Supply chain management is essential to a company's success in today's fiercely competitive business environment, regardless of the industry. Businesses are increasingly turning to big data analytics as a useful tool for supply chain improvement to meet the expanding needs of customers, save operational costs, and boost overall efficiency. In order to improve critical areas including demand forecasting, inventory management, logistics optimization, and risk reduction, research has been conducted on the collecting and use of big data in supply chain management. Big data analytics and supply chain management integration can result in more cost-effective operations, better customer service, and increased resilience to disturbances. Organisations that effectively use big data will gain a competitive advantage in the constantly changing supply chain landscape as technology and data capabilities continue to grow.
TopIntroduction
Supply chain management (SCM), in today's complex and interconnected business environment, has developed into a crucial task for organizations looking to keep a competitive advantage. The gathering and analysis of large data (Kumar, M. S. et al., 2021) is one of the primary facilitators of efficient SCM. Big data, which is the term for enormous and varied collections of information (Pandey, B. K. et al., 2023) created from numerous sources, has the potential to fundamentally alter how businesses plan, carry out, and optimize their supply chain activities (Malhotra, P. et al., 2021). The process of obtaining, storing, and analyzing big data for supply chain management entails gathering information from numerous sources all along the supply chain (Richey, R. G. et al., 2022).
These resources may consist of:
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Internet of Things (IoT) (Kumar Pandey, B. et al., 2021) Sensors: Sensors attached to goods, machines, and other items can deliver real-time information about their whereabouts, state, and performance. For keeping track of the flow of commodities and assets, this information is crucial.
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Enterprise Resource Planning (ERP) Systems: ERP software (Kirubasri, G. et al., 2022) gathers and combines data from a range of corporate operations, such as purchasing, production, and inventory control. A thorough supply chain analysis can be performed using this centralized data. Data from suppliers and partners can offer insights into performance, lead times, and quality. This data comes from suppliers, logistics partners, and other external stakeholders. For managing suppliers and evaluating risks, this information is essential.
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Customer Information: Information on orders, preferences, and feedback from customers is crucial for predicting demand and developing products.
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Social media and market data analysis can assist businesses in better understanding consumer behaviour and adapting to changes in demand.
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Historical Data: For trend analysis, benchmarking, and continuous improvement, records of earlier supply chain operations and performance measures are essential.
Once this data has been gathered, it needs to be processed and examined in order to yield useful insights. To find trends, anomalies, and opportunities in the data, advanced analytics techniques like machine learning (Singh, H. et al., 2022) and predictive modeling (Babu, S. Z. D. et al., 2022, July) are frequently used.
Figure 1.
Components and processes involved in leveraging big data analytics (Varela Rozadosn& Tjahjono, 2014) to enhance supply chain operations
A block diagram in Figure1for a Big Data (Nguyen, Truong et al., 2017) system in supply chain management would encompass the various components and processes involved in leveraging big data analytics to enhance supply chain operations. This block diagram represents the core components and processes in a Big Data system for supply chain management (M. K. Daoud,et al.,2023). Such a system leverages advanced analytics and data-driven insights to optimize supply chain operations, improve efficiency, reduce costs, and enhance overall performance (Wang, C.et al.,2023).