Sound conflict management and resolution for virtual-enterprise collaborations. Issue 3 (September 2016)

• Record Type: Journal Article
• Authors: Narendra, Nanjangud; Norta, Alex; Mahunnah, Msury; Ma, Lixin; Maggi, Fabrizio
• Is Part Of: Service oriented computing and applications. Volume 10:Issue 3(2016:Sep.)
• Page Start: 233
• Page End: 251
• Publication Date: 2016-09
• Subjects: Virtual enterprise -- Smart contract -- e-governance -- Exceptions -- Conflict management -- Service orientation -- Agents -- Decentralized autonomous organizations -- Industry 4.0; Electronic data processing -- Distributed processing -- Periodicals; Web services -- Periodicals; Electronic commerce -- Periodicals; 004
• DOI: 10.1007/s11761-015-0183-0
• Languages: English
• ISSNs: 1863-2386

Abstract

Abstract For fulfilling customer requests, enterprises are increasingly part of collaboration networks with peers. Such collaborations, also known asvirtual enterprises (VE), are governed by pre-defined contracts that restrict the behaviour of each participating enterprise. However, since each enterprise is autonomous, the potential arises for conflicts during collaboration. In this paper, we extend our earlier work on virtual-enterprise modelling and address the existing gap of conflict modelling, management and resolution in VEs. Our approach works as follows: first, to detect a conflict by analysing the exceptions reported during execution; second, to uncover the conflict type, origin and impact of an exception; and third, depending on the nature of an exception, to implement the appropriate conflict negotiation and resolution strategy among the participating entities of a VE. Crucial to this approach, and serving as one of the key contributions of this paper, is the conflict ontology. This ontology helps to model conflict types along with related exceptions, negotiation and resolution strategies, thereby enabling conflict management and resolution. Throughout this paper, we illustrate our ideas with a running example and also present a detailed evaluation based on a case study from the automotive production domain.