Pre-warning analysis in traceability systems for food production supply chains

Production quality in the food production supply chain is studied in this paper. The deficiency of quality monitoring existing in traceability systems is analyzed. An abnormality diagnosis algorithm, pre-warning method and pre-warning system are presented. The potential production abnormality of the logistics unit in the whole supply chain is diagnosed; a warning is generated; and decision support information is given. Four abnormal data types are analyzed; and a standard hierarchy evaluation indicator system for abnormalities is developed. A mathematical model for abnormality detection is developed by combining a radial base function (RBF) neural network, fuzzy control, and statistical analysis methods. The model can accurately find different types of abnormalities, especially hidden problems. Instance analysis and simulations show that the proposed pre-warning system can effectively identify abnormality types, and accurately determine whether a warning should be issued, depending on the warning level when an abnormality is detected by the system.