Sections

Stop Wishing And Start Optimizing Supply Chain Performance

Chris Durkin

In manufacturing supply chains, inherent mismatches in speed, or cadence, can seriously impact agility and performance. While the pace of modern commerce offers businesses little hope, CPG_BIBif any, for balancing those mismatches, there is significant opportunity for improving performance. It lies in adjusting some pivotal business processes to compensate for the mismatches, keeping supply chains – especially demand-driven supply chains of consumer products companies – from taking performance hits. In fact, some specific, well-targeted adjustments hold the promise of performance optimization.

Traditions and transitions in supply chains

IDC research points out that the demand side of manufacturing supply chains have traditionally operated at a faster cadence than the supply side. Not necessarily a bad thing, and manufacturers have found ways (like inventory buffers) to compensate for these mismatches.

But for consumer product companies, demand volatility far exceeds supply volatility, so they experience the greatest cadence mismatch between the demand and supply sides of their supply chains. The mismatch is too substantial for approaches like inventory buffering to compensate – at least not well.

Compounding the issue is the steady transition in supply chain dynamics: The demand side of the consumer products supply chain is becoming even more demand driven. This is due, in no small part, to external pressures triggered by mobile, social connectivity, and e-commerce technologies – not to mention modern consumers who want yesterday what they purchase today.

Without some process adjustments, the resulting cadence mismatches within the supply chain could be great enough to deteriorate inventory quality, cause out of stocks, and reduce service levels.

The ultimate goal: A resilient supply chain

IDC defines a resilient supply chain as one capable of ensuring and preserving continuous, consistent product supply in order to meet product delivery and customer service obligations despite short- or long-term disruption. But if you’re a demand-driven consumer products company, how can you reach this state of supply chain perfection?

There’s no one-size-fits-all answer to the question. It would differ from company to company. It all depends on core principles of the business, the processes supporting those principles, and which processes lend themselves to the kinds of improvements that would compensate substantially for mismatches in supply chain cadences.

Adjustment possibilities

Business process adjustments that ultimately result in a resilient supply chain – one capable of responding quickly to market opportunities – can take many forms. The key lies in adjusting for longer-term supply requirements in a way that efficiently and cost-effectively satisfies the dynamics of near-term demand.

Based on real-world instances of cadence mismatches in supply chains, IDC research suggests some adjustments that demand-driven consumer products companies can make to eliminate those mismatches. These include:

  • Shortening supply lead time through a more balanced approach to global sourcing
  • Improving supply planning though adoption of modern, faster planning tools
  • Consuming new supply chain capabilities faster through the cloud

Evaluating current performance

Of course, before making adjustments, you’d want to evaluate current performance in some key areas to determine if cadence mismatches are taking a toll. IDC suggests evaluating:

  • Supply network agility: Does your company have long lead times, inflexible factories, and locked-down contracts with key suppliers?
  • Demand planning and forecast accuracy: Are forecast errors reflective of typical volatility or of actual flaws in your planning process? And can changes in demand be built quickly into a new view of the forecast?
  • Inventory management efficiency: Does your company have the right overall inventory level but the wrong things in the wrong places? Can you adapt your inventory quickly to meet changing business requirements?
  • Cost consensus: Are all areas of your business working from the same set of numbers? Or is lack of cost consensus across business functions interrupting product supply?

Answers to these questions might indicate that it’s time for some adjustments.

Tools for achieving resiliency

Tools that offer or enhance functionalities in some key areas can make achieving supply chain resiliency a more reachable goal. IDC research suggests looking at tools that support:

  • Demand sensing to improve forecasting by improving the downstream capture of consumer demand data
  • Sales and operations planning to align demand and supply plans with financial goals by giving all stakeholders in cost planning a unified view of demand, supply, and financial data
  • Inventory management and optimization to help ensure that goods are consistently available at the right time and right place
  • Cost visibility to gain a full view of costs that would also reflect benefits gained from shorter lead times achieved through proximity sourcing

And certainly, tools capable of leveraging newer functionalities – courtesy of social media, Big Data analytics, cloud computing, and mobile technologies – stand to bring even more options to the table.

For references highlighting the principles and suggestions offered here, check out the IDC infographic and whitepaper Clock Speed in the Consumer Product Supply Chain.

future-of-business

Comments

chrisdurkin

About chrisdurkin

Chris Durkin is the Director of Marketing at SAP responsible for marketing numerous SAP solutions in North America. Specialties include Strategic and Tactical Marketing, Branding, Lead Generation, Event Management, Public Relations, Analyst Relations, Partner Marketing, Direct/Email Marketing, Social Media and Cloud Solutions.

Zhena’s Gypsy Tea Brews Sustainable Growth On Cloud ERP

David Trites

Recently I had the pleasure of hosting a podcast with Paula Muesse, COO and CFO of Zhena’s Gypsy Tea, a small, organic, fair-trade tea company based in California, and Ursula Ringham from SAP. We talked about some of the business challenges Zhena’s faces and how the company’s ERP solution helped spur growth and digital transformation.

Small but complex business

~ERP helped Zhena’s sustain growthZhena’s has grown from one person (Zhena Muzyka) selling hand-packed tea from a cart, into a thriving small business that puts quality, sustainability, and fair trade first. And although the company is small its business is complex.

For starters, tea isn’t grown in the United States, so Zhena’s has to maintain and import inventory from multiple warehouses around the world. Some of their tea blends have up to 14 ingredients, and each one has a different lead time. That makes demand-planning difficult. In addition, the FDA and US Customs require designated ingredients be traced and treated a certain way to comply with regulations.

Being organic and fair trade also makes things more complicated. Zhena’s has to pass an annual organic compliance audit for all products and processing facilities. And all products need to be traceable back to the farms where the tea was grown and picked to ensure the workers (mostly women) are paid fair wages.

Sustainable growth

Prior to implementing its new ERP system, Zhena’s was using a mix of tools like QuickBooks, Excel, and paper to manage the business. But to sustain growth and ensure future success, the company had to make some changes. Zhena’s needed an integrated software solution that could handle all facets of the business. It needed a tool that could help with cost control and profitability analysis and facilitate complex reporting and regulatory requirements.

The SAP Business ByDesign solution was the perfect choice. The cloud-based ERP solution reduced both business and IT costs, simplified processes from demand planning to accounting, and enabled mobile access and real-time reporting.

Check out the podcast to hear more about how Zhena’s successfully transformed its business by moving to SAP Business ByDesign.

 This article originally appeared on SAP Business Trends.

Building a successful company is hard work. SAP’s affordable solutions for small and midsize companies are designed to make it easier. Simple to install and use, SAP SME Solutions help you automate and integrate your business processes to give real-time, actionable insights. So you can make decisions on the spot. Find out how Run Simple can work for you. Visit sap.com/sme.

Comments

David Trites

About David Trites

David Trites is a Director of SAP Global Marketing. He is responsible for producing interesting and compelling customer stories that will humanize the SAP brand, support sales and marketing teams across SAP, and increase the awareness of SAP in key markets.

Haier Asia Builds A Digital Platform To Speed Innovation And Win Consumers’ Loyalty [VIDEO]

Dinesh Sharma

08 Apr 2013 --- Intersection, Germany. --- Image by © Markus Hanke/www.MarkusHanke.de/CorbisFew words scare the corporate world like the term “disruption.” No matter the language, disruption conjures the fear of dilution, alteration, and disturbance. And as the world becomes increasingly hyperconnected, disruption seems to be an ever-present threat.

Nevertheless, the C-suite is remaining vigilant by embracing the digital economy as the new reality. According to a recent study conducted by the Economist Intelligence Unit, 80% of executives view hyperconnectivity positively – indicating that it presents more opportunities than threats. All the while, they are carefully watching the competitive landscape and anticipating the arrival of overnight digital sensations and the inventiveness of long-time adversaries.

However, this is only one side of the transformational change hyperconnectivity is bringing. Disruption is not just happening on the corporate side of the consumer market – consumers are steadily disrupting everything a business touches.

The secret? Go beyond the competition to find disruptive opportunity

Not that long ago, most businesses followed a one-time transaction model. They would manufacture the product and ship it to the retailer, and consumers would purchase it. However, hyperconnectivity has changed the rules – making this experience a distant memory.

Consumers are more connected to information and no longer interested in listening corporate rhetoric. By drastically changing everything in our lives, the Internet is giving more power to the consumer, putting them in a position to guide the conversation and dictate product and service offerings. From this perspective, it is easy to see that hyperconnectivity and its impact on social behavior are the true disruptors.

Haier Asia, a top-ranking multinational consumer electronics and home appliances company, is one of those few companies that quickly recognized how hyperconnectivity is powering consumer-based disruption. “When you look around, no consumer life business is making money. Why is that? Margins are so slim,” cites Yoshiaki Ito, president and CEO of Haier Asia. “Consumers are far, far faster than manufacturers because they are getting new information on a daily basis. In the meantime, traditional companies produce their products – taking 24 months. So the gap is just widening every second.”

Instead of surrendering to these challenges, Haier decided to disrupt itself and the market it serves. With a two-prong approach to digital transformation, the company created a service-based model to seize the potential of new consumer behaviors and accelerate its product development cycles.

“My strategy for Haier Asia is to double up the digital platform. This is a great opportunity to bring us to the next level by becoming a services provider and gaining a steady stream of new revenue,” says Ito.

How did Haier take advantage of hyperconnectivity to gain the attention of stakeholders and consumers? Watch the video below to find out.

This article originally appeared on SAP Business Trends.

Want more insight on managing digital disruption? See Three Keys To Winning In A World Of Disruption.

Comments

Dinesh Sharma

About Dinesh Sharma

Dinesh Sharma is the Vice President, Marketing, Internet of Things, at SAP. He is a GM-level technology executive with leadership, technical innovation, effective strategic planning, customer and partner engagement, turnaround management and focused operational execution experience at both large enterprise and startup companies.

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!

Comments

Erik Marcade

About Erik Marcade

Erik Marcade is vice president of Advanced Analytics Products at SAP.

Tags:

The New Digital Healthcare Patient Experience

Martin Kopp

Digitized healthcare has arrived. And it is only going to get better. Since the 1950s, information technology has had a growing influence on the healthcare industry. And today, more than three-quarters of all patients expect to use digital services in the future. That is, if they are not using them already. Healthcare consumers have become more informed and proactive.

Today, a pregnant woman can schedule a gynecology appointment electronically. Her insurance company probably offers a smartphone app to monitor her health. She can download the app and self-register. The app documents her ongoing health as she updates the profile data. And because her data is stored in the cloud, her gynecologist has immediate access to it.

These are a few examples of the important trends shaping the patient experience with digital innovation. The latest digital solutions are bringing the patient and the healthcare industry closer together. And this digital connectivity means more personalized patient care.

Digital technology is changing the role of the patient. Patients are better informed and more involved in their own health decisions. With greater access to information, they can sometimes self-diagnose certain health issues. Due to digitization, they have better communication with healthcare providers and easier access to their own test results.

Monitoring illness

Healthcare providers are better equipped to gather and analyze data. So, healthcare outcomes are faster and easier to realize. Providers can react earlier to conditions. And they can even sometimes predict medical conditions before any symptoms appear. Therapies are transforming to a more user-centric design. This is all possible because digital networking of data informs caregivers earlier and keeps them informed. We have moved past the patient’s chart as the most important source of information.

Improving wellness

The ability to predict medical conditions gives providers a tool to promote wellness. This is changing the healthcare value chain. Remote monitoring is possible, making trips to the clinic or doctor’s office less necessary. Wearable monitoring devices have changed the medial landscape. And the use of wearable devices is expected to grow. According to the McKinsey Global Institute (MGI), 1.3 billion people will be using fitness trackers by the year 2025. In some regions, this will account for up to 56% of the population. The millennial generation sums up the benefits in a word: convenience.

The blending of physical and digital realms into a common reality is referred to as the Internet of Things (IoT). The IoT makes many things possible that were only dreamed of a few years ago. It extends the reach of information technology. From remote locations, we can electronically monitor and control things in the physical world. Basically, it is the digitizing of the physical world.

With the IoT, MGI predicts a savings in healthcare treatment costs of up to $470 billion per year by 2025. But even more important is the improvement in healthcare. In addition to driving down treatment costs, this will extend healthy life spans and improve the quality of life for millions of people. And it will improve access to healthcare for those who are underserved in the present system. Plus, this extensive use of fitness tracking devices will create a multi-billion dollar industry.

Re-shaping the patient experience

The patients of today and tomorrow have more information and more options than ever before. Patients are already seeing increased value from the Big Data that healthcare professionals now have access to. Patients are more engaged in their own care. We are entering an age of personalized healthcare based on far-reaching knowledge bases.

Because of digital innovation, healthcare consumers can more easily seek relief when they are sick. They can be more involved in disease prevention and self-supported care. With patient-owned medical devices, they are connected to the Big Data of cloud computing. This cloud-based information provides proven treatments and outcomes for specific conditions.

Value chain improvements

The digital value network connects all aspects of the healthcare ecosystem in real time. This connectivity drives better healthcare outcomes that are specifically relevant to the patient. Digital innovation in healthcare improves interactions to provide personalized care based on Big Data. In that respect, you can think of it as Big Medicine for the little guy. A massive database gives healthcare providers a 360-degree view of the patient. Data is stored in the cloud and processed in the core platform.

Services and functions that this efficient system provides include medication reminders for patients. It tracks your health for you, your family, and friends. Remote home monitoring and emergency detection offer an increased level of safety and protection. Remote diagnostics can mean you stay at home instead of being hospitalized. Prediction of organ or other physical failures before they happen can save lives.

SAP software provides a single platform that brings together healthcare providers, patients, and value-added services. It offers a seamless digitization of the entire patient experience. And it provides results in real time, available to all parts of the healthcare ecosystem. This broad connectivity creates an omni-channel, end-to-end patient experience.

To learn more about Digital Transformation for Healthcare, click here.

Comments

Martin Kopp

About Martin Kopp

Martin Kopp is the global general manager for Healthcare at SAP. He is responsible for setting the strategy and articulating the vision and direction of SAP's healthcare-provider industry solutions, influencing product development, and fostering executive level relationships key customers, IT influencers, partners, analysts, and media.