Open Access

Sila Sever

• n the broadest sense, data analytics can be defined as "the application of processes and techniques that transform raw data into meaningful information to improve decision making." According to PwC's Global Data and Analytics Survey 2016, companies are seeking ways to use data analytics in order to understand risk and leverage data. There is a vast amount of data in the companies' supply chain such as transactional, time phased and sensor data that can be used in order to understand operational risks. Especially, companies having extremely complex supply chains with thousands of suppliers that are more fragile to risks and try to come up with data analytics solutions to increase supply chain resilience by detecting potential risks in advance. The thesis will follow an inductive research approach. A systematic literature review will be done in order to understand useful data analytics methods such as predictive and prescriptive analytics for the supply chain risk management. A comparative case study will also be done based on the already conducted supply chain risk management data analytics projects to analyse what type of data analytics method can be useful with which type of supply chain risk. The methods determined by the systematic procedure will be evaluated and placed in a framework, which has to be developed. The framework will help to understand levers that influence successful applications of supply chain risk management data analytics methods. Also it will provide a structured approach about how to use quantitative data in order to increase supply chain resilience with the help of data analytics. Validation of the framework will be done by working in a cooperation with a German automotive supplier company.