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Using Predictive Analytics For Planning, Forecasting – And Decision-Making

Henner Schliebs

In a global survey of 1,544 CFOs and finance executives by CFO Research, three-quarters anticipate making effective use of Big Data over the next five years. Finance organizations will need to make significant changes in their processes, skills, and technology to use this data to their advantage.

The best finance organizations are using predictive analytics to forecast future performance and drive strategic decision-making. To be clear, predictive analytics refers to the organizational capability to discover and communicate meaningful patterns in data to predict and improve business performance, recommend action, and guide decision-making. Rather than relying primarily on historical information, finance organizations can use predictive analytics to identify trends, analyze key variables, examine what-if scenarios, and so on – live.

Here are some examples of how the smart use of predictive analytics has contributed to the success of some companies and enhanced the impact of the finance organization.

Increasing forecasting frequency, reducing cycle time

Many Americans look to the American Automobile Association (AAA) for travel assistance, insurance, and emergency towing. To optimize service from the AAA motor clubs across the United States and Canada, the AAA national office built a centralized “action center” to provide better insight into member needs. With next-generation predictive analytics, AAA could better understand customers’ needs by having real-time access to data. This led to enhanced marketing campaigns and reduced customer attrition rate.

Live Oak Bank lends exclusively to small businesses and specific professions like veterinarians and pharmacists, and due to the nature of these customers, the bank values speed and flexibility. The bank is guided by executives who are industry leaders in both finance and technology, bringing innovation and efficiency to the lending process. The bank leverages real-time analytics to make better decisions more quickly. With the predictive analytics platform, decision-makers now enjoy greater collaboration and transparency, and the team can respond quickly to managers.

As the UK’s largest insurer and a leading provider of insurance and asset management, Aviva protects around 31 million customers worldwide with insurance, savings, and investment products. Tapping into predictive analytics models helped Aviva gain the insight needed to serve clients with offers most relevant to their interests. The company made use of predictive analytics to generate propensity models for more targeted customer groups, rather than a generic group, which allowed staff members to make better decisions and more accurate projections for clients.

One of the world’s most renowned manufacturers of skylights, VELUX from Denmark, uses predictive finance to optimize the balance sheet by better understanding the financial impact of warranty claims, and therefore improve customer service.

Use cases for different financial processes can add some ideas to your agenda.

Harnessing unstructured data

Traditionally, software has been useful in reading and analyzing structured data, but the volume of unstructured data – from external financial reporting systems, RFID sensors, and social media, for example – is exploding. Predictive analytics can help CFOs harness it for more accurate planning, forecasting, and decision making based on what’s happening now and what’s likely to happen, rather than what happened in the past.

To learn more about how finance executives can empower themselves with the right tools and play a vital role in business innovation and value chain, review the SAP finance content hub, which offers additional research and valuable insights.

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About Henner Schliebs

Henner Schliebs is the Vice President and Head of Global Finance Audience Marketing at SAP. He is a progressive sales/marketing executive with 15+ years business software solutions focused on corporate functions, offering superb marketing and Go To Market skills and proven track record in enterprise software solutions, along with significant experience in solution management and customer engagement.

Real-Time Analysis Tools Critical To Improving Finance Performance [INFOGRAPHIC]

Viki Ghavalas

The majority of finance executives agree that real-time analysis tools are key to making better business decisions, according to a report by CFO Research and SAP titled “The Future of Financial Planning and Analysis.” However, executives polled also believe that their current systems still need more improvement to be able to make a positive impact on the business. Executives surveyed point to four main priorities for their FP&A tools.

Finance executives surveyed expect the demand for real-time analysis tools to grow in the coming years. However, the survey also shows that having these tools is not enough and that stakeholders also expect analysis and insights from finance that are simple and actionable.

Data in financial planning and analysis

Learn more about what finance executives are projecting for FP&A by downloading the “The Future of Financial Planning and Analysis” report.

Are you monitoring business performance in real time? If not, read Boosting Efficiency For CFOs And The Finance Function.

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About Viki Ghavalas

Viki Ghavalas is worldwide program manager for the finance line of business at SAP.

Why Banks Should Be Bullish On Integrating Finance And Risk Data

Mike Russo

Welcome to the regulatory world of banking, where finance and risk must join forces to banking executiveensure compliance and control. Today it’s no longer sufficient to manage your bank’s performance using finance-only metrics such as net income. What you need is a risk-adjusted view of performance that identifies how much revenue you earn relative to the amount of risk you take on. That requires metrics that combine finance and risk components, such as risk-adjusted return on capital, shareholder value added, or economic value added.

While the smart money is on a unified approach to finance and risk, most banking institutions have isolated each function in a discrete technology “silo” complete with its own data set, models, applications, and reporting components. What’s more, banks continually reuse and replicate their finance and risk-related data – resulting in the creation of additional data stores filled with redundant data that grows exponentially over time. Integrating all this data on a single platform that supports both finance and risk scenarios can provide the data integrity and insight needed to meet regulations. Such an initiative may involve some heavy lifting, but the advantages extend far beyond compliance.

Cashing in on bottom-line benefits

Consider the potential cost savings of taking a more holistic approach to data management. In our work with large global banks, we estimate that data management – including validation, reconciliation, and copying data from one data mart to another – accounts for 50% to 70% of total IT costs. Now factor in the benefits of reining in redundancy. One bank we’re currently working with is storing the same finance and risk-related data 20 times. This represents a huge opportunity to save costs by eliminating data redundancy and all the associated processes that unfold once you start replicating data across multiple sources.

With the convergence of finance and risk, we’re seeing more banks reviewing their data architecture, thinking about new models, and considering how to handle data in a smarter way. Thanks to modern methodologies, building a unified platform that aligns finance and risk no longer requires a rip-and-replace process that can disrupt operations. As with any enterprise initiative, it’s best to take a phased approach.

Best practices in creating a unified data platform

Start by identifying a chief data officer (CDO) who has strategic responsibility for the unified platform, including data governance, quality, architecture, and analytics. The CDO oversees the initiative, represents all constituencies, and ensures that the new data architecture serves the interests of all stakeholders.

Next, define a unified set of terms that satisfies both your finance and risk constituencies while addressing regulatory requirements. This creates a common language across the enterprise so all stakeholders clearly understand what the data means. Make sure all stakeholders have an opportunity to weigh in and explain their perspective of the data early on because certain terms can mean different things to finance and risk folks.

In designing your platform, take advantage of new technologies that make previous IT models predicated on compute-intensive risk modeling a thing of the past. For example, in-memory computing now enables you to integrate all information and analytic processes in memory, so you can perform calculations on-the-fly and deliver results in real time. Advanced event stream processing lets you run analytics against transaction data as it’s posting, so you can analyze and act on events as they happen.

Such technologies bring integration, speed, flexibility, and access to finance and risk data. They eliminate the need to move data to data marts and reconcile data to meet user requirements. Now a single finance and risk data warehouse can be flexible and comprehensive enough to serve many masters.

Join our webinar with Risk.net on 7 October, 2015 to learn best practices and benefits of deploying an integrated finance and risk platform.

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About Mike Russo

Mike Russo, Senior Industry Principal – Financial Services Mike has 30 years experience in the Financial Services/ Financial Software industries. His experience includes stints as Senior Auditor for the Irving Trust Co., NY; Manager of the International Department at Barclays Bank of New York; and 14 years as CFO for Nordea Bank’s, New York City branch –a full service retail/commercial bank. Mike also served on Nordea’s Credit, IT, and Risk Committees. Mike’s financial software experience includes roles as a Senior Banking Consultant with Sanchez Computer Associates and Manager of Global Business Solutions (focused on sale of financial/risk management solutions) with Thomson Financial. Prior to joining SAP, Mike was a regulator with the Federal Reserve Bank in Charlotte, where he was responsible for the supervision of large commercial banking organizations in the Southeast with a focus on market/credit/operational risk management. Joined SAP 8years ago.

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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