Siloed Diversity Holds Us All Back

Jill Willcox

There is conflict over an issue in the field of diversity and inclusion that needs to be addressed. Here are the two opposing schools of thought:

  1. Diversity is a long process, and every disenfranchised group can’t get there at once. As we work on gender and racial diversity, we will open the door for other groups.
  1. The siloing of advancement in diversity is holding every group back. The very idea of diversity and inclusion is that every group should be equal and represented fully in business. You can’t have partial diversity and consider that a success.

In recent years, businesses have been paying closer attention to diversity issues, since studies continue to show that diverse workforces are more productive and more profitable than homogenous ones. They are also more creative because people coming from diverse backgrounds with varied life experiences bring different approaches. It only makes sense that diversity of thought breeds more creative and effective problem solving.

So, for the purposes of this discussion, we can agree that diversity is good for business. The sticking point has always been how to actually make it happen in the workplace. The business world is rife with consultants whose purpose is to help organizations shift their hiring practices and create more diverse employee workforces, but it has been slow going at many companies. This slow movement is obviously of great concern for everyone shut out by homogenized work cultures, but this is critically important to those individuals who do not fit the two groups most talked about in the movement – gender and racial diversity. If it has taken this long to work towards a CONVERSATION on diversity for those groups, how long will individuals with differences not connected to race and gender have to wait for their turn to be included?

The reality of diversity today

In order to get to the heart of the matter, let’s look at where diversity in business stands today:

  • 2016 Fortune 500 list includes only 21 women CEOs, down from 24 last year (the highest number ever reached)
  • There is no research on the employment rate of people with speech and language disorders

Now let’s look at the U.S. population:

  • 13% of the population is African-American
  • 51% are female (2010 U.S. Census)
  • More than 3.5 million Americans have autism

Not only do we have the persistent issue of primarily all white male executive leadership in business, the employment numbers of those with a disability of any type are startling. So how do we fix it?

One for all, and all for one

In order to understand why separate movements for diversity are less effective than an inclusive one, we only have to look at the Women’s Suffragette movement. Lucy Stone, the founder of the original movement, broke with her friends Susan B. Anthony and Elizabeth Stanton because, unlike her, they did not support the 14th and 15th Amendments, giving black men citizenship and the right to vote. Their movements splintered as Susan B. Anthony moved closer to radical anti-black groups, and many scholars argue that this separation caused a delay in the movement accomplishing their goals.

Sadly, a thorough study of the impact of diversity outside of race and gender has yet to be undertaken. The UK’s Department for Business, Innovation, and Skills undertook an international assessment of diversity studies and found:

“There are very few workplace studies that attempt to quantify the impacts of diversity on business outcomes, when considering disability, religion, and sexual orientation. In many instances this is a result of data limitations. Very few private sector firms collect systematic and useable data on religion and sexual orientation (see for instance, 2012 Diversity League Tables). Even if they do, response rates can be very low and data on disability are often hard to analyse as they are often self-reported and can cover a wide range of conditions.”

One of our goals at The Speech Factor is to raise awareness and promote the undertaking of a study that we believe will show that diversity in all forms holds a clear benefit for businesses.

It is obvious that if all marginalized groups gathered together under a unified front they’d have a far greater number of people pushing towards the same goal, with an undeniably louder voice. Diversity can’t work in silos, because the silos are exactly what it needs to destroy in order to have success.

Learn How to Design a Flexible, Connected Workspace.


Improving Mobility With Smart Traffic In Metro Operations

Konstanze Werle

The increasing demands of a larger population are a growing concern. Estimates suggest that by 2030, around 60 percent of the world population will live in large cities. By 2040, this will increase to around 75 percent.

Urbanization raises specific concerns for metro operators, who must consider the increased traffic and safety risks associated with a growing population. Developing smart traffic strategies helps optimize the flow of people and goods in the city to reduce congestion and the risk of safety hazards. It also helps metro operators handle problems that may arise from increased traffic.

Frequency and route

Adequate transport services are a priority for transit operators, particularly as populations increase. The behavior of regular commuters, tourists, and occasional commuters, and the traffic resulting from events impact transit routes and availability of public transportation. Metro operators need to determine when to deploy transportation and the appropriate size of transport vehicles in different areas and at different times of the day. Metro operators and city officials must understand the travel patterns of patrons to provide effective transportation services.

According to World Transit Research, many residents of major cities in the United States avoid bus transit that requires them to transfer to a different bus. If schedules and routes are complicated, many residents look for alternative forms of transportation to reach their destination. World Transit Research points out that the bus transportation system in Barcelona was designed to cover the entire city. With a greater number of routes and frequency of buses, residents are more likely to transfer to a different bus to move throughout the city. Evaluating the travel patterns of local residents in a city provides an opportunity to address potential problems with frequency and available routes on public transportation.

Reducing delays from malfunctions

The Florida Department of Traffic points out that a smart traffic system actually improves the urban environment by setting up solutions in every area of traffic control and management. Cameras on traffic lights or in metro stations, for example, allow operators to identify problems at an early stage. Metro operators can then take measures to correct them in a timely manner.

Smart traffic systems gather accurate data and place sensors on public transportation to limit risks to passengers. The sensors also determine when problems develop and can catch malfunctions before they cause significant delays. Sensors help operators determine when to handle maintenance on trains or other areas of technology. The result: Better solutions to problems and fewer delays.

Greater management of incidents on public transportation

Safety management systems in public transportation have come a long way in supporting the management of risks that contribute to accidents. According to the Florida Department of Traffic, metro operators handle incidents that occur on public transportation in relation to the technology, doors, and overall systems. Smart traffic systems enhance safety and give operators access to more data from sensors, cameras, and technological tools.

The information allows operators to address incidents that may result in inconveniences, delays, or poor traffic management. As a result, risk management tools available to the operators will help professionals anticipate incidents and address potential concerns in the future.

Optimized traffic control

According to the Oregon Metro, smart traffic systems allow metro operators to limit or even prevent problems due to congestion. Smart traffic systems focus on every area of urban traffic, from driving to taking a bus. By setting up a system that directs traffic around an accident or informs drivers about potential delays with updated data, it reduces the number of incidents on the road.

Optimizing traffic control plays a significant role in the safety of residents and commuters. Metro operators improve the coordination of traffic signals to prevent accidents with trains or other traffic. It also provides real-time information about current traffic conditions, weather information in relation to the roads, and appropriate signals for transit priority or large trucks.

The result of better coordination throughout traffic control in an urban environment is greater safety. The risk of accidents reduces by providing passengers of public transportation and drivers of their own vehicles real-time data about road conditions or other potential factors. Smart traffic gives metro operators advanced incident management solutions for every area of traffic control.

Smart traffic in metro operations provides a solution for safety concerns and frustrations with transportation in urban areas. As urban populations grow, metro operators face greater challenges in relation to commuter safety. Setting up a smart traffic system allows the metro operators to stay up-to-date with real-time incidents and handle malfunctions before they cause injuries or safety concerns.

Learn how to innovate at scale by incorporating individual innovations back to the core business to drive tangible business value: Accelerating Digital Transformation in Transportation.


Konstanze Werle

About Konstanze Werle

Konstanze Werle is a Director of Industries Marketing at SAP. She is a content marketing specialist with a particular focus on the travel and transportation, engineering and construction and real estate industries worldwide. Her goal is to help companies in these industries to simplify their business by sharing latest trends and innovation in their industry.

Air Cover For The Endangered

Rick Price

David Allen sits in a semi-dark trailer, eyes on a computer screen. He is scanning an image from an aerial drone skimming the treetops at the Dinokeng Game Reserve in South Africa. It is searching for elephants. A cloud of dust appears in the camera’s field of view – the plane banks toward it, and there! A big bull, ears flapping with each step, a tracking collar around his neck. The ERP Air Force has found its target, with the aim not to destroy, but to save the creature from his nemesis, the human poacher.

The link to poverty

The group behind this – a non-governmental organization called Elephants, Rhinos, & People or ERP, was founded in 2014 by to preserve and protect Southern Africa’s wild elephants and rhinos through alleviating rural poverty. A look at some numbers shows the link between poverty and threats to the elephants and rhinos.

According to, rhino horn is worth up to hundreds of times the per-capita income in poor rural areas of Southern Africa. On the global black market, it can fetch up to $80,000 per kilogram. Depending on the species, a rhino horn can weigh three to four kilos, so one horn can command more than $300,000 – an unimaginable fortune to most people there.

Grim statistics

The result is carnage. Wild African elephants are being killed at the rate of four an hour. At the end of 2016, the population was 352,000, according to Group Elephant. Rhinos are in far worse shape: The total population as of this writing is 19,682 Southern White Rhinos and 5,042 Black Rhinos for a total of 24,724 in all of Africa. The government of South Africa says 529 rhinos were poached in that nation alone in the first half of 2017. ERP says all in all, three rhinos are killed every day. Any effort to preserve this species must give people an alternative to poaching and a stake in the animals’ survival, through jobs and economic opportunities like tourism. Conservation initiatives also have to make the most of scarce resources, and that requires cutting-edge technology.

Covering a lot of ground

These are big animals, they can move quickly, and their stomping grounds are large. Dinokeng alone encompasses more than 45,000 acres. It would be impractical to hire enough people to keep tabs on every elephant and every rhino there every moment of every day. Inside the reserve, they are safer, but if they break out into the surrounding suburban area, just north of Pretoria, they face threats, including exposure to poachers who might not risk the reserve itself. ERP works with Dinokeng to patrol the fences and help keep the animals in, but there’s a limit to what they can accomplish. That’s where the ERP Air Force, the tracking collars, and Big Data come in.

Eyes in the sky

Back in the trailer, the GPS collar monitoring system has alerted drone pilot David Allen that the bull is getting too close to the fences. The drone follows the elephants as they approach the limits of the reserve. The operator can then direct rangers in SUVs or a helicopter to nudge the herd away from the boundary. That reduces the poachers’ ability to kill the animals – outside Dinokeng, there is less protection. The drones will also watch for poaching within the reserve, and ERP hopes to use them for other initiatives.

Big Data to protect big creatures

ERP uses cutting-edge digital technology to save these wild elephants and rhinos, both by keeping them in safe areas and by alleviating the poverty of the local population. All of that requires the ability to wrangle huge amounts of data quickly, from knowing where the elephants are, to routing the people to them. A group of technology companies, including SAP and its partner EPI-USE, have collaborated with ERP to build a system capable of collecting, organizing, storing, and retrieving that information.

Next on the roadmap is predictive analytics: As EPI-USE’s Jan van Rensburg said in May 2017, “We rely upon analytics in the background, a platform where we can predict where poaching will happen. And we use the data that we build up and store in our in-memory database so that we can more efficiently use the drones, and send them to the spots where poachers are more likely to be. And when a poacher is found by the drone, we can dispatch a ranger to deal with it.”

A weapon to save, not kill

So for ERP, Big Data and the ability to use it in real time have become a weapon in the fight to save endangered elephants and rhinos, while helping people in poverty create new ways to earn a living and turn away from poaching.

Want to know more about how ERP uses Big Data and drones to save endangered wild Elephants and Rhinos through alleviating rural poverty? Watch the video.


Rick Price

About Rick Price

Rick Price is an Emmy Award-winning journalist who now works at SAP, where he tells stories of customers' digital transformation.

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.

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. Please feel to connect on: Linkedin: Twitter: