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How High Will Your Supply Chain Jump To Build Customer Centric Business Processes?

Richard Howells

The 21st century consumer is always on, always connected, and doesn’t make a move without consulting the Internet. The millennial generation is emerging as a major demand driver, with access to huge amounts of information about products and trends on “what’s hot and what’s not,” via social media.

Take my teenage son (please!!!) for example, a basketball player in his high school. Every season, he wants the latest and greatest sneakers that will make him run, jump, and rebound better and faster. So what does he do? He turns on his tablet to go online to check out what’s available. And here he can not only see and buy what the NBA stars are wearing, but he can also customize the shoes to match his school colors and have his name and team number stitched into each sneaker. Through the advent of omni-channel sales, he can (once he gets my credit card of course) order from anywhere, on any device he has access too. Long gone are the days of the only option being the white high top!

And what happens when my son posts a pic of his new kicks on his social networks? Inevitably many of his friends are going to jump on their devices and order a pair for themselves. Is your business prepared to deliver to meet demand when your product is deemed “hot” and goes viral among the millennials?

Point of sales at the store is not the ONLY source of data to drive the replenishment process. It is no longer good enough just to be “demand driven.” We need to be “market driven.” We not only need the information from orders and sales forecasts, but also from weather forecasts, traffic reports, market share reports, and customer sentiment. We need to know what is being tweeted about our products, what and where products are hot, and what is being said, both good and (sometimes more importantly) bad via social media. This involves all the sources of data that describe the market and where the market is going at the most granular level.

Why is this important? Because the customer, or consumer, is becoming more demanding. If I order something online, I expect the delivery on the same day, or at least the next day. That introduces the need to think about demand differently. It is not possible to compete with aggregated demand for a product family in a region. To respond with speed, we need the information at the detailed level, so that it can be aggregated and analyzed to service a channel, market sector, customer, and specific order.

It sounds so simple, but to deliver those custom basketball sneakers to my son before he gets impatient, how high does the supply chain have to jump to enable him to make that three pointer?

Here are a few ways that we have to re-imagine this business process to be centered around the customer.

  • Design a customizable sneaker, with all possible allowed combinations of colors.
  • This design is handed over to both the sales and manufacturing organizations, to enable customers to design their own sneaker, and to the manufacturing site to produce it.
  • At the moment an order is placed, through any sales channel (in store, online etc.), the specific demand is instantly visible. The customer can specify unique text or numbers to be stacked onto the sneaker. They can also determine the shipping rules and instructions.
  • The specific order is planned and scheduled at an appropriate manufacturing facility.
  • The production line is set up to create all combinations of the sneaker for a “lot size of one.
  • At the final manufacturing step the unique text is stitched onto each sneaker.
  • The logistics processes are configured to uniquely pack and shipped to the desired pick up or delivery location based on the shipping rules determined by the customer.
  • Processes need to be in place to capture real demand signals coming from social media as demand for these custom sneakers take off in certain markets, regions, or even cities.

This is just a simple example of some of the business processes that have to be adjusted, and how leveraging a digitized extended supply chain can deliver a personalized solution by putting the customer in the center of the process.

The benefits of this scenario are significant:

  • Improved customer service and engagement
  • Stronger competitive differentiation
  • Compelling and holistic brand experience
  • Improved revenue

For more on supply chain optimization, download the free e-book How to Attack Supply Chain Fraud, Waste, and Abuse: The Quick Guide.

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About Richard Howells

Richard Howells is a Vice President at SAP responsible for the positioning, messaging, AR , PR and go-to market activities for the SAP Supply Chain solutions.

Innovator Or Follower? Reimagined Processes Keep Distributors In The Lead

Karen Lynch

The world is moving ever faster. Customers demand a wider variety of products and services with shorter delivery times. Competitive markets demand that wholesale distributors become more efficient.

To survive in this environment, distributors must rethink their business processes. Leading companies are using digital technology to optimize business outcomes. They are implementing bold initiatives to automate core administrative business processes. They are changing the way they operate by changing or eliminating fundamental business processes. They are moving from being reactive and slow to agile, proactive, and insight-driven.

Distribution companies are taking a fresh look at their key processes. They often find that any process can be more modern or digitized. To stay ahead of competition, the time to plan and execute is now.

Here are some ways distributors are approaching this challenge.

Become easier to do business with

The customer experience today is a lifecycle, not just an individual interaction. Customers expect a consistent experience during every touch point with your organization. Leading distributors provide a variety of ordering channels. These include call centers, websites, text messages, and in-person interaction. The goal is to deliver the exact products and services customers need at the time they need them.

Establish detail-driven customer engagement

Distributors are becoming more efficient by focusing on their best customers. They are gathering information through segmentation and stratification of their customer base. These details can help transform marketing activities, personalize user experiences, and grow customer loyalty. Companies can then nurture the most profitable customers. And they can take insight-driven action to improve relationships with the least profitable customers.

Enable predictive analytics

Companies should use data should to forecast and predict, not to reminisce. Imagine being able to tell your customers which products they will need and when they will need them. Companies use predictive analytics and up-to-the-minute insight to react to trends in real time.

Address operational efficiency and bottom-line results

With an eye toward higher efficiency, distributors are reimagining their operations as well. They negotiate the best deals with suppliers to improve margins, pricing, and inventory levels. They use solutions for integrated strategic sourcing and supplier management to achieve 8–14% savings. Companies use optimized operations for complex, high-volume financial processing. This can be valuable for their own company or as a service for business partners. For many companies, rebate and chargeback processes are still manual. This can leave significant revenue on the table. But digital solutions can automate these processes, identifying every opportunity to collect.

Leverage business networks

Distributors are leveraging the value of business networks. They can automatically order and pay for goods and services, both direct and indirect. Pre-defining suppliers, pricing, safety stock and reorder points enables touch-less and automated processing. Automation of these manual processes gives employees time to focus on customer-centric activities. This helps improve loyalty and increase sales.

Innovator or follower?  The choice is yours

In today’s digital economy, wholesale distributors must reimagine business processes to remain competitive and best serve customers. Distributors with vision are leading the way for the rest of the industry.

To learn more about digital transformation for wholesale, click here.

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Karen Lynch

About Karen Lynch

Karen Lynch is the Vice President, Global Wholesale Distribution Industry Business Unit at SAP. She sets the vision and direction and execute the go to market plan to address the needs of Wholesale Distributors across the globe by using SAP solutions.

Top Tech Partnerships Transforming The IT Industry

Kevin Ichhpurani

The recent unprecedented partnership announcement between SAP and Apple is just the latest in a string of game-changing partnerships and mergers in the tech industry. Driven by the relentless pace of innovation, the technology sector is only one of numerous industries that are moving quickly to ensure they stay relevant.

For example, leading healthcare companies are partnering with technology goliaths like Google to create life-changing innovations that will transform our world, such as surgical robots for non-invasive surgery. Industries ranging from automotive and academia to insurance, banking, and beyond are feeling the impact of digital disruption, competing through new digital ecosystems, and responding in ways that will reinvent how we live and work.

Let’s take a closer look at some of the latest key technology partnerships.

1. Apple and SAP

The recently announced SAP and Apple partnership is a revolutionary breakthrough for business. It will combine native apps for iPhone and iPad with the enterprise capabilities of the SAP HANA Cloud Platform. It allows developers, partners, and customers to build iOS apps tailored to their business needs in order to manage critical business operations.

The business capabilities for apps enabled by this partnership are endless. Imagine having a virtual assistant who not only responds to your requests, but also proactively reaches out to let you know when there are situations you need to be aware of. In fact, some of the first apps will contain functionality, referred to as co-pilot, which is touted as the first true digital assistant in the enterprise.

Here’s an example of how co-pilot functionality works. A machine in the production line is overheating. A sensor flags the issue to the inspector bot and to your app. The app with co-pilot functionality notifies you – in natural human language via text, action, or voice – and provides relevant information on the issue and suggestions on the best course of action to take. The app monitors the situation and helps you to quickly solve the problem by facilitating the proper service procedures and ensuring that you are kept abreast of the resolution. Importantly, the app will learn from behavioral data to improve its recommended actions in the future.

Tim Cook, Apple’s CEO stated, “This partnership will transform how iPhone and iPad are used in enterprise by bringing together the innovation and security of iOS with SAP’s deep expertise in business software. As the leader in enterprise software and with 76% of business transactions touching an SAP system, SAP is the ideal partner to help us truly transform how businesses around the world are run on iPhone and iPad.”  

2. Cisco and Ericsson

“In a world driven by mobility, cloud, and digitalization, the networks of the future will require new design principles to ensure they are agile, autonomous, and highly secure.” So said technology giants Cisco and Ericsson when they announced a sweeping strategic partnership to create the “networks of the future”.

The multifaceted partnership touches virtually all aspects of both companies, from sharing patents and intellectual property rights to joint development and creation of new products to collaborating on global services capabilities including consulting, integration, and support.

Together, the companies plan to offer the best of both companies – routing, data center, networking, cloud, mobility, management and control, and services – across network architectures from devices and sensors to access and core networks to the enterprise IT cloud.

Cisco and Ericsson are dramatically reinventing themselves to meet the realities of the digital economy, and enabling their customers to accelerate their own business transformations. By delivering an end-to-end product and services portfolio and joint innovation, they plan to provide the mobile enterprise experience of the future and speed the platforms and services needed to digitize countries and create the Internet of Things ecosystem.

Customers will not be the only beneficiaries of this partnership. The companies expect an incremental revenue opportunity of $1 billion or more for each company by calendar year 2018. Plus, in February 2016, the partners expanded their agreement to include Intel and Verizon, with the goal to develop a 5G router for business and residential service, and accelerate 5G innovations. (Apple, Nokia, and Qualcomm are also members of a Verizon-led initiative to develop and test 5G wireless technologies.)

Cisco CEO Chuck Robbins commented on the need for partnerships these days, saying, “In today’s fast-paced world, next-generation strategic partnerships allow us to innovate and move with greater speed.”

3. Dell and EMC

This is a merger as opposed to a partnership. But I had to mention it because it is one of the largest tech mergers to date, and it will have a widespread impact on the IT industry and beyond.

At the EMC World event this month, Dell Chairman and CEO, Michael Dell, said the merger will create a next-breed technology company that strategically covers the “customers’ entire infrastructure, from hardware to software services, from the edge to the core to the cloud.”

The $67 billion Dell and EMC deal also directly involves several well-known companies that they refer to as the ‘family of companies’ currently owned by one of the two tech giants, including VMware, Pivotal, SecureWorks, RSA, and Virtustream. The merger still has some hurdles to cross, but if it closes as expected by October, the combined company will have close to 170,000 employees.

The ripple effect across industries

These significant deals are part of a larger global trend of partnerships, alliances, and consolidations. The impact of some of these major technology partnerships and mergers is expected to cause a ripple effect across multiple sectors and the global business landscape.

This is only the beginning; I’m eager to see where these technology partnerships can take us.

Learn more about the Apple and SAP partnership to revolutionize work on iPhone and iPad.

For an in-depth look at how technology is changing the business landscape, download the SAP eBook, The Digital Economy: Reinventing the Business World.

To learn more about the multiple factors driving digital transformation, download the SAP eBook, Digital Disruption: How Digital Technology is Transforming Our World.

Discover how the Mobile Industry is Now Simpler Then Ever.

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Kevin Ichhpurani

About Kevin Ichhpurani

Kevin Ichhpurani is EVP of Strategic Business Development at SAP.

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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