The Key To Gaining ROI From IoT

Daniel Kehrer

On the enterprise technology hype scale, the Internet of Things is a heavyweight champ. There’s only one problem. So far, this “transformative trend” – as Gartner calls it in the firm’s 2016 IoT Hype Cycle report – has remained largely that: Hype.

But that’s about to change. New enabling technologies – including Bluetooth 5, proximity awareness, and others – will speed the path to IoT value creation. The rollout of Bluetooth 5 in early 2017, for example, will quadruple Bluetooth range, double its speed and boost data broadcasting capacity by 800%.

These speed, range, and capacity improvements will open vast new IoT opportunities for companies to build a more accessible and interoperable IoT. This in turn will finally make hypothetical enterprise and industrial IoT use cases a reality.

According to a recent McKinsey Global Institute (MGI) report, the hype surrounding IoT may in fact understate its full potential. McKinsey predicts that if policymakers and businesses get it right, IoT’s linking of physical and digital worlds will generate between $4 trillion (their low estimate) and $11.1 trillion per year in economic value by 2025.

And the bulk of that value – nearly 70% of it, says McKinsey – will come from B2B applications such as construction and manufacturing where IoT technology helps optimize equipment placement and maintenance, improve safety and security, and much more.

Meanwhile, technology suppliers are ramping up IoT-related platforms to help enterprises design, implement, and operate solutions that fill the gap between the ability to collect data and the capacity to capture, analyze, and act on it.

The power of proximity-awareness technology

One of the most powerful tools in helping enterprise organizations gain ROI from IoT is proximity awareness. Smart proximity awareness technology will play a critical role in how enterprise organizations extract value from IoT or, alternatively, IoE – the Internet of Everything.

It’s also IoE because the value-creation chain includes more than just things. It involves people, data streams, locations, equipment, communication systems, and more, all connected to the Internet. Proximity-awareness technology brings these scattered pieces of IoT together into a cohesive, cyber-physical system that organizations can analyze and act on to solve problems, optimize time, and improve productivity. This is increasingly important as connected “things” gain autonomy and begin taking more actions on their own.

Proximity solutions enable organizations to gain greater order, efficiency, automation, and predictability from IoT. They solve for situations where people and things are dispersed haphazardly and sometimes unaccounted for, eliminating guesswork and costly inefficiencies. They enable organizations to see where things are, what’s happening with them, and how to make them more effective and productive.

Smart proximity awareness technology will also enable value creation from enterprise “wearables” – always on, connected computing displays worn on the body for easy, hands-free access to show contextually relevant information – as these devices replace bar code scanners and handheld GPS.

Proximity makes IoT work

Many companies already generate large amounts of data from IoT but only use a fraction of it. That’s because they focus mainly on detecting breakdowns or other anomalies, rather than envisioning new, value-building uses. By deploying smart proximity-awareness technology, companies can realize greater value from IoT by using it to predict and optimize a wide range of activities. As this happens, the old mindset of repair and replace becomes a new mindset of predict and prevent.

Enriching corporate data with proximity awareness pushes several things forward. Knowing where your people, inanimate assets, suppliers, supplies, and customers are – and when their joint movements are actionable – allows automated responses to specific conditions of convergence and divergence.

And by standardizing proximity services in an open platform, enterprises can gather mobile and IoT telemetry, track assets and people in motion, determine when any two or more of them are converging or diverging, and act by triggering prox­imity-aware messages or instructions to both people and things.

As the world becomes increasingly networked with nearly everything linked to everything else, production and supplier networks are expected to grow enormously, meaning manufacturers will need to coordinate more global suppliers. At the same time, boundaries that now separate individual factories and other facilities will be eliminated as IoT and prox­imity awareness connect multiple factories and the people who run them.

According to MGI, “The potential value that could be unlocked with IoT applications in factory settings could be as much as $3.7 trillion in 2025, or about one-third of all potential economic value. Cities are the next largest, with value of up to $1.7 trillion per year.”

Building flexible solutions at scale

But building IoT systems and solutions as vertical silos and operational islands inhibits the ability to gain strategic value. Smart proximity awareness based on a scalable and horizontal technology foundation lowers barriers and makes it easier to integrate all of the pieces into a single whole that is easy to operate, expand, and maintain.

Now is the time to consider the IoT business opportunities at hand, set a vision, establish a plan, and put smart proximity awareness to work as a strategic differentiator. As McKinsey points out, “Businesses that fail to invest in IoT capabilities, culture, and processes, as well as in technology, are likely to fall behind competitors that do.”


About Daniel Kehrer

Daniel Kehrer has 20+ years leadership and hands-on execution experience as a technology, content marketing and digital media entrepreneur and industry thought leader. He has built & scaled multi-channel and global marketing and content creation teams and engines for VC- & PE-backed tech companies leading to acquisitions totaling nearly $1 billion. He is currently Founder & CEO of BizBest Media Corp. and, working with select startup and growth-stage tech companies. He’s written for Forbes, Harvard Business Review, The New York Times and Digitalist Magazine, among many other publications, writes a syndicated weekly column, is the author of seven books and earned his MBA from UCLA Anderson.

3 Ways The Internet Of Things Will Change Your Workplace In 2017

Melissa Burns

The Internet of Things is a transformational technology that will change the future of business. In 2008 there were officially more devices connected to the Internet than there were human beings, and according to the original estimates, we will see 50 billion connected devices by 2020.

A large variety of industries, including logistics, marketing, and manufacturing, are already using IoT technology today, and experts predict that number this will only increase as large and small companies increasingly implement it.

For most of us, the Internet of Things already plays a role in everyday life. One of the earliest examples goes back to the early 1970’s, when the first ATMs went online. Some items we already own also incorporate the IoT technology, including smart home appliances and fitness trackers. Other everyday items will also soon include features enabled by IoT, such as cars and streets that “communicate” to facilitate rush hour traffic. IoT is similarly poised to change the way companies work by making business processes more productive and efficient.

How exactly will IoT technology change the workplace?

Increased efficiency and productivity

The latest and greatest IoT developments will enable you to get more in less time. Imagine being able to fulfill large-scale tasks faster while making fewer mistakes in your work.

If you’re a business owner, you will be able to follow every aspect of your company—from controlling inventory to managing field service employees. When every tool and device is connected to one centralized system, it becomes much easier to control them. This will improve data analysis and management results and broaden the opportunity to expand your business.

What’s more, the enhanced connectivity IoT offers lets companies more easily connect with their customers and clients, preventing potential problems and creating new revenue streams based on automatically collected feedback.

Cheaper, greener technologies

In the U.S., 30% of the energy used in an average commercial building is wasted. The potential to reduce energy consumption is enormous. Imagine if these buildings could shift to a “nighttime mode” outside business hours, when power consumption is reduced to a minimum and all unnecessary electricity is deactivated. Prior to the start of the business day, the heating and ventilation systems would switch to standby mode to improve the indoor air quality. Then, at the start of workday. all functions are automatically activated depending on whether anyone is in the room. Moreover, the room temperature controller ensures the optimal indoor climate through proper heating and ventilation. A sun protection collector opens the window blinds and allows daylight into the room while also preventing too much light or heat.

Thanks to IoT, companies can save energy while keeping the office work environment comfortable. Such tools are already on the market or in development, and their quality is only going to improve.

Better workplace collaboration

Although some worry that IoT could create a more isolated workplace experience, studies suggest that it will have the opposite effect, potentially improving interactions among employees. According to a Harvard Business Review survey, companies are already seeing important benefits of IoT-based initiatives, and 58% respondents say they have seen increased collaboration within the business. For example, thanks to technology that enables remote workers, companies can create a real presence for employees anywhere in the world. Soon, video conferencing that previously required use of a fixed device such as a computer or smartphone will be possible from any part of an enterprise base.

While some companies have already adopted IoT technology into their internal business processes, it is not yet ubiquitous in the business world. It is important to realize that office connectivity involves more than just printers and computers; it is a complex, constantly evolving ecosystem with the potential to transform objects into smart services. Barriers remain before this technology will fundamentally reshape our lives, but the direction is clear.

For more insight on future workplace trends, see The Future Of Work Is Here Now, But Does Work Have A Future?


Melissa Burns

About Melissa Burns

Melissa Burns is an entrepreneur and independent journalist. She spends her time writing articles, overviews, and analyses about entrepreneurship, startups, business innovations, and technology. Follow her @melissaaburns.

How To Build A Smart Factory That Transforms Your Business Model

Susan Galer

One of the biggest mistakes companies can make on the road to connected manufacturing is taking ideas to developers while keeping decision-makers from the business out of the loop. I recently listened to two experts at the annual SAPPHIRE NOW & ASUG Conference who had a different take on how companies can build a smart factory quickly to improve efficiencies, reduce costs, strengthen competitiveness, and create new routes to market.

“We hear the term ‘fail fast’ all the time, but why fail at all,” asked Raymond Russ, senior director of SAP Solutions at the Fujitsu Center of Excellence. “If you do the right amount of planning with the business, you can create an IoT strategy that will work to connect your company.”

Here are the highlights of the Fujitsu Smart Factory framework Russ laid out with his colleague James Zhang, director of SAP Connected Manufacturing at the Fujitsu Center of Excellence.

Band-aid solutions aren’t the cure

Leading analysts predict the IoT market will impact GDP by $10 trillion, and manufacturing facilities comprise a hefty slice of the opportunity pie. But the stark truth is that 20% of North American manufacturing companies haven’t connected their machines to the Internet. Among those organizations that are exploring IoT, Zhang said many are missing the mark with only quick proofs of concept and pilots in targeted areas.

“The major challenge we’ve found is that you need to also understand the big picture,” said Zhang. “To solve your business process you don’t need a Band-Aid solution. You need to optimize the entire manufacturing model. You need to look at what the smart factory means for your company and what business outcomes you need.”

Understand it’s a journey

Zhang said the ideal approach to creating a connected enterprise is to develop a road map that shows the future, while also focusing on projects that deliver business value in a few months.

“Combining Fujitsu’s DNA as a manufacturer with platform and technologies from SAP is helping our shared customers transform their supply chain and demonstrate a strong business case,” he said. “When you talk with your C-suite in this way, they can see it’s a strong initiative.”

Connected manufacturing case study

Using predefined accelerators, companies can speed up co-creation of IoT-powered solutions for faster outcomes. One example Zhang shared was a building materials company with over 50 facilities worldwide and aggressive growth plans to double its business in the next year. They needed to improve operational efficiency and quality by connecting information from shop floor machines with processes and workers including inspection, planning, production execution, predictive maintenance, and reliability. Using digital innovations, the company reduced time-to-value by over 50% with outcomes in just five weeks.

Transforming business models

The smart factory streams vast amounts of real-time information from machines on the shop floor or in the field to people doing their daily jobs. Sensors and software deliver actionable intelligence that not only changes planning, operations, and maintenance for the better, but also business models. Zhang said that SAP and Fujitsu are co-innovating with companies to use artificial intelligence (AI) with natural language processing, as well as augmented reality and wearables for maintenance instructions, remote collaboration, and warehouse picking.

“There’s big potential for these technologies to increase efficiencies on the shop floor, saving time and increasing productivity,” said Zhang. “Think about if you link this technology with your service organization. You can improve your whole business model from selling products and equipment to selling services.”

Learn 6 Surprising Ways 3D Printing Will Disrupt Manufacturing.


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


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.


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.


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.