The Analytical Network Process (ANP) in turn provides a structure and process that guide the decision maker in weighing the various KM adoption in SC enabler's perspectives, BSC perspectives and SC performance measures to achieve the stated objectives ( Sevkli et al., 2012 Tavana, Momeni, Rezaeiniya, Mirhedayatian, & Rezaeiniya, 2013 Tjader, Maya, Shang, Vargas, & Gao, 2013). The balanced scorecard (BSC) is a good performance assessment framework developed by Kaplan and Norton (1992, 1993, 1996a, 1996b) to provide holistic consideration of financial and non-financial measures. The SC evaluation that relies on financial measures alone is not suitable. In the assessment of SC performance based on KM adoption, it is crucial to understand how KM adoption contributes to improve SC performance. The objective of this study is to explore the SC performance measures and evaluate the impact of KM adoption on SC performance.Įvaluating SC performance is a complex undertaking, in part because this is a transversal process involving several actors cooperating to achieve given organizational objectives ( Estampe, Lamouri, Paris, & Brahim-Djelloul, 2013). It needs that effective performance measurements to be established and hence, a SC performance measurement framework to evaluate the impact of KM adoption in SC is required. Measuring KM enabled SC performance can facilitate a better understanding of the influence of KM adoption on SC performance. There is a lack of studies measuring the impact of KM practices on the SC performance ( Marra et al., 2011). After making such a big investment in resources for KM adoption organizations should ask themselves in what aspects of performances they are trying to improve and how performances should be measured. In order to manage and improve the SC, many organizations have adopted KM in their SC to gain more competitive advantages against their competitors. SC performance measurement is still a fruitful research area and there is the need of further research ( Akyuz & Erkan, 2010). 1.1 Research motiveĪlthough the large number of researchers emphasized on the importance of performance measuring and providing approaches and methods, there is a lack of a comprehensive model to link the strategy to the SC performance measures ( Najmi & Makui, 2012). KM enabled SC members can provide a guarantee for the chain members to access the external knowledge, but also it is helpful to improve overall competitiveness of the SC ( Lia & Hu, 2012). The KM adoption in SC enables a collaborative environment for the chain to be more adaptive and responsive for achieving an improved strategic competitive position in the market place. There has been increasing academic and practitioner interest in understanding and isolating the factors that contribute to effective knowledge transfer between SC actors ( He et al., 2013). KM adoption in SC is recognized as an important source of competitive advantage. KM is a major enabler of SC, and is a critical element in the information intensive and multi-cultured enterprise environments ( Marra, Ho, & Edwards, 2011). Knowledge Management (KM) is a systematic, organized, explicit and deliberate ongoing process of creating, disseminating, applying, renewing and updating the knowledge for achieving organizational objectives ( Pillania, 2008). Knowledge transfer improves the performance of SC organizations ( He, Ghobadin, & Gallear, 2013 Lawson, Petersen, Cousins, & Handfield, 2009). The efficient knowledge flows and knowledge sharing process among SC partners ensure agility, adaptability, and alignment in the chain ( Lee, 2004). SC is no longer confined to the study of logistics, information flow and cash flow. Effective SC relies on information integration and implementation of best practice across the chain ( Agrell & Hatami-Marbini, 2013). A highly efficient SC would bring great benefits to organizations such as integrated resources, reduced logistics and high quality of overall level of services ( Fan, Zhang, Wang, Yang, & Hapeshi, 2013). Supply Chain (SC) is a set of organizations directly and indirectly interlinked and interacted to transform inputs into outputs for efficient delivery to the end customer ( Elgazzar, Tipi, Hubbard, & Leach, 2012).
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