Network-analysis approaches to deal with causal complexity in a supply network

Bezuidenhout, C. N.; Bodhanya, Shamim; Sanjika, Thawani Mpatama; Sibomana, Milindi; Boote, Gordon Louis Nelson

doi:10.1080/00207543.2011.575088

Cited by 21 publications

(18 citation statements)

References 29 publications

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“…The nodes (in the supply chain) refer to different supply chain parties which are connected by many links, representing physical activities in the supply chain network. Applications of SNA in supply chain analysis are reported in Bezuidenhout et al (2012), Swaminathan et al (2002) and Lazzarini et al (2001).…”

Section: Applying Network Coefficients Of Nodes In a Supplymentioning

confidence: 99%

Using social network analysis to combat counterfeiting

Ting

Tsang

2013

International Journal of Production Research

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Counterfeiting undoubtedly produces great harm to companies and the society. This paper presents a study on the application of Social Network Analysis (SNA) to combating this problem. The supply chain can be viewed as a network of parties involved in delivering value to consumers. Normally, SNA is used to analyze relationships between people. In this study, it is utilized to analyze supply chains for identification of parties that are likely to be involved in counterfeiting activities. After identifying the suspects in a supply chain, the company can deter and detect counterfeiting by tightening surveillance on these parties. The feasibility of using SNA as an anti-counterfeiting tool is investigated in a case study, the findings of which indicate that SNA can effectively help companies to prevent sources of counterfeit products from infiltrating into the supply chain. Problematic parties can be identified by characterizing the following features: high degree of centrality, high closeness of centrality and high betweenness of centrality.Keywords: Anti-counterfeiting, RFID, Social Network Analysis, Supply Chain Management This is the Pre-Published Version.

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Section: Applying Network Coefficients Of Nodes In a Supplymentioning

confidence: 99%

Using social network analysis to combat counterfeiting

Ting

Tsang

2013

International Journal of Production Research

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“…For example, a substantial amount of OSM empirical research conceives of a supply chain as consisting of simply a customer and a supplier or perhaps a supply chain triad (Choi & Wu, 2009). While researchers readily acknowledge that real supply chains are more like networks (Basale & Belamy, 2014;Benzudenhout, et al, 2012;Choi, et al, 2001), there is a tendency to focus on small fragments of supply chains. This may be justified as learning about fragments to generalize to a broader network (Benton & Maloni, 2005), but it many cases, this conceptual oversimplification is simply for the researchers' convenience.…”

Section: Conceptual Oversimplification Oversimplified Constructs Andmentioning

confidence: 99%

The best of times and the worst of times: empirical operations and supply chain management research

Melnyk

Flynn

Awaysheh

2017

International Journal of Production Research

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We assess the current state of empirical research in operations and supply chain management (OSM), using Dickens' contrast between the best of times and the worst of times as a frame. The best of times refers to the future that empirical OSM research is now entering, with exciting opportunities available using big data and other new data sources, new empirical approaches and analytical techniques, and innovative tools for developing theory. These are well aligned with new research questions related to the digital economy, Industry 4.0, the impact of the millennial generation as consumers, social media, 3D printing, etc. However, we also explore how it is the worst of times, focusing on the challenges and problems that plague empirical OSM research. Our goal is to show how OSM researchers can learn from the worst of times, in order to be poised to take advantage of the best of times. We introduce the research diamond as a vehicle for emphasizing the importance of a balanced research perspective that treats the research problem, theory, data collection and data analysis as equally important, requiring alignment between them. By learning and addressing the issues in this period of the best of times and the worst of times, we can take advantage of the opportunities facing our field to generate research that is balanced, insightful, rigorous, relevant, impactful and interesting.

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“…There are currently limited techniques available in the sugar industry, to quantify and predict the impacts of a decision, such as the harvest date of a cane field (Higgins et al, 2007;Lejars et al, 2008;Amu et al, 2013). Furthermore, Bezuidenhout et al (2012a) argued that a ''one-size-fits-all'' approach to optimising systems is unlikely to be a successful solution in the sugar industry. This is due to each mill being unique because of its history and the various biophysical issues on the ground, at different times .…”

Section: Introductionmentioning

confidence: 98%