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Digital Operations: Test-And-Learn Beats Wait-And-See

Dr. Achim Krüger

Digitalization is dramatically shaping supply chain operations in the 21st century.

By embracing the latest Internet of Things (IoT) applications and other cutting-edge technology, supply chain organizations are creating new business models and more efficient ways to work.

Smart logistics processes optimize warehousing operations and delivery, creating enhancements in inventory and transportation. Through smart manufacturing, businesses can accelerate production, increase agility, and make strides toward more easily delivering product customization. Meanwhile, smart engineering enables designers to simulate manufacturing and identify challenges before products are actually built.

Digitalization and a seamlessly digital supply chain clearly have their advantages. The adoption of the latest technology, however, comes with a unique set of challenges. Primarily, leaders seeking to innovate and incite change must strike a delicate balance, walking the knife’s edge between chasing digital initiatives – a metaphorical collection of “shiny objects” – and dismissing every new digital technology as “hype.”

While you certainly don’t want to waste your time implementing initiatives that don’t benefit your enterprise, you can’t afford to hold off on adopting innovative technology too long, or you risk falling too far behind to ever catch up.

Taking a test-and-learn approach at your organization

Contrary to previous technology waves, like enterprise resource planning and mobile communications, sticking to standard business processes won’t lead to the successful application of digital technology.

How should this realization impact your plans for digitalization and your investment in people, processes, and technology moving forward?

SCM World research advises taking a test-and-learn approach rather than clinging to a wait-and-see mindset. Thinking strategically about which technologies to implement and invest in instead of waiting for early adopters to forge the path is key for success in a hypercompetitive business landscape.

To begin instituting test-and-learn at your organization, you should first consider your business holistically through three levels of analysis:

  1. How will digitalization change business processes and customer expectations?
  2. How will digital disruptions and IoT change business models within your industry?
  3. How will digitalization impact the nature of work itself?

Answer these questions to determine where digital investment makes the most sense, then take action – with the intent to constantly refine and improve for the future.

These top companies prosper with test-and-learn

A number of leading companies are already doing just that. From analyzing new data streams and leveraging advanced analytics, to making improvements in 3D printing and advanced robotics, to delivering digital products via the cloud, organizations worldwide are reaping the benefits of taking a test-and-learn approach and experimenting with digitalization.

While heavy hitters such as Amazon and Uber are often-cited digital disruptors, additional examples of companies getting ahead with test-and-learn include:

  • Schneider Electric: Tailored Supply Chain, Schneider’s comprehensive effort to divide its supply chain according to six unique customer segments, uses a series of advanced analytics tools to understand customer behavior. The initiative will help the company enhance data insights and develop a new business process focused on improving demand sensing capabilities. The program, which didn’t require a large investment in new technology, has so far already generated 340 million euros worth of incremental margin contribution.
  • DB Schenker: DB Schenker recently leveraged the concept of the sharing economy and forged a partnership with UShip that connects shippers to trucking companies online – similar to what Airbnb does for travelers looking for a place to stay. So far, this collaboration has not only impacted speed and selection for DB Schenker’s customers, it’s also improved efficiency by making use of previously underutilized assets.
  • Harley-Davidson: Harley-Davidson’s product planning approach decreased from a 21-day fixed plan to a six-hour window. In addition to positively impacting on-hand inventory, the initiative has added more agility and flexibility around scheduling and accommodating orders. All this is largely due to an increasingly digital supply chain, which offers the company greater visibility.

Get smart: Adopt a test-and-learn approach today

These organizations are just a few of the many that are prospering with a test-and-learn approach. From increasing agility and precision to reducing costs and waste, they demonstrate how smart investments in digital technology must strike a balance between inaction and overkill to revolutionize the nature of supply chain.

For more examples of how top companies are using test-and-learn to positively impact business outcomes, and to learn how you can successfully implement the concept at your own organization, download the full SCM World report: Smart Operations and the Internet of Things.

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Dr. Achim Krüger

About Dr. Achim Krüger

Dr. Achim Krüger is Vice President of Operational Excellence (EAM and EH&S) at SAP. After starting his career as an officer with the German Air Force, he held several positions in the areas of maintenance of helicopters and transport aircraft as well as systems engineering, before he worked in higher commands as a logistics general staff officer. Joining SAP in 2002, Dr. Krüger first served as a consultant before establishing the SAP for Defense & Security industry portfolio and later assumed several other duties in Solution Management and Development,

4 Steps To Revolutionize Your Industrial Manufacturing [VIDEO]

Conor Brophy

For generations, we have lived in a mass production society. Imagine a world where a 50,000-square foot warehouse, once filled with products and inventory as far as the eye can see, now sits empty. Inventory has shifted to a new location: the cloud. Products are now manufactured, or “printed,” on-demand and delivered to the location where the demand exists.

UPS and Fast Radius are two innovative companies with a shared vision for the future of manufacturing. Fast Radius is UPS’s partner in 3D printing and a pioneer in on-demand manufacturing. UPS is a global leader in logistical services. Together, these two companies are teaming up and leveraging IoT technology in order to rewrite the rules of manufacturing.

The digital supply chain

“[There] are problems that [manufacturing] companies have been trying to solve for decades. How do I make my inventory management more efficient? How do I match the supply of product that I’m making with the actual demand?” said Rick Smith, co-founder of Fast Radius, at the Fast Radius facility in Chamblee, Georgia.

To answer these questions, we must take a step back and look at the big picture. Over the last 100-plus years, conventional manufacturing has been based on a simple equation: “The more things you make, the lower the cost of each of those things,” according to Rick Smith. This equation presents problems in cost and infrastructure when considering factory build and maintenance, tooling and molds, and piles of excess inventory. Instead of asking how to simply make this process more efficient, UPS and Fast Radius are taking a step further by enabling a digital supply chain.

Simplifying industrial manufacturing in four easy steps

1. Design

The first step towards on-demand manufacturing requires a seamlessly integrated solution for collaboration. Fast Radius is leveraging a distributed manufacturing solution to virtually collaborate with customers and ensure design specifications and the correct level of certifications are met. This platform is the digital workshop where designers and engineers, on both sides, can make sure a printed product will be the same as one produced on an assembly line.

2. Internet of Things (IoT)

UPS is using an IoT platform which specializes in making machines smarter and drives end-to-end digital transformation. A distributed manufacturing solution, sensors, automation, robotics, and 3D printers feed massive amounts of data processed within an IoT portfolio, enabling more dynamic data leading to seamless customer experiences.

“It’s really an ecosystem of technologies that work together. It’s not just one thing, it’s many things that are working together,” said Alan Amling, VP of strategy at UPS.

3. 3-D printing

3D printing is the third step in the process to set forth on-demand manufacturing. After taking the world by storm, the technology continues to evolve every day. “Traditionally with printing, you would be stuck with a very limited amount of materials. You could replicate some of the harder types with traditional methods, but with the new technologies and new materials coming to market, you can almost replicate any kind of production,” said Rick Smith.

4. Logistics

Finally, a designed, printed product must do one last thing: Get to the customer. How? By leveraging UPS and its areas of logistical expertise. Simply box it up and slap on a UPS Next-Day Air sticker. The customer will receive the part within 24 hours of initial purchase.

By harnessing the power of an IoT platform, UPS and Fast Radius are quickly revolutionizing product manufacturing. They have streamlined the process of design, IoT, 3-D printing, and logistics to create a seamless customer experience. The impact to manufacturers, to businesses, and to individuals will undoubtedly be disruptive. Watch the video below to learn more.

Effective IoT connectedness requires a unifying foundation. SAP has addressed this need by introducing the SAP Leonardo portfolio, an innovative IoT solution portfolio designed to help organizations digitally transform existing processes and evolve to new digital models. Learn more by downloading an SAP Leonardo brochure, reading about real-world use cases, attending our flagship event Leonardo Live this summer, visiting sap.com/iot, and following us on Twitter at @SAPLeonardo.

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Conor Brophy

About Conor Brophy

Conor Brophy works with the SAP Global Marketing Customer Storytelling team as a content specialist. When he is not writing about stories of "Run Simple," he enjoys golf, hiking and spending time with his fiance, friends and family.

Amp The Supply Chain

Hans Thalbauer and Michael S. Goldberg

Earlier this decade, manufacturing executives were skeptical about the benefits of digitizing their operations. According to various studies, only 37% believed digital business could drive revenue growth; 25% thought the sector would be highly impacted by digital transformation within the following five years; and fewer than 10% were implementing digital technologies to transform their businesses end to end.

That was then. The future is arriving fast.

Now every manufacturing C-suite in the world is on the path to digital transformation, with the supply chain at its heart. A transformed supply chain is the enabler for companies to deploy technology for personalizing products, accelerating delivery, and meeting rising customer expectations—all while constantly probing the boundaries of their existing business models.

Researchers at IDC have identified a clear turning point ahead: they predict that half of manufacturers will be benefiting from digital transformation in their supply chains by 2019.

Charging Ahead with
Supply Chain Transformation

When successfully implemented, digital supply chain technologies will lead to revenue gains, boost service quality, help cut innovation costs, and speed product-to-market times. The evidence is already apparent.

 2018

90% of supply chains will use B2B commerce networks to collaborate. By enabling decentralized collaboration among members of networks, blockchain technology is beginning to demonstrate its potential to automatically speed up supply chain network transactions. CoinDesk reports that BHP Billiton, one of the world’s largest mining companies, has started using blockchain technology to automatically share data with vendors (including geologists and shipping firms) that collect and analyze mining samples instead of relying on spreadsheets.

Manufacturing centers and microfactories with 3D printers will receive 500% more funding. Ford is testing 3D printing to make parts, starting with plastic molding for auto interiors and spoilers that go on racing models. The technology has potential to speed delivery of parts and save money in assembly and service processes.

Data: IDC

2019

Supply chain productivity and efficiency using Internet of Things (IoT) sensors will improve 30%. IoT-based sensors that enable the collection and analysis of data—and the analytics tools that make good on the variety and speed of that data—make productivity and efficiency improvements possible. Following a model with jet engines made famous by General Electric, Kaeser Compressors has fitted its air compressors with internet-connected sensors and is selling metered air compressor services rather than the equipment itself. Not only does this represent a new business model for Kaeser, it also improves uptime and service quality for customers, because the manufacturer, not the user, is responsible for maintenance.

50% of supply chains will benefit from digital transformation, while others will lag due to outdated business models and systems. The creation of local factories and mini-warehouses will put subsets of products closer to where they are needed and will locate production processes and products closer to customers. Adidas is building a “Speedfactory” in Atlanta, slated to open in 2017, that will bring customized products to American retail customers faster than could be done when manufacturing is executed primarily in Asia. The Atlanta facility, modeled after a factory in Germany, will use robots to automate production processes that can, for example,  customize shoe styles and fit to match customer specifications.

Data: IDC

2020

50% of mature supply chains will use artificial intelligence and advanced analytics for planning and forecasting. Intelligent systems can make faster and better predictions than people can. The healthcare unit at Merck KGaA is working on an initiative to bring sensors and intelligent software algorithms to bear on its supply chain, according to The Wall Street Journal. The goals: better data about how products do in the market and an accelerated planning process.

50% of manufacturers will deliver directly to consumers. The McDonald’s supply chain once stopped at the restaurant door. But after offering delivery services in Asia and the Middle East, the company has begun pilots to bring burgers to customers—even partnering with ride-sharing digital natives at Uber in Florida to deliver meals.

Data: IDC

Digital Power Source

The opportunities for supply chain transformation are real, although the path forward is challenging. An SAP-sponsored study by research and advisory firm Longitude notes that while many enterprises appear to be digitized, the foundations of their operations—supply chain, procurement, and logistics—are still analog. Market forces are placing these companies under great strain, making them susceptible to disruption by digital startups.

Transformation means converting analog processes into digital supply networks—now. While every company’s digitization strategy will be different, enabling these processes requires the following:

  • Ask the right questions. To avoid being overtaken by a lean startup, you need to continually evaluate your operations against competitors. Some questions to ask, according to Peter Weill and Stephanie L. Woerner in the MIT Sloan Management Review: Are the products you make ordered and delivered digitally? Can you equip them with data to make them more valuable? Are there other firms serving your customers that could become competitors? Can a digital offering replace your products now or in the future?
  • Have the right data systems in place. You need information from everything in your production ecosystem—including sensors, machines, factory and warehouse equipment, trucks, and even products—in forms that you can analyze to improve production processes.
  • Commit to automation. Machine-learning technologies make your systems more intelligent, so you can pursue the right opportunities and produce the right outcomes. For example, blockchain technology applied to supply chain systems can configure order processes so they happen immediately.
  • Include every process. The digitization effort should cover manufacturing processes from product design and configuration to supply chain planning, manufacturing, shipping, and after-sales service.

These points are where the discussion starts. Every C-suite will have its own approach to how these elements come together for their firm to succeed. Many companies are executing their strategies now. The rest need to head that way. D!

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Hans Thalbauer

About Hans Thalbauer

Hans Thalbauer is globally responsible for solution management and the go-to-market functions for SAP digital supply chain solutions and the SAP Leonardo portfolio of Internet of Things solutions. In this role, he is engaged in creative dialogues with businesses and operations worldwide, addressing customer needs and introducing innovative business processes, including the vision of creating a live business environment for everyone working in operations. Hans has more than 17 years with SAP and is based out of Palo Alto, CA, USA. He has held positions in development, product and solution management, and the go-to-market organization. Hans holds a degree in Business Information Systems from the University Vienna, Austria.

Michael S. Goldberg

About Michael S. Goldberg

Michael S. Goldberg is an independent writer and editor focusing on management and technology issues.

Heroes in the Race to Save Antibiotics

Dr. David Delaney, Joseph Miles, Walt Ellenberger, Saravana Chandran, and Stephanie Overby

Last August, a woman arrived at a Reno, Nevada, hospital and told the attending doctors that she had recently returned from an extended trip to India, where she had broken her right thighbone two years ago. The woman, who was in her 70s, had subsequently developed an infection in her thigh and hip for which she was hospitalized in India several times. The Reno doctors recognized that the infection was serious—and the visit to India, where antibiotic-resistant bacteria runs rampant, raised red flags.

When none of the 14 antibiotics the physicians used to treat the woman worked, they sent a sample of the bacterium to the U.S. Centers for Disease Control (CDC) for testing. The CDC confirmed the doctors’ worst fears: the woman had a class of microbe called carbapenem-resistant Enterobacteriaceae (CRE). Carbapenems are a powerful class of antibiotics used as last-resort treatment for multidrug-resistant infections. The CDC further found that, in this patient’s case, the pathogen was impervious to all 26 antibiotics approved by the U.S. Food and Drug Administration (FDA).

In other words, there was no cure.

This is just the latest alarming development signaling the end of the road for antibiotics as we know them. In September, the woman died from septic shock, in which an infection takes over and shuts down the body’s systems, according to the CDC’s Morbidity and Mortality Weekly Report.

Other antibiotic options, had they been available, might have saved the Nevada woman. But the solution to the larger problem won’t be a new drug. It will have to be an entirely new approach to the diagnosis of infectious disease, to the use of antibiotics, and to the monitoring of antimicrobial resistance (AMR)—all enabled by new technology.

But that new technology is not being implemented fast enough to prevent what former CDC director Tom Frieden has nicknamed nightmare bacteria. And the nightmare is becoming scarier by the year. A 2014 British study calculated that 700,000 people die globally each year because of AMR. By 2050, the global cost of antibiotic resistance could grow to 10 million deaths and US$100 trillion a year, according to a 2014 estimate. And the rate of AMR is growing exponentially, thanks to the speed with which humans serving as hosts for these nasty bugs can move among healthcare facilities—or countries. In the United States, for example, CRE had been seen only in North Carolina in 2000; today it’s nationwide.

Abuse and overuse of antibiotics in healthcare and livestock production have enabled bacteria to both mutate and acquire resistant genes from other organisms, resulting in truly pan-drug resistant organisms. As ever-more powerful superbugs continue to proliferate, we are potentially facing the deadliest and most costly human-made catastrophe in modern times.

“Without urgent, coordinated action by many stakeholders, the world is headed for a post-antibiotic era, in which common infections and minor injuries which have been treatable for decades can once again kill,” said Dr. Keiji Fukuda, assistant director-general for health security for the World Health Organization (WHO).

Even if new antibiotics could solve the problem, there are obstacles to their development. For one thing, antibiotics have complex molecular structures, which slows the discovery process. Further, they aren’t terribly lucrative for pharmaceutical manufacturers: public health concerns call for new antimicrobials to be financially accessible to patients and used conservatively precisely because of the AMR issue, which reduces the financial incentives to create new compounds. The last entirely new class of antibiotic was introduced 30 year ago. Finally, bacteria will develop resistance to new antibiotics as well if we don’t adopt new approaches to using them.

Technology can play the lead role in heading off this disaster. Vast amounts of data from multiple sources are required for better decision making at all points in the process, from tracking or predicting antibiotic-resistant disease outbreaks to speeding the potential discovery of new antibiotic compounds. However, microbes will quickly adapt and resist new medications, too, if we don’t also employ systems that help doctors diagnose and treat infection in a more targeted and judicious way.

Indeed, digital tools can help in all four actions that the CDC recommends for combating AMR: preventing infections and their spread, tracking resistance patterns, improving antibiotic use, and developing new diagnostics and treatment.

Meanwhile, individuals who understand both the complexities of AMR and the value of technologies like machine learning, human-computer interaction (HCI), and mobile applications are working to develop and advocate for solutions that could save millions of lives.

Keeping an Eye Out for Outbreaks

Like others who are leading the fight against AMR, Dr. Steven Solomon has no illusions about the difficulty of the challenge. “It is the single most complex problem in all of medicine and public health—far outpacing the complexity and the difficulty of any other problem that we face,” says Solomon, who is a global health consultant and former director of the CDC’s Office of Antimicrobial Resistance.

Solomon wants to take the battle against AMR beyond the laboratory. In his view, surveillance—tracking and analyzing various data on AMR—is critical, particularly given how quickly and widely it spreads. But surveillance efforts are currently fraught with shortcomings. The available data is fragmented and often not comparable. Hospitals fail to collect the representative samples necessary for surveillance analytics, collecting data only on those patients who experience resistance and not on those who get better. Laboratories use a wide variety of testing methods, and reporting is not always consistent or complete.

Surveillance can serve as an early warning system. But weaknesses in these systems have caused public health officials to consistently underestimate the impact of AMR in loss of lives and financial costs. That’s why improving surveillance must be a top priority, says Solomon, who previously served as chair of the U.S. Federal Interagency Task Force on AMR and has been tracking the advance of AMR since he joined the U.S. Public Health Service in 1981.

A Collaborative Diagnosis

Ineffective surveillance has also contributed to huge growth in the use of antibiotics when they aren’t warranted. Strong patient demand and financial incentives for prescribing physicians are blamed for antibiotics abuse in China. India has become the largest consumer of antibiotics on the planet, in part because they are prescribed or sold for diarrheal diseases and upper respiratory infections for which they have limited value. And many countries allow individuals to purchase antibiotics over the counter, exacerbating misuse and overuse.

In the United States, antibiotics are improperly prescribed 50% of the time, according to CDC estimates. One study of adult patients visiting U.S. doctors to treat respiratory problems found that more than two-thirds of antibiotics were prescribed for conditions that were not infections at all or for infections caused by viruses—for which an antibiotic would do nothing. That’s 27 million courses of antibiotics wasted a year—just for respiratory problems—in the United States alone.

And even in countries where there are national guidelines for prescribing antibiotics, those guidelines aren’t always followed. A study published in medical journal Family Practice showed that Swedish doctors, both those trained in Sweden and those trained abroad, inconsistently followed rules for prescribing antibiotics.

Solomon strongly believes that, worldwide, doctors need to expand their use of technology in their offices or at the bedside to guide them through a more rational approach to antibiotic use. Doctors have traditionally been reluctant to adopt digital technologies, but Solomon thinks that the AMR crisis could change that. New digital tools could help doctors and hospitals integrate guidelines for optimal antibiotic prescribing into their everyday treatment routines.

“Human-computer interactions are critical, as the amount of information available on antibiotic resistance far exceeds the ability of humans to process it,” says Solomon. “It offers the possibility of greatly enhancing the utility of computer-assisted physician order entry (CPOE), combined with clinical decision support.” Healthcare facilities could embed relevant information and protocols at the point of care, guiding the physician through diagnosis and prescription and, as a byproduct, facilitating the collection and reporting of antibiotic use.

Cincinnati Children’s Hospital’s antibiotic stewardship division has deployed a software program that gathers information from electronic medical records, order entries, computerized laboratory and pathology reports, and more. The system measures baseline antimicrobial use, dosing, duration, costs, and use patterns. It also analyzes bacteria and trends in their susceptibilities and helps with clinical decision making and prescription choices. The goal, says Dr. David Haslam, who heads the program, is to decrease the use of “big gun” super antibiotics in favor of more targeted treatment.

While this approach is not yet widespread, there is consensus that incorporating such clinical-decision support into electronic health records will help improve quality of care, contain costs, and reduce overtreatment in healthcare overall—not just in AMR. A 2013 randomized clinical trial finds that doctors who used decision-support tools were significantly less likely to order antibiotics than those in the control group and prescribed 50% fewer broad-spectrum antibiotics.

Putting mobile devices into doctors’ hands could also help them accept decision support, believes Solomon. Last summer, Scotland’s National Health Service developed an antimicrobial companion app to give practitioners nationwide mobile access to clinical guidance, as well as an audit tool to support boards in gathering data for local and national use.

“The immediacy and the consistency of the input to physicians at the time of ordering antibiotics may significantly help address the problem of overprescribing in ways that less-immediate interventions have failed to do,” Solomon says. In addition, handheld devices with so-called lab-on-a-chip  technology could be used to test clinical specimens at the bedside and transmit the data across cellular or satellite networks in areas where infrastructure is more limited.

Artificial intelligence (AI) and machine learning can also become invaluable technology collaborators to help doctors more precisely diagnose and treat infection. In such a system, “the physician and the AI program are really ‘co-prescribing,’” says Solomon. “The AI can handle so much more information than the physician and make recommendations that can incorporate more input on the type of infection, the patient’s physiologic status and history, and resistance patterns of recent isolates in that ward, in that hospital, and in the community.”

Speed Is Everything

Growing bacteria in a dish has never appealed to Dr. James Davis, a computational biologist with joint appointments at Argonne National Laboratory and the University of Chicago Computation Institute. The first of a growing breed of computational biologists, Davis chose a PhD advisor in 2004 who was steeped in bioinformatics technology “because you could see that things were starting to change,” he says. He was one of the first in his microbiology department to submit a completely “dry” dissertation—that is, one that was all digital with nothing grown in a lab.

Upon graduation, Davis wanted to see if it was possible to predict whether an organism would be susceptible or resistant to a given antibiotic, leading him to explore the potential of machine learning to predict AMR.

As the availability of cheap computing power has gone up and the cost of genome sequencing has gone down, it has become possible to sequence a pathogen sample in order to detect its AMR resistance mechanisms. This could allow doctors to identify the nature of an infection in minutes instead of hours or days, says Davis.

Davis is part of a team creating a giant database of bacterial genomes with AMR metadata for the Pathosystems Resource Integration Center (PATRIC), funded by the U.S. National Institute of Allergy and Infectious Diseases to collect data on priority pathogens, such as tuberculosis and gonorrhea.

Because the current inability to identify microbes quickly is one of the biggest roadblocks to making an accurate diagnosis, the team’s work is critically important. The standard method for identifying drug resistance is to take a sample from a wound, blood, or urine and expose the resident bacteria to various antibiotics. If the bacterial colony continues to divide and thrive despite the presence of a normally effective drug, it indicates resistance. The process typically takes between 16 and 20 hours, itself an inordinate amount of time in matters of life and death. For certain strains of antibiotic-resistant tuberculosis, though, such testing can take a week. While physicians are waiting for test results, they often prescribe broad-spectrum antibiotics or make a best guess about what drug will work based on their knowledge of what’s happening in their hospital, “and in the meantime, you either get better,” says Davis, “or you don’t.”

At PATRIC, researchers are using machine-learning classifiers to identify regions of the genome involved in antibiotic resistance that could form the foundation for a “laboratory free” process for predicting resistance. Being able to identify the genetic mechanisms of AMR and predict the behavior of bacterial pathogens without petri dishes could inform clinical decision making and improve reaction time. Thus far, the researchers have developed machine-learning classifiers for identifying antibiotic resistance in Acinetobacter baumannii (a big player in hospital-acquired infection), methicillin-resistant Staphylococcus aureus (a.k.a. MRSA, a worldwide problem), and Streptococcus pneumoniae (a leading cause of bacterial meningitis), with accuracies ranging from 88% to 99%.

Houston Methodist Hospital, which uses the PATRIC database, is researching multidrug-resistant bacteria, specifically MRSA. Not only does resistance increase the cost of care, but people with MRSA are 64% more likely to die than people with a nonresistant form of the infection, according to WHO. Houston Methodist is investigating the molecular genetic causes of drug resistance in MRSA in order to identify new treatment approaches and help develop novel antimicrobial agents.

The Hunt for a New Class of Antibiotics

There are antibiotic-resistant bacteria, and then there’s Clostridium difficile—a.k.a. C. difficile—a bacterium that attacks the intestines even in young and healthy patients in hospitals after the use of antibiotics.

It is because of C. difficile that Dr. L. Clifford McDonald jumped into the AMR fight. The epidemiologist was finishing his work analyzing the spread of SARS in Toronto hospitals in 2004 when he turned his attention to C. difficile, convinced that the bacteria would become more common and more deadly. He was right, and today he’s at the forefront of treating the infection and preventing the spread of AMR as senior advisor for science and integrity in the CDC’s Division of Healthcare Quality Promotion. “[AMR] is an area that we’re funding heavily…insofar as the CDC budget can fund anything heavily,” says McDonald, whose group has awarded $14 million in contracts for innovative anti-AMR approaches.

Developing new antibiotics is a major part of the AMR battle. The majority of new antibiotics developed in recent years have been variations of existing drug classes. It’s been three decades since the last new class of antibiotics was introduced. Less than 5% of venture capital in pharmaceutical R&D is focused on antimicrobial development. A 2008 study found that less than 10% of the 167 antibiotics in development at the time had a new “mechanism of action” to deal with multidrug resistance. “The low-hanging fruit [of antibiotic development] has been picked,” noted a WHO report.

Researchers will have to dig much deeper to develop novel medicines. Machine learning could help drug developers sort through much larger data sets and go about the capital-intensive drug development process in a more prescriptive fashion, synthesizing those molecules most likely to have an impact.

McDonald believes that it will become easier to find new antibiotics if we gain a better understanding of the communities of bacteria living in each of us—as many as 1,000 different types of microbes live in our intestines, for example. Disruption to those microbial communities—our “microbiome”—can herald AMR. McDonald says that Big Data and machine learning will be needed to unlock our microbiomes, and that’s where much of the medical community’s investment is going.

He predicts that within five years, hospitals will take fecal samples or skin swabs and sequence the microorganisms in them as a kind of pulse check on antibiotic resistance. “Just doing the bioinformatics to sort out what’s there and the types of antibiotic resistance that might be in that microbiome is a Big Data challenge,” McDonald says. “The only way to make sense of it, going forward, will be advanced analytic techniques, which will no doubt include machine learning.”

Reducing Resistance on the Farm

Bringing information closer to where it’s needed could also help reduce agriculture’s contribution to the antibiotic resistance problem. Antibiotics are widely given to livestock to promote growth or prevent disease. In the United States, more kilograms of antibiotics are administered to animals than to people, according to data from the FDA.

One company has developed a rapid, on-farm diagnostics tool to provide livestock producers with more accurate disease detection to make more informed management and treatment decisions, which it says has demonstrated a 47% to 59% reduction in antibiotic usage. Such systems, combined with pressure or regulations to reduce antibiotic use in meat production, could also help turn the AMR tide.

Breaking Down Data Silos Is the First Step

Adding to the complexity of the fight against AMR is the structure and culture of the global healthcare system itself. Historically, healthcare has been a siloed industry, notorious for its scattered approach focused on transactions rather than healthy outcomes or the true value of treatment. There’s no definitive data on the impact of AMR worldwide; the best we can do is infer estimates from the information that does exist.

The biggest issue is the availability of good data to share through mobile solutions, to drive HCI clinical-decision support tools, and to feed supercomputers and machine-learning platforms. “We have a fragmented healthcare delivery system and therefore we have fragmented information. Getting these sources of data all into one place and then enabling them all to talk to each other has been problematic,” McDonald says.

Collecting, integrating, and sharing AMR-related data on a national and ultimately global scale will be necessary to better understand the issue. HCI and mobile tools can help doctors, hospitals, and public health authorities collect more information while advanced analytics, machine learning, and in-memory computing can enable them to analyze that data in close to real time. As a result, we’ll better understand patterns of resistance from the bedside to the community and up to national and international levels, says Solomon. The good news is that new technology capabilities like AI and new potential streams of data are coming online as an era of data sharing in healthcare is beginning to dawn, adds McDonald.

The ideal goal is a digitally enabled virtuous cycle of information and treatment that could save millions of dollars, lives, and perhaps even civilization if we can get there. D!

Read more thought provoking articles in the latest issue of the Digitalist Magazine, Executive Quarterly.


About the Authors:

Dr. David Delaney is Chief Medical Officer for SAP.

Joseph Miles is Global Vice President, Life Sciences, for SAP.

Walt Ellenberger is Senior Director Business Development, Healthcare Transformation and Innovation, for SAP.

Saravana Chandran is Senior Director, Advanced Analytics, for SAP.

Stephanie Overby is an independent writer and editor focused on the intersection of business and technology.

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Small And Midsize Businesses Have The Capacity To Drive Europe’s Future As A Digital Superpower

Katja Mehl

Part 10 of the “Road to Digital Transformation” series

Representing 99.8% of all companies throughout Europe, small and midsize businesses have tremendous power when it comes to impacting the region’s economy. One innovation at a time, they’re transforming entire industries, propelling emerging industries forward with adjacent offerings, and even supersizing a favorite childhood toy to make living conditions better for the poor and homeless. But perhaps the greatest evolution is found in the growing adoption of technology among firms.

According to the IDC InfoBrief “The Next Steps in Digital Transformation: How Small and Midsize Companies Are Applying Technology to Meet Key Business Goals with Insights for Europe,” sponsored by SAP, 35.4% of all European firms feel that their adoption of digital technology is either advanced or well underway. Germany and France are great examples of countries that are embracing advanced business networks and automation technology – such as the Internet of Things – to boost productivity and computerize or consolidate roles left empty due to long-term labor shortages.

Despite the progress made in some countries, I am also aware of others that are still resistant to digitizing their economy and automating operations. What’s the difference between firms that are digital leaders and those that are slow to mature? From my perspective in working with a variety of businesses throughout Europe, it’s a combination of diversity and technology availability.

digital transformation self-assessment

Source: “The Next Steps in Digital Transformation: How Small and Midsize Companies Are Applying Technology to Meet Key Business Goals with Insights for Europe,” IDC InfoBrief, sponsored by SAP, 2017. 

Opportunities abound with digital transformation

European companies are hardly homogenous. Comprising 47 countries across the continent, they serve communities that speak any of 225 spoken languages. Each one is experiencing various stages of digital development, economic stability, and workforce needs.

Nevertheless, as a whole, European firms do prioritize customer acquisition as well as improving efficiency and reducing costs. Over one-third of small and midsize companies are investing in collaboration software, customer relationship management solutions, e-commerce platforms, analytics, and talent management applications. Steadily, business leaders are finding better ways to go beyond data collection by applying predictive analytics to gain real-time insight from predictive analytics and machine learning to automate processes where possible.

Small and midsize businesses have a distinct advantage in this area over their larger rivals because they can, by nature, adopt new technology and practices quickly and act on decisions with greater agility. Nearly two-thirds (64%) of European firms are embracing the early stages of digitalization and planning to mature over time. Yet, the level of adoption depends solely on the leadership team’s commitment.

For many small and midsize companies across this region, the path to digital maturity resides in the cloud, more so than on-premise software deployment. For example, the flexibility associated with cloud deployment is viewed as a top attribute, especially among U.K. firms. This brings us back to the diversity of our region. Some countries prioritize personal data security while others may be more concerned with the ability to access the information they need in even the most remote of areas.

Technology alone does not deliver digital transformation

Digital transformation is certainly worth the effort for European firms. Between 60%–90% of small and midsize European businesses say their technology investments have met or exceeded their expectations – indicative of the steady, powerhouse transitions enabled by cloud computing. Companies are now getting the same access to the latest technology, data storage, and IT resources.

However, it is also important to note that a cloud platform is only as effective as the long-term digital strategy that it enables. To invigorate transformative changes, leadership needs to go beyond technology and adopt a mindset that embraces new ideas, tests the fitness of business models and processes continuously, and allows the flexibility to evolve the company as quickly as market dynamics change. By taking a step back and integrating digital objectives throughout the business strategy, leadership can pull together the elements needed to turn technology investments into differentiating, sustainable change. For example, the best talent with the right skills is hired. Plus, partners and suppliers with a complementary or shared digital vision and capability are onboarded.

The IDC Infobrief confirms what I have known all along: Small and midsize businesses are beginning to digitally mature and maintain a strategy that is relevant to their end-to-end processes. And furthering their digital transformation go hand in hand with the firms’ ability to ignite a transformational force that will likely progress Europe’s culture, social structure, and economy. 

To learn how small and midsize businesses across Europe are digitally transforming themselves to advance their future success, check out the IDC InfoBrief “The Next Steps in Digital Transformation: How Small and Midsize Companies Are Applying Technology to Meet Key Business Goals with Insights for Europe,” sponsored by SAP. For more region-specific perspectives on digital transformation, be sure to check every Tuesday for new installments to our blog series “The Road to Digital Transformation.”

 

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Katja Mehl

About Katja Mehl

Katja Mehl is Head of Marketing for Europe, Middle East, and Africa at SAP.