Lingzhe Liu

Business Intelligence for Improving Supply Chain Risk Management

The risk management over a supply chain has to be founded on the management controls in each of the partner companies in the chain. Inevitably, the business relationship and operations dependence bind the control efforts of partner companies together. This proposes challenges for supply chain risk management and at the same time for the BI application. In this paper we analyse the management control situations where business intelligence technology can be applied and describe the concepts of systematic risk analysis to improve the management controls, based on causal analysis of business exceptions. The analysis process is driven by diagnostic drill-down operations following the equations of the information structure in which the data are organised. Using business intelligence, the analysis method can generate explanations supported by the data. A “risk template” is provided to assist analysts to fully comprehend the risk scenario in the practical business setting, so as to evaluate and re-design the existing controls, and to apply BI for management improvement.

Auditing Data Reliability in International Logistics

Data reliability closely relates to the risk management in international logistics. Unreliable data negatively affect the business in various ways. Due to the competence specialization and cooperation among the business partners in a logistics chain, the business in a focal company is inevitably dependent on external data sources from its partner, which is impractical to control. In this paper, we present a research-in-progress on an analysis method with Bayesian networks. The goal is to support auditorâ??s assessment on the reliability of the external data. A case study is provided to illustrate the merits of Bayesian networks when dealing with the data reliability problem.