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Announcement
Exploring client server architecture for supply chain optimization, visibility and sustainability using APIs

Student name: Mr Aayush Verma
Guide: Dr Anand Madhukar
Year of completion: 2023
Host Organisation: SESG, BDO India LLP
Supervisor (Host Organisation): Mr Pranjjal Shukla
Abstract:

Supply chain visibility is crucial for businesses to understand the flow of goods and information throughout their supply chain network. In simple words, with visibility and optimization, operation efficiency of a company improves. Providing a powerful tool for analysing and optimizing supply chain processes enables organizations to track the movement of goods and assets and trace products from the point of origin to the point of consumption, which later help in analysing transportation routes, costs, improving traceability and accountability. If used the right way can provide insights into the environmental and social impact of supply chain operations, one of them being analysing carbon footprint of their supply chain, areas of improvement, manage potential risks and develop sustainable strategies, through which an organisation can make data-driven decisions and optimize their operations. The client-server relationship is at the heart API Systems. This relationship involves a networked system where clients, such as end-users or applications, communicate with servers that provide resources and services. It includes several different rendering approaches such as server-side rendering, client-side rendering, and hybrid approaches. This study aims at exploring these relationships and related computations on offline and online rendering for vehicle route calculations, such as network analysis using djikstra’s algorithm or the shortest path analysis and supply chain optimization simulation using clarke and wright savings algorithm. The distance travelled and the routes taken by trucks are plugged in to the respective emission factors to find out the emission factors per kg CO2 for the routes travelled to find out the carbon emissions produced by the trucks based on different sizes.