Collaborative intelligence in knowledge based service planning

Abstract

E-services are provided by several web-enabled companies. While some of them are competitive in nature, others are collaborative. There are few methodologies currently existing for measuring the value of e-collaboration among these e-service providers. The objective of this research is to measure collaborative intelligence (CI) in the knowledge based service (KBS) industry and to identify measures for finding the best collaborators during the formation and functioning stages of collaborative networks. The model developed in this research, CIMK (collaborative intelligence measure of KBS), measures CI by multi-objective optimization on parameters for collaboration, and suggests optimal operating points for various clients with greater flexibility. CIMK allows decision makers to customize the model based on their knowledge of the industry. Then, the CNOA (Collaborative Network Optimization Algorithm) is applied to select the best providers for requests based on their CI levels. CNOA has been implemented over a HUB-CI (HUB with CI) platform, which is a next generation collaboration support system developed at Purdue University. Three analytic experiments are designed and performed to validate the models (1) in terms of usability, (2) to compare the CIMK with alternative methods, and (3) to find the relative advantages of the CIMK model. The results of the experiments indicate that the average service cost can decrease by close to 50% when operating points with high CI, suggested by CIMK model, are implemented. The CI level computed by CIMK is successfully used as a decision parameter for on-going matching e-service providers to different requests.