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Industry 4.0 Leads Into The Innovation Economy

Kerstin Geiger

Industry 4.0 – the Next Industrial Revolution

Industry 4.0 Leads Into The Innovation EconomyWe are entering the era of the 4.0 industrial revolution. This nomenclature implies that we already had some revolutions or at least distinct phases of improvements. But what makes the current change big and unique?

In the last 40 years we build up the IT, electronics and telecom infrastructures that resulted in the first level of automation of industrial processes, the globalization of the economy and the Internet. Innovation and focus was mainly targeting to improve and optimize the existing processes and increase efficiency.

New Technologies Open the Way to Industry 4.0

In the last year innovation was adding further ingredients to the mix – mobile, cloud, social media and big data.  What we see today is, that these innovations come together to build a perfect symbiosis (not to say the perfect storm).

Industry 4.0 describes these changes on two levels: the vertical automation, digitalization and networking of production systems and the horizontal integration of supply and value chains. In addition to that products and systems become smarter and they are connected.

It looks to me as if we are leaving the classical industrial era and entering an innovation- based economy. The changes ahead will be as big as the changes from an agrarian society to an industrial one, as we leave the structure, markets and business processes of the industrial age.

It’s a Hyper-Connected World

One of the main driving factors is the hyper-connected world. The global market is increasingly leveled due to the highly networked character of our economy and as a result traditional competitive factors – access to resources, scale, and market share – are being joined by other factors, such as knowledge, innovation, IP and access to top talent. Furthermore, the newly forming business networks now allow companies and business networks to form new business models.

As the production gets smarter, companies will be able to respond much better to customer requirements, even down to level of ‘make-for-me’. In addition to that customers are much more outcome oriented, for instance they desire a good cup of coffee instead of owning a coffee machine or they want holes instead of owning a drilling device. In response to this requirements value chains get fluid and adaptive, and what truly matters are several factors: real-time insights, fast responses to customer demands, a high product quality associated with the best services around.

Taking a Holistic Approach

How is that translating into actions? Companies will still want to increase the efficiency of their manufacturing processes but in addition they will invest in innovation and service. As business processes are progressively more linked with each other, we at SAP think that only a holistic approach, a principle we call idea to performance, will guide companies through the upcoming transformations.

SAP laid down many of the business foundations of the IT-based industrial era and we are ready to take our customers into the next phase as well. Will you join us?

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Dr. Kerstin Geiger is Global Head of Industry Business Solutions at SAP AG.

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Kerstin Geiger

About Kerstin Geiger

Kerstin Geiger is the SVP of Industry Value Engineering at SAP Asia Pacific Japan. Her specialties include business intelligence, go-to-market strategy, enterprise software, business strategy and development.

Compelling Shopping Moments: 4 Creative Ways Stores Connect With Their Customers

Ralf Kern

compelling shopping momentsOn a recent morning, as I was going through my usual routine, my coffeemaker broke. I cannot live without coffee in the morning, so I immediately looked up my coffeemaker on Amazon and had it shipped Prime in one day. My problem was solved within minutes. My Amazon app, and my loyalty account with that company, was there for me when I needed it most.

It was in this moment that I realized the importance of digital presence for retailers. There is a chance that the store 10 minutes from my house carries this very same coffeemaker; I could have had it in one hour, instead of one day. But the need for immediate access to information pushed me to the online store. My local retailer was not able to be there for me digitally like Amazon.

Retail is still about reading the minds of your customers in order to know what they need and create a flawless experience. But the days of the unconnected shopper in a monochannel world are over. I am not alone in my digital-first mindset; according to a recent MasterCard report, 80% of consumers use technology during the shopping process. I, and consumers like me, use mobile devices as a guide to the physical world.

We don’t need to have an academic discussion about multichannel, omnichannel, and omnicommerce and their meanings, because what it really comes down to for your consumers, or fans, is shopping. And shopping has everything to do with moments in your customers’ lives: celebration moments, in-a-hurry moments, I-want-to-be-entertained moments, and more. Most companies only look for and measure very few moments along the shopping journey, like the moment of coupon download or the moment of sales.

Anticipating these moments was easier when mom and pop stores knew their customers by name. They knew how to be there for their shoppers when, where, and how they wanted it. And shoppers didn’t have any other options. Now it is crucial for companies to understand all of these moments and even anticipate or trigger the right moments for their customers.

In today’s digital economy the way to achieve customer connection is with simple, enjoyable, and personalized front ends that are supported by sophisticated, digital back ends. Then you can use that system to support your customer outreach.

Companies around the world are using creative and innovative methods to find their customers in various moments. Being there for customers comes in many different shapes and forms. Consider these examples:

Chilli Beans

A Brazilian maker of fashion sunglasses, glasses, and watches, Chilli Beans has a loyal following online and at over 700 locations around the world. Chilli Beans keeps its customers engaged by releasing 10 limited-edition styles each week. If customers like what they see, they have to buy fast or risk missing out.

Bonobos

Online men’s fashion retailer Bonobos reaches its customers with its Guide Shops. While they look like traditional retail outlets, the shops don’t actually sell any clothes. Customers come in for one-on-one appointments with the staff, and if they like anything that they try on, the staff member orders it for them online and it is shipped to their house. The 20 Guide Shops currently open have proven very successful for the company.

Peak Performance

Peak Performance, a European maker of outdoor clothing, has added a little magic to its customer experience. It has created virtual pop-up shops that customers can track on their smartphones through CatchMagicHour.com, and they are only available at sunrise and sunset at exact GPS locations. Customers who go to the location, be it at a lighthouse or on top of a mountain, are rewarded with the ability to select free clothing from the virtual shop that they have unlocked on their phones.

Shoes of Prey

The customer experience is completely custom at Shoes of Prey, a website where women can design custom shoes. From fabric to color, the customer picks every element, and then her custom creation is sent directly to her house. Shoes of Prey has even shifted its business model based on customer feedback. Its customers wanted to get inspiration and advice in a physical store. So Shoes of Prey made the move from online-only to omnicommerce and has started to open stores around the world.

While the customer experience for each of these connections is relatively simple – a website, a smartphone, an online design studio – the back end that powers them has to be powerful and nimble at the same time. These sophisticated back ends – powering simple, enjoyable, and personalized front ends – will completely change the game in retail. They will allow companies to engage their customers in ways we can’t even begin to imagine.

Technology will help you be there in the shopping moment. The best technology won’t annoy your customers with irrelevant promotions or pop-up messages. Instead, like a good friend, it will know how to engage with customers and when to leave them alone – how to truly connect with customers instead of manage them. Consequently, customer relationship management as we know it is an outdated technology in the economy of today – and tomorrow. Technologies that go beyond CRM will help retailers to differentiate. Aligning your organization and those technologies will be the Holy Grail to creating true and sustainable customer loyalty.

Learn more ways that business will never be the same again. Learn 99 Mind-Blowing Ways The Digital Economy Is Changing The Future Of Business.

Find out how SAP can help you go beyond CRM and support your retail business.

Ralf Kern is Global Vice President Retail for SAP and a retail ambassador for SAP. Interested in your feedback. You can also get in touch on Twitter or LinkedIn

This blog also appeared on SAP Customer Network.

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Ralf Kern

About Ralf Kern

Ralf Kern is the Global Vice President, Business Unit Retail, at SAP, responsible for the future direction of SAP’s solution and global Go-to-Market strategy for Omnicommerce Retail, leading them into today’s digital reality.

IoT Can Keep You Healthy — Even When You Sleep [VIDEO]

Christine Donato

Today the Internet of Things is revamping technology. IoT image from American Geniuses.jpg

Smart devices speak to each other and work together to provide the end user with a better product experience.

Coinciding with this change in technology is a change in people. We’ve transitioned from a world of people who love processed foods and french fries to people who eat kale chips and Greek yogurt…and actually like it.

People are taking ownership of their well-being, and preventative care is at the forefront of focus for both physicians and patients. Fitness trackers alert wearers of the exact number of calories burned from walking a certain number of steps. Mobile apps calculate our perfect nutritional balance. And even while we sleep, people are realizing that it’s important to monitor vitals.

According to research conducted at Harvard University, proper sleep patterns bolster healthy side effects such as improved immune function, a faster metabolism, preserved memory, and reduced stress and depression.

Conversely, the Harvard study determined that lack of sleep can negatively affect judgement, mood, and the ability retain information, as well as increase the risk of obesity, diabetes, cardiovascular disease, and even premature death.

Through the Internet of Things, researchers can now explore sleep patterns without the usual sleep labs and movement-restricting electrode wires. And with connected devices, individuals can now easily monitor and positively influence their own health.

EarlySense, a startup credited with the creation of continuous patient monitoring solutions focused on early detection of patient deterioration, mid-sleep falls, and pressure ulcers, began with a mission to prevent premature and preventable deaths.

Without constant monitoring, patients with unexpected clinical deterioration may be accidentally neglected, and their conditions can easily escalate into emergency situations.

Motivated by many instances of patients who died from preventable post-elective surgery complications, EarlySense founders created a product that constantly monitors patients when hospital nurses can’t, alerting the main nurse station when a patient leaves his or her bed and could potentially fall, or when a patient’s vital signs drop or rise unexpectedly.

Now EarlySense technology has expanded outside of the hospital realm. The EarlySense wellness sensor, a device connected via the Internet of Things, mobile solutions, and supported by SAP HANA Cloud Platform, monitors all vital signs while a person sleeps. The device is completely wireless and lies subtly underneath one’s mattress. The sensor collects all mechanical vibrations that the patient’s body emits while sleeping, continuously monitoring heart and respiratory rates.

Watch this short video to learn more about how the EarlySense wellness sensor works:

The result is faster diagnoses with better treatments and outcomes. Sleep issues can be identified and addressed; individuals can use the data collected to make adjustments in diet or exercise habits; and those on heavy pain medications can monitor the way their bodies react to the medication. In addition, physicians can use the data collected from the sensor to identify patient health problems before they escalate into an emergency situation.

Connected care is opening the door for a new way to practice health. Through connected care apps that link people with their doctors, fitness trackers that measure daily activity, and sensors like the EarlySense wellness sensor, today’s technology enables people and physicians to work together to prevent sickness and accidents before they occur. Technology is forever changing the way we live, and in turn we are living longer, healthier lives.

To learn how SAP HANA Cloud Platform can affect your business, visit It&Me.

For more stories, join me on Twitter.

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Christine Donato

About Christine Donato

Christine Donato is a Senior Integrated Marketing Specialist at SAP. She is an accomplished project manager and leader of multiple marketing and sales enablement campaigns and events, that supported a multi million euro business.

Running Future Cities on Blockchain

Dan Wellers , Raimund Gross and Ulrich Scholl

Building on the Blockchain Framework

Some experts say these seemingly far-future speculations about the possibilities of combining technologies using blockchain are actually both inevitable and imminent:


Democratizing design and manufacturing by enabling individuals and small businesses to buy, sell, share, and digitally remix products affordably while protecting intellectual property rights.
Decentralizing warehousing and logistics by combining autonomous vehicles, 3D printers, and smart contracts to optimize delivery of products and materials, and even to create them on site as needed.
Distributing commerce by mixing virtual reality, 3D scanning and printing, self-driving vehicles, and artificial intelligence into immersive, personalized, on-demand shopping experiences that still protect buyers’ personal and proprietary data.

The City of the Future

Imagine that every agency, building, office, residence, and piece of infrastructure has an entry on a blockchain used as a city’s digital ledger. This “digital twin” could transform the delivery of city services.

For example:

  • Property owners could easily monetize assets by renting rooms, selling solar power back to the grid, and more.
  • Utilities could use customer data and AIs to make energy-saving recommendations, and smart contracts to automatically adjust power usage for greater efficiency.
  • Embedded sensors could sense problems (like a water main break) and alert an AI to send a technician with the right parts, tools, and training.
  • Autonomous vehicles could route themselves to open parking spaces or charging stations, and pay for services safely and automatically.
  • Cities could improve traffic monitoring and routing, saving commuters’ time and fuel while increasing productivity.

Every interaction would be transparent and verifiable, providing more data to analyze for future improvements.


Welcome to the Next Industrial Revolution

When exponential technologies intersect and combine, transformation happens on a massive scale. It’s time to start thinking through outcomes in a disciplined, proactive way to prepare for a future we’re only just beginning to imagine.

Download the executive brief Running Future Cities on Blockchain.


Read the full article Pulling Cities Into The Future With Blockchain

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Dan Wellers

About Dan Wellers

Dan Wellers is founder and leader of Digital Futures at SAP, a strategic insights and thought leadership discipline that explores how digital technologies drive exponential change in business and society.

Raimund Gross

About Raimund Gross

Raimund Gross is a solution architect and futurist at SAP Innovation Center Network, where he evaluates emerging technologies and trends to address the challenges of businesses arising from digitization. He is currently evaluating the impact of blockchain for SAP and our enterprise customers.

Ulrich Scholl

About Ulrich Scholl

Ulrich Scholl is Vice President of Industry Cloud and Custom Development at SAP. In this role, Ulrich discovers and implements best practices to help further the understanding and adoption of the SAP portfolio of industry cloud innovations.

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Are AI And Machine Learning Killing Analytics As We Know It?

Joerg Koesters

According to IDC, artificial intelligence (AI) is expected to become pervasive across customer journeys, supply networks, merchandizing, and marketing and commerce because it provides better insights to optimize retail execution. For example, in the next two years:

  • 40% of digital transformation initiatives will be supported by cognitive computing and AI capabilities to provide critical, on-time insights for new operating and monetization models.
  • 30% of major retailers will adopt a retail omnichannel commerce platform that integrates a data analytics layer that centrally orchestrates omnichannel capabilities.

One thing is clear: new analytic technologies are expected to radically change analytics – and retail – as we know them.

AI and machine learning defined in the context of retail

AI is defined broadly as the ability of computers to mimic human thinking and logic. Machine learning is a subset of AI that focuses on how computers can learn from data without being programmed through the use of algorithms that adapt to change; in other words, they can “learn” continuously in response to new data. We’re seeing these breakthroughs now because of massive improvements in hardware (for example, GPUs and multicore processing) that can handle Big Data volumes and run deep learning algorithms needed to analyze and learn from the data.

Ivano Ortis, vice president at IDC, recently shared with me how he believes, “Artificial intelligence will take analytics to the next level and will be the foundation for retail innovation, as reported by one out of every two retailers globally. AI enables scale, automation, and unprecedented precision and will drive customer experience innovation when applied to both hyper micro customer segmentation and contextual interaction.”

Given the capabilities of AI and machine learning, it’s easy to see how they can be powerful tools for retailers. Now computers can read and listen to data, understand and learn from it, and instantly and accurately recommend the next best action without having to be explicitly programmed. This is a boon for retailers seeking to accurately predict demand, anticipate customer behavior, and optimize and personalize customer experiences.

For example, it can be used to automate:

  • Personalized product recommendations based on data about each customer’s unique interests and buying propensity
  • The selection of additional upsell and cross-sell options that drive greater customer value
  • Chat bots that can drive intelligent and meaningful engagement with customers
  • Recommendations on additional services and offerings based on past and current buying data and customer data
  • Planogram analyses, which support in-store merchandizing by telling people what’s missing, comparing sales to shelf space, and accelerating shelf replenishment by automating reorders
  • Pricing engines used to make tailored, situational pricing decisions

Particularly in the United States, retailers are already able to collect large volumes of transaction-based and behavioral data from their customers. And as data volumes grow and processing power improves, machine learning becomes increasingly applicable in a wider range of retail areas to further optimize business processes and drive more impactful personalized and contextual consumer experiences and products.

The transformation of retail has already begun

The impacts of AI and machine learning are already being felt. For example:

  • Retailers are predicting demand with machine learning in combination with IoT technologies to optimize store businesses and relieve workforces
  • Advertisements are being personalized based on in-store camera detections and taking over semi-manual clienteling tasks of store employees
  • Retailers can monitor wait times in checkout lines to understand store traffic and merchandising effectiveness at the individual store level – and then tailor assortments and store layouts to maximize basket size, satisfaction, and sell through
  • Systems can now recognize and predict customer behavior and improve employee productivity by turning scheduled tasks into on-demand activities
  • Camera systems can detect the “fresh” status of perishable products before onsite employees can
  • Brick-and-mortar stores are automating operational tasks, such as setting shelf pricing, determining product assortments and mixes, and optimizing trade promotions
  • In-store apps can tell how long a customer has been in a certain aisle and deliver targeted offers and recommendations (via his or her mobile device) based on data about data about personal consumption histories and preferences

A recent McKinsey study provided examples that quantify the potential value of these technologies in transforming how retailers operate and compete. For example:

  • U.S. retailer supply chain operations that have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years. Using data and analytics to improve merchandising, including pricing, assortment, and placement optimization, is leading to an additional 16% in operating margin improvement.
  • Personalizing advertising is one of the strongest use cases for machine learning today. Additional retail use cases with high potential include optimizing pricing, routing, and scheduling based on real-time data in travel and logistics, as well as optimizing merchandising strategies.

Exploiting the full value of data

Thin margins (especially in the grocery sector) and pressure from industry-leading early adopters such as Amazon and Walmart have created strong incentives to put customer data to work to improve everything from cross-selling additional products to reducing costs throughout the entire value chain. But McKinsey has assessed that the U.S. retail sector has realized only 30-40% of the potential margin improvements and productivity growth its analysts envisioned in 2011 – and a large share of the value of this growth has gone to consumers through lower prices. So thus far, only a fraction of the potential value from AI and machine learning has been realized.

According to Forbes, U.S. retailers have the potential to see a 60%+ increase in net margin and 0.5–1.0% annual productivity growth. But there are major barriers to realizing this value, including lack of analytical talent and siloed data within companies.

This is where machine learning and analytics kick in. AI and machine learning can help scale the repetitive analytics tasks required to drive leverage of the available data. When deployed on a companywide, real-time analytics platform, they can become the single source of truth that all enterprise functions rely on to make better decisions.

How will this change analytics?

So how will AI and machine learning change retail analytics? We expect that AI and machine learning will not kill analytics as we know it, but rather give it a new and even more impactful role in driving the future of retail. For example, we anticipate that:

  • Retailers will include machine learning algorithms as an additional factor in analyzing and  monitoring business outcomes in relation to machine learning algorithms
  • They will use AI and machine learning to sharpen analytic algorithms, detect more early warning signals, anticipate trends, and have accurate answers before competitors do
  • Analytics will happen in real time and act as the glue between all areas of the business
  • Analytics will increasingly focus on analyzing manufacturing machine behavior, not just business and consumer behavior

Ivano Ortis at IDC authored a recent report, “Why Retail Analytics are a Foundation for Retail Profits,” in which he provides further insights on this topic. He notes how retail leaders will use new kinds of analytics to drive greater profitability, further differentiate the customer experience, and compete more effectively, “In conclusion, commerce and technology will converge, enabling retailers to achieve short-term ROI objectives while discovering untapped demand. But implementing analytics will require coordination across key management roles and business processes up and down each retail organization. Early adopters are realizing demonstrably significant value from their initiatives – double-digit improvements in margins, same-store and e-commerce revenue, inventory positions and sell-through, and core marketing metrics. A huge opportunity awaits.”

So how do you see your retail business adopting advanced analytics like AI and machine learning? I encourage you to read IDC’s report in detail, as it provides valuable insights to help you invest in – and apply – new kinds of analytics that will be essential to profitable growth.

For more information, download IDC’s “Why Retail Analytics are a Foundation for Retail Profits.

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Joerg Koesters

About Joerg Koesters

Joerg Koesters is the Head of Retail Marketing and Communication at SAP. He is a Technology Marketing executive with 20 years of experience in Marketing, Sales and Consulting, Joerg has deep knowledge in retail and consumer products having worked both in the industry and in the technology sector.