Research Publications

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Subject: search.filters.subject.Manufacturing Industry

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Now showing 1 - 2 of 2
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    PublicationOpen Access
    Digital Distilleries: Navigating Industry 4.0 Trends in Sri Lanka’s Beverage Industry
    (ICSDB 2024 and SLIIT Business School, 2024-12-10) Thakshila, P.; Kushani, M.; Darshana, S.; Herath, B.; Pathirana, G.; Jayasinghe, P.; Ehalapitiya, S.
    Industry 4.0 is a rapidly evolving paradigm allowing industries to explore digital transformation opportunities to provide services and products to both existing and new markets at competitive prices. Developed nations have largely capitalized on Industry 4.0, creating fresh market opportunities by embedding advanced technologies in industrial processes. This research assesses the readiness for implementing Industry 4.0 in Sri Lanka’s beverage sector through primary data. Using focus group interviews with managerial-level employees in large-scale beverage firms, this study identifies technical, operational, and market/environmental factors as key influences in adopting Industry 4.0 in this sector. The findings contribute actionable insights into strategic pathways for digital transformation.
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    PublicationOpen Access
    Topological structure of manufacturing industry supply chain networks
    (Hindawi, 2018-10-03) Perera, S. S; Bell, M. G. H; Piraveenan, M; Kasthurirathna, D; Parhi, M
    Empirical analyses of supply chain networks (SCNs) in extant literature have been rare due to scarcity of data. As a result, theoretical research have relied on arbitrary growth models to generate network topologies supposedly representative of real-world SCNs. Our study is aimed at filling the above gap by systematically analysing a set of manufacturing sector SCNs to establish their topological characteristics. In particular, we compare the differences in topologies of undirected contractual relationships (UCR) and directed material flow (DMF) SCNs. The DMF SCNs are different from the typical UCR SCNs since they are characterised by a strictly tiered and an acyclic structure which does not permit clustering. Additionally, we investigate the SCNs for any self-organized topological features. We find that most SCNs indicate disassortative mixing and power law distribution in terms of interfirm connections. Furthermore, compared to randomised ensembles, self-organized topological features were evident in some SCNs in the form of either overrepresented regimes of moderate betweenness firms or underrepresented regimes of low betweenness firms. Finally, we introduce a simple and intuitive method for estimating the robustness of DMF SCNs, considering the loss of demand due to firm disruptions. Our work could be used as a benchmark for any future analyses of SCNs.