Demirel, Kenan CemŞahin, AhmetAlbey, Erinç2021-09-202021-09-202020978-303054594-91865-0929http://hdl.handle.net/10679/7565https://doi.org/10.1007/978-3-030-54595-6_6In this study we construct a decision support system (DSS), which utilizes the production process parameters to predict the quality characteristics of final products in two different manufacturing plants. Using the idea of regressor chains, an ensemble method is developed to attain the highest prediction accuracy. Collected data is divided into two sets, namely “normal” and “unusual”, using local outlier factor method. The prediction performance is tested separately for each set. It is seen that the ensemble idea shows its competence especially in situations, where collected data is classified as “unusual”. We tested the proposed method in two different real-life cases: textile manufacturing process and plastic injection molding process. Proposed DSS supports online decisions through live process monitoring screens and provides real time quality predictions, which help to minimize the total number of nonconforming products.enginfo:eu-repo/semantics/restrictedAccessConference paper12559611410.1007/978-3-030-54595-6_6Industry 4.0Quality predictionEnsemble methodsRegressor chainsDecision support system2-s2.0-85089312976