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Top1. Introduction
The latest advancements in information technology have created a decision support systemwith the supply chain. The implementation of the decision support system has helped in decreasing the complexity of decision making. Various decisions and forecasting related to the market trends can be easily made using the decision support system. This scheme helps the decision making authority to make correct decisions (W. A. Teniwut and C. L. Hasyim, 2020). All companies and individual contributors to a product range from raw materials to finished products comprise a supply chain. Examples of supply chain activities include agriculture, refining, design, manufacturing, packaging, and transport. Various supply chain partners work together to ensure the sustainability of the supply chain. This collaboration is designed such that there are minimal risks involved. During the collaboration, the first step is data collection. Supply Chain management refers to managing transactions engaged in raw material procurement, processing, and distribution to end-users. Their transformation into finished goods, moral management of the supply chain ensures a balance between demand and supply. The second step is data analysis, and the third step is data visualization. The final step is the result interpretation (M. V. C. Fagundes, et al. 2020). To reach the desired supply chain management system that enables competitive administrations, inter-organizational systems allow the data flux between organizations to be automated. This supports customer requirements and product and service delivery.
Big data analytics is another important component of the supply chain system. It is used to analyse the data generated using the internet of things (IoT) devices. These devices collect data in real-time from numerous sensors, and the collected data is stored in the cloud. The collected data can be processed using big data analytics systems to design supply chains (A. K. Jha, et al. 2020). With the development of a more digital environment in which the value chains are linked, and distribution systems are increasingly intelligent autonomy, and automation the future of supply chains is being transformed globally. Various decision support models are designed for the improvement of support chain systems. These models are used for the standardization of the overall framework. Thus they are implemented to improve the finance and the manufacturing process. The digitization phase enables gathering numerous real-time data to infer useful information (K. Yildiz and M. T. Ahi, 2020).
The development of support tools is a recently trending area that can be used for automating decisions (V. Vedanarayanan, et al. 2020). Algorithms based on machine learning, deep learning, artificial intelligence, big data analysis, etc., can be used to integrate software tools into the support chain environment. These techniques are used for the optimization of the decision-making models (N. Akbarian-Saravi, et al. 2020). Decision support in the support chain is done based on the optimization criterion. The optimization is done using genetic algorithms. These algorithms perform the optimization of the available resources using bio-inspired models (M. E. Bounif and M. Bourahla, et al. 2013). The efficiency of the supply chain is thus the way to get the right product to the correct position at the lowest cost at the right time. While processors want to measure the efficiency of their supply chain, customers often judge them.