Machine Learning Approaches to Improve Visibility in Global Supply Chains

Literature review is the main aspect of this research where special attention is given to learning the ways of applying machine learning to enhancing transparency of supply chains all over the world. This is achieved with the help of big data to blend a classical model with several features of the improved ML tool of demand forecasting, anomaly detection, or better route optimization. Data is the whole information collected for analyzing while pre processing means the step through which data is passed before analysis. Model development refers to the process of developing a model which will be followed in practice while implementation refers to the act of practicing what was envisaged in the development of the model. Last but not least, control is the last step in the model implementation process where the outcome of the model is critically determined. The outcome of the research has presented that reliability and effectiveness has been established to achieve an enhanced level of predictability in terms of hit ratio, delivery performance, inventory and overall organizational parameters. It reveals that, if helms properly, then the ML has the capability to increase the effectiveness and visibility of the global supply chain – which is a vital element of any business. . Learning, knowledge creation cannot be a one time process and hence needs continuous application where the stakeholders are the active participants. This study provides informed guidance on the factors that can be applied in integrating ML in supply management.