Smart risk analytics design for proactive early warning

Abstract

Purpose: Automobile manufacturers are highly dependent on supply chain performance which is endangered by risks. They are not yet able to proactively manage these risks, often requiring reactive bottleneck management. A proactive and digitalized early warning method is needed. Methodology: The publication provides methodological-empirical contribution to proactive early warning resulting in a smart risk management approach. The methodological approach is carried out according to the design science research approach. Findings: The developed smart risk management enables an automated, objective and real-time ex-ante-assessment of supply chain risks in to secure the supply of the automobile manufacturer. Smart risk analytics based on artificial intelligence is shown with its suitability for proactive early warning using the example of inaccurate demand planning. Originality: The analytical approach provides insights into the flexibility of supply chains under risk and the impact over time, which is applied in the proactive early warning design. Artificial intelligence is applied to predict and assess supply chain risk events.