Abstract
Electric‐mechanical equipment manufacturing industries focus on the implementation of intelligent manufacturing systems in order to enhance customer services for highly customized machines with high‐profit margins such as electric power transformers. Intelligent manufacturing consists in using supply chain data that are integrated for smart decision making during the production life cycle. This research, in cooperation with a large electric power transformer manufacturer, provides an overview of critical intelligent manufacturing (IM) technologies. An ontology schema forms the terminology relationships needed to build two intelligent supply chain management (SCM) modules for the IM system demonstration. The two core modules proposed in this research are the intelligent supplier selection and component ordering module and the product quality prediction module. The intelligent supplier selection and component ordering module dispatches orders that match the best options of suppliers based on combined analytic hierarchy process (AHP) analysis and multiobjective integer optimization. In the case study, the intelligent supplier selection and component ordering module demonstrates several acceptable Pareto solutions based on strict constraints, which is a very challenging task for decision makers without assistance. The second module is the product quality prediction module which uses multivariate regression and ARIMA to predict the quality of the finished products. Results show that the R square values are very close to 1. The module shortens the time for the company to accurately judge whether the two semifinished iron cores for the product meet the quality requirements. The component supplier selection module and the finished product quality prediction module developed in this research can be extended to other IM systems for general high‐end equipment manufacturers using mass customization.
Highlights
supply chain management (SCM)Supplier ManagementOntology of Smart Manufacturing manufacturing execution systems (MES)Quality Management IoTCloud Computing e intelligent supplier selection module workflow.CriteriaQCDS scorePerformance Evaluation System performance parameterAnalytic Hierarchy Process analytic hierarchy process (AHP) weights
Calvello [29] proposed a method that combines statistical machine learning algorithms and autoregressive integrated moving average (ARIMA) for forecasting water levels. e literature reviews show that training ARIMA using historical data can achieve optimal accuracy. is research explores research in related fields to improve the accuracy of predicting the quality of finished products
We choose the scenario of selecting a transformer casing supplier for the company. e QCDS score of the transformer casing supplier is used as input of the AHP model, which will generate the weight required in the multiobjective integer programming model
Summary
Supplier selection is an important issue for supply chain management. Previous research shows that many methodologies have been proposed to solve the supplier selection problem. [4] proposed a combined FUCOM (FUll COnsistency Method)–Rough SAW model to examine the problem of sustainable evaluation of supplier performance and selection. Nunic [5] proposed a FUCOMMABAC (Multi-Attributive Border Approximation Area Comparison) model for evaluating and selecting the PVC manufacturer. Among these previous research studies, the analytical hierarchy process (AHP) is among the most popular methods continuingly being used. Combining AHP with multiexpression programming (MEP) for suppliers’ performance evaluations [7] and using quality function deployment (QFD) provide an order preference that is very satisfactory for supplier selection [8]. Supplier selection is improved by using an intelligent decision module combined with a hybrid method containing AHP and a multiobjective integer programming (MOIP) model. In order to build the intelligent decision modules proposed in this research, the literature review covers the analytic
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