Are Project Managers Really Admins?

Paul Dandurand

The world of project management was obsessed with metrics: in 2013, 62% of businesses felt they couldn’t properly track time and costs for their projects. This blew the door wide open for the development of project management software that would facilitate this kind of tracking. With KPIs now so easily accessible, PMs could easily communicate real-time reports to their stakeholders, and stakeholders could demand more frequent reporting, knowing that the information was at the tips of our fingers.

The thought process was: with better data and better tracking, surely there will also be better project success! Unfortunately, that’s not the case. Since 2013, there are in fact fewer successful projects every year. The 2015 success rate for IT projects was only 29%, according to Standish Group survey. This is hard on other projects areas like new product development projects and professional services client engagements.

Time and cost tracking added more stress on project managers to spend most of their time gathering the data to populate the project management tools or to manually create spreadsheet and PowerPoint reports. This is what I call project “administration” work.

So what’s going on here?

I believe the problem is too much project administrating and not enough managing and leading. Part of the blame could be from executives and sponsors demanding more detailed cost and schedule data without balancing it with project facilitation, execution, people engagement, and process improvement, which could lead to better business value success.

The three project management hats

Let’s take a minute to imagine this scenario in a different light: a restaurant. There are three main areas of management in a restaurant: the business, the food, and the service. These are usually divided among the general manager, executive chef, and maitre d’, respectively. Responsibilities are divided between all three roles and are specific to each without any overlap. If an executive chef starts managing servers and training them, the chef won’t have time to manage the sous chefs, as well. While the service may greatly increase in quality (or not), the food preparation will suffer. Bottom line: it takes a balanced effort across all three areas to successfully run a restaurant.

The same parallel can be drawn to project management: The success of a project requires three different types of tasks: administering, managing, and leading.

Some organizations have the resources, especially for very large projects, to have a separate person responsible for admin, managing, and leading. However, I certainly know that is not the case for most of us who need to survive with one person wearing all hats. The problem is that one of the hats (administration) gets worn much more than the other two—to our detriment.

Let’s look at the three hats.

1) Project administration hat (maitre d’ thinking):

The administrative portion of project management consists of tracking costs, time and resources, creating status reports, documenting, and setting things up.

2) Project management hat (executive chef thinking):

The managerial portion of a project is mostly about proper communication and facilitation. The goal here is to ensure proper communication within the team members, stakeholders, sponsors, and the end project customers. Management also takes the form of facilitation, where processes become streamlined and barriers or obstacles are removed. Managing is also about executing the process and solving problems. This includes motivating people to ask for help and to help others when needed. The manager should foster lessons-learned discussions and innovation to improve the process.

3) Project-leading hat (general manager thinking):

Leading entails identifying or understanding the project’s goals and communicating the vision and strategy to the team and stakeholders. Leaders see the bigger picture, challenge the status quo, and look to innovate. They also engage the team, but do so with executive management’s support.

In our past blog about why projects fail (Source: Standish Group), Lawrence Dillon, former ARAMARK Healthcare CIO and current COO at ENKI, LLC, found that none of the top five reasons for project failure had anything to do with the project administrator side of our project world. Here are the top five reasons for failure:

  1. Lack of executive support.
  2. Missing emotional maturity.
  3. Poor user involvement.
  4. No optimization.
  5. Not enough skilled staff.

The meat of these points relates to the duties of the project manager and project leader hats.

Project manager’s secret sauce: three hats on one head

Let’s assume we have no choice but to have one project manager for our project. How do we do so much with one head? Think of a small cafe or specialty restaurant where the owner is playing the role of general manager, executive chef, and maitre d’. This owner knows the business will not last long if strategy, innovation, and administrating is not done in balance.

How can one person manage a project with the three hats? Here are some ideas on how to not get over-consumed by project admin tasks:

  • Define project success: Establish a new definition of your project’s success with executives and leadership. In other words, what business value are you striving to achieve for them?
  • Prioritize metrics: Obtain agreement with leadership on what critical and minimal metrics are needed to best manage the success of meeting business value. Prioritize metrics between must-have and nice-to-have, and obtain and ensure that you have the power to choose to drop the nice-to-haves in favor of other leadership duties when there are time constraints.
  • Implement best project process: Choose the right project process recipes and ingredients needed for best project success. The process should have enough of the secret sauce to ensure that junior team members have the how-too information at their fingertips.
  • Streamline process: Mold your chosen process for any custom needs and ensure that the added information also has the steps on how things get them done for best end results.
  • Facilitate and execute: Fire up the oven and execute the process while solving problems.
  • Engage people: Engage team members to be accountable, ask for help, help others, and chime in on how to improve the process from lessons learned and new ideas.
  • Improve and repeat: Scale the process for future repeatability with flexibility in mind to forever increase business value.

If some of us are finding that we’re project admins due to time limitations, let’s see what we can do to lower the admin time and increase management and leadership time.

What about project software tools? As mentioned above, most tools focus their features for the project administrator hat: scheduling, resource allocation, progress percent, and financial data. I believe that future project tools will become more balanced with a focus on project manager and project leader hats. This will be about driving process with flexibility, engaging people to solve issues together, and improving the future process from lessons learned and new ideas.

For information about project management software for driving processes, please visit SAP App Center.

This article originally appeared on the Pie blog and is republished by permission.


Paul Dandurand

About Paul Dandurand

Paul has a background in starting and growing companies. Prior to PieMatrix, he was co-founder of FocusFrame, where he wore multiple hats, including those of co-president and director. He helped position FocusFrame as the market leader with process methodology differentiation. FocusFrame was sold to Hexaware in 2006. Previously, he was a management consulting manager at Ernst & Young (now Capgemini) in San Francisco and Siebel Systems in Amsterdam. Paul enjoys photography, skiing, and watching independent films. He earned a B.A. degree in Economics from the University of California at Berkeley.

“The Digital CIO”: An Experience In The Digital Economy

Thomas Saueressig

Introducing the Digital CIO series

The way business success was measured in the past doesn’t hold up against the demands of an ever-expanding digital environment. The digital reality has changed forever the way we live and do business. And no one better understands the opportunities and the challenges of digital enterprise transformation than the CIOs of today. Staying engaged on this topic is critical to ensure the success of the digital journey.

That is why I am excited to introduce the “The Digital CIO,” a new blog series in the CIO Knowledge section of the Digitalist Magazine by SAP. The premise of this blog series is to be a continuous source of insight and inspiration from CIOs to their peers all over the world. Top executives from global industries will share their experience in incorporating sophisticated new technologies, what they have learned, and what has been successful for them. This series is a reflection based on real stories told by CIOs who have taken necessary steps to improve speed and efficiency, eliminate wasteful practices, and avoid or overcome costly mistakes in their organizations.

Meet our first digital CIO

“The Digital CIO” launches with Cristiano Barbieri, the CIO and director of Customer Relationship and Technology Strategies of SulAmérica in Brazil. His first blog examines the rise of the dual-role CIO, and how merging responsibilities for both IT and customer relationships is helping SulAmérica maintain its leadership position in Brazil’s fast-paced insurance market. In his blog series, Cristiano explains how he and his team recognized the importance of an internal cultural shift to take digital transformation from cliché to reality.

 A CIO-to-CIO community

In my work with executives around the world, I am fortunate to meet leading CIOs with invaluable insights about advancing their organizations towards technological innovations. This is an experience I would like to share, and it inspired the idea of the Digital CIO blog series. The objective is to create a community of CIOs who will discuss what they have learned about work ethics in the digital era and how their organizations were changed by leveraging groundbreaking technologies to address the fast-paced demands of our digital economy.

Each blogger will provide personal guideposts on how to navigate what may seem difficult and uncharted territory. These are executive accounts of shared facts, figures, and findings to help other leaders make their best technology decisions. It will guide them on how to interact successfully with CEOs and CFOs, and how to make a compelling business case for technological innovation to their leadership.

I hope you are as excited about this series as I am, and I wish you all the best for your organization’s digital journey. I welcome your input on this new series and on what area of technology you’d like to learn more about. Please feel free to comment below.

With IT increasingly central to every business – from the customer experience to the offering to the business model itself – we all need to start thinking like CIOs. Learn how to Hack the CIO.


Thomas Saueressig

About Thomas Saueressig

Thomas Saueressig is chief information officer, global head of IT Services, and a member of the SAP Chief Technology Officer circle. In his role as SAP CIO, he represents the entire IT organization internally and externally. He works to enable SAP’s IT organization to become agile, user-centric, and business-driven, with a cloud-first approach. His teams enable new business models and optimize business processes by leveraging the latest technologies and innovations, to provide a modern workplace. Thomas has vast experience in the global IT organization, starting with building up the Enterprise Mobility organization and leading all cross functions, over to heading the entire IT Project Delivery and Client IT organization globally. His focus is to create a user-centric IT organization, that delivers great user and customer experiences and changes the perception of IT. Prior to this, he supported Executive Board Member Gerhard Oswald as Executive Board Assistant in his daily operations and strategic projects. Thomas started his career in SAP Consulting where he successfully led multiple CRM customer projects. Thomas was honored to be included on Fortune’s 40 Under 40 list in 2016 and was recognized in 2017 as one of Constellation Research’s Business Transformation 150, a list that recognizes the top global executives leading transformation efforts in their organizations. He has a degree in Business Information Technology from the University of Cooperative Education in Mannheim (Germany), and a joint executive MBA from ESSEC (France), and Mannheim Business School (Germany).

Growing Businesses Prepare For Act 2 Of Their Digital Evolution

Meaghan Sullivan

Competing against a slate of long-established brands and disruptive startups is a stressful and exhausting reality for many small and midsize businesses. They are not only up against rivals with extensive resources, capital, and brand recognition, but also first-mover visionaries fueled by nonstop creativity, out-of-the-box thinking, and an “everything to gain, nothing to lose” spirit.

This environment may appear as the death knell for small and midsize businesses, but according to Oxford Economics’ 2017 “The Transformation Imperative for Small and Midsize Companies,” sponsored by SAP, digital transformation initiatives are helping them stay competitive. The survey of 3,000 executives across 17 countries reveals that over half of growing companies view digital transformation as a core survival objective that will increase in urgency by as much as 32% over the next five years.


Source:The Transformation Imperative for Small and Midsize Companies,” Oxford Economics, sponsored by SAP, 2017.


What’s compelling most small and midsize businesses to pay attention and invest in digital transformation? It seems that the answer to this question points to the buzzworthy wave of next-generation technology that’s emerging as a viable – sometimes, necessary – enabler of capabilities required to operate competitively.

Sweeping, technology-driven changes advance the importance of digital transformation

For small and midsize businesses, the road to digital transformation has been hard-won. This level of technology-driven change was once considered an option reserved for large enterprises that could afford it. However, the growth and improvement they are experiencing now from their digital initiatives closely resemble those realized by their much larger rivals. According to the Oxford Economics report, businesses with annual revenues between US$100 million and $499.9 million are using digital transformation to attract and retain the right talent and increase profitability – nearly as much as companies generating millions and billions more.


Source:The Transformation Imperative for Small and Midsize Companies,” Oxford Economics, sponsored by SAP, 2017.


While this is all excellent news, small and midsize businesses are far from done with digital transformation. Emerging technologies such as self-service analytics, the Internet of Things, artificial intelligence, machine learning, and blockchain are finding their way into conversations among business leaders, analysts, and trusted solution providers.

For any growing company that is already evolving their practices with analytics, cloud solutions, mobile applications, or digital commerce platforms, the capabilities enabled by these new digital investments are highly accessible. For example, the Internet of Things is enabling anything from high-precision hyperlocal advertising and online search to the tracking of shipments and customers. Companies can now take their seemingly random collection of data to pull insights on product performance, customer behavior, and brand sentiment. And with information like this, decision-makers can have a better sense of how they can increase revenue while avoiding oncoming risks and preventable costs.

Taking advantage of these next-generation technologies doesn’t necessarily mean that you have to actually implement them. It is highly likely that you are using – or will use – software or services that tap into these advanced technologies. Machine learning is turning data analytics is a simpler, more-decisive tool. Cybersecurity solutions are leveraging blockchain to validate authentication and secure data governance. Artificial intelligence is being embedded in business applications to guide users through processes, workflows, and information search.

So here’s my bet for 2018: Every company will touch technology more than ever before – whether through active adoption or passive use. Small and midsize businesses that know how to take advantage of these opportunities will reap tremendous rewards. But as their digital transformation matures, companies will be able to pick out the digital winners that will make their employees’ lives easier, add value to the customer experience, and build a stronger brand name.

Help ensure that your small or midsize business is ready to take on digital transformation to continually improve operations, processes, and products, and services. Check out Oxford Economics’ study, “The Transformation Imperative for Small and Midsize Companies,” sponsored by SAP.

This article originally appeared on Growth Matters Network and is republished by permission.


Meaghan Sullivan

About Meaghan Sullivan

Meaghan Sullivan is the vice president of Global Channel Marketing at SAP. In this role, she is tasked with accelerating global indirect revenue through channel marketing practices with a focus on VARs and Distributors. Sullivan focuses on Partner-Lead Demand Generation activities to provide SAP partners with innovative programs, campaigns and resources that enable them to more efficiently market their SAP solutions and services.

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.