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Real Time Is Key To Furthering Digital Agility

Kevin Benedict

It seems we read daily about the increasing role and importance of information, data analysis, and data security on politics, national security, and global economies.

More data is being generated than ever before, and successful companies are investing in business analytics and Big Data solutions to mine it for competitive advantages. There is a new sense of urgency today as businesses realise data has a shelf life, and the value of it diminishes rapidly over time. In an always-connected world where consumers and their needs are transient, timing is everything, and a special type of data is needed: real-time data. In order to capture competitive advantages and contextual relevance before data expires, enterprises must deploy information logistics systems (ILS) that deliver on the potential fast enough to exploit it.

Mobile consumers are impatient and demand instant results. IT infrastructures must be able to support real-time mobile and Internet of Things (IoT) interactions, and this requirement will increase as mobile commerce is predicted to grow to 47% of all e-commerce by 2018. Supporting real-time information requires not only real-time IT environments, but also digital transformation across the entire organisation. In order to succeed, businesses must react to location-based and time-sensitive information while it is still contextually relevant.

Data is the lifeblood of e-commerce, mobile commerce, and increasingly physical stores where the digital and physical worlds are rapidly converging. As commerce rapidly shifts online and to mobile, the success of products, brands, and companies are increasingly dependent on data and systems that consume it in order to support the demand for more personalised digital experiences. How an organisation makes sense of data, protects it, and disseminates it is a complex and challenging issue.

Data strategies

Data strategies and the execution of them will determine the market winners of the future, and the future is now. Businesses are learning from Amazon, Google, Facebook, Netflix, and others that effective data and analytics strategies are the secret to success in digital markets. Information dominance is now the strategic business goal.

In the book Code Halos, authors Malcolm Frank, Paul Roehrig, and Benjamin Pring describe how the revolution today in data and analytic strategies are impacting industry structures in “consistent and violent patterns.” In another recent study, 82% of investors and equity analysts believe industries are being disrupted by innovations in data and analytics. They believe these innovations will alter competitive dynamics.

In addition to investments in IT, achieving real-time operational tempos in a enterprise takes rethinking business models, organisational structures, and business processes. It requires new ways of thinking and employee training. Supporting the tempo of real-time operations is a daunting task many will fail to prioritise, and they will suffer as a consequence.

The winners of a digital tomorrow will invest in four key areas:

  1. the quality and speed of their information logistics systems
  1. supporting real-time operational tempos
  1. business agility
  1. the ability to use real-time contextually relevant data to personalise digital user experiences

Businesses that embrace digital transformation will optimise their organisational structures and business models to support the operational tempos required by a mobile and connected world. Increasingly mobile and connected device users demand real-time responses. To support real-time responses requires an enterprise to move beyond “human time” and into the realm of “digital time.” Humans as biological entities operate at a pace governed by the sun, moon, and the physical requirements to keep our carbon-based bodies alive. These requirements and mental limitations make scaling humans beyond these time-cycles impossible without augmentation. Augmentation takes the form of intelligent process automation, artificial intelligence, and algorithms. These types of augmentation technologies have the advantage of being able to work 24/7, 365 days a year, and don’t (as yet) ask for holidays off.

Once an organisation is capable of supporting real-time tempos, and can support the personalised interactions mobile users demand, the challenge becomes business agility.

Agility is the speed at which a business can recognise, analyse, react, and profit from rapidly changing consumer demands in a hypercompetitive market. Businesses that can accurately understand customer demand and their competition, and then respond faster, will soon dominate those that are slower. The military strategist John Boyd called these competitive advantages, “getting inside of your competitor’s decision and response curves.” This means your actions and responses are occurring at a pace that surpasses your competitions’ ability to comprehend it.

Sub-optimal information logistics systems and the glacial operational tempos of yesteryear will not succeed in today’s or tomorrow’s world, and company valuations have already begun to reflect this. One-third of investors and equity analysts surveyed believe that good data and analytics strategies are rewarding companies with higher valuations. Gartner’s Douglas Laney has even coined the phrase “infonomics” to describe how information, as a new asset class, can be measured to estimate its impact on company valuations.

To succeed in the digital future, CIOs must implement innovative data strategies and information logistics systems capable of winning in a real-time world where contextually relevant, instant, and personalised experiences are required. They must develop company cultures where change is viewed as an opportunity. They must digitally transform their businesses to operate at real-time tempos and move beyond “human-time” limitations to algorithm supported “digital-time.” They must understand that rapidly changing digital consumer behaviours mandate companies operate in a more agile manner capable of rapid responses to new opportunities and competitive threats.

For more insight on aligning your digital and in-store sales and marketing (and why it’s essential to your customers’ satisfaction), see Our Digital Planet: See It, Click It, Touch It, Buy It.

This article originally appeared on the Guardian Media & Tech Network’s Digital Business hub and was syndicated with the author’s permission.

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Kevin Benedict

About Kevin Benedict

Kevin Benedict is senior analyst at Cognizant's Center for the Future of Work, SAP mentor alumnus, speaker, writer, and mobile and digital strategies expert. He is a popular keynote speaker who, in the past three years, has shared his insights into mobile and digital strategies with companies in 17 different countries. Benedict has over 30 years of experience working with enterprise applications, and he is a veteran mobile industry executive. He wrote the forward to SAP Press' best-selling book on enterprise mobility, "Mobilizing Your Enterprise with SAP," and has published over 3,000 articles. Find him on Twitter @krbenedict.

5 Reasons Manufacturing SMEs Need Cloud More Than Ever

Lindsey LaManna

The business environment for sales teams in manufacturing and engineering industries is increasingly demanding: IT infrastructures have become complex, whilst everyday sales activities claim simplicity and ease. Additionally, CEOs demand a clear overview of incoming opportunities and the business process.

So how do cloud solutions help sales teams in manufacturing and high-tech industries to survive?

Before looking into the specifics of cloud solutions for sales teams, we need to understand the current situation:

Outsourcing of IT has failed

In the last decade, there was a major shift in IT operations. Starting in the late 90s, companies optimized their IT budget by shifting personal costs to project costs via outsourcing. Furthermore, decisions of “buy vs. build” often were made in favour of building software. At this time, standard software often did not exactly match the companies’ demands, and they preferred a custom-tailored solution.

The outsourcing and custom-tailored solutions approach of the past is now becoming a legacy of many companies. As a result, IT is sitting on outdated solutions that are either expensive to maintain or are not maintained at all. Upgrade projects often fail since they do not offer additional benefits to the business. At the same time, sales people are left to standard office software such as Microsoft Excel to price quotes and calculate discounts, and to MS Word, which is prone to errors and lacks efficiency, to create proposals.

However, the rise of software-as-a-service changed the availability of new specialized software as well as the IT operations model dramatically. Chances arise even for small or medium enterprises (SMEs) to support their sales people in a way that is not only affordable but also the least disruptive in regards of their current sales practices. 

As business becomes more demanding, the role of IT grows more strategic than ever

In the past, IT was more focused on operating internal systems and developing custom solutions – or managing outsourced teams. Being faced with fast-paced business environments and CEOs demanding transparency and control over business processes, their mandate is becoming more strategic. Nowadays CIOs are key to strengthen and optimize business processes and thus take over a more consultative role, which supports the business owners’ decision-making process.

Performing this shift in responsibilities, CIOs must also balance and reorganize their financial and human resources to cater for more consultative workloads. Cloud software can facilitate this shift as the operations, maintenance, and support responsibilities move to specialists, which are rented in the sense of software-as-a-service (SaaS). At the same time, the incurring costs are shared among the customers of a software product and shift from CapEx (Capital Expenditures) to OpEx (Operational Expenditures) via a monthly or yearly subscription fee.

TCO comparison cloud vs. traditional software

Centralized internal systems do not work for sales people on the road

Another trend that emerged during the last decade—not only in manufacturing—was to centralize major systems like orders and material being maintained in one ERP system. This worked out nicely for personnel with a fixed work place, but is a problem for mobile people such as the sales force.

Opening up central systems for external access to provide sales reps with relevant information where they need it poses many security and data compliance risks. IT departments try to cover this issue by management of the internal network, reverse proxies, and VPN. However, secure operations of complex network setups remain complex, and users often experience these approaches as slow and cumbersome. Sales reps depend on fast and reliable access to sales relevant data anywhere and at any time.

Cloud applications can be the answer as they offer standardized, securely managed ways to synchronize or access internal systems data like ERP data and make these accessible in the cloud. Managing bandwidths and network to the users are both off the shoulders of IT, and become part of the SaaS package.

Collaboration needs vs. ad-hoc processes

Collaboration is Key in manufacturing_web
Nowadays, especially in manufacturing, the value chain of a company with several locations can appear scattered, hence hard to support by IT. Sales reps are located where the customers are, whilst manufacturing premises are built and operated in lower-cost areas. Nevertheless, the need for collaboration between sales reps, manufacturing sites, and engineering experts is key to produce marketable solutions in an engineering-to-order scenario and to produce accurate customer quotes.

Supporting this essential part of the business process is no major feature of traditional software, but rather managed externally. Employees use email or other collaboration tools and applications. These gap fillers do not only pose security risks to sensitive data, they also don’t help structuring the often ad-hoc initiated collaboration process. Mailboxes are clogged, data gets lost, and IT finds themselves surrounded by a jungle of shadow IT.

A cloud solution can tackle this challenge when the respective collaborative processes are backed deeply into the system itself. Collaborative processes such as information exchange, collaborative quote inputs by different parties, document sharing, and approval processes happen directly in the software and on the objects that run the business processes rather than externally; for example, via email. Leveraging the numeral integration capabilities of a reasonable cloud solution, the users such as sales reps and managers can enjoy seamless integration with email programs like Microsoft Outlook.

Usability is key for sales people

As reality shows, IT cannot force users to stick to outdated software with insufficient features to support their everyday business challenges. Furthermore, users also stop using given software or tools if the usability is not satisfactory. The so-called “consumerization of business software” describes this trend and explains the resulting behaviour: Sales people go back to using pen and paper, look for easy-to-use applications running on personal devices like tablets, or don’t document their sales process at all. This is not only inefficient and non-scalable for an entire company, it also leads to a lack of transparency of the sales process, and managers are unable to pull production forecasts or make accurate revenue predictions.

Cloud software usually covers only a small business process in comparison to the much bigger on-premise suites of the past. This enables cloud solutions to focus on the specific requirements of users like sales reps and sales managers. Hence, usability is backed into the DNA of cloud companies, and this term does not only cover design and interaction patterns, but also general performance and response times which are essential in fast-paced business environments. The integration of existing workflows via Excel uploads and Excel, Word, or PDF downloads further help to increase the user adoption rate, thereby increasing productivity.

The 5 reasons to shift sales operations to the cloud

Summarizing the main challenges that CIOs in manufacturing and engineering businesses face nowadays, the following chances for a shift of the sales operations to the cloud arise:

1.  For SMEs, cloud solutions are an affordable and least disruptive way to replace legacy custom solutions or error-prone and inefficient gap-filling solutions.

2.  With the software-as-a-service model, IT operations, maintenance, and support can be shifted to experts whilst costs are moved from CapEx to OpEx, and the total cost of ownership (TCO) decreases drastically.

3.  Cloud solutions relieve the negative effects of data centralization for sales reps on the road, and deliver relevant sales knowledge to them in a reliable manner and under high security standards.

4.  A reasonable cloud software not only fulfills collaboration needs amongst the users, but also deeply backs collaboration processes into the system and ensures compliance with security standards and internal company approval structures.

5.  A strong focus on usability in cloud solutions leads to a high user adoption rate, and thus enables an efficient sales process in the organisation.

outdated software user experience vs. high usability in cloud

Confronted with the role shift for IT departments from operations to strategic responsibilities – a fast-paced and highly competitive business environment and a highly demanding user group – CIOs of today need to invest in cloud solutions if they want to keep up. Leveraging smart and lean cloud solutions that limit costs and multiply productivity can be seen as a road to business success for SMEs in manufacturing and engineering industries.

This article originally appeared on the In Mind Computing Blog.

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Lindsey LaManna

About Lindsey LaManna

Lindsey LaManna is a Marketing Manager at SAP. Her specialties include social media marketing, marketing strategy, and marketing communications.

Data: The Foundation Of Real-Time Digital Business

R “Ray” Wang

From Big Data to small data, the digital world measures and values every interaction. Digital technology enables every touch point, click, conversation, picture, and byte of digital exhaust to be used to improve decision-making. In fact, data provides the foundation for success in a real-time digital business. This is why organizations must carefully design a data strategy as the first step in digital transformation.

To get started, successful organizations map out a data-to-decisions framework (see Figure 1). This framework uses all types of upstream and downstream data (for example, structured, unstructured, big, small, and contextual) to align with business processes, creating information flows. From order to cash, procure to pay, campaign to lead, hire to retire, and incident to resolution, context is applied to information flows.

In the next step, algorithms apply context attributes such as role, relationship, weather, product, geo-spatial location, time, sentiment, and even intent to the information flows. The bigger the data set, the more opportunities for algorithms to find patterns of insight. The goals are to ask questions of the data and expose patterns of insight, using performance, deduction, inference, and prediction.

Traditionally, most systems stop after discovering insight. In a digital business, though, insight powers the ability to guide decision-making. By using the ability to take actions based on data, organizations can consider how to identify the next best actions, make recommendations, suggest risk mitigation, and even suggest that no actions be taken. By designing a data-to-decisions framework, organizations gain the ability to build a digital business and enable real-time business.

Once a data-to-decisions foundation is established, organizations can think about how they can apply the framework to augment decision-making. Successful leaders start by putting together a list of questions they seek answers for. They prioritize that list and then begin addressing these questions within the data-to-decisions framework. The secret to success is not what answers can be provided, but what questions should be asked. Successful organizations learn how to ask questions that have never been asked before, sometimes by employing techniques such as design thinking.

Figure 1: Use the data-to-decisions framework to drive real-time business
Data-real time

With mastery of data to decisions, organizations eventually will move from real-time to right-time models. Real-time immediately provides data to decisions as requested, resulting in a data deluge. Unfortunately, real time on its own may not be fast enough. Organizations may need to anticipate when data should be delivered. Why? Real time describes the speed at which the transformation from data to decisions must occur. Right time is about the precision that relevant, contextual information can provide once cognitive capabilities are applied to the data-to-decisions framework. In other words, right-time systems ensure organizations see what they need to see before they even know they need it.

So where do you begin?

1. Start by identifying the questions your organization seeks to answer.
2. Ask what traits make up the most valuable products, employees, customers, and suppliers. These traits drive the questions around what context matters.
3. Determine the information flows and business processes that drive context.
4. Understand the people and devices touched to provide the next level of journey mapping.
5. Apply the data sources and channels of data to recommendation engines and decision frameworks.

After taking these 5 steps, you can then start creating big data business models powered by insight. Digital technologies, data, and algorithms should all be aggressively used to create business models that take advantage of insights. Visibility, relevance, and immediacy will come from these insights-based business models. The goals are to simplify the complexity of decision making and enable real-time digital business.

Learn more how real-time business is impacting companies like yours.

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R “Ray” Wang

About R “Ray” Wang

R “Ray” Wang is the Principal Analyst, Founder, and Chairman of Silicon Valley based Constellation Research, Inc. He’s also the author of the popular business strategy and technology blog “A Software Insider’s Point of View”. With viewership in the 10’s of millions of page views a year, his blog provides insight into how disruptive technologies and new business models such as digital transformation impact brands, enterprises, and organizations. Wang has held executive roles in product, marketing, strategy, and consulting at companies such as Forrester Research, Oracle, PeopleSoft, Deloitte, Ernst & Young, and Johns Hopkins Hospital. His new best selling book Disrupting Digital Business, published by Harvard Business Review Press and globally available in Spring of 2015, provides insights on why 52% of the Fortune 500 have been merged, acquired, gone bankrupt, or fallen off the list since 2000. In fact, this impact of digital disruption is real. However, it’s not the technologies that drive this change. It’s a shift in how new business models are created.

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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How Digital Supply Chains Are Changing Business

Dominik Erlebach

In the real world, customer demands change and supply disruptions happen. Consumers and collaborative business partners have new expectations. Is your supply chain agile enough to respond?

As technology expands and the Internet of Things becomes increasingly prevalent, a digitized supply chain strategy is moving into the core of business operations. Today’s high-tech companies must adapt – speed and accuracy are crucial, and collaboration is more important than ever. The right digital tools are the key to developing an extended, fast supply chain process. The evolution of the extended supply chain can be seen with McCormack and Kasper’s study on statistical extended supply chains.

Ultimately, however, everything comes down to the efficient use of data in decision making and communication, improving overall business performance, and opening a whole new world of opportunities and competitive differentiation.

The changing horizon of supply chains in technology

Supply chain management now extends further than ever before. Until recently, the task for supply chains was simple: Companies developed ideas, produced goods, and shipped them to distribution points, with cost efficiency as one of the most prevalent factors. As the supply chain evolves into extended supply chain management, however, companies must plan beyond these simple steps.

The Digitalist summarizes these new market conditions as collaborative, customer-centric, and individualized. New products need to hit markets much faster, and customers expect premium service and collaboration. Increasingly, the supply chain must meet rapidly changing consumer demands and respond accordingly. Management can now be conducted by analysis of incoming data or by exception, as discussed in a presentation by Petra Diessner. Resilience and responsiveness have clearly become differentiators in the cutthroat high-tech market. To keep up, high-tech companies must change their method of conducting business.

What is the extended supply chain?

The extended supply chain refers to widening the scope of supply chain planning and execution, not only across internal organizations, but beyond a company’s boundaries. High-tech companies must involve several tiers of suppliers, manufacturers, distributors, and customers. Some businesses also study the popularity and dynamics of a product directly, such as through mining social media data. To lead the competition, modern businesses must orchestrate the extended supply chain to keep up with consumer demands, as consumers now look for fully integrated service experiences.

What makes the supply chain work in extended form?

There are several key factors to consider when updating your supply chain model. The first, access to information, is essential to managing digitized extended supply chains and unleashes benefits that can help your company grow beyond traditional models. All companies gather information, but companies that use this information effectively will excel where others do not.

World Market Forum discusses this in its report on extended supply chains. Digitizing the supply chain process can help manage even an extended, complex network and provide key access to critical information. The success of digitization relies on gathering accurate, pertinent information in a format that can be readily applied and used.  While speed is important, it is not enough. The information itself must be applicable to the company’s needs and it must provide data that allows companies to make more informed decisions about product growth, marketing, and supply chain distribution.

Real-time information access is a primary benefit of digitized supply chains. Market trends become instantly visible with real-time data and cloud-based analytics, as Marcus Schunter notes in this analysis. This insight allows companies to plan for new products and platform development as the market shifts. Eliminating the need to wait months to access and analyze data is critical for the high-speed technology market and also allows for assessment of critical situations. Resolving problems as they occur is more efficient than post-mortem analysis and allows companies to act rather than react. In extending your supply chain, look for technological advancements that allow you to gather data and analyze it in real time.

Consolidation of digital information is another major benefit to the digitized supply chain. Localized digital information can be gathered and analyzed to form executable action plans. Data becomes part of the planning and problem-solving cycle, improving overall communication and allowing for fuller, more meaningful problem-solving and planning. With digitization, speed is a factor, but the relevancy and application of information separates successful companies from the pack. For companies looking to extend their supply chain, this is a key element when searching analysis tools and platforms.

Is your high-tech organization ready to meet the challenges of a complex market and navigate the digital economy?

To learn more about digital transformation in high-tech supply chain, visit here.

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