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How Smart, Connected Products Are Transforming Companies

The evolution of products into intelligent, connected devices is revolutionizing business. In a November 2014 article, How Smart, Connected Products Are Transforming Competition, Harvard Business School professor Michael Porter and PTC president and CEO James Heppelmann looked at how this shift is changing the structure of industries and forcing firms to rethink their strategies. In this companion article, the authors look at the effects inside firms, examining the impact that smart, connected products have on operations and organizational structure. The new capabilities and vast quantities of data that smart, connected products offer are redefining the activities of the core functions of companies—sometimes radically. As software and cloud-based operating systems become integral to products, new product-development principles emerge, manufacturing components and processes change, and IT security becomes the job of every function. Companies need different skills and expertise, which creates new imperatives for HR. In the marketing function, the ability to track a product’s condition and use shifts the focus to maximizing the product’s value to the customer over time. Customer relationships become continuous and open-ended, service becomes more efficient and proactive, and new business models are enabled. The rich data on location and environment that products provide take logistics to a whole new level. Smart, connected products also alter interactions between functions, in ways that hold major implications for organizational structure. Intense, ongoing coordination becomes necessary across multiple functions, including design, operations, sales, service, and IT. Functional roles overlap and blur. Entirely new functions – unified data organizations, dev-ops, and customer success management- begin to emerge. What is under way is the most substantial change in the manufacturing firm since the Second Industrial Revolution, and the effects are spreading to other industries, like services, as well.

The Framework of Business Model in the Context of Industrial Internet of Things

The purpose of this article is an attempt to develop the concept of a business model dedicated to companies implementing technologies of the Industrial Internet of Things. The proposed concept has been developed to support traditional companies in the transition to the digital market. The study was based on the available literature on the impact the Industrial Internet of Things has on the economy and business models.

A Manager’s Guide to Augmented Reality

There is a fundamental disconnect between the wealth of digital data available to us and the physical world in which we apply it. While reality is three-dimensional, the rich data we now have to inform our decisions and actions remains trapped on two-dimensional pages and screens. This gulf between the real and digital worlds limits our ability to take advantage of the torrent of information and insights produced by billions of smart, connected products (SCPs) worldwide. Augmented reality, a set of technologies that superimposes digital data and images on the physical world, promises to close this gap and release untapped and uniquely human capabilities. Though still in its infancy, AR is poised to enter the mainstream – according to one estimate, spending on AR technology will hit $60 billion in 2020. AR will affect companies in every industry and many other types of organizations, from universities to social enterprises. In the coming months and years, it will transform how we learn, make decisions, and interact with the physical world. It will also change how enterprises serve customers, train employees, design and create products, and manage their value chains, and, ultimately, how they compete. In this article we describe what AR is, its evolving technology and applications, and why it is so important. Its significance will grow exponentially as SCPs proliferate, because it amplifies their power to create value and reshape competition. AR will become the new interface between humans and machines, bridging the digital and physical worlds. While challenges in deploying it remain, pioneering organizations, such as Amazon, Facebook, General Electric, Mayo Clinic, and the U.S. Navy, are already implementing AR and seeing a major impact on quality and productivity. Here we provide a road map for how companies should deploy AR and explain the critical choices they will face in integrating it into strategy and operations.

The Cambridge Business Model Innovation Process

Organisations increasingly understand that meeting their sustainability ambitions does not only require new technologies, but innovation on the business model level. To facilitate the design of more sustainable business models, a range of new tools and techniques have been developed. While this resulted in the design of a wide range of promising business models, only very few are successfully implemented. The Cambridge Business Model Innovation Process is a framework developed to guide organisations’ business model innovation efforts and map the necessary activities and potential challenges. In this paper, we introduce the framework and present an exploratory attempt of applying it to a social start-up. The preliminary result of this experience led us to build a comprehensive research agenda that aims at developing tools and processes to help organisations in bridging the design-implementation gap in sustainable business model innovation.

What can machine learning do? Workforce implications

Digital computers have transformed work in almost every sector of the economy over the past several decades. We are now at the beginning of an even larger and more rapid transformation due to recent advances in machine learning (ML), which is capable of accelerating the pace of automation itself. However, although it is clear that ML is a general purpose technology, like the steam engine and electricity, which spawns a plethora of additional innovations and capabilities, there is no widely shared agreement on the tasks where ML systems excel, and thus little agreement on the specific expected impacts on the workforce and on the economy more broadly. We discuss what we see to be key implications for the workforce, drawing on our rubric of what the current generation of ML systems can and cannot do [see the supplementary materials (SM)]. Although parts of many jobs may be suitable for ML (SML), other tasks within these same jobs do not fit the criteria for ML well; hence, effects on employment are more complex than the simple replacement and substitution story emphasized by some. Although economic effects of ML are relatively limited today, and we are not facing the imminent end of work as is sometimes proclaimed, the implications for the economy and the workforce going forward are profound.

An Explanatory Study on the Co-evolutionary Mechanisms of Business IT Alignment

Business IT Alignment is considered an enduring topic in academic and practitioners’ literature. The interest in the subject is justified by the link, demonstrated by several studies, between alignment and corporate performances. However, alignment research has not yet been translated into practices, theoretically demonstrated in literature and applied to companies. The interpretation of alignment as a function of independent factors and the underestimation of the complex nature of alignment process are considered key barriers in alignment achievement. The present study is based on a multi-case study analysis carried out in two companies that implemented alignment processes. We conceptualise alignment as a co-evolution process and derive four mechanisms and three types of parameters and explain their role in alignment implementation. The contribution is theoretical, since we analyse and describe mechanisms and factors that govern alignment, and for the practitioners, since knowledge of these mechanisms is precondition for an effective alignment implementation.