Production and operations management for intelligent manufacturing: a systematic literature review

Zhou, Liping; Jiang, Zhibin; Geng, Na; Niu, Yimeng; Feng, Chun; Liu, Kefei; Qi, Nanshan

doi:10.1080/00207543.2021.2017055

Cited by 67 publications

(24 citation statements)

References 246 publications

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“…With quality management as an important component of the core competencies of manufacturing companies [27], manufacturing industries can effectively achieve performance improvement goals and competitive advantages by implementing quality management practices to meet the challenges of new global competition [28]. Emerging technologies such as industrial big data [29], the industrial Internet of Things (IIoT) [30], and cyberphysical systems [31] are driving the manufacturing industry toward a new era of smart manufacturing [32]. Smart manufacturing, represented by shared manufacturing and cloud manufacturing, is facing many challenges while helping to transform and upgrade the manufacturing industry.…”

Section: Advances In Quality Innovation and Quality Synergy Researchmentioning

confidence: 99%

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Zhang

Wang

et al. 2022

Processes

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Shared manufacturing provides a new path for the transformation and development of the manufacturing industry, but challenges such as low quality and poor positivity for quality improvement limit the positive role of shared manufacturing. Considering the influences of heterogeneous emotions of subjects on quality decision making, the theory of rank-dependent expected utility (RDEU) and evolutionary game theory were integrated to establish an evolutionary game model of shared manufacturing quality innovation synergy with multi-agent participation and analyze how sentiment affects motivation for quality improvement. The study showed that: (1) emotions, an irrational factor, can significantly change the stable state of the evolution of the shared manufacturing quality innovation synergetic system by influencing the decision-making behavior of decision makers; (2) in terms of the specific microscopic influence mechanism, rationality is the key to ensuring that the behavioral decisions of decision makers do not enshrine large systemic deviations. (3) In terms of the mechanism of heterogeneous emotions, when one party is optimistic, the deepening of the other party’s pessimism tends to bring positive effects; when one party is pessimistic, the deepening of the other party’s optimism tends to bring negative effects. The main management insights are as follows: (1) correctly recognizing and treating heterogeneous emotions of decision makers and regulating the formation and role of heterogeneous emotions of decision makers; (2) appropriately creating an atmosphere of pessimistic emotions, and guiding shared manufacturing to pay attention to manufacturing quality innovation synergy; (3) appropriately releasing favorable information about quality innovation synergy, and continuously promoting high-quality development of shared manufacturing. This study broadens the path of quality improvement in shared manufacturing and the scope of application of emotion theory in a certain sense.

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Section: Advances In Quality Innovation and Quality Synergy Researchmentioning

confidence: 99%

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Zhang

Wang

et al. 2022

Processes

View full text Add to dashboard Cite

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“…The continuously improved sensor technology and data processing capability with big data, artificial intelligence, cloud computing, and edge technology are widely used in industrial equipment, process control, and factory management, leading to the further development of intelligent manufacturing. Intelligent manufacturing combines real-time data analysis, artificial intelligence (AI), and other machine learning technologies in manufacturing to improve production quality, reliability, and resource efficiency [ 4 , 5 , 6 ]. Sensors are attached to the manufacturing machines to collect and process real-time data through sensor networks for intelligent manufacturing, whereas AI algorithms based on the acquired data are designed to coordinate all operations of the manufacturing, such as equipment maintenance, manufacturing practices, and final product testing.…”

Section: Introductionmentioning

confidence: 99%

Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors

Fei²,

Zhang³

2022

Sensors

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The Industrial Internet of Things (IIoT) connects industrial assets to ubiquitous smart sensors and actuators to enhance manufacturing and industrial processes. Data-driven condition monitoring is an essential technology for intelligent manufacturing systems to identify anomalies from malfunctioning equipment, prevent unplanned downtime, and reduce the operation costs by predictive maintenance without interrupting normal machine operations. However, data-driven condition monitoring requires massive data collected from smart sensors to be transmitted to the cloud for further processing, thereby contributing to network congestion and affecting the network performance. Furthermore, unbalanced training data with very few labelled anomalies limit supervised learning models because of the lack of sufficient fault data for the training process in anomaly detection algorithms. To address these issues, we proposed an IIoT-based condition monitoring system with an edge-to-cloud architecture and computed the relative wavelet energy as feature vectors on the edge layer to reduce the network traffic overhead. We also proposed an unsupervised deep long short-term memory (LSTM) network module for anomaly detection. We implemented the proposed IIoT condition monitoring system for a manufacturing machine in a real shop site to evaluate our proposed solution. Our experimental results verify the effectiveness of our approach which can not only reduce the network traffic overhead for the IIoT but also detect anomalies accurately.

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“…However, there is still no authoritative conclusion on what kind of valuation method can be adopted to assess the value of carbon assets scientifically and accurately, especially the research on the valuation of carbon assets of power enterprises, the main force of the carbon trading market, which can be collected. The traditional market approach, the income approach, and the cost approach are all limited and inapplicable to the valuation of carbon assets and project carbon assets of power enterprises [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.

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Production and operations management for intelligent manufacturing: a systematic literature review

Cited by 67 publications

References 246 publications

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Evolutionary Game Analysis of Shared Manufacturing Quality Innovation Synergetic Behavior Considering a Subject’s Heterogeneous Emotions

Edge-to-Cloud IIoT for Condition Monitoring in Manufacturing Systems with Ubiquitous Smart Sensors

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

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