Big data fueled process management of supply risks: Sensing, prediction, evaluation and mitigation

Abstract

Supplier risks jeopardize on-time or complete delivery of supply in a supply chain. Traditionally, a company can merely do an ex-post evaluation of a supplier's performance, and handles emergencies in a reactive rather than a proactive way. We propose an agile process management framework to monitor and manage supply risks. The innovation is two fold - Firstly, a business process is established to make sure that the right data, the right insights, and the right decision-makers are in place at the right time. Secondly, we install a big data analytics component, a simulation component and an optimization component into the business process. The big data analytics component senses and predicts supply disruptions with internally (operational) and external (environmental) data. The simulation component supports risk evaluation to convert predicted risk severity to key performance indices (KPIs) such as cost and stockout percentage. The optimization component assists the risk-hedging decision-making.