Beyond The Numbers: Overcoming Challenges In Business Financial Analysis

Erwan Philippe

Money doesn’t make the world go ’round anymore. Data does.

In today’s rapidly changing business landscape, successful business owners are learning to harness data, particularly financial data. Unfortunately, many entrepreneurs either: (a) take this for granted or (b) have no clue how to use it.

This blog post will help you make sense of your financial data and maximize its potential through financial management and financial analysis.

Every time you make a judgment on how your business is performing based on your financial situation or your operating performance, or when you make predictions on what could happen, you’re doing financial analysis. When you make financial decisions within your business, such as securing loans, acquiring new assets, or issuing stocks and bonds, you’re performing financial management.

Preparing financial reports for financial management and analysis

Financial management and financial analysis start with accurate and complete financial statement data, which come from three financial reports:

1. Income statement: This shows your business activities as far as how much revenue or sales you are bringing in, minus your expenses.

2.  Balance sheet: This is an overview of your assets (what you own) and liabilities (what you owe).

3. Cash flow: This statement shows how cash goes in (cash inflows) and out (cash outflows) of your business.

Most businesses prepare financial reports, as these are necessary for compliance with government regulations, external and internal transparency, and comprehensive and integrated financial management. But to promote successful financial management practices, you need a sound financial analysis.

Financial analysis goes beyond looking at numbers, graphs, and charts; it involves using different techniques and tools, depending on the nature of your business. In other words, the data are just a set of random photos on a wall, and what’s important is how you put them together to tell a story.

Many small business owners, however, are stuck with old practices and misconceptions that impair their ability to come up with good financial analysis.

Common misconceptions about business financial analysis: Are you still making these mistakes?

1. Doing the math…and ending it there

As mentioned, financial analysis involves more than just putting numbers together and compiling them to generate fancy-looking reports. It also isn’t just about calculating ratios and percentages and filling out variables in formulas at the end of every month, quarter, or year. The numbers are just building blocks. In a nutshell, financial analysis should answer the question, “How did my business do and why?” in more qualitative terms.

To achieve this, start by asking the right questions. Which of the numbers or ratios are useful to evaluate your business? What are you supposed to compare or use as benchmarks? Depending on the nature of your business, you also need to know:

  • Which metrics are critical in your industry? How well did you do in those metrics?
  • What factors affect these metrics?
  • How will external and internal changes or trends affect your business?
  • What steps does management need to take to address these?

2. Analyzing financial data for generic reasons

Different businesses have different needs and objectives. To meet your unique business requirements, clearly define the purpose of performing financial analysis in more specific terms. To come up with this, ask the following questions:

  • What purpose will your financial analysis serve?
  • What problem or issue are you trying to resolve?
  • What scope and depth of analysis can you achieve?
  • Are you doing a horizontal/dynamic financial analysis (comparing financial statements over a number of years) or vertical/static (just for one year)?
  • What kind of data do you have? How are these variables related to each other (dependent or independent?), and how will your data affect your analysis?

3. Using generic methods to analyze financial data

Once you’ve answered the “why” of financial analysis, figure out how you will go about it by choosing the financial analysis techniques that are appropriate for your objectives. Ratio analysis, cash flow analysis, comparative financial statements, and trend analysis are just a few examples.

Although there are different ways to analyze data based on your needs, the process must be scientific. As a guide, you may use the following framework:

  • Define your purpose and context. Find out what your objective is, what issues you intend to address, what kind of report you aim to produce, when you plan to accomplish your analysis, and, if applicable, how much you plan to spend on it.
  • Data collection. This is where you gather both qualitative and quantitative data such as financial statements, other financial reports, industry news, discussions with stakeholders, and other information relevant to your analysis.
  • Data processing. You need to process or adjust data before analyzing it for the numbers to make sense. For instance, raw data may not be enough to compare profitability of a small business to a large one, or to compare a business from a high-income locality to a low-income one. Adjusting and processing the data will enable you to perform cross-section analysis.
  • Data analysis and interpretation. This is where you relate, compare, or contrast your different variables with each other, taking into consideration the context of the problem or issue at hand.
  • Conclusions and recommendations. From the data gathered and your analysis of it, address the objectives and issues you identified in the beginning of the process.
  • Follow-up. What are your next steps? How does your report help your business in the long term? While your report may answer some questions, it may also stir up more questions, which you may also ask within this step

4. Making errors in accounting and financial reports. 

Even the smallest inaccuracies can throw your entire financial report off course. Whether it’s due to human error or deliberate attempts to conceal suspicious activities, these mistakes not only harm the integrity of your financial analysis; they can also do permanent damage to your business.

Data entry errors, misclassification of data, omission, and transposition errors are common mistakes that can diminish your ability to stay competitive. In preparing balance sheets, for instance, a common error is the misclassification of assets and liabilities. Entering a long-term liability into the current liability column can make your business appear less solvent, which could turn off potential financiers or investors. Missed entries on income statements, meanwhile, can make your business look less profitable than it actually is.

Because of these errors, you can miss out on opportunities to secure funding or make wrong decisions based on your financial health. No matter how time-consuming you think it might be, it’s wise to invest time and resources into reviewing your financial data by implementing efficient review processes to ensure the accuracy and integrity of your reports.

5. Creating complex, error-prone spreadsheets

Inaccuracies in accounting can be largely attributed to the use of manual spreadsheets. Setting it up, manually entering formulas, controlling who has access to it, managing which versions of which spreadsheets to update – you have to do all these steps and more to ensure that your reports are accurate. But despite all the time and labor you put into your spreadsheets, mistakes are still inevitable. Besides, perfecting your spreadsheets doesn’t even accomplish half of what financial analysis and financial management require.

As a business owner, you would make better use of your time by analyzing the data and figuring out what decisions to make based on the data.

6. Using outdated, manually entered data

In today’s fast-paced business landscape, your numbers can change quickly. Transactions move at the speed of light, the value of stocks and currencies fluctuate, and that report your accountant spent all last night preparing may be totally useless after everything that happened this morning. To ensure accurate, updated information, invest in accounting tools that can process your data in a matter of seconds and show your financial information and status in real-time.

Modernizing financial reports and financial analysis

More businesses are modernizing their financial management functions through automating processes that otherwise take too many resources and are prone to human error. One way to do this is through investing in business technologies such as enterprise resource planning (ERP) solutions.

With these tools, small and midsized enterprises can reduce up to 100% of financial forecasting errors with dynamic planning and analysis tools while cutting as much as 50% of working days spent on closing annual books through instant profit-and-loss insights, real-time cost control, and more.

Businesses are also moving away from traditional data infrastructure that may incur delays in delivering information by shifting to cloud-based solutions that automate processes, process data in real-time, and deliver vital information to decision-makers. This helps business owners and managers shift the use of resources from tedious, repetitive calculations to tasks that could use more human judgment and intelligence. Instead of spending hours crunching spreadsheets, business owners could use that time coming up with creative solutions to business challenges while also minimizing the cost of human error and delays.

Through technology, business owners and finance professionals can put resources to better use and come up with more efficient ways to look at data and data analytics beyond looking at the numbers.

A cloud-based ERP solution that consolidates all your financial transactions, reports, and spreadsheets empowers you to do this. It gives you a clearer picture that allows you to tell your story more accurately.

With SAP Business One, you can prepare, access, and consolidate your financial reports, including profit and loss statements, balance sheets, profitability reports, cash flow analysis, budget reports, and multi-period comparisons. Start your free SAP Business One trial and see how it can help you meet your financial management and financial analysis goals.

Follow SAP Finance online: @SAPFinance (Twitter)  | LinkedIn | FacebookYouTube


Erwan Philippe

About Erwan Philippe

Erwan Philippe is the head of SAP Business One Asia Pacific Japan, which also includes Greater China. Working in the APJ region for over 15 years, his career spans over 13 years in the IT sector, which includes various leadership positions in sales, business development, and operations. Today, Erwan is responsible for driving sales, operations, expansion, and growth of SAP Business One across Asia.

Lessons On GRC Platforms From The Forrester Wave: Keep Your Eyes On The Road

Bruce McCuaig

On February 15, Forrester released The Forrester Wave: Governance, Risk, and Compliance Platforms, Q1 2018. SAP continues to be rated a leader. But the market for governance, risk, and compliance (GRC) platforms is maturing, and at SAP, we believe the criteria for evaluating and selecting a vendor are evolving.

Should you choose the GRC vendor with the strongest current offering or the GRC vendor with the strongest strategy?

SAP occupies neither position. But is there a third and better option?

Keep your eyes on the road, not the vendor

Anyone buying a new car has access to consumer reports comparing and rating vehicle manufacturers and models against a variety of criteria. Vehicle entertainment systems are among the criteria rated.

However, for most vehicles made in the last 5-10 years, the supplier of the original equipment vehicle entertainment system is not displayed. In some cases, the supplier is not even mentioned in the owner’s manual. There may be a lesson here for customers looking for GRC solutions.

In automobiles, the entertainment system is an outcome, not a separate process. The outcome is evaluated, not the vendor. Customers evaluate and purchase the complete vehicle, not an individual component.

In my recent vehicle purchase, the UI of the OEM entertainment system was rated poorly in several analyst reports.

The real issue, however, isn’t whether the vehicle entertainment system’s UI is easy to learn. That may be a useful criterion for an in-home entertainment system. But the critical criterion for the UI in a vehicle entertainment system is this: Can you use it while keeping your eyes on the road? That is the essential outcome. Acoustic performance is a given. But can the system add value and synergies from other vehicle systems through native integration and continuous monitoring?

Does the entertainment system provide continuous monitoring of the vehicle navigation system, integration with the alarm system, compatibility with the mobile communication and vehicle maintenance systems? Does it provide audio alerts for maintenance and safety issues? Will it warn you of hazards ahead? Does it connect to the cloud?

Does it make the vehicle better?

The specific stand-alone features and UI of the entertainment system in your vehicle are important but secondary to the main goal. Integration of the system into the vehicle’s overall performance is the key criterion. Criteria that help you select a home entertainment system are not useful for evaluating a system for your car. And beyond the core capabilities that are important for your home entertainment system, are there other features that add value and influence the car-buying decision?

The path for ERP providers in the GRC market

The goal is balance and integration. GRC performance without strategy is short-lived. GRC strategy without strong capability is not effective. Growing both simultaneously is difficult, but in the end, is the only sustainable option.

I’d suggest that is exactly where an ERP provider should be positioned and exactly the trajectory to follow.

Eventually, GRC systems, like car entertainment systems, will be subsumed into the ERP landscape of your business. The value of the GRC systems will lie in their integration into the underlying ERP system and the cloud and their contribution to performance, not in their stand-alone virtues.

Download and read The Forrester Wave: Governance, Risk, and Compliance Platforms, Q1 2018.

Follow SAP Finance online: @SAPFinance (Twitter) | LinkedIn | Facebook | YouTube


Bruce McCuaig

About Bruce McCuaig

Bruce McCuaig is director Product Marketing at SAP GRC solutions. He is responsible for development and execution of the product marketing strategy for SAP Risk Management, SAP Audit Management and SAP solutions for three lines of defense. Bruce has extensive experience in industry as a finance professional, as a chief risk officer, and as a chief audit executive. He has written and spoken extensively on GRC topics and has worked with clients around the world implementing GRC solutions and technology.

The CFO’s Strategy Playbook: Seven Key Elements

Johannes Vogel

Part 1 of the 4-part CFO Strategy Playbook series

In my first blog, “Seven Finance Strategy Questions That Can Start the Paradigm Shift in the CFO Domain,” I discussed the reasons why many CFOs are starting to look at their finance team’s functional strategy. They revisit the way the finance teams are set up, what values and vision the team stands for, how to better serve the “customers” of the CFO team, and what changes are needed to implement this transformation.

In this article, I will explore which key content elements should be part of a functional finance strategy. The article can be used as a finance strategy playbook.

The elements provide a framework for the CFO team’s strategic organizational development. The focus of this post will be on strategic content and less on approach. In working with my customers’ finance teams, I do sense a growing interest in reviewing or fine-tuning their functional finance strategies, combined with a willingness toward more experimentation and smaller initiatives to try out innovation options. This frame of mind provides a good starting point for starting any finance transformation. The finance strategy creates the foundation and framework for transformation of the finance function.

Let’s explore what questions should be addressed in developing a company’s finance strategy:

  • How does the overall company strategy inform and impact the CFO team’s functional strategy?
  • Which megatrends and innovations are relevant for the company at large and for the finance team in particular?
  • What are the requirements of the business and of capital market stakeholders or investors, which the finance team needs to address in three to five years?
  • Which finance activities provide the most value and need to be strengthened—for example, decision support, capital allocation, active business partnering?
  • What should be the vision and mission for the finance team looking at the needs of the rest of the company and the finance team for the next three to five years?
  • Which are the topics of strategic focus for the CFO team, what are “strategic thrust areas”?
  • How does finance define its role relative to the business, and what operating model and governance are needed to sustain that role?
  • What people and cultural aspects should be considered, and what changes for job profiles are required?
  • What is the status quo of the finance team and what initiatives are needed?
  • How can we track and measure success?
  • What is the roadmap and implementation plan to operationalize and execute the strategy?

Finance strategy – a definition

On a more abstract level, functional finance strategies deal with external and internal requirements for the finance team. They consider external megatrends and innovation and make specific what the vision and mission should be and what strategic thrust areas are relevant. The strategic thrust areas make transparent what areas the finance team will create value for the business. They also address how business insight is generated and made available to the operative business. In addition, there is typically an organizational update of roles and responsibilities.

Ideally, the finance strategy also covers the operating model (team structures and in- and outsourcing decisions), at team governance, and at ways of working and required skill profiles and cultural aspects. Finally, the finance strategy should include actionable initiatives and tasks, showing what is needed to master the transformation and how the change will be implemented.

The seven finance strategy elements

Let’s assume that a CFO together with a team wants to start the definition of their functional finance strategy. The structure might look like this:

  1. The context
  1. The objectives
  1. Governance
    • Design principles
    • Strategy development process
  1. Status quo and requirements
    • Megatrends and innovation relevant for finance
    • Status quo of the CFO team (as-is state)
    • Requirements for the finance team
  1. Finance strategy dimensions*
    • Finance strategic thrust areas
    • Finance strategy vision and mission statements
    • Finance strategy details (by strategic thrust area)
    • Impact on the Finance operating model and organizational structure
    • Summary to-be state
  1. The finance strategy gap (as-is state vs. to-be state)
  1. Implementation road gap
    • Initiative summaries
    • Finance initiative portfolio and prioritization
    • Roadmap and implementation plan

In upcoming posts, I will delve into more detail.

*Please note that the various aspects of a company’s financing strategy, which frequently is also viewed as part of the overall finance strategy, warrant a separate article.

For more insight on financial leadership, see Why Complacency Is An Expensive Mindset For The CFO.


Johannes Vogel

About Johannes Vogel

Johannes Vogel is director of Finance & Regulatory for Finance Strategy & Digital CFO Services at BearingPoint. Johannes is an experienced professional in management consulting in the areas of finance strategy, finance process improvement, digital CFO services, and process management. He has worked with national and international clients in creating functional strategies for CFO teams preparing to support the business by creating value and business insights, while running a cost-efficient yet technologically modern organization. Other projects Johannes conducted with his clients included digital CFO assessments and benchmarking, as well as ERP and finance process implementations. Prior to consulting, Johannes worked in various finance functions of an Atlanta-based international media group. Johannes is a lecturer for a Master Program at the Universität der Künste in Berlin and likes to post on finance topics. In his time off, he enjoys playing guitar with friends and tries to spend as much time as possible with his sailing buddies somewhere off the coast of Croatia or elsewhere.

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.