Smart Service Systems: Analytics, Cognition and Innovation Minitrack
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Smart service systems (smart services, smart devices etc.) can be characterized by: (1) the types of offerings to their customers and/or citizens, (2) the types of jobs or roles for people within them, and (3) the types of returns they offer investors interested in growth and development, through improved use of technology, talent, or organizational and governance forms, which create (dis)incentives that (re)shape behaviors.
An important trend in smart service systems is the increasing availability of cognitive assistants (e.g., Siri, Watson, Jibo, Echo, Cortana) to boost productivity and creativity of all the people inside them. There is a need to apply robust research findings in the appropriate management and organizational contexts related to innovation of smart service systems, quality, architecture, design and delivery, and the resulting customer satisfaction and business value.
Because of analytics and cognitive systems, smart service systems adapt to a constantly changing environment to benefit customers and providers with deep and wide learning capabilities. Using big data analytics, service providers try to compete for customers by (1) improving existing offerings to customers, (2) innovating new types of offerings, (3) evolving their portfolio of offerings and making better recommendations to customers, (4) changing their relationships to suppliers and others in the ecosystem in ways their customers perceive as more sustainable, fair, or responsible.
The goal of this track is to explore the challenges, issues and opportunities related to innovation of smart service systems that enable value co-creation with analytics, cognitive and human systems. We are interested in novel theories, approaches and applications for innovation of smart service systems.
Possible topics of applied, field and empirical research include, but are not limited to:
- Theories, approaches and applications for innovation of smart service systems
- Value co-creation processes, metrics and analytics for smart innovation processes
- Methods scale the benefits of new knowledge globally, rapidly, and profitably
- Service-oriented agile IT realization platform for smart service co-creation
- Place of cognitive systems, computing, system engineering, cloud for smart service systems
- Innovation ecosystems with internet and internet-of-things
- Theories and approaches for integrating analytical and intuitive thinking processes
- Open innovation and social responsibility
- Planning, building and managing design and innovation infrastructures and platforms
- Technology and organizational platforms support rapid scaling processes (smart phones, franchises, etc.)
- service systems include the customer, provider, and other entities as sources of capabilities, resources, demand, constraints, rights, responsibilities in value co-creation processes, and includes current applications of human and cognitive systems
- Analytics models, tools and engine for analytics support
- Agile business development platform for operational enablement: business processes, rules, real-time event management
- The commoditization of business processes (e.g. out-tasking, ITIL, SCORE), software (e.g. the software-as-service model, software oriented architecture, application service providers) and hardware (e.g., on-demand, utility computing, cloud computing, software oriented infrastructure with virtualized resources, infrastructure service providers for innovations
- Self-service and smart technologies & management for sustainable innovations
- Services implications to value chains, networks, constellations and shops
- Collaborative innovation management in B2B and B2C e-commerce
Haluk Demirkan (Primary Contact)
University of Washington - Tacoma
James C. Spohrer
IBM Almaden Research Center
Ralph D. Badinelli
ItemTraining IBM Watson Using Automatically Generated Question-Answer Pairs( 2017-01-04)IBM Watson is a cognitive computing system capable of question answering in natural languages. It is believed that IBM Watson can understand large corpora and answer relevant questions more effectively than any other question-answering system currently available. To unleash the full power of Watson, however, we need to train its instance with a large number of well-prepared question-answer pairs. Obviously, manually generating such pairs in a large quantity is prohibitively time consuming and significantly limits the efficiency of Watson’s training. Recently, a large-scale dataset of over 30 million question-answer pairs was reported. Under the assumption that using such an automatically generated dataset could relieve the burden of manual question-answer generation, we tried to use this dataset to train an instance of Watson and checked the training efficiency and accuracy. According to our experiments, using this auto-generated dataset was effective for training Watson, complementing manually crafted question-answer pairs. To the best of the authors’ knowledge, this work is the first attempt to use a large-scale dataset of automatically generated question-answer pairs for training IBM Watson. We anticipate that the insights and lessons obtained from our experiments will be useful for researchers who want to expedite Watson training leveraged by automatically generated question-answer pairs.
ItemThe Social Factory: Connecting People, Machines and Data in Manufacturing for Context-Aware Exception Escalation( 2017-01-04)Manufacturing environments are socio-technical systems \ where people have to interact with machines to achieve \ a common goal. The goal of the fourth industrial revolution is \ to improve their flexibility for mass customization and rapidly \ changing production conditions. As a contribution towards \ this goal, we introduce the Social Factory: a social network \ with a powerful analytics backend to improve the connection \ between the persons working in the production environment, \ the manufacturing machines, and the data that is created \ in the process. We represent machines, people and chatbots \ for information provisioning as abstract users in the social \ network. We enable natural language based communication between \ them and provide a rich knowledge base and automated \ problem solution suggestions. Access to complex production \ environments thus becomes intuitive, cooperation among users \ improves and problems are resolved more easily.
ItemRebuilding Evolution: A Service Science Perspective( 2017-01-04)This paper explores a simple idea and asks a simple question: What determines the speed limit of evolutionary processes, and might there be ways to speed up those processes for certain types of systems under certain conditions? Or even more simply, how rapidly can complex systems be rebuilt? To begin with, the universe can be viewed as an evolving ecology of entities. Entities correspond to types of systems - from atoms in stars to organisms on Earth to ideas in the heads of people. Service science is the study of the evolving ecology of service system entities, complex socio-technical systems with rights and responsibilities – such as people, businesses, and nations. We can only scratch the surface in this paper, but our explorations suggest this is an important research question and direction, especially as we enter the cognitive era of smart and wise service systems. For example, it takes a child multiple years of experience to learn language and basic social interactions skills, but could machine learning algorithms with the proper data sets learn those capabilities in a fraction of the time?