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Increasing complexity of industrial products and manufacturing processes have challenged conventional statistics based quality management approaches in the circumstances of dynamic production. A Bayesian network and big data analytics integrated approach for manufacturing process quality analysis and control is proposed. Based on Hadoop distributed architecture and MapReduce parallel computing model, big volume and variety quality related data generated during the manufacturing process could be dealt with. Artificial intelligent algorithms, including Bayesian network learning, classification and reasoning, are embedded into the Reduce process. Relying on the ability of the Bayesian network in dealing with dynamic and uncertain problem and the parallel computing power of MapReduce, Bayesian network of impact factors on quality are built based on prior probability distribution and modified with posterior probability distribution. A case study on hull segment manufacturing precision management for ship and offshore platform building shows that computing speed accelerates almost directly proportionally to the increase of computing nodes. It is also proved that the proposed model is feasible for locating and reasoning of root causes, forecasting of manufacturing outcome, and intelligent decision for precision problem solving. The integration of bigdata analytics and BN method offers a whole new perspective in manufacturing quality control.

Customer requirement analysis has become a primary concern for companies who compete in the global market. Kano’s model, as a customer-driven tool, has been widely used for customer requirement analysis in product improvement. Although a number of authors have improved the traditional Kano’s model, there has been a limitation of dealing with the fuzzy and uncertainty of human thought under multi-granularity linguistic environment. Furthermore, the traditional Kano’s model faces problems regarding quantitative data computation and customer requirements importance assessment. In this article, an improved fuzzy Kano’s model is proposed to analyze customer requirements under uncertain environment. A 2-tuple linguistic fuzzy Kano’s questionnaire is developed to model the uncertainty and diversity of customers’ assessments using 2-tuple linguistic variables under multi-granularity linguistic environment. Then, a comprehensive and systematic methodology is presented to prioritize customer requirements through quantitative analysis of improved fuzzy Kano’s model. This method integrates subjective judgments assigned by decision maker, objective weights based on maximizing deviation method and customer satisfaction contribution to determine the priority ratings of customer requirements. A case study of combine harvester development is presented to evaluate the proposed model.

The transformation to service economy and rapid development of smart technologies have greatly changed traditional commodity trading based business models. There is an irresistible trend developing to the new business paradigm of Smart Product Service Ecosystem (SPSE), which integrates value co-creation network, service ecological thinking and Information & Communication Technologies (ICT). A conceptual framework of SPSE is proposed, covering the study on its definition, characters, components, mutual interactions and development roadmap. Business model innovations of SPSE are analysed from three aspects, including interaction modes, resource allocation principles and cooperation mechanisms. First, smart interactions offer new approaches and opportunities for manifest customer demand acquiring and implicit demand reasoning. Then, the dynamic sharing resource pool is introduced for service resource allocation optimization. The integration of distributed layout and centralized management and scheduling of resources will significantly reduce operation costs, improve resource utilization efficiency, and accelerate service response speed. Last, cooperative mechanisms will be developed based on risk sharing, ecological diversification and node association multi-polarization, to achieve a win-win situation and improve the stability, reliability and competitiveness of SPSE. The proposed theory is expected to offer possible guidelines and roadmaps for those transforming and emerging industries.

The study mainly focuses on resource allocation optimization for industrial product-service systems (IPS2). The development of IPS2 leads to sustainable economy by introducing cooperative mechanisms apart from commodity transaction. The randomness and fluctuation of service requests from customers lead to the volatility of IPS2 resource utilization ratio. Three basic rules for resource allocation optimization are put forward to improve system operation efficiency and cut unnecessary costs. An approach based on fuzzy multiple linear regression (FMLR) is developed, which integrates the strength and concision of multiple linear regression in data fitting and factor analysis and the merit of fuzzy theory in dealing with uncertain or vague problems, which helps reduce those costs caused by unnecessary resource transfer. The iteration mechanism is introduced in the FMLR algorithm to improve forecasting accuracy. A case study of human resource allocation optimization in construction machinery industry is implemented to test and verify the proposed model.

With the development of the internet of things (IoTs), big data, smart sensing technology, and cloud technology, the industry has entered a new stage of revolution. Traditional manufacturing enterprises are transforming into service-oriented manufacturing based on prognostic and health management (PHM). However, there is a lack of a systematic and comprehensive framework of PHM to create more added value. In this paper, the authors proposed an integrative framework to systematically solve the problem from three levels: Strategic level of PHM to create added value, tactical level of PHM to make the implementation route, and operational level of PHM in a detailed application. At the strategic level, the authors provided the innovative business model to create added value through the big data. Moreover, to monitor the equipment status, the health index (HI) based on a condition-based maintenance (CBM) method was proposed. At the tactical level, the authors provided the implementation route in application integration, analysis service, and visual management to satisfy the different stakeholders&rsquo, functional requirements through a convolutional neural network (CNN). At the operational level, the authors constructed a self-sensing network based on anti-inference and self-organizing Zigbee to capture the real-time data from the equipment group. Finally, the authors verified the feasibility of the framework in a real case from China.

The increasing complexity of product and radical changing environment lead companies towards innovation network (IN) collaboration. Although recent researches highlight the importance and benefits of innovation network (IN) to new product development (NPD), this subject is still developing and it has the potential to expand into new dimensions. However, there is a significant lack in the literature regarding holistic, systematic and integrated framework when it comes to manipulating the challenge of true NPD projects. The current researches mainly focus on the strategy level which provides less application guidance for NPD. Thus, in this work, the authors propose an integrative framework of innovation network for NPD based on the literature review. In addition, a case study of new refrigerator development reveals the feasibility and potentials of the overall framework to its broad usage in industry.

With the fast development of service economy, researches and applications of Industrial Product–Service Systems have become popular. Advantages and benefits of Industrial Product–Service Systems are widely discussed, while risks and uncertainties resulting from this transformation tide are rarely investigated. There are strong appeals from the industry for a holistic Industrial Product–Service Systems risk management architecture. Based on a full review on the existing work, an Industrial Product–Service Systems risk management framework integrating multiple methods is proposed trying to provide a possible solution. Structure–process correlation analysis is used for Industrial Product–Service Systems modeling. Risk recognition matrix and graphic risk units integrating with dynamic Bayesian network are developed for risk recognition and modeling. Entropy reduction value–based and effect chains’ visualized analysis offer an efficient approach for key risk and key node recognition. The proposed methods get tested and verified with a case study of risk management for hydraulic system maintenance service in excavator Industrial Product–Service Systems.

Process trimming (also can be called as trimming for process) helps to trim and eliminate process operations by redistributing their functions among other operations. It offers one way to eliminate key disadvantages in product and manufacturing process that other methods do not see. However, few methods have been disclosed in a structured way for process trimming effectively. Therefore, this article proposes an integrated process focused on technological process and product innovation to solve the key problems with process trimming-based TRIZ (theory of inventive problem-solving) approach. This method helps to identify and inventively solve the key problems and maximize use resource of system and supersystem. First, process trimming candidates are identified based on component function model, component trimming rules, process function model analysis, component–process interaction matrix, and root cause analysis. Then, three types of process trimming strategies are presented. Algorithm of process trimming is developed to identify key problems in technological process. TRIZ problem-solving tools are used to solve these key problems. Finally, a case study of refrigerator door foam innovative design and manufacturing process is investigated to test the efficiency of the approach. The innovative solution significantly decreases manufacturing defects and service cost.

One critical factor in collaborative part and tooling development is the information integration and sharing. After analyzing characteristics of the collaboration process, the problem of information integration and sharing is transformed into a problem of domain ontology integration and sharing. First, an activity model is presented to describe interactions among domains of part, process, and tooling. Next, three local ontologies are constructed to serve as formal, explicit specifications of domain-specific information. To integrate local ontologies as global ontology, the relations and constrains among domains are explicitly represented using Semantic Web Rule Language. Then, the ontology sharing method via similarity matching is studied. Finally, the proposed approach is demonstrated by a case.

Patent design around is an effective way to walk around competitive patents and avoid patent infringement. This paper proposes a TRIZ-based Trimming method for Patent design around. The 4 stages of Patent design around include design around target definition, design around problem identification, problem solving, and solution evaluation. In the 4 stages, Technical Features (TF) are identified to define design around target based on patent claim decomposition. In order to avoid literal infringement and doctrine of equivalents, the design around problem identification process of Trimming one or more patent Technical Features is developed. Then, the problem solving tools based on Modern TRIZ tools (such as Physical Contradiction resolution, and Function-Oriented Search) are introduced to solve the design around problem. Two Trimming scenarios of Core Ejector System is investigated to illustrate the method. The project successfully circumvents target patent and generates several new granted patents in China. Establish an integrated TRIZ-based framework for Trimming strategy to design around competitive patent.Present a step by step Trimming Technical Features process to generate Trimming scenarios and Trimming problems for innovation inspiration.Generate two Trimming scenarios for Core Ejector System improvement.

The proper balancing of geographically distributed task schedules and the associated workforce distributions are critical determinants of productivity in any people-centric production environment. The paper has investigated the cross-trained workers scheduling problem considering the qualified personal allocation and temporally cooperation of engineers simultaneously. A 0–1 programming model is developed and the non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to deal with the NP-hard problem. In order to enforce the NSGA-II, significant improvements are made to function the approach in a more efficient way. It is observed that the improved NSGA-II outperforms the original NSGA-II in the experimental test. The promising outcomes of the formulation in the experiment make its implementation easily customisable and transferable for solving other intricate problems in the context of skilled workforce scheduling. Furthermore, the modified NSGA II can be used as an efficient and effective tool fo...

With the continuous improvement of living standards, customers are gradually pursuing products which are individualization, entertainment, and scene. The production model of the enterprise has changed from the traditional inventory model to the manufacturing model driven by the customer orders. By analyzing the evolution process of customer to business, we get the overall framework of customer to business based on customer platform-connecting manufactory platform-connecting service. A case study in the automotive industry is introduced, which takes customer to business as the implementation of enterprise strategy. The detailed implementation process is discussed from the four aspects of open and networked demand for personalized customer (C), open and networked manufacturing (M), open and networked service (S), open and networked collaborative platform (P). The objective of this article is to provide general references for enterprises to change from traditional business-to-customer model to open and interconnected customization model by combining the improved customer-to-business framework and its application in automotive industry. The framework we put forward has achieved good results in related customer-to-business projects we participated in. The research results of this article can be used as a reference for enterprises to design, set, and carry out the model of customer to business.

Product innovative design process is complex and requires innovative methods and knowledge to support. TRIZ (Theory of Inventive Problem Solving) and Patent Circumvention can help to find innovative solutions using patent knowledge. However, so far no method has been proposed to integrate TRIZ and Patent Circumvention effectively. Therefore, this paper proposes an innovative design process based on TRIZ and Patent Circumvention which consists of four stages, including Initiate Problem Description, Problem Identification, Problem-Solving, and Specific Solution. The advantages of the framework include integrating Modern TRIZ tools to identify key problems for innovation, effectively using Patent Circumvention technology to adopt or walk around competitor's patent solution. Then, a case study is proposed to test the efficiency of the approach. At the end, recommendations for future research are suggested.

The transformation to service economy calls for the development of Industrial Product Service Systems (IPS2), which has changed traditional business models greatly. In recent years, there are trends and needs from the industry to further develop IPS2 by integrating advanced information technologies. While the emergence of Cyber-Physical Systems (CPS) provides whole new viewpoints and approaches for IPS2 construction, there are still no public recognized systematic solutions. To solve this problem, the concept of intelligent IPS2 and a framework integrating IPS2 with CPS is proposed in this work. The system decomposition and intellectualization process of intelligent IPS2 are analyzed, trying to figure out the interaction mechanisms between IPS2 and CPS. Then A general architecture of CPS supported intellectualization of IPS2 is developed, which is constructed by five layers. The model for intelligent IPS2 is put forward based on industry investigation, trying to offer possible guidelines and roadmap for those service transforming companies.

Along with the change of global economic landscape and the development of manufacturing technologies, cyber–physical-system-integrated smart manufacturing system has become a general solution for both developed and developing countries to upgrade their manufacturing industries. To bridge the gap from those theories developed without much practice to those strategies put forward in recent years by typical countries, a framework of smart manufacturing workshop is proposed in this work, trying offer a possible solution in workshop level to the intellectualization of manufacturing processes. To measure the unbalanced development levels, a comprehensive model for quantitative capability maturity evaluation of smart manufacturing workshops is developed, providing directive guidelines and roadmap for the transformation of manufacturing companies. A complete and detailed application of automotive body-in-white manufacturing is also given to demonstrate the implementation and potentials of the framework.