Barriers to IoT Business Model Innovation

The vision of an Internet of Things (IoT), in which virtually all physical things become connected to the internet, promises enormous economic potential. The IoT might disrupt entire industries and it forces companies to rethink their current business activities. In light of these challenges, research on business model innovation (BMI) can offer promising insights. This research paper aims to contribute to the emerging BMI literature by identifying innovation barriers in an IoT context. 16 barriers are identified on the basis of ten expert interviews that were conducted with employees from five multinational companies. The contributions of our study might lay a fruitful ground for future research, e.g. in respect to prescriptive IoT BMI processes or quantitative investigations of IoT success

IoT business models in an industrial context

There is a broad consensus that the transformative power of the Internet of Things (IoT) will affect all kinds of industries; or, to put it in a more optimistic light, that almost no domain is excluded from the opportunities to leverage the IoT. But, what does this mean for the future of industrial processes? This article introduces the concept of high-resolution management (HRM). IoT enables the collection of high-resolution data for the physical world where, as in the digital world, every aspect of business operations can be measured in real-time. This capability facilitates high-resolution management, such as short optimization cycles in industrial production, logistics and equipment efficiency, comparable to methods like A/B-Testing or Search Engine Optimization, which are state of the art in digital business. We take the following two perspectives on leveraging high-resolution management. First, through greater insights into their industrial processes, companies that apply HRM in their operations are able to achieve higher efficiency, quality and flexibility. The example of vehicle fleet management illustrates this effect. Second, we build upon the St. Gallen Business Model Navigator in order to look in greater detail on how the IoT affects industrial processes. Gassmann et al. Gassmann, O., Frankenberger, K., and Csik, M. (2014). The business model navigator: 55 models that will revolutionise your business. Financial Times. introduce 55 generic business model patterns, of which our extended research identified 20 that could profit significantly from the IoT Fleisch, E., Weinberger, M., and Wortmann, F. (2014). Geschäftsmodelle im Internet der Dinge. HMD Praxis der Wirtschaftsinformatik, 51(6), 812-826; Gassmann, O., Frankenberger, K., and Csik, M. (2014). The business model navigator: 55 models that will revolutionise your business. Financial Times. . Analyzing these 20 patterns allowed for the identification of six key components: Remote Usage and Condition Monitoring, Object Self Service, Digital Add-on, Digital Lock-in, Product as a Point of Sales and Physical Freemium. These building blocks help companies to supply HRM-supported offerings. Finally, the example of remote monitoring of process parameters shows that these business model components can also be deployed to create offerings that enable others to apply HRM

Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data

An ever growing variety of smart, connected Internet of Things (IoT) devices poses completely new challenges for businesses regarding security and privacy. In fact, the adoption of smart products may depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology could be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. To support this, we designed and developed an instantiation of an SDPS (CertifiCar) in three iterative cycles intented to prevent the fraudulent manipulation of car mileage data. Following the explication of our SDPS, we provide an ex post evaluation of our design theory considering CertifiCar and two additional use cases in the areas of pharmaceutical supply chains and energy microgrids. Our results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner

How Digital Transformation Affects Large Manufacturing Companies’ Organization

In light of emerging digital technologies, executives across industries are rethinking their companies’ business models and organizational structures. To meet future customer expectations, large manufacturing companies in particular are challenged to integrate two distinct worlds: the physical world (i.e. the design, production, and maintenance of complex hardware products) and the digital world (i.e. software, data analytics and digital services). Large manufacturing companies often possess various business units, a diversified business model portfolio, and complex IT landscapes including traditional, embedded, and digital IT types. Hence, they face specific organizational issues, which so far have only received limited attention among professionals. Based on 16 in-depth expert interviews with companies across the Internet of Things (IoT) ecosystem, we have identified six main issues regarding how digital transformation will affect large manufacturing companies’ overall organizational structure

IoT business models in an industrial context

here is a broad consensus that the transforma-tive power of the Internet of Things (IoT) will affect allkinds of industries; or, to put it in a more optimistic light,that almost no domain is excluded from the opportuni-ties to leverage the IoT. But, what does this mean for thefuture of industrial processes? This article introduces theconcept of high-resolution management (HRM). IoT en-ables the collection of high-resolution data for the phys-ical world where, as in the digital world, every aspect ofbusiness operations can be measured in real-time. Thiscapability facilitates high-resolution management, suchas short optimization cycles in industrial production, lo-gistics and equipment efficiency, comparable to methodslike A/B-Testing or Search Engine Optimization, which arestate of the art in digital business. We take the followingtwo perspectives on leveraging high-resolution manage-ment. First, through greater insights into their industrialprocesses, companies that apply HRM in their operationsare able to achieve higher efficiency, quality and flexibil-ity. The example of vehicle fleet management illustratesthis effect. Second, we buildupon the St. Gallen BusinessModel Navigator in order to look in greater detail on howthe IoT affects industrial processes. Gassmann et al.¹in-troduce55generic business model patterns, of which our xtended research identified20that could profit signifi-cantly from the IoT².Analyzingthese20patterns allowedfor the identificationof six key components:Remote Usageand Condition Monitoring,Object Self Service,Digital Add-on,Digital Lock-in,Product as a Point of SalesandPhysicalFreemium. These building blocks help companies to sup-ply HRM-supported offerings. Finally, the example of re-mote monitoring of process parameters shows that thesebusiness model components can also be deployed to cre-ate offerings that enable others to apply HRM.ISSN:0178-2312ISSN:2196-677

How the IoT Affects Multibusiness Industrial Companies: IoT Organizational Archetypes

Innovative technologies, including sensors, cloud-based data analytics and artificial intelligence, are altering the logic of how manufacturing companies conduct business. More specifically, the Internet of Things (IoT), and hence the transition from off

Driving Process Innovation with IoT Field Data

The Internet of Things (IoT) promises to deliver tremendous business value and disrupt various industries. However, many companies are taking much longer than anticipated to realize these opportunities. To exploit the digital data streams flowing from their smart, connected products (IoT field data), companies need to identify new opportunities that go beyond well-known product and service innovations. We describe how manufacturing companies are leveraging IoT field data to innovate in their internal processes at all stages of the product lifecycle