"Strategic application and Critical analysis of Machine Learning to manage and mitigate Risks within the [COMPANY_XXX] Supply Chain"
Topic:
"Strategic application and Critical analysis of Machine Learning to manage and mitigate Risks within the [COMPANY_XXX] Supply Chain"
Abstract:
In todays complex supply networks, disruptions caused by diverse events represent major unknown operational conditions and risk factors requiring research on supply chain risk management. (Bodendorf, Sauter and Franke, 2023). Given the complexity of modern supply chains we argue that a data-driven approach to supply chain risk management enhances the assessment and prediction of risks. The goal of this Thesis is to Predict High Risk suppliers in [COMPANY_XXX] supply chain using Machine learning and Big data analytics. The studys findings will help [COMPANY_XXX] avoid supply chain disruptions and align their purchasing strategies better. The study will be beneficial to [COMPANY_XXX] in preparing for supplier risks and tackling any disruptions caused in the supply chain due to suppliers.
Introduction:
In todays complex supply chain networks risks of disruptions due to organizational factors within and environmental factors outside of supply chains represent major challenges for both practitioners and researchers (Baghersad and Zobel, 2021). In recent years, accompanied by the surge in big data and the increasing demand for business analytics, the importance of empirical research based on observational data in the field of supply chain risk management is experiencing a significant boost (Ho et al., 2017). By incorporating financial indicators which reflect the financial liquidity data of suppliers can be a valuable source of information to predict their risk and prepare for any disruptions that may arise.
Purpose:
The purpose of this application-oriented master thesis is to help [COMPANY_XXX] identify supply chain risks prior to its occurrence and to proactively anticipate supply chain risk affecting the company. Hence with this prior information making [COMPANY_XXX] better prepared for supplier failures and disruptions caused in near future.
Methodology:
This methodology used in this study is a secondary data analysis with the aid of ML techniques.
Impact:
The impact of this study will be significant in helping [COMPANY_XXX] prepare for supplier risks and tackle any disruptions caused in the supply chain. Another benefit would be related to decision making capacity due to a more strategic perspective. Advantage of having risk knowledge in prior intensifies the supplier relationship through an improved information flow.