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Parts machining outsourcing (PMO) can be regarded as a strategic weapon to increase product quality and productivity as well as cost cutting in many manufacturing enterprises. However, making an outsourcing decision based on accumulated experience is neither effective nor scientific due to subjective judgement and lack of systematic analysis. Therefore, to systematically help the decision-maker survey and select suppliers to outsource and also to timely achieve the most beneficial portfolio among suppliers in a non-cooperative game structure, this paper proposes an integrated decision support model for PMO using ontology, constraint-based reasoning, fuzzy VIKOR and game theory in three phases, namely supplier prequalification, ultimate selection and order coordination. In the decision-making process, decision factors such as quality, cost and production capability are categorised and put into different phases, respectively, by their characteristics for being measured at a more reasonable method. Finally, a case from a leading printing machinery enterprise is analysed to validate the proposed model. This research is expected to improve the quality and effectiveness of decision-making in PMO. [ABSTRACT FROM AUTHOR]

As an essential element in the overall designing of a service system, service planning plays an important role in improving final service quality and user experience. Service planning is a customer-oriented approach that facilitates the translation of customer requirements into activity process features or engineering characteristics. The trend is towards learning decision-support knowledge from massive service case data to facilitate service planning. However, when learning from imbalanced data, existing methods have poor predictive ability to identify minority patterns. To improve the quality and efficiency of the service planning, an innovative approach based on the combination of granular computing and deep learning are presented. It employs an inductive paradigm, clustering examples in a best granularity, sampling and refining into a more balanced example set, and feeding them into a deep learning model. The proposed approach can mine the planning patterns between the customer requirements and service process features, thereby facilitating knowledge transferring in service planning under the social manufacturing context. [ABSTRACT FROM AUTHOR]

There is an increasing unstructured text data produced in cross-enterprise social interaction media, forming a social interaction context that contains massive manufacturing relationships , which can be potentially used as decision support information for cross-enterprise manufacturing demand-capability matchmaking. How to enable decision-makers to capture these relationships remains a challenge. The text-based context contains high levels of noise and irrelevant information, causing both highly complexity and sparsity. Under this circumstance, instead of exploiting man-made features carefully optimized for the relationship extraction task, a deep learning model based on an improved stacked denoising auto-encoder on sentence-level features is proposed to extract manufacturing relationships among various named entities ( e.g. , enterprises, products, demands, and capabilities) underlying the text-based context. Experiment results show that the proposed approach can achieve a comparable performance with the state-of-the-art learning models, as well as a good practicality of its’ web-based implementation in social manufacturing interaction context. The ultimate goal of this study is to facilitate knowledge transferring and sharing in the context of enterprise social interaction, thereby supporting the integration of the resources and capabilities among different enterprises. [ABSTRACT FROM AUTHOR]