What the Top Restaurant in US Can Teach About the Digital Economy

Stephanie Overby

Celebrity chef Grant Achatz and business partner Nick Kokonas have been shaking up the fine-dining scene in Chicago since launching Alinea in 2005. There, they combined art and science to molecularly deconstruct classic dishes, serving them in unconventional ways. Now they aim to disrupt the decades-old dining reservations process with a cloud-based platform that enables restaurants to sell seats the way theaters and sports teams do: with tiered pricing and same-day discounts to shift demand.

By charging customers ahead of time, the system also transfers the risk of costly no-shows to the customer and enables restaurants to better manage food and labor costs. Their company, Tock, boasts investors such as former Twitter CEO Dick Costolo and restaurateurs Thomas Keller and Kimbal Musk (brother of Tesla Motors CEO Elon).

Recipe for a new revenue model

Kokonas, a derivatives trader and investor in more than a dozen companies, hates it when someone says, “That’s just how we do it.”

2016_Q1_creators_01“I like hackers,” he says. “People who probe for answers. They put together a quick solution, try it, and then quickly improve it. And then they do that again. It’s a mindset. Most corporations do not have that mindset.”

After years of dealing with what Kokonas calls the “absurdity” of reservations at Alinea—70% of people requesting prime-time weekend slots, three full-time employees answering phones just to tell people no, no-shows hovering at 10% or more, and dejected would-be diners—he and Achatz invented another way. When they launched their second restaurant, Next, in 2011, they created a proprietary software application to sell reservations to every seating. Guests buy nonrefundable tickets to enjoy the 18- to 22-course tasting menu, the same way they would if they wanted to see a play or watch a Cubs game. After all, fine dining was itself a sort of theater, Kokonas thought. In 2012, Alinea adopted the system; the restaurant that lost more than a quarter million dollars a year on no-shows virtually eliminated them.

Last year, Kokonas and Achatz hired a chief technology officer and developed a commercial, software-as-a-service version of that system for other restaurants. “I knew it would have demand if we rebuilt it robust enough to handle thousands of restaurants rather than just ours,” says Kokonas, who reportedly raised several million dollars to launch Tock in December 2014. A year later, Tock had 50 customers, from San Francisco’s Coi, where tickets cost US$220–275 per person, to Portland’s midrange Thai restaurant Pok Pok, where patrons pay a nonrefundable $20 deposit per seat. “We are adding three to five customers a week now,” Kokonas says, predicting that Tock will have a few hundred customers by mid-2016.

Spicing Up Bookings

Online platforms such as OpenTable or Yelp’s SeatMe have digitized the old-school reservation process, charging restaurants for each booking. Tock instead charges dining establishments a flat $695 fee and enables them to customize the system. Restaurants can offer patrons traditional, no-cost reservations; tickets that serve as deposits on their final bill; or all-inclusive entry. Journeyman, outside of Boston, for example, offers no-fee reservations, a $10–20 discount for those who prepay for their meal, and tickets for special events and holiday parties. And restaurants are able to price tickets dynamically based on demand. Alinea, for example, has offered heavily discounted tickets on slow days, such as Super Bowl Sunday or July 4.

By making pricing and seat availability transparent, Kokonas thinks restaurants can create more trust with diners. And few appear to have balked at paying more for prime-time seating: Alinea has remained full on weekends. Customers accept the different prices, Kokonas says, so long as the choice is theirs.

The digital ticketing system enables more personal interaction between restaurant and guest. “It’s actually easier to spend more time with customers,” Kokonas says. “Once a customer makes a booking, the restaurant can reach out by e-mail or text or through social media to confirm choices or dietary restrictions or to learn more about customer needs and desires and can do so efficiently and quickly.”

Dining on Data

Deep knowledge of regular customers’ habits and preferences is part of what defines the high-end dining experience. But the CRM system in most traditional reservation systems is “just a glorified Post-it note” of customer information, according to Kokonas. Tock users can import their own data into their existing CRM applications.

2016_Q1_creators_02“We can learn about the demographics, purchasing decisions, and timing of a restaurant’s customers in entirely new ways,” Kokonas says. “We can track customer preferences like type of water, coffee service, whether they’re left- or right-handed—as granular as a restaurant wants to get in order to serve a patron better. That certainly makes service seem more magical to the guest.”

Early Tock customer Daniel Patterson, owner of Coi, has said that by diminishing reservations uncertainty, the system enables his restaurant to staff more appropriately and spend more time focusing on the customer experience.

Fresh Thinking

The challenge Kokonas and Achatz are facing with Tock is convincing restaurants that there is value in change. “I thought they’d see this great system and immediately go for it,” Kokonas says. “We have to teach restaurants how to think creatively. [Some] are hesitant to embrace change, so we have to do a lot of handholding at first.”

That’s because restaurants typically pay more attention to food and service than to business operations, Kokonas says. And when it comes to technology, they opt for what’s cheapest or best known. As with getting diners excited about a new venue, good word of mouth helps. “In the past few weeks, restaurants have moved from a ‘fear of change’ to a ‘fear of missing out.’”

This story has been updated to reflect information obtained after publication. Tock is no longer hosted on Amazon Web Services and does not pass along its data hosting costs.


Global Findings On IoT For Consumer Products

Don Gordon

The massive impact of Internet of Things (IoT) on consumer products is now beyond question, with more than 75 billion connected devices expected by 2025. That’s a significant change from 2017’s 8.4 billion IoT devices in use, which already represents an increase of 31% from 2016.

From iPhones to smart home devices like Nest and the Amazon Echo, IoT-enabled devices have captured people’s imaginations and wallets. But our brand-new study reveals that the IoT presents massive opportunities to consumer products companies in ways that are often unseen to consumers.

Currently, 60% of surveyed global manufacturers are implementing analytics on IoT data to optimize processes and production. But, as the study shows, this is just a small part of the picture. You can get a copy of our study here. However, these numbers have been mostly conjecture with regards to consumer products IoT.

Earlier this year, SAP conducted an in-depth international survey to create a more accurate overall picture. Our professionals received data from respondents in five countries in varying states of development. The individuals surveyed represent a wide range of consumer products industries and many different professional positions. Research demographics are available in the full study document. Here’s a short look at the results of this survey.

A common set of business challenges point to IoT

The top business challenge faced by these manufacturers were raw material cost fluctuation at 35%. This was followed by high logistics costs and shrinking operational margins, at 32% and 31%, respectively. After the top three, high lead time for products and inventory and the slow pace of innovation followed, at 28% each.

The top actions put in place to deal with these concerns were led by faster reaction to demand and capacity changes, at 32%. After this was improving product lead time and product quality and compliance, at 27% and 24%. Following this was focusing on more product innovation and increasing transport efficiencies, at 23% each. The compelling trend here is that many of these challenges are most hindering consumer products companies are areas where IoT has high potential to drive strong benefits.

Understanding and applying IoT technology

But why are they investing in IoT? The survey found that 41% of respondents have a clear picture of what IoT is and what it can do for their company. That still leaves a large number of respondents who know it is important but don’t have a strong grasp of IoT and the benefits it can provide. For overall reasons, having areas of applicability to the business and IoT’s potential value to their business top the list. But what areas are the companies planning on implementing first?

There are several key areas where implementation of IoT technology has been planned: Quality control came in at a high 61%. This suggests that this is the main driver for many businesses. After this: logistics management, distribution center management, inventory movement control, and transportation management.

The companies are taking several different approaches to implementing IoT into their operations. Key initiatives used include creating processes for managing IoT. Following that is training or acquiring IoT-capable staff, learning from early adopters’ actions, increasing budget, and building an organizational consensus.

Leaders and laggards

Leaders and laggards in this group tend to divide on several aspects. One aspect includes strategic drivers for companies implementing IoT into their supply chain. Leaders focus on improving product lead time, reacting to demand and capacity changes more quickly, and improving product quality and compliance. These aspects take a forward-focused approach. Laggards focus on decreasing out-of-stocks, improving cost-to-serve initiatives, and increasing product innovation. This shows a focus on catching up to market changes.

There was also a difference in implementing initiatives to improve IoT use. Leaders focus on creating IoT management processes, learning from early adopters and allocating more funds for the process. Though laggards also focus on creating IoT management processes, it is third on their list, after establishing partnerships to exploit IoT and building an organizational consensus of IoT technology implementation.

This survey helps to prove the potential of consumer products IoT. The results help us gain a stronger understanding of key issues with IoT implementation. We discovered that improving understanding of IoT capabilities needs to be addressed. Understanding allows companies to take advantage of IoT’s benefits. There were also many differences between countries, company sizes, and positions. More detailed information and many insights can be found in our original study report than can fit in this article. To take advantage of this information, download the full report.

By undertaking this survey, SAP has been able to assemble a large number of statistics that are specific to the consumer products IoT industry. As a leader in that industry, we believe it’s important to have accurate numbers to help consumer products companies move forward.  If you need help developing a comprehensive plan to bring IoT technology into your product line or company, please feel free to contact us today for more information.

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?


About Don Gordon

Don Gordon leads global Consumer Products industry marketing for SAP. Previously he led global Retail industry marketing for IBM. He lives in Philadelphia, considered by many to be the finest city on earth.

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.


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.

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.