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Digital Kitchener: Putting Smart City Concepts Into Action

Dan Murray

Many Canadians might not realize it, but in their neighborhood parks, soil sensor networks will soon tell the city parks department when specific trees need watering. Although Kitchener doesn’t yet boast a “connected trees” system, the city has taken on numerous smart city initiatives over the past few years that have contributed to its already burgeoning reputation as a tech powerhouse in Canada.

If you live in or visit Kitchener, you may see or hear about our plans for a network of 18,325 energy-efficient streetlights. You may also know about our work with world-class tech leaders and incubators in the city, or our crowdsourcing project to test the speed and quality of Internet connections across the city.

In January 2017, all this progress was pulled together under a new four-year Digital Kitchener strategy – a vision to build a smarter, more connected city using innovative digital tools, engaging our residents and working with community partners.

Digital Kitchener is about building a connected, innovative, inclusive and on-demand city. This means establishing digital infrastructure to support features like our recent tax and utility billing system, which helps citizens connect to efficient city services. Our infrastructure helps streamline billing and financial processes and improves customer service.

In April 2017, Kitchener will introduce an online portal that offers customers 24-7 access to their property tax and utility accounts. They will be able to track utility consumption, request online customer service, submit meter readings, and more.

Digital Kitchener involves more than third-party tools and tech. The new online billing service shows how tools can help establish infrastructure for a smart city. At its core, Digital Kitchener is about using infrastructure, resources, and relationships to connect residents to their government and to each other.

Investment in public wi-fi access, including at all city facilities and major green spaces, is another example of how a Digital Kitchener will offer information when and where people want it. We’re also exploring additional possibilities of a narrowband LED streetlight network. We already stand to save $1 million a year (it will have paid for itself in just over eight years), and energy costs will be cut by 15%. Now we’re looking into how streetlight sensors could help to organize everything from parking and traffic to black ice detection and storm water management.

The other big part of building a Digital Kitchener – the bit where startups and our tech community comes in – is the Civic Innovation Lab. Situated in the Communitech Hub, it’s the place where city staff and local innovators get together to develop new services and products for our smart city.

This allows us to bring our ideas and thoughts to the tech community and have them weigh in. And we’re not just talking about any old tech community here – Kitchener-Waterloo has been called “Silicon Valley of the North” and “Startup City,” and is home to the likes of Google and SAP. Adding further excitement to this great reputation is the soon-to-be-opened Catalyst137, which will be the world’s largest IoT manufacturing facility.

There is much potential coming from the growth of IoT and many opportunities to look at how we can do things differently. The path we’re taking — laying the digital foundations and working with the tech community to see what it can do — means we’ll be able to make the most of it. It’s an exciting time to be a technologist, especially in a city like Kitchener.

To learn more, visit the Digital Kitchener website and hear my colleagues Alex Ahkoon and Joyce Evans talk at SAP Smart Cities Forum on March 7. Join the City of Kitchener, alongside leaders and technologists from cities around the world, at the SAP Smart Cities Forum in Toronto on March 7.

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Dan Murray

About Dan Murray

Dan Murray is Director of Technology Innovation and Services at the City of Kitchener, a thrivving city located in Southern Ontario, Canada, with a population of 230,000.

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.

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.

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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.

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.