Service innovation

Service innovation is used to refer to many things. These include but not limited to:The Finnish research agency TEKES defines service innovation as 'a new or significantly improved service concept that is taken into practice. It can be for example a new customer interaction channel, a distribution system or a technological concept or a combination of them. A service innovation always includes replicable elements that can be identified and systematically reproduced in other cases or environments. The replicable element can be the service outcome or the service process as such or a part of them. A service innovation benefits both the service producer and customers and it improves its developer’s competitive edge. A service innovation is a service product or service process that is based on some technology or systematic method. In services however, the innovation does not necessarily relate to the novelty of the technology itself but the innovation often lies in the non-technological areas. Service innovations can for instance be new solutions in the customer interface, new distribution methods, novel application of technology in the service process, new forms of operation with the supply chain or new ways to organize and manage services.'Many literatures on what makes for successful innovations of this kind comes from the New Service Development research field (e.g. Johne and Storey, 1998; Nijssen et al., 2006). Service design practitioners have also extensively discussed the features of effective service products and experiences. One of the key aspects of many service activities is the high involvement of the client/customer/user in the production of the final service. Without this co-production (i.e. interactivity of service production), the service would often not be created. This co-production, together with the intangibility of many service products, causes service innovation to often take forms rather different from those familiar through studies of innovation in manufacturing. Innovation researchers have, for this reason, stressed that much service innovation is hard to capture in traditional categories like product or process innovation. The co-production process, and the interactions between service provider and client, can also form the focus of innovation. A growing number of professional association have service sections that promote service innovation research, including INFORMS, ISSIP, and others.There have been many methods for service innovation design and one of these is Dominant Innovation methodology which is a systematic approach to discover the gaps of service value delivery through a scenario-based mapping. Dominant Innovation methodology is based on a visible/ and invisible matrix to map the growth gaps of product value and further develop an innovation ecosystem between evidence and supply sources. Design and service innovation have also been combined in the context of business acceleration using the Minimum Valuable Service model. The MVS is an open source methodology that integrates lean startup and Service Design and it has been largely adopted by startups and large organizations like Ge and Cisco in Silicon Valley.In the traditional product-service system (PSS) business model, industries develop product with value-added service instead of single product itself, and provide their customers services that are needed. In this relationship, the market goal of manufacturers is not one-time product selling, but continuous profit from customers by total service solution, which can satisfy unmet customers’ needs. Most of PSS systems focus on ‘human-generated or human-related data’ instead of ‘machine-generated data or industrial data’, which may include machine controllers, sensors, manufacturing systems, etc. Early work using web-based product monitoring for remote product services including GM OnStar Telematics, Otis Remote Elevator Maintenance (REM), and GE Medical InSite during 1990s. Service innovation using web-based system was further developed for e-service applications. Recently, more advanced approaches using predictive analytics and cyber-physical systems (CPS) can harvest large amounts of data of a variety of types to uncover hidden patterns, unknown correlations and other useful information from industrial and manufacturing systems and integrate with business automation software for improved productivity and innovation.In recent years policy makers have begun to consider the potential for promoting services innovation as part of their economic development strategies. Such consideration has, in part, been driven by the growing contribution that services activities make to national and regional economies. It also reflects the emerging recognition that traditional policy measures such as R&D grants and technology transfer supports have been developed from a manufacturing perspective of the innovation process.