Predict Risk Assessment in Supply Chain Networks with Machine Learning

Supply chain gradually becomes a core factor to operate and develop for businesses. Using machine learning, especially with neural networks, to assess the risk in supply chain network has been attracted many research and become potential approaches. Via machine learning particular to Bayesian neural network, risk evaluation in supply chain network can be performed effectively to support supply chain partners to assess, identify, monitor, and mitigate risks. In detail, by using reliability theory, supply chain network’s risk is divided in alternative scales (from Very high risk to Very low risk). The Bayesian neural network allows to treat the weights and outputs as the variables in order to find their marginal distributions that best fit the data. By taking the advantage of Bayesian neural network in deep learning, the experiment in this paper shows a very high accuracy rate in supply chain risk prediction. This implicates the performance of using machine learning in supporting of managerial decision making in selecting suppliers.