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Myths in Risk Management — You Don’t Need To Start With A Risk

Bruce McCuaig

Recently I was perusing a relatively unknown corner of ISO 31000 Risk Management —Principles and Guidelines— and long dormant memories flooded back.

The ISO section I was reading, Monitoring and Review (s 5.6), deals with the sorts of metrics that should be monitored to ensure the risk management system’s working. For example, it suggests monitoring indicators of control effectiveness, incidents (near misses), issues, key risk indicators, loss events, and other relevant variables important to the risk management process.

Years ago, I was appointed manager of accounting for what was then a mid-sized oil and gas company with operations in Canada, Russia, and Indonesia. I had a staff of about 200 people and one of my first assignments was to design and implement a completely new financial accounting system.

Eager to begin, I met with the project team and asked them for their plan. I was told that implementation could not begin for several months. The first step in the financial systems project plan was developing a new chart of accounts. And developing the new chart of accounts required a detailed analysis of what the system needed to report for the business to be managed successfully. After all, why develop a new financial accounting system if it didn’t help us manage our business?

It strikes me now that the same principle could be applied to implementing enterprise risk management (ERM). Basically, ERM implementation could start with gleaning the information we want from what ISO calls monitoring and review.

This would involve a process flow that’s nearly the opposite of what risk management practitioners currently follow:

  • Step 1: Determine the ERM information that’s the most important to the business managers and external stakeholders.  In my example, as an upstream oil and gas company, the critical information was related to finding and developing new oil and gas reserves, and reporting oil and gas production. We needed financial, economic, operating, and engineering data.
  • Step 2: Determine what information the managers need to know about these key identifiers. Essential information regarding reserves and production could be categorized as:
    • Control failures (not just the accounting kind – think of well blow out preventers that fail)
    • Incidents, such as delays in drilling
    • Loss events, such as dry wells or spills
    • Key risk indicators (KRI), such as trends in finding and development costs, production rates, etc.
  • Step 3: Determine the organization and process structure where this information can be found or where it’s created.
  • Step 4: Figure out why the control failures, incidents, and losses occur, and/or what risks they cause, and why the KRIs are over the threshold (or why they aren’t). The answers to these questions will provide the list of  risks that must be managed.

I’ve just outlined an upside-down risk management process— pretty much the opposite of traditional risk management. Of course it’s a little more complicated than this, but not by much  (after all, this is just a blog).

This risk management system implementation uses the same principles as my financial system implementation years ago, which, by the way, was successful, on time, and on budget. It boils down to figuring out what’s important, how to measure it, and how to report on it. The rest pretty much takes care of itself. Conversely, if I’d based my financial system implementation on today’s risk management practices, I would’ve begun instead by guessing what numbers I would have in the accounting records.

What’s the Lesson Here?

One lesson is that risk managers need a laser focus— not on the risks, but on the information that tells them how risk’s managed.

The second lesson is that extraneous information is avoided with this approach. By starting with the essential information needed to drive the business, all the secondary stuff’s avoided.

And the third lesson is to follow the principle of “begin with the end in mind”.

The good news is that never in the history of risk management have better tools been available. We have mobile devices, the cloud, business intelligence, predictive analytics, in-memory processing, web scanning, and more available to build and maintain the dashboards we need to monitor and review risks. These tools should be the backbone of risk management.

Risk managers should use them. The purpose of ERM is not to create pretty heat maps of risks, but to create information about how risks are being managed to drive business performance. We need to start ERM implementations with a dashboard of what information is required to manage the risk.

I’m interested in your experiences. Please share them with me.

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Bruce McCuaig

About Bruce McCuaig

Bruce McCuaig is the Director of Solution Marketing, Governance Risk and Compliance at SAP. His specialties include Enterprise Risk Management, Governance, Management Consulting and Strategy.

The Future Of Supplier Collaboration: 9 Things CPOs Want Their Managers To Know Now

Sundar Kamak

As a sourcing or procurement manager, you may think there’s nothing new about supplier collaboration. Your chief procurement officer (CPO) most likely disagrees.
Forward-thinking CPOs acknowledge the benefit of supplier partnerships. They not only value collaboration, but require a revolution in how their buying organization conducts its business and operations. “Procurement must start looking to suppliers for inspiration and new capability, stop prescribing specifications and start tapping into the expertise of suppliers,” writes David Rae in Procurement Leaders. The CEO expects it of your CPO, and your CPO expects it of you. For sourcing managers, this can be a lot of pressure.

Here are nine things your CPO wants you to know about how supplier collaboration is changing – and why it matters to your company’s future and your own future.

1. The need for supplier collaboration in procurement is greater than ever

Over half (65%) of procurement practitioners say procurement at their company is becoming more collaborative with suppliers, according to The Future of Procurement, Making Collaboration Pay Off, by Oxford Economics. Why? Because the pace of business has increased exponentially, and businesses must be able to respond to new market demands with agility and innovation. In this climate, buyers are relying on suppliers more than ever before. And buyers aren’t collaborating with suppliers merely as providers of materials and goods, but as strategic partners that can help create products that are competitive differentiators.

Supplier collaboration itself isn’t new. What’s new is that it’s taken on a much greater urgency and importance.

2. You’re probably not realizing the full collective power of your supplier relationships

Supplier collaboration has always been a function of maintaining a delicate balance between demand and supply. For the most part, the primary focus of the supplier relationship is ensuring the right materials are available at the right time and location. However, sourcing managers with a narrow focus on delivery are missing out on one of the greatest advantages of forging collaborative supplier partnerships: an opportunity to drive synergies that are otherwise perceived as impossible within the confines of the business. The game-changer is when you drive those synergies with thousands, not hundreds of suppliers. Look at the Apple Store as a prime example of collaboration en masse. Without the apps, the iPhone is just another ordinary phone!

3. Collaboration comes in more than one flavor

Suppliers don’t just collaborate with you to provide a critical component or service. They also work with your engineers to help ensure costs are optimized from the buyer’s perspective as well as the supplier’s side. They may even take over the provisioning of an entire end-to-end solution. Or co-design with your R&D team through joint research and development. These forms of collaboration aren’t new, but they are becoming more common and more critical. And they are becoming more impactful, because once you start extending any of these collaboration models to more and more suppliers, your capabilities as a business increase by orders of magnitude. If one good supplier can enable your company to build its brand, expand its reach, and establish its position as a market leader – imagine what’s possible when you work collaboratively with hundreds or thousands of suppliers.

4. Keeping product sustainability top of mind pays off

Facing increasing demand for sustainable products and production, companies are relying on suppliers to answer this new market requirement.

As a sourcing manager, you may need to go outside your comfort zone to think about new, innovative ways to collaborate for achieving sustainability. Recently, I heard from an acquaintance who is a CPO of a leading services company. His organization is currently collaborating with one of the largest suppliers in the world to adhere to regulatory mandates and consumer demand for “lean and green” lightbulbs. Although this approach was interesting to me, what really struck me was his observation on how this co-innovation with the supplier is spawning cost and resource optimization and the delivery of competitive products. As reported by Andrew Winston in The Harvard Business Review, Target and Walmart partnered to launch the Personal Care Sustainability Summit last year. So even competitors are collaborating with each other and with their suppliers in the name of sustainability.

5. Co-marketing is a win-win

Look at your list of suppliers. Does anyone have a brand that is bigger than your company’s? Believe it or not, almost all of us do. So why not seize the opportunity to raise your and your supplier’s brand profile in the marketplace?

Take Intel, for example. The laptop you’re working on right now may very well have an “Intel inside” sticker on it. That’s co-marketing at work. Consistently ranked as one of the world’s top 100 most valuable brands by Millward Brown Optimor, this largest supplier of microprocessors is world-renowned for its technology and innovation. For many companies that buy supplies from Intel, the decision to co-market is a strategic approach to convey that the product is reliable and provides real value for their computing needs.

6. Suppliers get to choose their customers, too

Increased competition for high-performing suppliers is changing the way procurement operates, say 58% of procurement executives in the Oxford Economics study. Buyers have a responsibility to the supplier – and to their CEO – to be a customer of choice. When the economy is going well, you might be able to dictate the supplier’s goods and services – and sometimes even the service delivery model. When times get tough (and they can very quickly), suppliers will typically reevaluate your organization’s needs to see whether they can continue service in a fiscally responsible manner. To secure suppliers’ attention in favorable and challenging economic conditions, your organization should establish collaborative and mutually productive partnerships with them.

7. Suppliers can help simplify operations

Cost optimization will always be one of your performance metrics; however, that is only one small part of the entire puzzle. What will help your organization get noticed is leveraging the supplier relationship to innovate new and better ways of managing the product line and operating the business while balancing risk and cost optimization. Ask yourself: Which functions are no longer needed? Can they be outsourced to a supplier that can perform them better? What can be automated?

8. Suppliers have a better grasp of your sourcing categories than you do

Understand your category like never before so that your organization can realize the full potential of its supplier investments while delivering products that are consistent and of high quality. How? By leveraging the wisdom of your suppliers. To be blunt: they know more than you do. Tap into that knowledge to gain a solid understanding of the product, market category, suppliers’ capabilities, and shifting dynamics in the industry, If a buyer does not understand these areas deeply, no amount of collaboration will empower a supplier to help your company innovate as well as optimize costs and resources.

9. Remember that there’s something in it for you as well

All of us want to do strategic, impactful work. Sourcing managers with aspirations of becoming CPOs should move beyond writing contracts and pushing PO requests by building strategic procurement skill sets. For example, a working knowledge in analytics allows you to choose suppliers that can shape the market and help a product succeed – and can catch the eye of the senior leadership team.

Sundar Kamak is global vice president of solutions marketing at Ariba, an SAP company.

For more on supplier collaboration, read Making Collaboration Pay Off, part of a series on the Future of Procurement, by Oxford Economics.

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The CFO Role In 2020

Estelle Lagorce

African American businessman looking out office window --- Image by © Mark Edward Atkinson/Blend Images/CorbisThe role of the CFO is undergoing a serious transformation, and CFOs can expect their role to continue to evolve, according to a recent CFO.com article by Deloitte COO and CFO Frank Friedman.

In the futurist article, Friedman says one of the biggest factors that will contribute to the CFO’s significant change over the next five years is technology.

Digital technology is obviously expected to drive change in high-tech companies, but Friedman says it’s industries outside of the tech sectors that are of particular interest, as they struggle to understand how to grasp and harness the digital capabilities available to them.

Working with high tech in low-tech industries

Five years from now, a finance team may be defined by how well it uses technology and innovative business tools, regardless of what industry it’s in. The article outlines some examples of ways that digital technology will increasingly be used by CFOs in “non-tech” sectors:

  • Predictive analytics: CFOs in manufacturing companies can forecast results and produce revenue predictions based on customer-experience profiles and current demand, instead of comparing to previous years as most companies still do today.
  • Social media and crowdsourcing: You may not think CFOs spend a lot of time on social media or crowdsourcing sites, but these methods can actually expedite finance processes, such as month-end responsibilities of the finance organization.
  • Big Data: CFOs already have a lot of data at their fingertips, but in 2020 they will have even more. CFOs in both tech and non-tech sectors who understand how to use that data to make valuable, informed decisions, can strategically guide their company and industry in a more digitally oriented world.

To do this, Friedman says CFOs can lead the way by addressing some critical areas:

  1. Know the issues: Gather the key questions that leaders expect Big Data analytics to answer.
  1. Make data easily accessible: Collect data that is manageable and easy to access.
  1. Broaden skills: The finance team needs people with the skills to understand and strategically interpret the data available to them.

The tech-savvy CFO

The role of today’s CFO has already expanded to include strategic corporate growth advice as well as managing the bottom line. In 2020, Friedman says expectations placed on the CFO are presumed to be even greater, and CFOs will likely need a much more diverse, multidisciplinary skill set to meet those demands.

The article details several traits and skills that CFOs will need in order to keep up with the pace of digital change in their role.

  1. Digital knowledge: CFOs must be tech-savvy in order to capitalize on technical innovations that will benefit their company and their industry as a whole.
  1. Data-driven execution: CFOs will need the ability to execute company strategy and operations decisions based on data-driven insights.
  1. Regulatory compliance: Regulations continue to be more stringent globally, so CFOs will need to be proficient at working closely with regulators and compliance systems.
  1. Risk management: With the growing global economy comes increased cyber and geopolitical risks worldwide. The CFOs of 2020, especially those in large multinational organizations, will need to have the expertise to monitor and manage risk in areas that may be unforeseen today.

The future CFO’s well-rounded resume

By 2020, the CFO role will require much more than just an accounting background. According to Deloitte’s Frank Friedman, “CFOs may need to bring a much more multidisciplinary skill set to the job as well as broader career experiences, from working overseas to holding positions in sales and marketing, and even running a business unit.”

So if you’re a current or aspiring CFO, you have five years to round out your resume with the necessary skills to be ready for the digitally driven role of the CFO in 2020.

The above information is based on the CFO.com article What Will the CFO Role Look Like In 2020?” by Deloitte COO & CFO, Frank Friedman – Copyright © 2015 CFO.com.

Want to learn more about best practices for transforming your finance organization? View the SAP/Deloitte Webinar, “Reshaping the Finance Function”.

For an in-depth look at digital technology’s role in business transformation, download the SAP eBook, The Digital Economy: Reinventing the Business World.

To learn more about the business and technology factors driving digital disruption, download the SAP eBook, Digital Disruption: How Digital Technology is Transforming Our World.

To read more CFO insights from a tech industry perspective, read the Wall Street Journal article with SAP CFO Luka Mucic: Driving Insight with In-memory Technology.

Discover 7 Questions CFOs Should Ask Themselves About Cyber Security.

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Estelle Lagorce

About Estelle Lagorce

Estelle Lagorce is the Director, Global Partner Marketing, at SAP. She leads the global planning, successful implementation and business impact of integrated marketing programs with top global Strategic Partner across priority regions and countries (demand generation, thought leadership).

Unlock Your Digital Super Powers: How Digitization Helps Companies Be Live Businesses

Erik Marcade and Fawn Fitter

The Port of Hamburg handles 9 million cargo containers a year, making it one of the world’s busiest container ports. According to the Hamburg Port Authority (HPA), that volume doubled in the last decade, and it’s expected to at least double again in the next decade—but there’s no room to build new roads in the center of Hamburg, one of Germany’s historic cities. The port needed a way to move more freight more efficiently with the physical infrastructure it already has.

sap_Q216_digital_double_feature1_images1The answer, according to an article on ZDNet, was to digitize the processes of managing traffic into, within, and back out of the port. By deploying a combination of sensors, telematics systems, smart algorithms, and cloud data processing, the Port of Hamburg now collects and analyzes a vast amount of data about ship arrivals and delays, parking availability, ground traffic, active roadwork, and more. It generates a continuously updated model of current port conditions, then pushes the results through mobile apps to truck drivers, letting them know exactly when ships are ready to drop off or receive containers and optimizing their routes. According to the HPA, they are now on track to handle 25 million cargo containers a year by 2025 without further congestion or construction, helping shipping companies bring more goods and raw materials in less time to businesses and consumers all across Europe.

In the past, the port could only have solved its problem with backhoes and building permits—which, given the physical constraints, means the problem would have been unsolvable. Today, though, software and sensors are allowing it to improve processes and operations to a previously impossible extent. Big Data analysis, data mining, machine learning, artificial intelligence (AI), and other technologies have finally become sophisticated enough to identify patterns not just in terabytes but in petabytes of data, make decisions accordingly, and learn from the results, all in seconds. These technologies make it possible to digitize all kinds of business processes, helping organizations become more responsive to changing market conditions and more able to customize interactions to individual customer needs. Digitization also streamlines and automates these processes, freeing employees to focus on tasks that require a human touch, like developing innovative strategies or navigating office politics.

In short, digitizing business processes is key to ensuring that the business can deliver relevant, personalized responses to the market in real time. And that, in turn, is the foundation of the Live Business—a business able to coordinate multiple functions in order to respond to and even anticipate customer demand at any moment.

Some industries and organizations are on the verge of discovering how business process digitization can help them go live. Others have already started putting it into action: fine-tuning operations to an unprecedented level across departments and at every point in the supply chain, cutting costs while turbocharging productivity, and spotting trends and making decisions at speeds that can only be called superhuman.

Balancing Insight and Action

sap_Q216_digital_double_feature1_images2Two kinds of algorithms drive process digitization, says Chandran Saravana, senior director of advanced analytics at SAP. Edge algorithms operate at the point where customers or other end users interact directly with a sensor, application, or Internet-enabled device. These algorithms, such as speech or image recognition, focus on simplicity and accuracy. They make decisions based primarily on their ability to interpret input with precision and then deliver a result in real time.

Edge algorithms work in tandem with, and sometimes mature into, server-level algorithms, which report on both the results of data analysis and the analytical process itself. For example, the complex systems that generate credit scores assess how creditworthy an individual is, but they also explain to both the lender and the credit applicant why a score is low or high, what factors went into calculating it, and what an applicant can do to raise the score in the future. These server-based algorithms gather data from edge algorithms, learn from their own results, and become more accurate through continuous feedback. The business can then track the results over time to understand how well the digitized process is performing and how to improve it.

sap_Q216_digital_double_feature1_images5From Data Scarcity to a Glut

To operate in real time, businesses need an accurate data model that compares what’s already known about a situation to what’s happened in similar situations in the past to reach a lightning-fast conclusion about what’s most likely to happen next. The greatest barrier to this level of responsiveness used to be a lack of data, but the exponential growth of data volumes in the last decade has flipped this problem on its head. Today, the big challenge for companies is having too much data and not enough time or power to process it, says Saravana.

Even the smartest human is incapable of gathering all the data about a given situation, never mind considering all the possible outcomes. Nor can a human mind reach conclusions at the speed necessary to drive Live Business. On the other hand, carefully crafted algorithms can process terabytes or even petabytes of data, analyze patterns and detect outliers, arrive at a decision in seconds or less—and even learn from their mistakes (see How to Train Your Algorithm).

How to Train Your Algorithm 

The data that feeds process digitization can’t just simmer.
It needs constant stirring.

Successfully digitizing a business process requires you to build a model of the business process based on existing data. For example, a bank creates a customer record that includes not just the customer’s name, address, and date of birth but also the amount and date of the first deposit, the type of account, and so forth. Over time, as the customer develops a history with the bank and the bank introduces new products and services, customer records expand to include more data. Predictive analytics can then extrapolate from these records to reach conclusions about new customers, such as calculating the likelihood that someone who just opened a money market account with a large balance will apply for a mortgage in the next year.

Germany --- Germany, Lower Bavaria, Man training English Springer Spaniel in grass field --- Image by © Roman M‰rzinger/Westend61/CorbisTo keep data models accurate, you have to have enough data to ensure that your models are complete—that is, that they account for every possible predictable outcome. The model also has to push outlying data and exceptions, which create unpredictable outcomes, to human beings who can address their special circumstances. For example, an algorithm may be able to determine that a delivery will fail to show up as scheduled and can point to the most likely reasons why, but it can only do that based on the data it can access. It may take a human to start the process of locating the misdirected shipment, expediting a replacement, and establishing what went wrong by using business knowledge not yet included in the data model.

Indeed, data models need to be monitored for relevance. Whenever the results of a predictive model start to drift significantly from expectations, it’s time to examine the model to determine whether you need to dump old data that no longer reflects your customer base, add a new product or subtract a defunct one, or include a new variable, such as marital status or length of customer relationship that further refines your results.

It’s also important to remember that data doesn’t need to be perfect—and, in fact, probably shouldn’t be, no matter what you might have heard about the difficulty of starting predictive analytics with lower-quality data. To train an optical character recognition system to recognize and read handwriting in real time, for example, your samples of block printing and cursive writing data stores also have to include a few sloppy scrawls so the system can learn to decode them.

On the other hand, in a fast-changing marketplace, all the products and services in your database need consistent and unchanging references, even though outside the database, names, SKUs, and other identifiers for a single item may vary from one month or one order to the next. Without consistency, your business process model won’t be accurate, nor will the results.

Finally, when you’re using algorithms to generate recommendations to drive your business process, the process needs to include opportunities to test new messages and products against existing successful ones as well as against random offerings, Saravana says. Otherwise, instead of responding to your customers’ needs, your automated system will actually control their choices by presenting them with only a limited group of options drawn from those that have already received the most
positive results.

Any process is only as good as it’s been designed to be. Digitizing business processes doesn’t eliminate the possibility of mistakes and problems; but it does ensure that the mistakes and problems that arise are easy to spot and fix.

From Waste to Gold

Organizations moving to digitize and streamline core processes are even discovering new business opportunities and building new digitized models around them. That’s what happened at Hopper, an airfare prediction app firm in Cambridge, Massachusetts, which discovered in 2013 that it could mine its archives of billions of itineraries to spot historical trends in airfare pricing—data that was previously considered “waste product,” according to Hopper’s chief data scientist, Patrick Surry.

Hopper developed AI algorithms to correlate those past trends with current fares and to predict whether and when the price of any given flight was likely to rise or fall. The results were so accurate that Hopper jettisoned its previous business model. “We check up to 3 billion itineraries live, in real time, each day, then compare them to the last three to four years of historical airfare data,” Surry says. “When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now,’ or ‘no, we think that fare is too expensive, we predict it will drop, and we’ll alert you when it does.’ And we can give them that answer in less than one second.”

When consumers ask our smartphone app whether they should buy now or wait, we can tell them, ‘yes, that’s a good deal, buy it now’.

— Patrick Surry, chief data scientist, Hopper

While trying to predict airfare trends is nothing new, Hopper has told TechCrunch that it can not only save users up to 40% on airfares but it can also find them the lowest possible price 95% of the time. Surry says that’s all due to Hopper’s algorithms and data models.

The Hopper app launched on iOS in January 2015 and on Android eight months later. The company also switched in September 2015 from directing customers to external travel agencies to taking bookings directly through the app for a small fee. The Hopper app has already been downloaded to more than 2 million phones worldwide.

Surry predicts that we’ll soon see sophisticated chatbots that can start with vague requests from customers like “I want to go somewhere warm in February for less than $500,” proceed to ask questions that help users narrow their options, and finally book a trip that meets all their desired parameters. Eventually, he says, these chatbots will be able to handle millions of interactions simultaneously, allowing a wide variety of companies to reassign human call center agents to the handling of high-value transactions and exceptions to the rules built into the digitized booking process.

Port of Hamburg Lets the Machines Untangle Complexity

In early 2015, AI experts told Wired magazine that at least another 10 years would pass before a computer could best the top human players at Go, an ancient game that’s exponentially harder than chess. Yet before the end of that same year, Wired also reported that machine learning techniques drove Google’s AlphaGo AI to win four games out of five against one of the world’s top Go players. This feat proves just how good algorithms have become at managing extremely complex situations with multiple interdependent choices, Saravana points out.

The Port of Hamburg, which has digitized traffic management for an estimated 40,000 trucks a day, is a good example. In the past, truck drivers had to show up at the port to check traffic and parking message boards. If they arrived before their ships docked, they had to drive around or park in the neighboring residential area, contributing to congestion and air pollution while they waited to load or unload. Today, the HPA’s smartPORT mobile app tracks individual trucks using telematics. It customizes the information that drivers receive based on location and optimizes truck routes and parking in real time so drivers can make more stops a day with less wasted time and fuel.

The platform that drives the smartPORT app also uses sensor data in other ways: it tracks wind speed and direction and transmits the data to ship pilots so they can navigate in and out of the port more safely. It monitors emissions and their impact on air quality in various locations in order to adjust operations in real time for better control over environmental impact. It automatically activates streetlights for vehicle and pedestrian traffic, then switches them off again to save energy when the road is empty. This ability to coordinate and optimize multiple business functions on the fly makes the Port of Hamburg a textbook example of a Live Business.

Digitization Is Not Bounded by Industry

Other retail and B2B businesses of all types will inevitably join the Port of Hamburg in further digitizing processes, both in predictable ways and in those we can only begin to imagine.

sap_Q216_digital_double_feature1_images4Customer service, for example, is likely to be in the vanguard. Automated systems already feed information about customers to online and phone-based service representatives in real time, generate cross-selling and upselling opportunities based on past transactions, and answer customers’ frequently asked questions. Saravana foresees these systems becoming even more sophisticated, powered by AI algorithms that are virtually indistinguishable from human customer service agents in their ability to handle complex live interactions in real time.

In manufacturing and IT, Sven Bauszus, global vice president and general manager for predictive analytics at SAP, forecasts that sensors and predictive analysis will further automate the process of scheduling and performing maintenance, such as monitoring equipment for signs of failure in real time, predicting when parts or entire machines will need replacement, and even ordering replacements preemptively. Similarly, combining AI, sensors, data mining, and other technologies will enable factories to optimize workforce assignments in real time based on past trends, current orders, and changing market conditions.

Public health will be able to go live with technology that spots outbreaks of infectious disease, determines where medical professionals and support personnel are needed most and how many to send, and helps ensure that they arrive quickly with the right medication and equipment to treat patients and eradicate the root cause. It will also make it easier to track communicable illnesses, find people who are symptomatic, and recommend approaches to controlling the spread of the illness, Bauszus says.

He also predicts that the insurance industry, which has already begun to digitize its claims-handling processes, will refine its ability to sort through more claims in less time with greater accuracy and higher customer satisfaction. Algorithms will be better and faster at flagging claims that have a high probability of being fraudulent and then pushing them to claims inspectors for investigation. Simultaneously, the same technology will be able to identify and resolve valid claims in real time, possibly even cutting a check or depositing money directly into the insured person’s bank account within minutes.

Financial services firms will be able to apply machine learning, data mining, and AI to accelerate the process of rating borrowers’ credit and detecting fraud. Instead of filling out a detailed application, consumers might be able to get on-the-spot approval for a credit card or loan after inputting only enough information to be identified. Similarly, banks will be able to alert customers to suspicious transactions by text message or phone call—not within a day or an hour, as is common now, but in a minute or less.

Pitfalls and Possibilities

As intelligent as business processes can be programmed to be, there will always be a point beyond which they have to be supervised. Indeed, Saravana forecasts increasing regulation around when business processes can and can’t be digitized. Especially in areas involving data security, physical security, and health and safety, it’s one thing to allow machines to parse data and arrive at decisions to drive a critical business process, but it’s another thing entirely to allow them to act on those decisions without human oversight.

Automated, impersonal decision making is fine for supply chain automation, demand forecasting, inventory management, and other processes that need faster-than-human response times. In human-facing interactions, though, Saravana insists that it’s still best to digitize the part of the process that generates decisions, but leave it to a human to finalize the decision and decide how to put it into action.

“Any time the interaction is machine-to-machine, you don’t need a human to slow the process down,” he says. “But when the interaction involves a person, it’s much more tricky, because people have preferences, tastes, the ability to try something different, the ability to get fatigued—people are only statistically predictable.”

For example, technology has made it entirely possible to build a corporate security system that can gather information from cameras, sensors, voice recognition technology, and other IP-enabled devices. The system can then feed that information in a steady stream to an algorithm designed to identify potentially suspicious activity and act in real time to prevent or stop it while alerting the authorities. But what happens when an executive stays in the office unusually late to work on a presentation and the security system misidentifies her as an unauthorized intruder? What if the algorithm decides to lock the emergency exits, shut down the executive’s network access, or disable her with a Taser instead of simply sending an alert to the head of security asking what to do while waiting for the police to come?

sap_Q216_digital_double_feature1_images6The Risk Is Doing Nothing

The greater, if less dramatic, risk associated with digitizing business processes is simply failing to pursue it. It’s true that taking advantage of new digital technologies can be costly in the short term. There’s no question that companies have to invest in hardware, software, and qualified staff in order to prepare enormous data volumes for storage and analysis. They also have to implement new data sources such as sensors or Internet-connected devices, develop data models, and create and test algorithms to drive business processes that are currently analog. But as with any new technology, Saravana advises, it’s better to start small with a key use case, rack up a quick win with high ROI, and expand gradually than to drag your heels out of a failure to grasp the long-term potential.

The economy is digitizing rapidly, but not evenly. According to the McKinsey Global Institute’s December 2015 Digital America report, “The race to keep up with technology and put it to the most effective business use is producing digital ‘haves’ and ‘have-mores’—and the large, persistent gap between them is becoming a decisive factor in competition across the economy.” Companies that want to be among the have-mores need to commit to Live Business today. Failing to explore it now will put them on the wrong side of the gap and, in the long run, rack up a high price tag in unrealized efficiencies and missed opportunities. D!

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Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

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Millennials Are Transforming How Finance Works

Estelle Lagorce

If you haven’t already, get used to it: Millennials are now officially the largest demographic group in the U.S. workforce. And they have very different aspirations and expectations than their fellow workers do about the nature of work.

Millennials are typically self-motivated and forward-looking. They understand the power of technology and social media, and all the different ways to use both to network and communicate with one another. So does that make them “dangerous” to how finance operates in the future? Their characteristic work style – caring deeply about workplace satisfaction, social networking, and mobile-addiction – changes the historically new-trend-and-technology-averse finance department. As a result, all financial professionals worldwide are now seeing their role and job within the corporate enterprise differently.

On the April 26, 2016 edition of Financial Excellence with Game-Changers Radio, produced by SAP’s Bonnie D. Graham, three thought leaders agreed: Yes, it could make them “dangerous.” (Listen to the episode here.)

According to Henner Schliebs, vice president and head of Global Finance Audience Marketing at SAP, it is not millennials themselves who are dangerous. Rather it is their work style that is being picked up by others in the finance department. As he said, “It’s not really a generational conflict. It’s a shift in society, it’s a shift in the workforce, and everybody is and should be a part of it.”

Another attitude

One of the ways millennials differ from previous generations is in their attitude toward money: They are more money-conscious. And according to John Arellano, senior manager with the Aerospace & Defense and Aviation group at Deloitte Consulting, millennials share 13 times more content about their personal finances on LinkedIn than the preceding Generation X. And this spills over into their style of financial planning.

“Many millennials have started saving for retirement in their early twenties,” John commented. “It’s really interesting how early they understand the importance of saving money and investing money for the future.”

Celina Rogers, vice president and editorial director for CFO Publishing, agreed. “I certainly think it’s interesting that millennials have such a keen interest in personal finance. And I think that feeds into the desire among some millennials to find more secure work – but also to find meaning within that work. And that’s where I think many finance functions will be challenged: to provide that sense of meaning together with the kind of security finance has offered in the past.”

In fact, millennials’ attitude towards work will put the traditional financial department under threat. Millennials want the flexibility to work when they want to – work on a Sunday, for example, and have Monday off. Henner added, “Technology advancement over the last couple of years makes this possible.”

Different expectations

The expectation from millennials that they can be more self-deterministic will undoubtedly be a challenge for many finance departments. But enlightened CFOs are embracing it, as Celina explained. “Many finance leaders see this as a tremendous opportunity. By absorbing millennials and their approach to work into finance, [leaders] can help transform their finance functions in ways that are incredibly important to the business in general.”

Millennials also expect the latest technology at work. They have grown up with the Internet and mobile phones. They want to use their own devices, not the aging company desktop. And they also expect to be able to access from anywhere the data and systems they need to do their jobs.

But according to Henner, there is something else that makes them different, and that will be a catalyst for change. “They want to see what their contribution is to the overall mission of the company and the company’s contribution to society,” he said. “This is a very drastic change in how a CFO should perceive his or her role.”

Fragile loyalty

With millennials bringing lots of fresh ideas to the table, the panel of experts believes it is important to communicate with them and incorporate their ideas. Simply seeing millennials as another resource is not enough. Millennials want their work to have meaning, which requires treating them differently; otherwise they could easily walk away from their job. As John put it, “There are a lot of great millennials in the workplace that a lot of firms need to retain.”

Gaining their loyalty requires rethinking the finance function, what its role in the organization should be, and, importantly, communicating this to everyone involved. Finance can no longer be seen as simply reporting the numbers. Finance needs to report what the company is giving back to society, from a social, environmental, and societal perspective.

Reverse mentoring

Millennials also want to work smarter, not harder. For them it’s not about a work-life balance; it’s about work-life integration. And this can also benefit other demographic cohorts in the workplace. Henner sees a kind of “reverse mentoring” happening, where the older generations are learning from millennials. For example, he observed, millennials are asking, “Why should I do a job that can be automated? If a machine can do it, why should I?”

Millennials also want to be connected to their companies 24×7 so they can work when they want to. “I think this convenience that improves our whole lives is something that we can learn from the millennials,” he continued.

The millennial CFO

So as millennials climb the corporate ladder (if there is such a thing any more) and start taking on the role of CFO, what changes do the experts think they will make?

For John, it will be technology. “With technology, you are going to see a lot of new capabilities.”

Celina added that it will happen quickly, even in the next five to ten years. “I think that the fresh perspective that millennials will bring to finance will really accelerate and drive change, if for no other reason than the value consciousness they will bring.”

For Henner, millennials are already having an effect. “They want this digitalized world because they grew up with it,” he explained. “They grew up with information all over the place and they are expecting the same within the organization [where they work].”

Prediction

So what are the experts’ future predictions regarding millennials? For John, it’s leadership. “Millennials are no longer leaders of tomorrow. They are now leaders of today. So we have to understand that, first and foremost.”

Celina pointed to the influence factor. “I see that in ten years, millennials will really dominate finance leadership. In that capacity, we’ll see tremendous improvement in the kind of enterprise technology that’s working in finance and an expansion of finance’s influence.”

And for Henner, it’s how millennials will manage the generation after them. “Given that 50% of the children going into elementary school today will have a job that does not currently exist, millennials will have to face bigger problems than we’re facing with them right now,” he says. “I wish them all the luck in the world.”

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Estelle Lagorce

About Estelle Lagorce

Estelle Lagorce is the Director, Global Partner Marketing, at SAP. She leads the global planning, successful implementation and business impact of integrated marketing programs with top global Strategic Partner across priority regions and countries (demand generation, thought leadership).