Also called: S2aaS, Sensing as a Service
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How: Collect, process, and sell sensor data and analysis across verticals to create new products, typically within real-time monitoring or predictive maintenance.
Why: Expand the use of data to not only serve one application, but to be shared and traded within the ecosystem of internet-enabled devices.
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The advent of sensor technology offers a plethora of opportunities for companies to enhance their offerings and strengthen customer relationships. By leveraging Sensors as a Service, businesses can provide a host of value-added services, such as tracking and monitoring product usage, optimizing system performance, and providing behavior-based services.
The utilization of Sensors as a Service allows for the transformation of traditional maintenance approaches, such as preventative or reactive maintenance, into a more sophisticated form of maintenance known as predictive maintenance. This is achieved by analyzing user data and identifying when a system requires maintenance or when replacement parts are needed.
Where did the Sensor as a Service business model pattern originate from?
The concept of “Sensors as a Service” has its origins in the field of Internet of Things (IoT) and Industry 4.0. As more and more devices and equipment are being outfitted with sensors to collect data, companies have begun to realize the potential value in using this data to improve operations and gain insights. Rather than having to invest in their own sensors and data infrastructure, companies are now able to access sensor data through a service provided by a third-party. This allows them to access the benefits of sensor data without having to make a large upfront investment. The term “Sensors as a Service” has been coined to describe this business model, which is becoming increasingly popular as more companies look to leverage the power of IoT and sensor data.
Applying the Sensor as a Service business model
Sensors as a Service can be applied in various industries to optimize performance and increase efficiency. The measurement data collected from the physical world is no longer confined to a single application, but rather it is being harnessed for a vast array of potential applications, comprising an eco-system that will pose one of the next big challenges on the Internet of Things (IoT).
For instance, Nestlé uses sensors to remotely optimize chocolate production on their SIG packaging machines. Similarly, FLSmidth employs sensors to monitor and optimize the complex systems installed in Holcim cement manufacturing sites worldwide. Another example is the remote diagnosis of printing machines from Heidelberger Druck, which can be parameterized remotely by means of a sophisticated engineering background.
Personalized Monitoring with Sensors
Wearable sensors, such as those found in armbands manufactured by Fitbit, provide individuals with the ability to continuously monitor their physical activity and sleep patterns. These sensors can be worn 24/7 and provide users with information on the number of steps taken, distance traveled, and calories burned during the day, as well as monitoring sleep rhythms at night. Additionally, users can make personal settings and track their progress through free online tools and a mobile app.
Data as currency
In this new data-driven pattern, the data-generating products or the resulting services are no longer the central focus. Instead, it is the data itself that has become the primary currency to be earned. An example of this phenomenon is Streetline, a company that installs sensors on municipal and private property to detect vacant parking places and sells the collected data to interested third parties. Car drivers can access the information for free through an app.
For city governments, the data processed in this manner holds tremendous value. The physical expenditure to identify parking offenders is dramatically reduced, the utilization of parking places increases, and the quality of information available to make the best use of them rises. Sensor as a Service represents a business model pattern that revolves around a multi-sided market for sensor data.
The advantages of servitization with sensors
Many servitization business models rely on the use of sensors embedded in their products to gather data and information. This allows service providers to continuously monitor the state of their products and provide necessary upgrades, repairs, or maintenance as needed, thereby adding extra value for the customer. An example of this is an industrial heating ventilation and air conditioning (HVAC) system, where an internal sensor can alert the HVAC supplier of a malfunction, allowing for quick repairs and eliminating the responsibility of noticing and fixing the problem for the end-user.
Servitization is happening across all industries, with the manufacturing sector already exploring the potential of Robots as a Service (RaaS) and widely adopting various Software as a Service (SaaS) models, such as Supervisory Control and Data Acquisition (SCADA) and Manufacturing Execution Systems (MES). Sensors as a Service (S2aaS) is the next step in this trend, allowing manufacturers to pay a monthly or annual charge for the deployment of sensors in their facility, including maintenance, support, and regular upgrades.
Challenges of the Sensors as a Service business model
One major issue is the integration of new sensors with existing technology, as most manufacturers use a range of equipment from different manufacturers, models, and production years. Some older machinery may not have sensors at all, while newer devices may have proprietary sensors that are difficult to replace. It is important to ensure that the Sensor as a Service provider can either collect data from existing sensors or provide advanced enough sensors to warrant replacement.
Another challenge is ensuring that the sensors offered by the Sensor as a Service provider are advanced enough to provide an improvement. For example, some standard sensors can only indicate the presence or absence of an object, while smart sensors can provide more data such as motion, proximity, weight, and image sensing. Additionally, smart sensors can also be used to monitor the health of equipment in a facility, using accelerometer sensing to detect mechanical problems or signs of breakdown.
Manufacturers should also ensure that the sensors offered by the Sensors as a Service provider are enabled with IO-Link technology, which is an open standard protocol that provides a common communication for a sensor’s parameters and features. This can greatly improve the amount and quality of data that is collected and used for analysis.
- How can you switch focus from the physical product itself to making sense of data across multiple sources?
- How can you build an ecosystem of Internet of Things-enabled devices to form an even larger product?
- How will my customers benefit from the data collected by these sensors?
- What is the cost of deploying and maintaining the sensors?
- How will I integrate the sensors with my existing technology infrastructure?
- What are the potential challenges of integrating and managing the data collected by the sensors?
- How will I ensure the security of the sensor data and protect against data breaches?
- How will I monetize the sensor data and generate revenue?
- How will I ensure that we can collect data from existing sensors to compile a complete report?
Streetline is a company that installs sensors on municipal and private property to detect vacant parking places, and sells the data collected to interested third parties. A car driver can access the information free of charge through an app. For city governments, the data processed has tremendous value: their physical expenditure to identify parking offenders is dramatically reduced, utilization of parking places increases, and the quality of information available to make the best use of them rises.
Lets customers manage their smart home to control lights, music, curtains, and locks, as they (their phone) leave the house.
GE offers a cloud-based platform called Predix, which allows customers to collect, analyze and monetize data from industrial equipment and infrastructure. Predix uses sensors to gather data on everything from wind turbines to jet engines, and offers customers real-time insights and predictive analytics to improve efficiency and reduce downtime
Source: GE Predix
Siemens’ MindSphere is an IoT operating system that allows customers to connect and manage their industrial equipment and systems. MindSphere uses sensors to gather data on things like energy consumption and machine performance, and provides customers with real-time insights and analytics to improve efficiency and reduce costs.
Source: Siemens MindSphere
The product “plugs in” data from third-party sensors and data infrastructures and offers a method of monetizing it, using blockchain-based smart contracts to ensure that data from sensors are both transmitted and charged for. Nokia claims that Sensing-as-a-Service has already been trialed with commercial operators and has powerful analytics baked in to ensure customers have full visibility
FLSmidth of Denmark utilizes sensors as a service in their complex cement plant systems installed at Holcim cement manufacturing sites worldwide. This allows for remote optimization of the cement production process, as well as the ability to perform predictive maintenance on the systems through analysis of user data.
- Business Models and the Internet of Things by Elgar Fleisch, Markus Weinberger, Felix Wortmann
- The IoT Business Model Builder by Bilgeri, Brandt, Lang, Tesch & Weinberger
- Apple HomeKit is a better smart home platform than you think. Here's why by David Priest
- New Business Model: Sensors As a Service by Jonathan Wilkins
- Sensing as a Service (S2aaS): Buying and Selling IoT Data by Charith Perera