Innovation Patterns

Sawatani, Yuriko; Nakamura, Fusashi; Sakakibara, Akira

doi:10.1109/scc.2007.71

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…Reference [6] described various innovation pattern categories, forms to capture innovations as patterns and innovation life cycle studies using innovation patterns. They also introduced a pattern language and compared various innovation cases.…”

Section: Related Workmentioning

confidence: 99%

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Optimization of value-network through clusters consolidation

Habib

2009

2009 IEEE Latin-American Conference on Communications

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Many researchers view the value of the enterprise network through its utilization. If the network utilization is low, then its economical value and benefit are diminished. Also, the rapidly changes in network technologies, customers' demands and economical factors increase the opportunity for viewing the network as a dynamic infrastructure, so that its values can be adapted accordingly. Based on these views periodical changes in networks necessitate in the reshaping the clusters (sub-networks). Here we have proposed a number of redesign operations on the customers' clusters, which are subnetworks in the value-network, with the aware to minimize the extra-traffic in the backbone network, and maximize the intratraffic within the clusters by considering customers' movement and clusters consolidation. We have utilized Simulated Annealing algorithm to search for the best solution to the clusters consolidation problem. Our simulation results show a better trade-off in minimizing the extra-traffic through clusters consolidation by 11.65% of the total backbone traffic and 40% in the number of clusters.

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Section: Related Workmentioning

confidence: 99%

“…The function COM() estimates the center-of-mass of a given cluster; moreover, the function Dist() calculates the Euclidian distance from a customer to its center-of-mass. Constraint (6) ensures that the total number of bound customers to a cluster should be balanced.…”

Section: Problem Formulationmentioning

confidence: 99%

Optimization of value-network through clusters consolidation

Habib

2009

2009 IEEE Latin-American Conference on Communications

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“…The first generational model is linear, where innovation is unidirectionally pushed from the research phase to the commercial application phase [2], [3]. The second model, the pull model, holds the consumer as the main focus of innovation as opposed to the designer [4].…”

Section: Introductionmentioning

confidence: 99%

Mining Authoritativeness of Collaborative Innovation Partners

Engler¹,

Kusiak

2010

INT J COMPUT COMMUN

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Abstract:The global marketplace over the past decade has called for innovative products and cost reduction. This perplexing duality has led companies to seek external collaborations to effectively deliver innovative products to market. External collaboration often leads to innovation at reduced research and development expenditure. This is especially true of companies which find the most authoritative entity (usually a company or even a person) to work with. Authoritativeness accelerates development and research-to-product transformation due to the inherent knowledge of the authoritative entity. This paper offers a novel approach to automatically determine the authoritativeness of entities for collaboration. This approach automatically discovers an authoritative entity in a domain of interest. The methodology presented utilizes web mining, text mining, and generation of an authoritativeness metric. The concepts discussed in the paper are illustrated with a case study of mining the authoritativeness of collaboration partners for microelectromechanical systems (MEMS).

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“…This growth of services encourages us to study basic business system changes from product-based to service economies, and to define fundamental challenges to accelerate service innovations [4], [6]. Beyond the followup sales of product-based services, the business strategies of manufacturing companies are changing to focus on services as key differentiators in their new business models [26], [27]. This shift to focus on services businesses of manufacturing industries affects their external business processes, as well as internal business processes, which include research organizations.…”

Section: Introductionmentioning

confidence: 99%

“…There are other approaches of the study in services innovations. To understand service innovations more detail, there are approaches to look into service innovations patterns [26], [27], service productivities [29], and knowledge creation [11], [17], [18], and to form unified theory for goods and services [30], [31]. Many researches are focusing more on the industry level analysis and enterprise levels of service businesses [3], [15].…”

Section: Introductionmentioning

confidence: 99%

Services research model for value co-creation

Sawatani¹,

Fujigaki

2008

PICMET '08 - 2008 Portland International Conference on Management of Engineering &Amp; Technology

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The impact of R&D to service business is increasing rapidly by the movement to service economies. The shift to focus on the services businesses, which is happening in manufacturing industries, affects their internal business processes, which include research organizations.In this paper, we study service research activities in a manufacturing enterprise, focusing on the advanced service research initiative, which was initiated as On Demand Innovation Services (ODIS) in 2003, as an exploratory research experiment in IBM. We propose services research model for value co-creation based on the ODIS experiment. We found that new knowledge is created through services activities, which are not created in a laboratory, traditional research activities. In addition, these are keys to the successful service projects. We focused on specific knowledge created during services research model for value co-creation, however, there are another kinds of knowledge also created. To understand and form the research base of service businesses and innovations, we should explore more on service innovation projects and understand critical factors, such as new knowledge created in a project, how they are created, and how the knowledge make the service research base more fruitful.

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Innovation Patterns

Cited by 10 publications

References 4 publications

Optimization of value-network through clusters consolidation

Optimization of value-network through clusters consolidation

Mining Authoritativeness of Collaborative Innovation Partners

Services research model for value co-creation

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