A neural network model for the assessment of partners’ performance in virtual enterprises

Sarı, Burak; Amaitik, Saleh M.; Kılıç, S. Engin

doi:10.1007/s00170-006-0642-z

Cited by 13 publications

(10 citation statements)

References 12 publications

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“…Complex event processing (CEP), the agent-based models approach [15], Sequential Probability Ratio Test (SPRT) [22], artificial neural networks (ANNs) [21] constitute well-known and widespread alternatives.…”

Section: Real Time Data Collection and Network Monitoringmentioning

confidence: 99%

Dynamic Manufacturing Networks Monitoring and Governance

Kokkinakos

Markaki

Panopoulos

et al. 2013

IFIP Advances in Information and Communication Technology

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Abstract. "Monitoring and Governance" is the most important phase of every Dynamic Manufacturing Network (DMN) lifecycle and aims at managing and controlling in a continuous way the operations of the network, resulting either in "small and corrective" actions towards the network's operation optimisation or to "larger and structural" changes, which are fed back to the initial phases of the network's lifecycle, for reconstructing the network towards better results. The study at hand aims to review current approaches for controlling and monitoring plant operation or traditional supply chains, and to examine thereby their maturity and adequacy for the management and monitoring of dynamic manufacturing networks, leading to useful conclusions with regard to the requirements and challenges encountered in this particular phase of the DMN lifecycle.

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Section: Real Time Data Collection and Network Monitoringmentioning

confidence: 99%

Dynamic Manufacturing Networks Monitoring and Governance

Kokkinakos

Markaki

Panopoulos

et al. 2013

IFIP Advances in Information and Communication Technology

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“…(Camarinha-Matos and Afsarmanesh, 2007;Sari et al, 2007) have all referred to this heterogeneity between possible partners as it exists in information technology infrastructures, working methods and business practice, as a possible obstacle to the VO operation.…”

Section: Heterogeneity Of Partnersmentioning

confidence: 99%

“…While Sari et al (2007) considered insufficient information about partners to be an obstacle to the VO selection, others including (Grabowiski and Roberts (1998); Chen and Fang (2002)) thought that a range of interests increased the risks to a VO.…”

Section: Wrong Partner/s Selectionmentioning

confidence: 99%

Interpretive structural modelling of risk sources in a virtual organisation

Alawamleh

Popplewell

2011

International Journal of Production Research

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Speedier network decision making together with shorter time to bring items to market together with lower network operating costs all result from enhanced knowledge sharing. In addition re-use of enterprise and network knowledge resulting from improved capture means that any risk of repeating earlier project work is limited, repetition of past mistakes is reduced. Decisions are made with greater awareness of any risks involved and therefore there is likely to be a reduction in costs arising from faulty decisions and failed collaborations. While there are many advantages attaching to the use of virtual organizations (VOs) there are also challenges, including risks that have become apparent through undertaking a review of the literature. In total 13 sources of risk were found stemming from the network related risks in a VO, where the emphasis of the study was placed. This paper contains a thorough study that will identify these threats as well as gaining a sound understanding of them by examining them one by one as they have been identified by the literature and previous studies. Subsequently, their relative importance will be analysed through the use of Structural Modeling (ISM) using information gathered in a questionnaire.

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“…Late or less submission of progress will create distrust between the partner and virtual enterprise. Therefore it is considered as a critical factor in the evaluation of partner performance [24].…”

Section: Related Research Studiesmentioning

confidence: 99%

“…Level of commitment or risk of having partner is measured as caution cost which is the cost that the partner must pay to the VE if the partner decides the give up before the assigned task is finished. On the other hand, past performance of a partner is assessed through an ANN model [24] which is based on delivery status of the task (late or early), quality of the completed task (% acceptable) and progress report submissions (late, early or none). (ii) AHP can display complicated selection factor in simple concepts of hierarchy, which can be accepted easily by a decision-maker.…”

Section: Related Research Studiesmentioning

confidence: 99%

Ahp model for the selection of partner companies in virtual enterprises

Sarı

Sen

Kılıç

2007

Int J Adv Manuf Technol

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Rising product variety and complexity, shorter time frames to respond, and the continual need to gain new capabilities through innovativeness force the trend of outsourcing to be replaced by strategic alliances, where enterprises or individuals work together towards a common goal and share their responsibilities as well as their profits. Recent developments in information technology have enabled relatively inexpensive, reliable and fast networking to support such alliances in real time. In this context, the virtual enterprise (VE) represents an appropriate cooperation alternative and competitive advantage for the enterprises. VE is a temporary network of independent companiessuppliers, customers, even rivals -linked by information technology (IT) to share skills, costs and access to one another's markets. In this emerging business model of virtual enterprise, the decision support functionality, which addresses issues such as partner company selection, is an important domain to be studied. In this paper, we propose an analytic hierarchy process model to contribute in the selection of the partner companies in the virtual enterprises.A case example is also covered to validate the feasibility of the adoption of the model in virtual enterprise situations.

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A neural network model for the assessment of partners’ performance in virtual enterprises

Cited by 13 publications

References 12 publications

Dynamic Manufacturing Networks Monitoring and Governance

Dynamic Manufacturing Networks Monitoring and Governance

Interpretive structural modelling of risk sources in a virtual organisation

Ahp model for the selection of partner companies in virtual enterprises

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