Towards a Conceptualization of Capabilities for Innovating Business Models in the Industrial Internet of Things

The emergence of Internet of Things (IoT) technologies offers promising value potentials for industrial manufacturers based on the combination of smart products and data-driven services. At the same time, many incumbent firms experience a threat to their traditional value proposition and are challenged to innovate and reconfigure their existing business models. However, many of these traditional manufacturers lack or are unaware of the required capabilities for successfully reinventing their business model using IoT technologies. We therefore adopt the lens of dynamic and operational capabilities and conduct an empirical analysis of organizational capabilities required for successful IoT-enabled business model innovation (BMI). Through an exploratory, qualitative study based on interviews with decision makers in industrial manufacturing companies and experts in practice-oriented research institutions, we identify eleven distinct dynamic and operational capabilities. Our findings provide useful insights for research and practice and advance the understanding of enablers in IoT-enabled BMI

Designing Business Models for the Internet of Things

This article investigates challenges pertaining to business model design in the emerging context of the Internet of Things (IOT). The evolution of business perspectives to the IOT is driven by two underlying trends: i) the change of focus from viewing the IOT primarily as a technology platform to viewing it as a business ecosystem; and ii) the shift from focusing on the business model of a firm to designing ecosystem business models. An ecosystem busi- ness model is a business model composed of value pillars anchored in ecosystems and fo- cuses on both the firm's method of creating and capturing value as well as any part of the ecosystem's method of creating and capturing value. The article highlights three major chal- lenges of designing ecosystem business models for the IOT, including the diversity of ob- jects, the immaturity of innovation, and the unstructured ecosystems. Diversity refers to the difficulty of designing business models for the IOT due to a multitude of different types of connected objects combined with only modest standardization of interfaces. Immaturity suggests that quintessential IOT technologies and innovations are not yet products and ser- vices but a "mess that runs deep". The unstructured ecosystems mean that it is too early to tell who the participants will be and which roles they will have in the evolving ecosystems. The study argues that managers can overcome these challenges by using a business model design tool that takes into account the ecosystemic nature of the IOT. The study concludes by proposing the grounds for a new design tool for ecosystem business models and suggest- ing that "value design" might be a more appropriate term when talking about business models in ecosystems

Designing Business Models for the Internet of Things

This article investigates challenges pertaining to business model design in the emerging context of the Internet of Things (IOT). The evolution of business perspectives to the IOT is driven by two underlying trends: i) the change of focus from viewing the IOT primarily as a technology platform to viewing it as a business ecosystem; and ii) the shift from focusing on the business model of a firm to designing ecosystem business models. An ecosystem business model is a business model composed of value pillars anchored in ecosystems and focuses on both the firm's method of creating and capturing value as well as any part of the ecosystem's method of creating and capturing value. The article highlights three major challenges of designing ecosystem business models for the IOT, including the diversity of objects, the immaturity of innovation, and the unstructured ecosystems. Diversity refers to the difficulty of designing business models for the IOT due to a multitude of different types of connected objects combined with only modest standardization of interfaces. Immaturity suggests that quintessential IOT technologies and innovations are not yet products and services but a "mess that runs deep". The unstructured ecosystems mean that it is too early to tell who the participants will be and which roles they will have in the evolving ecosystems. The study argues that managers can overcome these challenges by using a business model design tool that takes into account the ecosystemic nature of the IOT. The study concludes by proposing the grounds for a new design tool for ecosystem business models and suggesting that "value design" might be a more appropriate term when talking about business models in ecosystems

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Business Case and Technology Analysis for 5G Low Latency Applications

A large number of new consumer and industrial applications are likely to change the classic operator's business models and provide a wide range of new markets to enter. This article analyses the most relevant 5G use cases that require ultra-low latency, from both technical and business perspectives. Low latency services pose challenging requirements to the network, and to fulfill them operators need to invest in costly changes in their network. In this sense, it is not clear whether such investments are going to be amortized with these new business models. In light of this, specific applications and requirements are described and the potential market benefits for operators are analysed. Conclusions show that operators have clear opportunities to add value and position themselves strongly with the increasing number of services to be provided by 5G.Comment: 18 pages, 5 figure