monitor maintenance needs, and reduce losses due to spoilage or theft. Here’s why there’s never been a better time to adopt machine learning and achieve retail success:

Tracking every move

The increasingly complex supply networks and the need to coordinate logistics flows across multiple suppliers, present major challenges across the supply chain when tracking inventory. Slow reactions within the supply chain cause inefficient use of fleet, wasting both time and money, and failing customer expectations for on-time delivery.

Additionally, negligence during transportation can also result in damaged goods leading to more unsatisfied customers. These challenges prompt the need to monitor delivery as well as shipment status – and the involved connected logistics equipment (like trucks) – to have a real-time view of the current location of products. By compares the planned and current logistic flows, retailers can quickly react to unexpected conditions and deviation from plans.

Furthermore, to guarantee the on-time availability of components for the efficient processing of orders, retail companies need to implement technologies that control and monitor their complete supply and delivery chain. With an IoT-optimized tracking system, production, and delivery logistics are improved, inventory levels are increased, logistics costs are reduced, and on-time delivery rate is enhanced. This makes it possible for transportation management companies to increase customer satisfaction as products are received on-time, and in working condition.

If it’s broke, fix it

Cold chain monitoring, especially for companies with large global operations, represents both significant investment and maintenance challenges. Refrigerators, coolers, and freezers all see frequent use by restaurants and retailers, and while they are built to withstand wear and tear, asset failure not only comes with the cost to repair the appliances, but the cost of incurred losses from spoilage. Machine learning adapts the data provided through connected devices to practical applications. In this way, retailers can monitor and adjust average ambient temperatures and temperature variations from opening and closing of doors, ensuring consumers receive satisfactory products.

Additionally, today’s IoT connected appliances generate massive volumes of data from sensors and present a greater opportunity for continuous machine learning to turn this data into value-creating assets. With this data, retailers can establish a plan for predictive maintenance in advance of asset failure. By maximizing equipment uptime and ensuring consistent temperatures within pre-set tolerances, machine learning technology makes it possible for retailers to deliver the highest quality and full shelf-life products to their customers.

Stop shop loss

Retail companies selling beverages, refrigerated products, and frozen foods need to manage freezers, coolers, and other refrigeration units in their stores. These cooling units do present a significant ongoing investment in assets, maintenance, and inventory. These appliances need to be monitored to minimize or eliminate lost revenue due to spoilage or product expiration.

Machine learning, along with AI platforms, have also helped food and beverage retailers automate inventory management. By initiating machine-learning processes where employees take photos of store shelves, sensors within the platforms can identify which items are missing or incorrectly displayed. With this technology, store managers and warehouses can automatically be notified to organize or restock the shelves properly, ensuring that customer demand is met. Shelf management is an important part of reducing product loss, whether it’s from vandalism, damage, or theft.

In the food and beverage industry, retailers, producers, and restaurants are rapidly changing their business strategies to incorporate new, innovative technology to stay ahead of competition, meet consumer demands, and provide an enhanced experience.

Through the implementation of AI and machine learning technologies that provide a 360-degree view of both the consumer and their everyday operations, retailers can transform business processes and directly improve the customer journey. By investing in platforms that produce beneficial insights to facilitate this process, while also optimizing production and inventory, retailers within the food and beverage industry will see significant ROI and increase customer engagement.

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

This blog was originally posted on Chain Store Age, November 2017.


Lori Mitchell-Keller

About Lori Mitchell-Keller

Lori Mitchell-Keller is the Executive Vice President and Global General Manager Consumer Industries at SAP. She leads the Retail, Wholesale Distribution, Consumer Products, and Life Sciences Industries with a strong focus on helping our customers transform their business and derive value while getting closer to their customers.

The Blockchain Solution

By Gil Perez, Tom Raftery, Hans Thalbauer, Dan Wellers, and Fawn Fitter

In 2013, several UK supermarket chains discovered that products they were selling as beef were actually made at least partly—and in some cases, entirely—from horsemeat. The resulting uproar led to a series of product recalls, prompted stricter food testing, and spurred the European food industry to take a closer look at how unlabeled or mislabeled ingredients were finding their way into the food chain.

By 2020, a scandal like this will be eminently preventable.

The separation between bovine and equine will become immutable with Internet of Things (IoT) sensors, which will track the provenance and identity of every animal from stall to store, adding the data to a blockchain that anyone can check but no one can alter.

Food processing companies will be able to use that blockchain to confirm and label the contents of their products accordingly—down to the specific farms and animals represented in every individual package. That level of detail may be too much information for shoppers, but they will at least be able to trust that their meatballs come from the appropriate species.

The Spine of Digitalization

Keeping food safer and more traceable is just the beginning, however. Improvements in the supply chain, which have been incremental for decades despite billions of dollars of technology investments, are about to go exponential. Emerging technologies are converging to transform the supply chain from tactical to strategic, from an easily replicable commodity to a new source of competitive differentiation.

You may already be thinking about how to take advantage of blockchain technology, which makes data and transactions immutable, transparent, and verifiable (see “What Is Blockchain and How Does It Work?”). That will be a powerful tool to boost supply chain speed and efficiency—always a worthy goal, but hardly a disruptive one.

However, if you think of blockchain as the spine of digitalization and technologies such as AI, the IoT, 3D printing, autonomous vehicles, and drones as the limbs, you have a powerful supply chain body that can leapfrog ahead of its competition.

What Is Blockchain and How Does It Work?

Here’s why blockchain technology is critical to transforming the supply chain.

Blockchain is essentially a sequential, distributed ledger of transactions that is constantly updated on a global network of computers. The ownership and history of a transaction is embedded in the blockchain at the transaction’s earliest stages and verified at every subsequent stage.

A blockchain network uses vast amounts of computing power to encrypt the ledger as it’s being written. This makes it possible for every computer in the network to verify the transactions safely and transparently. The more organizations that participate in the ledger, the more complex and secure the encryption becomes, making it increasingly tamperproof.

Why does blockchain matter for the supply chain?

  • It enables the safe exchange of value without a central verifying partner, which makes transactions faster and less expensive.
  • It dramatically simplifies recordkeeping by establishing a single, authoritative view of the truth across all parties.
  • It builds a secure, immutable history and chain of custody as different parties handle the items being shipped, and it updates the relevant documentation.
  • By doing these things, blockchain allows companies to create smart contracts based on programmable business logic, which can execute themselves autonomously and thereby save time and money by reducing friction and intermediaries.

Hints of the Future

In the mid-1990s, when the World Wide Web was in its infancy, we had no idea that the internet would become so large and pervasive, nor that we’d find a way to carry it all in our pockets on small slabs of glass.

But we could tell that it had vast potential.

Today, with the combination of emerging technologies that promise to turbocharge digital transformation, we’re just beginning to see how we might turn the supply chain into a source of competitive advantage (see “What’s the Magic Combination?”).

What’s the Magic Combination?

Those who focus on blockchain in isolation will miss out on a much bigger supply chain opportunity.

Many experts believe emerging technologies will work with blockchain to digitalize the supply chain and create new business models:

  • Blockchain will provide the foundation of automated trust for all parties in the supply chain.
  • The IoT will link objects—from tiny devices to large machines—and generate data about status, locations, and transactions that will be recorded on the blockchain.
  • 3D printing will extend the supply chain to the customer’s doorstep with hyperlocal manufacturing of parts and products with IoT sensors built into the items and/or their packaging. Every manufactured object will be smart, connected, and able to communicate so that it can be tracked and traced as needed.
  • Big Data management tools will process all the information streaming in around the clock from IoT sensors.
  • AI and machine learning will analyze this enormous amount of data to reveal patterns and enable true predictability in every area of the supply chain.

Combining these technologies with powerful analytics tools to predict trends will make lack of visibility into the supply chain a thing of the past. Organizations will be able to examine a single machine across its entire lifecycle and identify areas where they can improve performance and increase return on investment. They’ll be able to follow and monitor every component of a product, from design through delivery and service. They’ll be able to trigger and track automated actions between and among partners and customers to provide customized transactions in real time based on real data.

After decades of talk about markets of one, companies will finally have the power to create them—at scale and profitably.

Amazon, for example, is becoming as much a logistics company as a retailer. Its ordering and delivery systems are so streamlined that its customers can launch and complete a same-day transaction with a push of a single IP-enabled button or a word to its ever-attentive AI device, Alexa. And this level of experimentation and innovation is bubbling up across industries.

Consider manufacturing, where the IoT is transforming automation inside already highly automated factories. Machine-to-machine communication is enabling robots to set up, provision, and unload equipment quickly and accurately with minimal human intervention. Meanwhile, sensors across the factory floor are already capable of gathering such information as how often each machine needs maintenance or how much raw material to order given current production trends.

Once they harvest enough data, businesses will be able to feed it through machine learning algorithms to identify trends that forecast future outcomes. At that point, the supply chain will start to become both automated and predictive. We’ll begin to see business models that include proactively scheduling maintenance, replacing parts just before they’re likely to break, and automatically ordering materials and initiating customer shipments.

Italian train operator Trenitalia, for example, has put IoT sensors on its locomotives and passenger cars and is using analytics and in-memory computing to gauge the health of its trains in real time, according to an article in Computer Weekly. “It is now possible to affordably collect huge amounts of data from hundreds of sensors in a single train, analyse that data in real time and detect problems before they actually happen,” Trenitalia’s CIO Danilo Gismondi told Computer Weekly.

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials.

The project, which is scheduled to be completed in 2018, will change Trenitalia’s business model, allowing it to schedule more trips and make each one more profitable. The railway company will be able to better plan parts inventories and determine which lines are consistently performing poorly and need upgrades. The new system will save €100 million a year, according to ARC Advisory Group.

New business models continue to evolve as 3D printers become more sophisticated and affordable, making it possible to move the end of the supply chain closer to the customer. Companies can design parts and products in materials ranging from carbon fiber to chocolate and then print those items in their warehouse, at a conveniently located third-party vendor, or even on the client’s premises.

In addition to minimizing their shipping expenses and reducing fulfillment time, companies will be able to offer more personalized or customized items affordably in small quantities. For example, clothing retailer Ministry of Supply recently installed a 3D printer at its Boston store that enables it to make an article of clothing to a customer’s specifications in under 90 minutes, according to an article in Forbes.

This kind of highly distributed manufacturing has potential across many industries. It could even create a market for secure manufacturing for highly regulated sectors, allowing a manufacturer to transmit encrypted templates to printers in tightly protected locations, for example.

Meanwhile, organizations are investigating ways of using blockchain technology to authenticate, track and trace, automate, and otherwise manage transactions and interactions, both internally and within their vendor and customer networks. The ability to collect data, record it on the blockchain for immediate verification, and make that trustworthy data available for any application delivers indisputable value in any business context. The supply chain will be no exception.

Blockchain Is the Change Driver

The supply chain is configured as we know it today because it’s impossible to create a contract that accounts for every possible contingency. Consider cross-border financial transfers, which are so complex and must meet so many regulations that they require a tremendous number of intermediaries to plug the gaps: lawyers, accountants, customer service reps, warehouse operators, bankers, and more. By reducing that complexity, blockchain technology makes intermediaries less necessary—a transformation that is revolutionary even when measured only in cost savings.

“If you’re selling 100 items a minute, 24 hours a day, reducing the cost of the supply chain by just $1 per item saves you more than $52.5 million a year,” notes Dirk Lonser, SAP go-to-market leader at DXC Technology, an IT services company. “By replacing manual processes and multiple peer-to-peer connections through fax or e-mail with a single medium where everyone can exchange verified information instantaneously, blockchain will boost profit margins exponentially without raising prices or even increasing individual productivity.”

But the potential for blockchain extends far beyond cost cutting and streamlining, says Irfan Khan, CEO of supply chain management consulting and systems integration firm Bristlecone, a Mahindra Group company. It will give companies ways to differentiate.

“Blockchain will let enterprises more accurately trace faulty parts or products from end users back to factories for recalls,” Khan says. “It will streamline supplier onboarding, contracting, and management by creating an integrated platform that the company’s entire network can access in real time. It will give vendors secure, transparent visibility into inventory 24×7. And at a time when counterfeiting is a real concern in multiple industries, it will make it easy for both retailers and customers to check product authenticity.”

Blockchain allows all the critical steps of the supply chain to go electronic and become irrefutably verifiable by all the critical parties within minutes: the seller and buyer, banks, logistics carriers, and import and export officials. Although the key parts of the process remain the same as in today’s analog supply chain, performing them electronically with blockchain technology shortens each stage from hours or days to seconds while eliminating reams of wasteful paperwork. With goods moving that quickly, companies have ample room for designing new business models around manufacturing, service, and delivery.

Challenges on the Path to Adoption

For all this to work, however, the data on the blockchain must be correct from the beginning. The pills, produce, or parts on the delivery truck need to be the same as the items listed on the manifest at the loading dock. Every use case assumes that the data is accurate—and that will only happen when everything that’s manufactured is smart, connected, and able to self-verify automatically with the help of machine learning tuned to detect errors and potential fraud.

Companies are already seeing the possibilities of applying this bundle of emerging technologies to the supply chain. IDC projects that by 2021, at least 25% of Forbes Global 2000 (G2000) companies will use blockchain services as a foundation for digital trust at scale; 30% of top global manufacturers and retailers will do so by 2020. IDC also predicts that by 2020, up to 10% of pilot and production blockchain-distributed ledgers will incorporate data from IoT sensors.

Despite IDC’s optimism, though, the biggest barrier to adoption is the early stage level of enterprise use cases, particularly around blockchain. Currently, the sole significant enterprise blockchain production system is the virtual currency Bitcoin, which has unfortunately been tainted by its associations with speculation, dubious financial transactions, and the so-called dark web.

The technology is still in a sufficiently early stage that there’s significant uncertainty about its ability to handle the massive amounts of data a global enterprise supply chain generates daily. Never mind that it’s completely unregulated, with no global standard. There’s also a critical global shortage of experts who can explain emerging technologies like blockchain, the IoT, and machine learning to nontechnology industries and educate organizations in how the technologies can improve their supply chain processes. Finally, there is concern about how blockchain’s complex algorithms gobble computing power—and electricity (see “Blockchain Blackouts”).

Blockchain Blackouts

Blockchain is a power glutton. Can technology mediate the issue?

A major concern today is the enormous carbon footprint of the networks creating and solving the algorithmic problems that keep blockchains secure. Although virtual currency enthusiasts claim the problem is overstated, Michael Reed, head of blockchain technology for Intel, has been widely quoted as saying that the energy demands of blockchains are a significant drain on the world’s electricity resources.

Indeed, Wired magazine has estimated that by July 2019, the Bitcoin network alone will require more energy than the entire United States currently uses and that by February 2020 it will use as much electricity as the entire world does today.

Still, computing power is becoming more energy efficient by the day and sticking with paperwork will become too slow, so experts—Intel’s Reed among them—consider this a solvable problem.

“We don’t know yet what the market will adopt. In a decade, it might be status quo or best practice, or it could be the next Betamax, a great technology for which there was no demand,” Lonser says. “Even highly regulated industries that need greater transparency in the entire supply chain are moving fairly slowly.”

Blockchain will require acceptance by a critical mass of companies, governments, and other organizations before it displaces paper documentation. It’s a chicken-and-egg issue: multiple companies need to adopt these technologies at the same time so they can build a blockchain to exchange information, yet getting multiple companies to do anything simultaneously is a challenge. Some early initiatives are already underway, though:

  • A London-based startup called Everledger is using blockchain and IoT technology to track the provenance, ownership, and lifecycles of valuable assets. The company began by tracking diamonds from mine to jewelry using roughly 200 different characteristics, with a goal of stopping both the demand for and the supply of “conflict diamonds”—diamonds mined in war zones and sold to finance insurgencies. It has since expanded to cover wine, artwork, and other high-value items to prevent fraud and verify authenticity.
  • In September 2017, SAP announced the creation of its SAP Leonardo Blockchain Co-Innovation program, a group of 27 enterprise customers interested in co-innovating around blockchain and creating business buy-in. The diverse group of participants includes management and technology services companies Capgemini and Deloitte, cosmetics company Natura Cosméticos S.A., and Moog Inc., a manufacturer of precision motion control systems.
  • Two of Europe’s largest shipping ports—Rotterdam and Antwerp—are working on blockchain projects to streamline interaction with port customers. The Antwerp terminal authority says eliminating paperwork could cut the costs of container transport by as much as 50%.
  • The Chinese online shopping behemoth Alibaba is experimenting with blockchain to verify the authenticity of food products and catch counterfeits before they endanger people’s health and lives.
  • Technology and transportation executives have teamed up to create the Blockchain in Transport Alliance (BiTA), a forum for developing blockchain standards and education for the freight industry.

It’s likely that the first blockchain-based enterprise supply chain use case will emerge in the next year among companies that see it as an opportunity to bolster their legal compliance and improve business processes. Once that happens, expect others to follow.

Customers Will Expect Change

It’s only a matter of time before the supply chain becomes a competitive driver. The question for today’s enterprises is how to prepare for the shift. Customers are going to expect constant, granular visibility into their transactions and faster, more customized service every step of the way. Organizations will need to be ready to meet those expectations.

If organizations have manual business processes that could never be automated before, now is the time to see if it’s possible. Organizations that have made initial investments in emerging technologies are looking at how their pilot projects are paying off and where they might extend to the supply chain. They are starting to think creatively about how to combine technologies to offer a product, service, or business model not possible before.

A manufacturer will load a self-driving truck with a 3D printer capable of creating a customer’s ordered item en route to delivering it. A vendor will capture the market for a socially responsible product by allowing its customers to track the product’s production and verify that none of its subcontractors use slave labor. And a supermarket chain will win over customers by persuading them that their choice of supermarket is also a choice between being certain of what’s in their food and simply hoping that what’s on the label matches what’s inside.

At that point, a smart supply chain won’t just be a competitive edge. It will become a competitive necessity. D!

About the Authors

Gil Perez is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Tom Raftery is Global Vice President, Futurist, and Internet of Things Evangelist, at SAP.

Hans Thalbauer is Senior Vice President, Internet of Things and Digital Supply Chain, at SAP.

Dan Wellers is Global Lead, Digital Futures, at SAP.

Fawn Fitter is a freelance writer specializing in business and technology.

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.



The Differences Between Machine Learning And Predictive Analytics

Shaily Kumar

Many people are confused about the specifics of machine learning and predictive analytics. Although they are both centered on efficient data processing, there are many differences.

Machine learning

Machine learning is a method of computational learning underlying most artificial intelligence (AI) applications. In ML, systems or algorithms improve themselves through data experience without relying on explicit programming. ML algorithms are wide-ranging tools capable of carrying out predictions while simultaneously learning from over trillions of observations.

Machine learning is considered a modern-day extension of predictive analytics. Efficient pattern recognition and self-learning are the backbones of ML models, which automatically evolve based on changing patterns in order to enable appropriate actions.

Many companies today depend on machine learning algorithms to better understand their clients and potential revenue opportunities. Hundreds of existing and newly developed machine learning algorithms are applied to derive high-end predictions that guide real-time decisions with less reliance on human intervention.

Business application of machine learning: employee satisfaction

One common, uncomplicated, yet successful business application of machine learning is measuring real-time employee satisfaction.

Machine learning applications can be highly complex, but one that’s both simple and very useful for business is a machine learning algorithm that compares employee satisfaction ratings to salaries. Instead of plotting a predictive satisfaction curve against salary figures for various employees, as predictive analytics would suggest, the algorithm assimilates huge amounts of random training data upon entry, and the prediction results are affected by any added training data to produce real-time accuracy and more helpful predictions.

This machine learning algorithm employs self-learning and automated recalibration in response to pattern changes in the training data, making machine learning more reliable for real-time predictions than other AI concepts. Repeatedly increasing or updating the bulk of training data guarantees better predictions.

Machine learning can also be implemented in image classification and facial recognition with deep learning and neural network techniques.

Predictive analytics

Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. Basic descriptive analytic techniques include averages and counts. Descriptive analytics based on obtaining information from past events has evolved into predictive analytics, which attempts to predict the future based on historical data.

This concept applies complex techniques of classical statistics, like regression and decision trees, to provide credible answers to queries such as: ‘’How exactly will my sales be influenced by a 10% increase in advertising expenditure?’’ This leads to simulations and “what-if” analyses for users to learn more.

All predictive analytics applications involve three fundamental components:

  • Data: The effectiveness of every predictive model strongly depends on the quality of the historical data it processes.
  • Statistical modeling: Includes the various statistical techniques ranging from basic to complex functions used for the derivation of meaning, insight, and inference. Regression is the most commonly used statistical technique.
  • Assumptions: The conclusions drawn from collected and analyzed data usually assume the future will follow a pattern related to the past.

Data analysis is crucial for any business en route to success, and predictive analytics can be applied in numerous ways to enhance business productivity. These include things like marketing campaign optimization, risk assessment, market analysis, and fraud detection.

Business application of predictive analytics: marketing campaign optimization

In the past, valuable marketing campaign resources were wasted by businesses using instincts alone to try to capture market niches. Today, many predictive analytic strategies help businesses identify, engage, and secure suitable markets for their services and products, driving greater efficiency into marketing campaigns.

A clear application is using visitors’ search history and usage patterns on e-commerce websites to make product recommendations. Sites like Amazon increase their chance of sales by recommending products based on specific consumer interests. Predictive analytics now plays a vital role in the marketing operations of real estate, insurance, retail, and almost every other sector.

How machine learning and predictive analytics are related

While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related. Basically, machine learning is a predictive analytics branch. Despite having similar aims and processes, there are two main differences between them:

  • Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data.
  • Unlike machine learning, predictive analytics still relies on human experts to work out and test the associations between cause and outcome.

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Shaily Kumar

About Shaily Kumar

Shailendra has been on a quest to help organisations make money out of data and has generated an incremental value of over one billion dollars through analytics and cognitive processes. With a global experience of more than two decades, Shailendra has worked with a myriad of Corporations, Consulting Services and Software Companies in various industries like Retail, Telecommunications, Financial Services and Travel - to help them realise incremental value hidden in zettabytes of data. He has published multiple articles in international journals about Analytics and Cognitive Solutions; and recently published “Making Money out of Data” which showcases five business stories from various industries on how successful companies make millions of dollars in incremental value using analytics. Prior to joining SAP, Shailendra was Partner / Analytics & Cognitive Leader, Asia at IBM where he drove the cognitive business across Asia. Before joining IBM, he was the Managing Director and Analytics Lead at Accenture delivering value to its clients across Australia and New Zealand. Coming from the industry, Shailendra held key Executive positions driving analytics at Woolworths and Coles in the past.