With manufacturing focused on multi-variety and small-batch intelligent production, the challenge is to guarantee and ensure good product quality . This paper proposes a novel small-batch product quality prediction approach considering complex interaction information. In this paper, first, to handle the complex nonlinear correlations among the manufacturing process variables, a kernel principal components analysis is used to extract the major features from the high-dimensional data. Second, we combine the discrete multivariate grey model and generalized discrete Choquet fuzzy integral to propose a discrete Choquet fuzzy grey model so as to capture the complex interaction information among the process variables. Next, an adjoint sensitivity analysis is applied to characterize the changes in the process variables on the predicted product quality index. We conduct an experiment on six semiconductor products made in China to validate the proposed model against a library of nine other methods. Our model yields an average reduction of 11.02% on MAPE, 20.47% on MSE, 13.56% on STD, and an average increase of 46.37% on EVS for six types of products. Our results inform that the proposed model is suitable for predicting manufacturing product quality when the process variables interact significantly, and we can prioritize the process variables for remedial action accordingly.

Disruptions are known to significantly affect a company's supply chain performance in today's highly volatile markets. However, most existing risk analysis tools to investigate the effects of disruptions are developed typically for specific supply chains. In this paper, we develop a mathematical model that utilizes the Time-To-Recovery (TTR) approach, through the characterization of the supply chain disruption recovery patterns. We are able to model the recovery patterns corresponding to different inherent characteristics of a supply chain, forecasting any type of supply chain's performance during the period of recovery or how long the TTR is after a disruption has occurred. The novelty of our model is that we allow a framework to capture the stochastic nature of TTR. Furthermore, the model is capable of estimating the TTR, which has tremendous commercial implication to the practitioners.