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SAPPOV: Why Chemical Companies Need The Cloud

Stefan Guertzgen

At around $3 trillion, the chemicals sector is one of the biggest and most important in the world. And while the largest companies take the lion’s share of this pie, there is a thriving, 

S&OP process for chemicalsfast-moving market for small and mid-sized players in the industry.

These companies typically have the same needs as the big guys – HR, finance, all of the standard line-of-business functions – plus chemistry-specific operations. But to find a competitive edge, they also need to be able to do flexible batch operations and rapidly set up new equipment and processes at a small scale.

Traditional operational models only go so far. To fight for a share and win it, they need every advantage they can get. In short, they need the cloud.

I can think of five compelling reasons why:

  1. Mobile workforce. Whether on the floor at a processing plant, with a client or on the move, empowering employees to sift real time data and make decisions on the fly revolutionizes their potential across the board.
  1. Minimize disruptions.Chemicals companies operate across a huge spread of regulatory environments. These aren’t all compatible and they often change. The right sort of cloud setup means that problems can be anticipated – and stakeholders informed – with time to do something about them.
  1. Scalability. A cloud-hosted solution available at short notice and on a hosted monthly subscription basis does not need the up-front capital back-up that larger players enjoy. If there’s a peak in business in an emerging market, for example, you can meet that need quickly and easily.
  1. Collaboration.In a flexible, fast-moving operation (and indeed many larger ones), not all of the key team members will use the same technologies. With the right technology, collaboration – as well as transparency and accountability – are easily managed.
  1. Differentiation through Innovation.Product life cycles are shrinking, customers get more and more demanding and competition gets tougher in today’s global world. Product innovation, but also rapid business model or process innovation are powerful weapons to survive or thrive in such an environment. Here cloud solutions can help to secure rapid access to latest technology innovations without going through time consuming and costly (re)-implementation projects.

While cloud computing has yet to fully penetrate the chemicals industry, other sectors, such as retail and healthcare, have picked it up enthusiastically. You don’t have to look far to see some intriguing examples of best practice and unprecedented agility. Amazon, for example, in its warehousing, or UPS in its logistics, not to mention much of the technology you use without even thinking on a smart phone.

It’s no great leap to see that the technology is relevant to more than one sector. The real revolution cloud-based solutions promise is not so much what they deliver as it is how they deliver it – with unprecedented speed, simplicity and scalability, plus reduced cost and risk.

To learn more about we can help you with your business challenges please have a look at SAP’s Solution Explorer for the Chemicals Industry: https://rapid.sap.com/se/#index?indids=i_chemical and find out more about our cloud solutions: http://www.sap.com/uk/pc/tech/cloud.html

What do you think about the issues discussed here? Continue the conversation in the comments below and on Twitter @SAP4Chemicals

Dr. Stefan GuertzgenDr. Stefan Guertzgen works for 6 years as Global Director for Industry Solution Marketing Chemicals at SAP. Prior to this assignment he has worked for 11 years in the chemical Industry (Chemtura) in various positions comprising R&D, Global Business Development, Sales and Business Process Management, and Sales & Operations Planning. In addition, he has 7 years experience in Presales and Management Consulting for the process industry (AspenTech, AT Kearney, and SAP Business Consulting) with focus on business operations.

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About Stefan Guertzgen

Dr. Stefan Guertzgen is the Global Director of Industry Solution Marketing for Chemicals at SAP. He is responsible for driving Industry Thought Leadership, Positioning & Messaging and strategic Portfolio Decisions for Chemicals.

More Resources, More Problems

Danielle Beurteaux

This is the second of a two-part series on resource volatility. As noted in the first post, globalization has created an environment of resource volatility. This post, with numbers 11 through 20 on the list, describes resources that are more stable than the previous 10. However, that doesn’t mean there isn’t turmoil, whether that’s environmental concerns in Indonesia’s palm oil production industry, or community organization for water rights in Chile. And, of course, whatever China does, the markets follow.

Top resources and trends

11. Natural Gas

According to the International Energy Agency, most natural gas comes from Russia, the United States, Canada, Qatar, and Iran, and the countries that use the most are the U.S., Russia, China, and Iran. There are sufficient reserves of natural gas, again according to the IEA’s projections, that should last past the year 2040. Liquefied natural gas, which is produced mostly by Qatar, with Australia set to overtake Malaysia for second place, has had a flat market recently. There isn’t the demand to keep up with increased production, so liquefied natural gas producers are looking for new markets, like cruise lines, to grow demand.

12. Tin

Most of the world’s tin comes from China and Indonesia. The tin market tanked last year because of less demand and lots of tin, although it did rally in July and then improve earlier this year, mostly because Indonesia is exporting less and easing the flood of tin on the market.

13. Gold

It seems like everyone’s crazy for gold right now. The precious metal is often perceived as a safer investment than other asset classes, and it’s up 20% this year. Famed investor George Soros just bought $264 million worth of shares in Barrick Gold. The Toronto-based gold-mining company is the world’s largest. Gold prices bumped down a bit while the market waited on the Federal Reserve’s meeting minutes, but some are saying gold will soon recover – and then some.

14. Nickel

Russia, Canada, and New Caledonia are the largest producers of nickel. Most is used to make stainless steel. Like several other commodities we’ve examined, there is more production than demand of nickel at the moment, which has led to depressed prices. China is a big consumer of nickel for stainless steel, and the country is using less because of a slowing real estate market.

15. Beef

The global demand for beef is up, but production is down due to a variety of factors. One is Australia’s decreased production due to drought conditions, which will mean 300,000 tons less beef for export this year. As Australia is a favored trading partner of the U.S., that will affect the American beef market. A recent study from Radobank predicts that China will increase live cattle imports for domestic processing, and Brazil will enter the U.S. market as well.

16. Wheat

It’s a good year for wheat. North American wheat production is doing well, although levels are down from the previous year, with five percent less planted in the U.S. and six percent less in Canada. According to the most recent USDA World Agricultural Supply and Demand Estimates report, total U.S. wheat supplies and use are up six percent and seven percent, respectively. Globally, the report projects a two percent increase in wheat supplies, and consumption will increase, too.

17. Iron Ore

Earlier this year, the iron ore market jumped, reportedly because of the Chinese government’s moves to help along the country’s economy. Things have settled down since then, with recent trading sending the per ton price downwards 22.9% from its high in April, which seems to be due to China’s increased crude steel production and also the government’s stopping speculative trading. They’ve also committed to transportation infrastructure projects, but there is still too much iron ore compared to demand.

18. Copper

As with iron ore, China’s announcement that it would be investing in transportation infrastructure affected the price of copper recently. This is likely a welcome piece of news, as copper had been trading at the lowest levels since March 2009. Output and demand are both projected for small increases this year. Chile has the largest open pit mine and the largest global reserves of copper, but it’s been facing difficulties in recent years including lack of water, which is essential for mining, and local community resistance.

19. Palm oil

Palm oil is a global big business to the tune of $50 billion, which is projected to increase to $88 billion by 2020. It’s in almost everything these days because it’s inexpensive, stable, and can be used for many applications. (It’s not always listed on ingredient labels as palm oil).  Most is produced in Malaysia. It’s also a bête noire of environmentalists – it’s linked to deforestation, the recent massive forest fires in Indonesia which were set, it’s thought, to clear land for plantations, and lost habitat for orangutans and increased worries about their extinction.

20. Aluminum

Aluminum rose overall in 2015, but took a dive in the last few months of the year. Market-watchers are hoping that China’s announcement that it will reduce aluminum output will help energize the market once oversupply is balanced. But one of the world’s biggest producers, Alcoa, is reorganizing, which could be an indication that the company is preparing for an era of depressed prices, despite continued healthy demand.

Digital transformation is affecting different industries at different speeds and on different scales. IDC reveals how in The Internet of Things and Digital Transformation: A Tale of Four Industries.

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Chemical Industry: 4 Opportunities Provided By Internet Of Things

Stefan Guertzgen

Chemical firms are embracing the Internet of Things, and in doing so, they are making new partnerships possible.

Technology improvements allow firms to partner with companies in many fields. With chemical manufacturing’s thin profit margins, these partnerships make prudent business sense.

Energy and tech firms are new potential partners, as are equipment makers. Firms with vision see possible ties with customers and subcontractors as well.

The Internet of Things (IoT) is driving these new connections. The IoT refers to the use of sensors, computers, and wireless connections to connect physical objects to each other.

By 2020, it’s estimated that between 30 billion and 50 billion objects will be connected. These connected objects will automate processes, find and self-correct problems, and record and send data to central servers. All of this data can be analyzed to modify and improve products and processes.

The Internet of Things and the chemical industry

As the cost of sensors and storage drops, so do the barriers to entry into the many possibilities available to the chemical industry. The technologies allow improved product security and safety. With connected products, processes, and people, firms can improve performance, minimize supply chain issues, and improve product quality.

Let’s take a closer look at some of the possibilities and partnerships these smart technologies offer.

Predictive maintenance

Downtime and unplanned maintenance are common issues in the chemical industry. Smart technology is solving those issues through the use of sensors that track quality and performance. Computers are raising or even addressing issues in real time to reduce equipment breakdowns. Equipment is more effective and maintenance is more efficient.

Connected devices generate vast amounts of data. Powerful analytics programs can interpret that data to improve quality. Augmented reality uses 3D visualization tools to improve maintenance and service.

Take, for example, the issue of batch quality. Most chemical makers can only assess a limited number of batches at a time. Big Data tools now enable thousands of batches to be analyzed together. This metadata lets companies improve production processes, yield rates, order fill rates, and per-batch costs.

Precision farming

Farmers today want to use chemicals in precise ways to produce higher yields. This “precision farming” requires a trusting partnership among many vested partners. Farmers need to work with agribusiness suppliers and chemical makers. Tech firms, equipment makers, and traders are also key players.

Successful precision farming requires tech platforms to handle large amounts of data. All stakeholders need to be able to access the data and collaborate in a secure virtual environment.

How does it all work? Imagine a system where sensors are constantly measuring soil quality. Data on water, nutrients, and pesticides are recorded and correlated. Analytics predict weather and its impact on a crop and adjust the rates and amounts of applied materials. Yields and quality are tracked and analyzed to find optimal ratios. Overlaid pricing and expense models recommend crops with the highest possible profit margins.

The results are significant in many areas. Farmers are more profitable. More people are fed with less environmental impact. Manufacturers improve future versions of equipment, seeds, and chemicals.

Improved logistics

Reducing friction along the logistics chain is much improved with the IoT. Sensors and RFID tags can ensure products remain quarantined or in specific locations. Contamination and attacks, either physical or cyber, can be detected faster and authorities alerted. Dispatchers can track transportation fleets in real time to predict and track deliveries.

Warehouse operations become far more efficient with these newer tools. With virtual reality, users can “see” products in real time, reducing the need for warehouse pick lists. Trackable specs and expiration dates can improve the efficiency of picking, packing, and put-away work. Data analysis can reveal the best use of available space and how to coordinate with suppliers on receivables.

Reducing energy expenses

Energy usage and regulatory controls are significant costs for most chemical manufacturers. IoT devices can address both concerns.

Installed sensors track energy usage and predict outages. Collected data ensure and verify regulatory compliance.

Analytics identify usage patterns and inefficiencies. Firms can make better decisions about energy purchases. Conservation measures can be identified. Not only do these tools offer cost reduction, they create greener operations.

Developing a strategy

So how do chemical firms develop a strategy that allows for these complex partnerships to develop and persist? Here are six considerations.

Innovate: Rapid advances in mobile. cloud and Big Data technologies are bound to continue. Firms that embrace these technologies and infuse them in planning are likely to take the lead and increase market share.

Think green: Whether your firm is B2B or B2C, IoT products can lead to greener outcomes and add marketable value to your line.

Global view: Connected supply chains, distribution, and products allow for a global operational perspective as well as global business opportunity.

Data and analytics: With more connected products comes more data. Chemical firms need to address storage capacities and tools to crunch all those numbers. Fortunately, cloud-based storage costs continue to drop and Big Data analytics tools are becoming more robust.

Infrastructure partners: Hardware, software, sensors, applications, telematics, and mobile devices are a part of your business now. View the vendors as strategic partners. Collaborate with them on new products and procedures.

Vigilance: Threats of attack and contamination are all too real in the chemical industry. Today firms need to also consider customer data protection and privacy. One downside to IoT is the proliferation of products that can be hacked, stolen, or tampered with.

Conclusion

Smart products provide extraordinary opportunity in the chemical industry. Firms that embrace the need to change and find vertical and horizontal partners will be well positioned. Rich data will allow for better-informed decisions on operations and revenue opportunities.

Start your journey now! Learn more about the value digital transformation brings to your company and establish the right platform and road map for transition.

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About Stefan Guertzgen

Dr. Stefan Guertzgen is the Global Director of Industry Solution Marketing for Chemicals at SAP. He is responsible for driving Industry Thought Leadership, Positioning & Messaging and strategic Portfolio Decisions for Chemicals.

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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Strengthening Government Through Data Analytics

Dante Ricci

When it comes to analyzing data, you could say that there is a clash in culture due to disconnect within the government workforce. This is partly due to the fact that many organizations don’t have people in place with the right technical skill sets. But government can uncover hidden insights to drive better results and create more value for citizens.

The need has never been greater to empower knowledge workers with a comprehensive – yet simple – integrated platform that helps unlock the real value in data for smarter decision-making.

Governments move toward constituent-centered platforms

The fact is, leading government organizations have begun to transform by using consumer-grade solutions to garner better insights from data. The key lies in self-service and automated analytics that do not require technical skill sets. Such solutions enable government personnel at all levels to shift from asking IT for historical reports to a real-time and predictive view that considers multiple data points to deliver a personalized view.

Poised with the right technology and collaborative mindset, governments can uncover new insights to make life better, safer, and healthier, when:

  1. Technology is intuitive and easy to use.
  2. Personnel can make decisions based on a combination of historical and real-time data rather than decisions based on historical perspective alone.
  3. Collaborative technology can include constituent insight and ideas for better decision making.

Digital transformation of government removes that massive barrier between agencies and departments using a platform that shares data and removes the friction that slows down the entire process. The result is that agencies are able to do more, produce better results, and still save money. Digital by default is the key. The rewards are significant for those who successfully leverage analytics: stretching their competitive advantage, driving innovation, and improving lives.

Predictive solutions that appear before your eyes

Digitalized governments run frictionless with decisions based on real contextual insights. Analytics help leaders see problems before or as they occur. That real-time connection identifies potential problems and gives management time to correct them. As real-time data becomes available through input from sensors, transactions, constituents, and other information channels, decisions can be made at the moment of opportunity.

Putting it together

What happens when you need to make decisions, but your data is two years old? What if you need to rewrite a policy that focuses on performance and cost — but you have no information about costs?

Those sorts of problems occur every day. In the first scenario, your decision may be wrong because the data changed. In the second scenario, the policy update may be late. Both potential outcomes reflect negatively on performance and can negatively impact the safety and quality of citizens’ lives. These are both examples of the friction that occurs within governments. They are also the reasons why relevant and timely data is necessary.

The power and tools that a digital government wields are transformative. The rewards for government are many: lower costs, improved services, safer communities, and a better overall quality of life.  Services become seamless. Systems become fluid. Operational costs drop and better outcomes occur.

In short, you make better decisions when they are based on facts and context, not feelings. People who need help get help quickly. Operational issues become identified and fixed. People are happy. And isn’t that the way government should work?

Are you ready for change?

Read about more about SAP’s perspective on digital government here.

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About Dante Ricci

Dante Ricci is the Global Public Services Marketing & Communications lead at SAP. His specialties include enterprise software, business strategy, business development, cloud computing and solution selling.