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Making It Personal With Individualized Products

Richard Howells

Personalized solutions and products are everywhere. You can design your own sneakers, customize your own drinks in vending machines, configure cars and motorbikes, and print your own personalized chocolates.

Consumers are now expecting the customer experience to also be a customizable experience.

As a result, companies are doing their best to understand the full potential between physical and digital assets and the Internet of Things (IoT). And we are witnessing new use cases across industries with breathtaking results.

Coca-Cola has Coca-Cola Freestyle, a touchscreen soda fountain that enables consumers to personalize their soda with over 100 different flavor combinations. Nike has NIKEiD, which lets customers personalize their own shoes, bags, backpacks, and other accessories. Logistics service providers are investing in 3D printing farms in order to provide value-added services at the end of the runway before a customized product is shipped around the world.

The platform for personalization

The common feature of most companies’ “personalization” strategy is a strong platform that is used as a base for customization. This has been taking place in the car industry for several years, with companies such as BMW allowing people to customize their base model to order. The Apple iPhone is another great example of a platform, as anybody can buy one of a few base models, and then customize his or her own device with apps and visual effects. This means that, from five or six base options, everybody has a personalized device.

Smart products drive new business models

IoT and Industry 4.0 are changing traditional business models by connecting people, products, and assets. Manufacturers are investigating how these new technologies can help their customers get more value and how new business engagements can change established business models.

Companies are embedding sensors in their products and, as a result, are becoming more and more like technology companies, hiring software engineers and rethinking the value delivered by their products.

John Deere tractors are now equipped with sensors to transmit moisture and temperature data from the fields. Kaeser Compressors reimagined its business and moved from selling products to selling a “compressed air by cubic meter” service. This business model requires metering compressed air remotely and bundling this information into the charging and billing process. The company has also leveraged smart sensors embedded into the compressors to minimize unscheduled machine downtime through IoT-enabled predictive and preventative maintenance.

3D printing can revolutionize industries

Over the past few years, we have seen the emergence of 3D printers having a growing effect on our extended supply chain processes.

Sneaker manufacturers are prototyping the ability to print a unique 3D-printed running shoe midsole that can be tailored to the cushioning needs of an individual’s foot, based on running style on a treadmill in the store.

Logistics service providers are investing in 3D printing farms to provide value-added customization services just prior to shipment.

Chocolate manufacturers are enabling customers to personalize their favorite treat by printing unique shapes or edible messages.

Additive layer manufacturing enables us to rethink how we design, produce, and bring products to market, as well as provide competitive differentiation and personalization to our products.

Manufacturing a lot size of one

As manufacturers seek to keep up with the need for both personalized products and the changing demand market, they are looking for the agility of a manufacturer with a lot size of one. Harley Davidson completely reconfigured its York, Pennsylvania, facility to enable all machinery and logistics devices to be equipped with sensors and location awareness. The factory reduced the lead time to produce customized motorbikes from a 21-day cycle to six hours, and now you do not find two bikes in sequence that are the same. Each model has more than 1,000 configuration options, and one motorcycle comes off the assembly line every 89 seconds.

Personalize or perish

Manufacturing in the age of product customization can be challenging. But with technological advancements such as 3D printing, IoT, and the shift to Industry 4.0 taking hold, offering personalization options to your customer base is now critical to the success of your business.

Looking to dominate your industry in the digital economy? Start digging deep into Algorithms: The New Means of Production.

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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.

How Big Data And IoT Are Transforming How We Buy And Drive Cars

Larry Stolle

In today’s tech-savvy world, most people are familiar with the idea of consumer and technical data collection. Business leaders use this data to get complete view of any given market segment. The sheer amount of business information that is currently collected has inspired the term Big Data. This Big Data means different things to different companies. It can include anything from records of retail purchases and buying patterns to product information and systematic feedback.

Many business professionals view Internet of Things (IoT) as the next big thing in the world of high technology. The online dictionary for IT professionals Webopedia defines IoT as “the ever-growing network of physical objects that feature an IP address for internet connectivity.”

The term also refers to “the communication that occurs between these objects and other Internet-enabled devices and systems.” IoT can accomplish great things in a number of industries when paired with the benefits of big data. Examples of “things” that can fall under the IoT umbrella include many consumer goods, including electronic appliances, connected security systems, household lights, speaker systems, and a full range of automotive vehicles.

Market research/analysis firm IDC recently released a study on the enormous potential of IoT and Big Data in a range of manufacturing industries. The study projected that worldwide manufacturing will generate $746 billion by 2018. This sentiment is echoed by Morgan Stanley, which estimates that IoT-driven automation in manufacturing could save $500 billion (2 to 4 percent based on a penetration rate of 50 percent).

The technological future of the automotive industry

A recent global expert survey by McKinsey & Co. on the manufacture of cars and trucks determined that automotive suppliers have big plans for Big Data and IoT. In fact, 92 percent of automotive industry leaders feel that these technologies will have a “huge impact” in the way their products are designed and manufactured. Big Data and IoT will also influence the way that automotive companies interact with, and sell to, their customer bases.

The global nonprofit Application Developers Alliance (ADA) regards automotive innovations as a microcosm of these digital technologies. According to ADA, industry experts estimate that every car will be connected in some way by 2025. It goes on to report that the market for connected vehicle technology will reach $54 billion by 2017.

Forbes also recently chimed in on this issue with the article “Big Data’s Big Impact Across Industries.” Writer Howard Balwin complied reports from the Center for Automotive Research and the Michigan news site MLive and concluded that Big Data is an “engine of innovation” and “about to get bigger for auto industry.”

Otto Schell of General Motors sums up the whole subject in a few words: “The use of data is not anymore questioned.” He goes on to report that the “entire business is changing.”

Car and truck companies are embracing the full potential of Big Data/IoT technologies. Modern digital technology within the automotive sector has already produced some stunning results. Business opportunities that are driven by Big Data and IoT include connected products and logistics that use embedded sensors to communicate.

This communication can occur among “smart” vehicles or between these vehicles and a strong digital core. These technologies can do great things, such as assisting in preventive maintenance and coordinating activities within a fleet of vehicles. They can also help detect engineering defects and determine driving patterns.

There are many other key IoT and Big Data trends in the automotive sector, including tracking customer sales and services and fostering seamless collaborations between automotive companies and high-tech companies such Google.

Perhaps the most exciting product of the marriage between automobile manufactures and IoT/Big Data technology is the self-driving car. These cars are still in development, but they stand to impact the automotive sector in profound ways. Morgan Stanley addressed the enormous potential of autonomous vehicles in its April 3, 2014 Blue Paper. The section entitled “The ‘Internet of Things’ Is Now: Connecting the Real Economy” reported that full penetration of self-driving vehicles would save the U.S. economy a total of $1.3 trillion. This figure increases to $5.6 trillion at the global level.

The Blue Paper goes on to estimate that self-driving cars and other state-of-the-art digital marvels will save the U.S. $158 billion in estimated fuel savings and $488 billion in estimated accident avoidance savings.

So while we look to big things from Big Data and IoT in the future, we cannot underestimate the exciting things that are happening right now. Otto Schell again sums things up nicely: “I think when we talk in general about cars, you see all over the place a lot of things are digitalization, connect the car.” He believes that the automotive industry must use digital technology to serve consumers today. “We give them options to go into their lives. And this option is very clearly digitalized.”

Learn more about digital transformation in the automotive industry here.

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Larry Stolle

About Larry Stolle

Larry Stolle is the senior global marketing director for the Automotive Industry at SAP. He has over 45 years of experience in the automotive industry with experience, ranging from dealerships to manufacturers and importers to technology companies such as IBM. Stolle currently holds two patents for dealer and manufacturer communications and for quality insights.

4 Reasons To Transform Products Into Connected Experiences And Outcomes

Kris Gorrepati

Several months passed before I finally got around to connecting our Nest smart thermostat to the Internet via a Wi-Fi network. And it was only then that I began to understand the power of product connectedness.

What used to be a moderately better, albeit well designed, thermostat suddenly transformed into an intelligent and accessible home comfort assistant that understood our preferences and patterns and worked on our behalf, without asking too much.

Connectedness has always been a highly desired feature for customers. But it’s also useful for engineers and product designers, as well. After all, what product designer wouldn’t want to be able to better understand how customers use their products? This information could help the product designer provide better customer support and continuously improve products, even while customers are actively using them.

This wasn’t that simple or cost-effective as recently as a couple of years ago. But with the near-universal availability of Wi-Fi, Bluetooth, and 3G/4G/LTE, combined with the plummeting costs of low-power computing platforms, it is even less of a problem now.

Four reasons your company’s products should be connected

Many products – toys, medical devices, industrial machinery, autos, kitchen equipment, washing machines, etc. – are candidates for connectedness. Moreover, there are plenty of worthy reasons for designing connected products:

  1. Developing a sticky customer relationship: A non-connected product generally leads toward a transactional customer relationship. A connected product moves the customer toward a long-term relationship – at least through the life of the product and usually beyond – by sharing data and providing more value with the help of cloud services. For example, Fitbit users usually get limited information from the fitness band itself. A connected Fitbit, however, can share information with Fitbit’s cloud services so the customer can receive richer, more insightful analysis. This information sharing and additional insight are prime sources of sticky customer relationships that can last well beyond a single product purchase.
  1. Changing business models: Connected products and services allow many organizations to establish a commercial relationship based on outcomes and performance.
  1. Improving products while they are actively being used: Tesla’s ability to change ground clearance to improve safety with an over-the-air software update is an amazing example of how connected products can be enhanced while in use.
  1. Obtaining extremely valuable usage, performance, and fault data: Companies can use this information to improve existing products, understand what is important for customers, and design even better products in the future. In addition, actual usage and status information can be used to guide customers toward more optimal maintenance and service protocols to reduce total lifecycle costs.

Developing the right strategies for your connected products

Clearly, the case to develop connected products is compelling for product designers and companies. However, designers and companies should approach connected products with a fundamentally different design and lifecycle management strategy. The normal approach is one of designing and creating a product – even if it is made up of mechanical, electrical, and software components – as a rigid object that is designed, manufactured, sold, and, eventually, forgotten. This approach is at odds with the key tenets of connected products.

Connected products require a product design and lifecycle management viewpoint that takes into consideration the following:

  • Products are malleable and can be improved even when they are being actively used
  • Products can be accessed with customer permission in real time
  • Cloud services are an extension of the product itself

The design strategy then will modularize and parameterize key capabilities, performance issues, and other components in order to modify/improve products with software and technology updates.

Key capabilities of connected products companies

Companies that offer connected products must also be able to:

  • Design, develop, deploy, and operate cloud services that complement and enhance product features and capabilities
  • Develop customer service and relationship management processes that can work directly with and through the connected product
  • Have the capacity to analyze historical performance and fault data to predict likelihood of failure and take appropriate action
  • Adjust maintenance and service recommendation processes to take into consideration the actual use of products
  • Modify existing supply chain and replenishment processes to meet real-time fulfillment needs

In short, connected products force companies to rethink and reimagine how they design products and how they operate to serve customers.

For more on why the Internet of Things’ value will be in the data, not the connections, see the white paper Live Business: The Importance of the Internet of Things.

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About Kris Gorrepati

Kris Gorrepati is part of the Solution Management team for Extended Supply Chain Solutions. He works with SAP's formidable ecosystem to support customer success and to promote excellence is Supply Chain Management, Manufacturing and Product Lifecycle Management. Kris has extensive experience in Supply Chain, Manufacturing and Product Development as a practitioner, designer, engineer and a thought leader.

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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