A non-conformance rate prediction method supported by machine learning and ontology in reducing underproduction cost and overproduction cost

Nonconformities are the major sources of waste in manufacturing process. Nonconformities cannot be fully eliminated but their occurrence rate can be predicted. This paper proposes a hybrid approach based on ontological modelling and machine learning for predicting the non-conformance rates of a manufacturing process and minimising its associated costs. Based on the proposed approach, the work orders, that are represented semantically using a formal ontology, are first clustered according to their semantic similarities and then, for each cluster, the appropriate models that predict the probability distribution of non-conformance rates are developed. When a new work order is created, the most similar work order is retrieved from historical records, and the probability distribution of its non-conformance rate is estimated by applying the predictive model of the cluster to which the work order belongs. The probability distribution is used to calculate the expected underproduction and overproduction cost and to determine the amount of production that minimises the expected costs. The proposed method was validated using a dataset obtained from a manufacturer of packaging for cosmetics. Compared to the expert’s opinions and other machine learning algorithms, the proposed method demonstrated better performance with respect to cost reduction.