3 best practices for sharpening IoT business models

Close up of a mind map written on a piece of paper.

Many entrepreneurs know only too well that the journey from an innovative idea to a viable business model can be a long one. As the recent example of Juicero showed, this journey might end tragically if not well managed. Despite the acquisition of venture capital amounting to a few hundred million dollars, Juicero had to shut down. The reason was a fundamental flaw in the core idea of its product. It turned out that the expensive connected juicer was not actually needed to squeeze the packs of cut-up fruit – manual squeezing did the job just fine.

So what went wrong? What can be learned from this failure?

When evaluating innovative ideas, it is crucial to gain a good understanding of the value drivers . The value propositions for target customer groups and key partners must be clear and compelling. Given the complexity of connected solutions and the associated ecosystem, this is easier said than done. However, there are several tools that can support this process and help reduce the risks and complexity of IoT solutions. Below we present a selection of our favorites, which evolved from the IoT Business Model Builder.

Become a Master in Business Model Innovation

The Inspiration Cube

The Inspiration Cube helps you generate and diversify your initial ideas. By providing business-related concepts / inspirations, it triggers a creative process that increases the output of new and enriched ideas.

Roll the dice and apply the concept to your idea, and you’ll find yourself generating new or alternative ones. The questions drive you into a different thought process, resulting in completely new approaches to existing ideas and paving the way towards innovation.

Spending some time considering questions raised by the Inspiration Cube (like the ones shown in the illustration above) might have helped Juicero identify and avoid the risks in its business model that made it fail in the end.

The IoT Value Network

Towards the end of the ideation phase, ideas need to be refined and sharpened further. The IoT Value Network is a powerful tool that supports this process. It provides a holistic view by illustrating how different stakeholders exchange services, supplies, and value in the context of an IoT solution. The following stakeholder roles are defined:

  • Business Owner: Operates the business model (usually the company that drives the business)
  • Customer or End User: Target customer benefitting from the solution
  • Supplier: Contributes to the business model by providing goods and/or services, for which they receive compensation. Their interest in the business model is usually limited to the direct commercial compensation.
  • Partner: Contributes to the business model by providing goods and/or services and is exposed to risk. Usually, CAPEX/OPEX are higher for Partners than for suppliers. However, if the model is successful, partners benefit from a higher return on investment (ROI).

When mapping the business model, different-colored arrows illustrate the flow of supplies/services and value/revenues. The video below provides some practical insights on using the tool.

You may also choose to include additional information such as value propositions or risks for each stakeholder. This takes the tool beyond visualizing the IoT network and turns it into an excellent method for conducting graphically supported risk assessment and scenarios analysis . Further fields of application include the determination of customer touchpoints as well as the definition of control points for stakeholder alignment.

The Assumption List

Usually, the early phases of business model development (i.e. ideation and refinement phases) involve the making of numerous assumptions. These should be documented as you go and should be verified towards the end of the refinement phase. We recommend populating your Assumption List throughout the entire process. This will help you keep up the pace during the ideation and refinement phases. It must not be slowed down or hindered by endless discussions about risks or uncertainties. If your team gets caught in such a situation, just make an assumption, put it on the list, and keep going.

However, it is crucial to verify the assumptions at some point to minimize uncertainties. Of course it would be impossible to eliminate all the uncertainties you will encounter, but it is important to make uncertain hypotheses as transparent as possible.

We often advise using an Excel document to keep track of the assumptions, and have provided you with a template below. For each assumption made during your business development process, you add a separate row to the list.

Screenshot of the assuption list.

For each row (assumption), the level of uncertainty as well as the impact is recorded. This allows you to arrange your assumptions by order of importance before beginning with the validation. Just start with the ones that are highly uncertain and have a high impact. Working your way through the list lets you reduce uncertainty and make critical assumptions transparent for the follow-on phases and decisions. In addition, you can categorize your assumptions and define your validation methods and tasks to keep track of critical information during the validation phase.

How to become a Master in Business Model Innovation

With innovations, a certain amount of trial and error is inevitable, but the above tools give you a better chance of seeing your ideas succeed . If you want to dive deeper into our tools and see how to put them into action to create sustainable IoT solutions, join our Master Class on IoT & Platform Business Model Innovation.

Join Master Class

More on business models

There has been much talk about the market potential of IoT. How does it impact the way companies do business?

Wondering how to initiate and deploy Industrial IoT initiatives? We tell you how to become an IIoT business model innovator.

The IoT changes industries, business models, and value chains. Large organizations have to adapt to this changing world.

The post 3 best practices for sharpening IoT business models appeared first on Bosch ConnectedWorld Blog.

Bosch ConnectedWorld Blog

The Best Opportunities are at the Intersection of Industries

The Best Opportunities are at the Intersection of Industries

Internet of Things (IoT) solutions are evolving day-by-day and opening up new gateways for progress and innovation. In a conversation with Paromik Chakraborty of Electronics For You, Shekhar Sanyal, director and country Head, IET India, talks about the business opportunities for startups in IoT space, impact of IoT on industries and endeavours of the IoT Congress


Shekhar Sanyal, Director and Country Head, IET India

Q. What are the business opportunities for startups in the IoT?

A. Right now, the best business opportunities in the IoT lie at the intersection of industries where no solution has been thought of yet. And by that, we mean intersection of data produced by industries. There lies a big opening for a startup if it can find the intersection where the data of one industry can be used for another using IoT solutions.

Most startups work on intersections where solutions are already available. The value comes from aligning the different datasets available and intersecting them so that the combination can benefit the users in new ways.

Q. What are the areas for investments in industrial IoT?

A. In sectors like automobile manufacturing, especially with the advent of electric cars, use of automation will keep increasing for the next three to five years. In the SME sector, the difference between the cost of investing in automated process and the cost of laboured process will start to narrow. So more and more SMEs will start implementing automation.
Speaking of specific technology, AI will play a key role. Nanotechnology will also get linked to IoT and play a bigger role. New technologies will develop in the background, and that is where the investment should be made.

Q. How can Indian IoT startups hit the bull’s eye?

A. Most startups are digitally savvy. They understand how the software and the technologies work. But, they lag behind in finding the link to the traditional industry. How many startups are looking at the electronics, electrical, power and energy, or automobile manufacturing industries, and providing solutions for them? These are the key infrastructure industries that are vital for the progress of the industrial economy. Since these industries are large and capital-intensive, startups tend to stay away from them.

Q. Please explain…

A. Let’s consider the energy industry, where a primary area for deep-diving is the smart grid—it will essentially change the way power is distributed. Another example is e-transportation, where electric vehicles need to optimise their electricity consumption. The internal combustion engine, which has a lot of moving parts today, will become much more digital. This leads to many new things that can be done over and above what is already present on the ground. The third example is energy storage. The renewable energy sector needs highly efficient solutions for storing and managing energy.

Q. How reliable are IoT networks currently?

A. Security and privacy are separate concerns. A lot of work is being done to ensure security on the cloud, and the available security levels are quite adequate. The challenge comes around privacy. There is presently no legal regulation or system for privacy handling. The situation is the same at global scale, and we all need to work towards bringing in some laws for data privacy.

Q. How will standardisation impact the industry?

A. Currently, there are about 15 different standards in various parts of the IoT, especially surrounding the way the data stack comes in and the kind of systems used to transfer the data. Availability of so many different standards creates a silos within the platform, disrupting interoperability—the main idea of the IoT. Having a single standard opens up the silos and makes the platform stronger and more valid.

From developers’ point of view, if there is a single common standard, you can just add onto the platform irrespective of what you create. It assimilates together and the value goes up.
With standardisation, anything developed around the globe will work in your country as well and you will not have to worry about compatibility.

Q. What is the main agenda for this year’s IoT Congress?

A. The theme of this year’s IoT Congress is healthcare, manufacturing and what’s next in IoT, with special attention to Clean Ganga project. It will focus on startups in IoT and also provide a peek into the virtual reality future of the IoT.


 

The post The Best Opportunities are at the Intersection of Industries appeared first on Internet Of Things | IoT India.

Internet Of Things | IoT India

Future Story Telling Disney Toys Could Pick Best Kids Bedtime Story!

Future Story Telling Disney Toys Could Pick Best Kids Bedtime Story!

Today’s news items begin with an exciting Disney research which teaches AI how to judge short stories. Going further, Intersil is offering robust, high-speed RS-485/RS-422 transceivers claimed to deliver industry’s highest working voltage and ultra-low EMI for industrial IoT networks. Finally, RS Components expands its intelligent, IO-Link communications range to boost the Industrial IoT.


Disney’s AI Judges Short Stories To Help Story-writers

The concept of using artificial intelligence (AI) to understand and evaluate narratives is an exciting topic for researchers for years. To address this challenge, researchers at Disney and the University of Massachusetts Boston have been working on neural networks that can evaluate short stories or narratives. While these AIs don’t (yet) analyze story like a professional literary critic, the software tries to predict which stories will be most popular and appeal a larger population. “Our neural networks had some success in predicting the popularity of stories,” said Disney Research scientist Boyang “Albert” Li in a statement. “You can’t yet use them to pick out winners for your local writing competition, but they can be used to guide future research.” Read more.


High-Speed RS-485/RS-422 Transceivers Underpin The IIoT

Ideal for today’s Industrial Internet of Things (IIoT) networks, Intersil has launched two new high-speed, isolated RS-485 differential bus transceivers that provide 40Mbps bidirectional data communication. The ISL32741E’s high working voltage of 1,000VRMS and 6kV of reinforced isolation is claimed to be more than 2x higher than competitive solutions, and suits most rigorous medical and high-speed motor control applications. The ISL32740E with 2.5kV of isolation and 600VRMS working voltage is claimed to feature the industry’s smallest package, enabling high channel density for programmable logic controllers (PLCs) in factory automation applications. Read more.


Intelligent, IO-Link Connected Sensors’ Range Expanded 

The key trend of smart sensors in factory automation environments is leading to a massive surge in the number of IO-link sensors worldwide. In line with this trend, RS Components (RS) has expanded its range of IO-Link compatible hardware and ancillaries, more than quadrupling stock of IO-Link compatible devices over the past six months. IO-link is a first standardised IO technology to communicate with industrial sensors and actuators, and playing an important part in the implementation of Industry 4.0 and the Industrial Internet of Things (IIoT) concepts.

 


 

The post Future Story Telling Disney Toys Could Pick Best Kids Bedtime Story! appeared first on Internet Of Things | IoT India.

Internet Of Things | IoT India

The Best Healthcare System In The World Is About To Change

Next year will mark the 70th anniversary of the National Health Service (NHS) in the United Kingdom, deemed the best healthcare system in the world by Commonwealth Fund. Its revered status is justified; the NHS provides consistent, high-quality care for families when they need it most. But these are complex and challenging times for UK’s most trusted and respected social institution.

The five-year forward view, published in October 2014 by NHS England, set out a vision of the biggest integrated care of any major western country. This transformational change is taking the form of “accountable care systems” (ACSs) covering seven million people, as highlighted in the five-year forward view refresh-2017.

ACSs are a way of transforming care and achieving system-wide resilience and efficiency because no single organization can solve current healthcare challenges on its own. They will break down organizational boundaries to streamline services and ultimately improve the experience of patients, caregivers, and citizens.

Strategic priorities of ACSs

Among other things, ACSs will be driving key initiatives in population health and personalized medicine to improve patient outcomes and the quality of their local health economy. Longer-term, by improving the health of the population, there will be reduced demand for health and social care services. ACSs must be able to make informed decisions to improve health outcomes for their population.

ACSs will also drive the direction of medical research in the UK by identifying the gaps. Driving these outcomes will require complete and accurate datasets, harnessed from across organizations in the network. The harnessing of data relies on interoperability of systems and bringing data together so it can be easily analyzed in a secure and private environment.

However, in any ACS geography, providers have chosen Electronic Patient Record (EPR) and other IT systems from multiple vendors. These IT systems do a decent job of cutting waste, eliminating red tape, and reducing the need to repeat expensive medical tests. What ACSs don’t have is the ability to bring together data from these disparate data-rich systems. The result? Patient data has simply moved from one “locker” (paper charts) to another (the cloud). This presents an enormous challenge for ACSs—information sharing is at the heart of their work, and yet the systems are “hiding” that data and hence not providing the necessary evidence and knowledge.

How the right technology platform can help

Simplifying the integration of all structured and unstructured data into an easily accessed, standards-based data warehouse makes it easier for care providers to view, search, and drive more value out of their healthcare data than ever before. It enables the health IT systems to be more efficient in several ways:

  • Consolidates, cleanses, and performs real-time analysis of clinical and genomics data from various source systems on an open, secure platform
  • Gets a holistic view across medical data stored in disconnected silos
  • Achieves faster time to value using a predefined, extensible data model
  • Optimizes patient outcomes through precision medicine and prevention support
  • Captures all hospital data using standard interfaces which can be used to capture new information or perform historic migrations
  • Maintains data quality, e.g. demographic updates
  • Ensures data is stored safely and efficiently and manages onward data migrations, so the EPR doesn’t have to

Where this is working today

The American Society of Clinical Oncology (ASCO) organized massive volumes of data of every cancer patient in the U.S. into usable knowledge via CancerLinq to connect and analyze electronic records to make more informed decisions about patient care. Today patient data from more than 1 million cancer patients is available on the CancerLinq platform. Instead of limiting insights to patients in clinical trials—which takes years to complete—ASCO is learning from nearly every patient as they undergo care and make that information available to experts across the country.

Learn more about SAP Healthcare here.

This story also appeared on the SAP Community.


Internet of Things – Digitalist Magazine

Building the Best Autonomous Brain

When I’m bumper-to-bumper in a sea of exhaust fumes and distracted drivers, it seems like autonomous driving can’t get here fast enough. Nor can the potential rewards that come along with fully autonomous vehicles, like far fewer accidents and mobility for people who struggle to get around on their own. To do my part, I’m focusing on how building the best autonomous brain for a car will get us there faster.

5 Things to Know About Autonomous Vehicles

Every day, we’re getting closer to the technology needed to power self-driving cars. But in-vehicle compute needs are complex, and autonomous driving algorithms are changing rapidly. So, the question is: What is the best long-term path to fast, safe decision-making? It all begins with the right compute for the right task. Here are five things you should know about the complex compute for autonomous driving.

 

It Takes More Than Deep Learning

Artificial intelligence is just one part of the story. And beyond that, AI is more than just deep learning. Yes, deep learning is key in teaching a car how to drive, especially when it comes to computer vision. But there will be several other types of AI at work in the fully autonomous vehicle, from traditional machine learning to memory- and logic-based AI. The fully autonomous vehicle will need a wide range of computing to support three intertwined stages of self-driving: sense, fuse and decide. Each stage requires different types of compute. In the first stage, the vehicle collects data from dozens of sensors to visualize its surroundings. During the second stage, data is correlated and fused to create a model of the environment. Finally, the vehicle must decide how to proceed. System designers need a flexible architecture to support all three stages, with an optimized combination of power efficiency and performance.

With a flexible, scalable architecture of CPUs, Intel Arria 10 FPGAs and other accelerators, our Intel GO automotive solutions portfolio leads the industry with a diverse range of computing elements that support all three stages of driving. But autonomous driving is much more than just in-vehicle compute; that’s why we offer a full car-to-cloud solution including 5G connectivity, data center technologies and software development tools to accelerate autonomous driving.
Smart AI consists of sensing, fusing and deciding.

 

No Fixed Architecture Can Keep Pace

Before system designers can achieve level four and five driving automation, they must determine how to best use different compute elements within the system to support each type of workload.

No fixed architecture can keep pace with the speed of innovation in AI and system design. Automakers and suppliers will need to be ready to change system designs down the road. Whether it’s to incorporate new algorithms or completely rethink compute to accommodate new workloads, system designers will need a flexible, scalable architecture. Simply put, they need interoperable and even programmable compute elements that don’t require them to start from the ground up every time they want to incorporate a new feature. With a flexible architecture of CPUs, FPGAs and other accelerators, future-ready solutions offer a diverse range of computing elements that can accommodate designs that may change long after hardware and vehicle design decisions have been made.

 

Driving the Future

Now is a time of tremendous opportunity as we continue to research and respond to the transformational changes before us. From powering Stanford University’s robotic car to serving as a premier board member of the University of Michigan Mobility Transformation Center’s Mcity, Intel is working alongside world-renowned research teams to understand the way people interact with connected cars. Intel has built autonomous vehicle labs in Arizona, California, Germany and Oregon. Here, we’re working hand in hand with our ecosystem partners to optimize customized solutions, road-test autonomous vehicles, and work toward common platforms that will speed broad industry innovation for the promising road ahead.

Learn more about the road to autonomous driving at intel.com/automotive. To stay informed about Intel IoT developments, subscribe to our RSS feed for email notifications of blog updates, or visit intel.com/IoTLinkedInFacebook and Twitter.

The post Building the Best Autonomous Brain appeared first on IoT@Intel.


IoT@Intel