Supply Chain Data Analysis with Innovative Logistics and Design Thinking Approaches

Supply chain management (SCM) involves the planning, coordination, and smooth execution of various tasks and processes related to manufacturing, sourcing, delivery, and distribution. Its goal is to ensure the efficient flow of products and information from suppliers to manufacturers or service providers and ultimately to customers. Perfect supply chain management is crucial for organizations as it helps reduce costs, improve product quality, meet customer demands, and maintain a competitive edge in the industry. One transformative approach to achieving this is through machine learning algorithms' supply chain data analysis. This approach optimizes operations, reduces costs, improves decision-making, and mitigates risks within the supply chain ecosystem. This abstract highlights the importance of utilizing machine learning algorithms to analyze supply chain data. By employing advanced analytics techniques, businesses can extract valuable insights from large and diverse datasets. These insights enable companies to make informed decisions that drive operational excellence. This research aims to thoroughly e-xamine the use of Data mining algorithms in analyzing supply chain data. It will cover each stage- of data analysis within the context of the supply chain, including data colle-ction, preprocessing, predictive- analytics, anomaly detection, optimization, real-time- monitoring, and risk management. Practical use case-s and benefits will be discusse-d for each stage in detail.