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4 Ways Digitalization Is Transforming R&D

Thomas Ohnemus

Fully one-quarter of the world’s economy will be digital by 2020, forecasts a new report from Accenture. But that prediction doesn’t tell the whole story. Because increasingly, all business processes will be not only digitized – converted from analog to digital – but also digitalized – transformed in a way that blurs the physical and virtual.

Many organizations are struggling to respond. In fact, only five percent of companies say they’ve mastered digital transformation to the point of competitive differentiation, according to Forrester.

The challenge is especially acute for manufacturers. From innovation to production to logistics, manufacturers are seeing their operations revolutionized by digital technologies.

That starts with research and development. Here are four key ways digitalization is transforming R&D:

1. End consumers are more empowered

Technology has put consumers in the driver’s seat. Customers now have instant, constant access to information about products, quality, and pricing – for both you and your competitors. In the past, if you had established yourself as a leader in a region, the competition was at a disadvantage. Today, customers know how you stack up against rivals around the world, and your past market leadership is irrelevant. This isn’t just a problem for sales and marketing. It’s also a problem for R&D, which must respond – in as near to real time as possible – to changing customer demands. The good news is that technology is also the solution. For example, by designing smart products that leverage Internet of Things (IoT) sensors, R&D can capture usage data to understand customer desires and capture performance data to learn how to improve products rapidly.

2. Transparency is rewriting how manufacturers collaborate

Information access is changing the way manufacturers interact both internally and with suppliers. This is true for every function, but especially for R&D.

As R&D creates more smart products, the skills it requires are changing. The automotive industry is a case in point. Fifteen years ago, cars began to incorporate electronics such as engine-control systems. Today, electronics are where most automotive R&D is happening, and within 10 years, electronics will allow cars to pretty much drive themselves.

That dramatically changes how cars are designed. In the past, mechanical engineers led design efforts, and electronics were merely an add-on. Today, software development – with its very different requirements and design cycles – is integral to the process. In the automotive industry and in virtually every other industry, product design will involve new stakeholders who must work together in new ways.

3. Business models are growing more flexible

In the past, product designers worked for companies that sold products. But increasingly, manufacturers will sell not products but services. That affects R&D in fundamental ways.

A good example is a midsize SAP client that makes industrial air compressors. Some years ago it realized customers wanted not air compressors but compressed air. So it began offering compressed air as a service. Before this time, it designed and manufactured air compressors and then sold them to customers. Now, it designs and manufactures air compressors, installs them at customer sites, and then charges for the compressed air customers consume.

That new business model changes how R&D develops products. First, it needs to design in IoT sensors to monitor the compressors in real time and enable predictive maintenance. Second, it needs to optimize longevity and ease of maintenance. One way the company achieves that is by having engineers regularly spend time with field service to see firsthand how equipment is performing.

4. Business processes are becoming more customer centric

In fact, 83% of executives believe digitalization is driving a shift from supply-side economies of scale to demand-side economies based on interconnection with customers and partners, according to the Accenture report.

Manufacturers will have to be more connected to customers, because new business models will demand it. Take the air compressor customer. It hasn’t invested in a capital-intensive air compressor; it’s simply contracted for compressed air. At the end of the contract, there’s little disincentive to switching to a more attractive contract. The same will be true for many products across many industries.

How does that change R&D? Design cycles will have to accelerate to maintain competitive differentiation. For example, most carmakers update a car’s electronics only if the customer happens to come in for service. Tesla has upped the ante by sending new features and functions directly to the consumer through regular software updates. Don’t be surprised if its competitors start to follow.

Ultimately, the digital economy begins and ends with the customer. Customers are more empowered, so companies need to become more customer-centric. And nowhere is that more true than in R&D.

In my next blog, I’ll look at how digitalization is transforming manufacturing.

For more insight on the new customer-centric digital economy, see Customer Relationship Status: It’s Complicated.

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Thomas Ohnemus

About Thomas Ohnemus

Thomas Ohnemus is the Vice President, Solution Marketing, Customer Value Office, at SAP. He is responsible for driving the go-to-market strategy, messaging, and demand generation. Thomas has over 25 years’ experience in business software solutions and his PLM expertise has awarded him key management positions in consulting, product management, service, and global marketing. He holds a master’s degree in engineering, and lives in Germany.

Real-Time Data Transforms Political Journalism, But Context Remains Vital

John Graham

The runup to the 2016 U.S. election is being covered in interesting new ways by the political media, with analysis of Big Data and real-time opinion polling offering journalists much deeper insight than ever before. The trend of “data journalism” is peaking as the media embraces advanced technologies that allow them to deliver a new breed of numbers-driven, fact-based journalism.

The tools being used for data journalism open up possibilities for fresh perspectives, more in-depth reporting, and new stories behind the numbers that have never been seen before. Traditional journalists are beginning to see how data journalism can complement their reporting, and the U.S. election is serving as an ideal testing ground. Political reporters are lapping up the improved data literacy and access to objective analysis, which is helping to make their reports more thorough and informative.

Consequently, American voters are becoming digital voters. They have access to real-time, data-driven information and public sentiment, which is empowering them with broader insight. They’re relying on this to help them make up their minds before they cast their vote, and it’s given many voters a renewed interest in becoming informed citizens able to make an educated choice.

However, the rise of data-driven journalism brings with it a potential pitfall for media organizations and readers alike. Digital information overload will bring about a fatigue around numbers if reporting quantity becomes more highly valued than quality. Having access to mountains of data is a huge benefit, but a reporter still has to be a journalist first to ensure they’re not getting buried under the numbers and missing the stories.

In other words, a political journalist still needs to be a politico, not just a statistician. They could fall into the trap of placing too much importance on meaningless correlations as indicators of voter sentiment, losing their grasp on what made them a great political reporter in the first place. As data gets bigger, this will become harder to resist. So they need to become experts in making Big Data small—rather than obsessing over the numbers, obsessing over figuring out what they really mean. In doing that, they have an unprecedented opportunity to make people more informed rather than simply overwhelming with them a series of conflicting data sets.

Some media organizations are already tackling the challenge of remaining relevant in a world of information overload. Using big data and visualizations, they are making great strides in making data journalism more accessible to reporters, politicos, and voters, which is proving its worth in giving political reporting a new lease of life.

Reuters’ Polling Explorer tool is an example of how this is being done, offering up customizable data visualizations focusing on the biggest talking points in the U.S. leading up to the election. It’s an entirely new scale of public opinion measurement, presented in a way anyone can understand and use, while enabling Reuters to usher in its own improved brand of accurate, fact-based, and timely journalism.

We can see the true potential of using real-time data analysis to measure up-to-the-minute public opinion in one poll on the most important problem facing the US today. Immediately after the Paris attacks in November, terrorism skyrocketed way above the economy as the number-one issue, rising sharply again straight after the December San Bernardino attack. For Reuters, this is just one of many examples of their greatly increased ability to find outliers in the data.

Reuters Polling Explorer runs on SAP HANA, an in-memory data platform that allows Reuters to access and analyze 100 million survey responses for quicker and more efficient reporting of public opinion.

For more on data analytics in today’s media environment, see How Big Data Is Changing The News Industry.

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John Graham

About John Graham

John Graham is president of SAP Canada. Driving growth across SAP’s industry-leading cloud, mobile, and database solutions, he is helping more than 9,500 Canadian customers in 25 industries become best-run businesses.

Smart Machines Create Markets For Cyber-Physical Advances

Marion Heindenreich

Today, industrial machines are more intelligent than ever before. These intelligent machines are changing companies in many ways.

Why smart machines?

Mobile networked computers were a key breakthrough for making smart machines. Big Data allows machines and computers to store information and analyze complex patterns. Cloud computing offers broad access to information and more storage.

These computerized machines are both physical and virtual. Some call them “cyber-physical” machines. Technology lets them be self-aware and connected to each other and larger systems.

Businesses change their approaches

Intelligent machines allow companies to innovate in many areas. For one, the value proposition for customers is evolving. Businesses now model and plan in different ways in many industries.

Makers of industrial machines and parts work in new ways within the organization. Engineering now partners with mechanical, electronic, and software staff to develop new products. Manufacturing now seamlessly ties what happens on the shop floor to the customer.

Service models are changing too. Scheduled and reactionary servicing of machines is fading. Now intelligent machines track themselves. Machines detect problems and report them automatically. Major problems or failures are predicted and reported.

A data mining example

One good industrial example is mining, which can be dangerous and difficult. As ores become scarce, the costs of mining have increased.

“Smart machines” started in mining in the late 1990s. Software and hardware let remote users change settings. Operators moved hydraulic levers from a safe distance. Sensors observed performance and diagnosed issues.

Data cables connected machines to computers on the surface. Continuous and remote monitoring of the machines grew. Over time, embedded sensors helped improve monitoring, diagnostics, and data storage.

The technology means workers only go underground to fix specific issues. As a result, accident and injury risk is lower.

New wireless technology now lets mining companies connect data from many mine sites. Service centers access large amounts of data and can improve performance. Maintenance is prioritized and equipment downtime is reduced.

Opportunity abounds

For companies the time is now. Today, mobile “connected things” generate 17% of the digital universe. By 2020 that share grows to 27%.

You might not be investing in this so-called “Internet of Things” (devices that connect to each other). But it’s a good bet your competitors are. A December 2015 study reported 33% of industrial companies are investing in the Internet of Things. Another 25% are considering it.

There are risks

This new dawning era of manufacturing is exciting. But there are concerns. Cyber attacks on the Internet of Things are not new. But as the use of intelligent machines grows, the threat of cyber attacks in industry grows.

Data confidentiality and privacy are concerns. So too are software and hardware vulnerabilities. Exposure to attack lies not just in the virtual space but the physical too. Tampering with unattended machines and theft pose serious risk.

To address these threats, industries must invest in cybersecurity along with smart machines.

Conclusion

The potential advantages of smart machines are staggering. They can reshape industries and change how companies produce new products and create new markets.

For more information, please download the white paper Digital Manufacturing: Powering the Fourth Industrial Revolution.

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Marion Heindenreich

About Marion Heindenreich

Marion Heidenreich is a solution manager for the SAP Industrial Machinery and Components Business Unit who focuses on solution innovations like Product Costing on SAP HANA and cloud solutions, as well as providing financial and business analysis for industry business strategy definition and business planning.

Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

Christopher Koch

Robots: Job Destroyers or Human Partners? [INFOGRAPHIC]

To learn more about how humans and robots will co-evolve, read the in-depth report Bring Your Robot to Work.

Download the PDF (91KB)

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Christopher Koch

About Christopher Koch

Christopher Koch is the Editorial Director of the SAP Center for Business Insight. He is an experienced publishing professional, researcher, editor, and writer in business, technology, and B2B marketing. Share your thoughts with Chris on Twitter @Ckochster.

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Building A Business Case For Financial Transformation

Nilly Essaides

There’s constant pressure on the CFO from the CEO to do better—to innovate, and to transform the finance organization into both a leaner and a more forward-looking analytics hub that provides insight and foresight to the enterprise. CFOs today must:

  • Interpret numbers instead of reporting them
  • Deploy enabling technology to automate low-value work
  • Scout for business and growth opportunities
  • Work effectively with Big Data to turn their teams into the brains of the organization
  • Act as true partners to the CEO, business leaders, and board of directors

Defining the ROI for transformation

Transformation sounds great in theory, but to get finance to literally go beyond its form—not an easy feat—executives need to see a strong business case and a tangible payback. After all, finance is all about the ROI.

Here are some solutions CFOs can wrap their heads around to help drive change:

  • Manage competitive disruption. Today’s business environment is rife with competitive threats. My last post listed five ways financial planning and analysis (FP&A) in its future form can help companies battle these threats. The cost of not transforming the finance function into the fast-thinking, forward-looking brains of the enterprise is the opportunity cost of falling behind. It’s the risk of becoming irrelevant through the inability to foresee competitive threats, or of lacking an action plan for dealing with the potential impact of such pressures on the financial health of the corporation.
  • Streamline processes. Obviously, there’s the dollars-and-cents savings that come from streamlining processes, using new technologies, and breaking down internal silos. For example, in many organizations, forecasting processes occur in different departments. Merging these disparate processes into one and using a single technology platform can save enormous resources in terms of systems and time. It eliminates duplicate entries of data and the need to reconcile discordant information, or the need to later argue about which number is right. It creates a single version of the truth.

Even within finance, things can be improved. Often the processes of budgeting, forecasting, and planning happen in isolation in different time frames. And operational and financial planning occur in different cycles and levels. By syncing up these processes, companies can get rid of redundancies. What’s more important, they can discover efficiencies and improve the quality of the end product.

  • Eliminate waste and free up strategic time. New technologies are enabling the finance function to automate low-value work and free up executives’ time to focus on strategic thinking, developing partnerships with the business, and advising management on how to drive growth. The payback is smarter decisions (faster growth, higher investment returns) while lowering operating expenses.
  • Look forward. Finance and FP&A today are shifting their focus from yesterday to tomorrow, from what happened to what’s going to happen. Transforming their mindset is key to helping the business move forward. Using techniques and technologies like driver-based modeling and predictive analytics, finance is remaking itself and producing faster, more frequent and—most importantly—more accurate forecasts. It’s giving management the one thing that matters most: time to pull business levers to affect future financial results. The payback is higher sales, wider margins, and lower cost of operations.
  • Change the mindset. There’s no transformation of the financial organization without a transformation of the financial skill set of executives. The first-quarter Deloitte CFO Signal Survey indicated that CFOs expect to embark on a wide range of efforts to improve the performance of their teams before the end of 2016. While foundational finance skills remain a must, to transform finance into the “A-team” of the future, executives must possess business acumen, diplomacy skills, intellectual curiosity, technology savvy, and a degree of comfort with ambivalence. They have to be okay with making decisions without 100% of the information. One can argue that the return on soft skills is soft. But it also means being able to move fast and grab windows of opportunity. Not all business cases are based on cost savings.
  • Build an analytics hub. The biggest challenge for CFOs today is to transform finance into the analytical hub of the organization and leverage Big Data to drive smarter business decisions—both in terms of cost cutting and in giving the business units advice on how to market, sell, develop, and grow their operations. That’s how finance fits within the digital enterprise. Finance needs to funnel Big Data from all corners of the organization—and outside it—to leverage its unique central viewpoint. It must bring the information together and run it through advanced analytics models to come up with causal relationships that explain what business initiatives are really moving the needle, what steps the company can take to improve results, and what its customers are doing and are likely to do. Digitizing finance has a huge payback: It allows companies to stay competitive in a digital economy.

Is finance transformation worth the effort? That may be the wrong question. The question is, can companies afford not to transform their finance function and remain relevant now and going forward?

Learn how the FP&A team at CF Industries Holdings Inc. prioritized business partnering options and transformed the organization to optimally support strategic goals by establishing an integrated business planning process at the AFP Annual Conference session, Driving Finance Transformation Through Integrated Business Planning.

For more of my insights on FP&A, subscribe to the monthly FP&A e-newsletter from my company, the Association for Financial Professionals. You can also connect with me on LinkedIn or follow me on Twitter.

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Nilly Essaides

About Nilly Essaides

Nilly Essaides is the director of the FP&A Practice at the Association for Financial Professionals. She has over 25 years of experience in the finance field. Nilly has written multiple in-depth research reports on FP&A and Treasury topics, as well as countless articles. She also speaks at conferences and moderates financial executives' roundtables across the country. Nilly has published a book on best-practice transfer and process excellence with the APQC, "If We Only Knew What We Know."