A novel smart product-service system configuration method for mass personalization based on knowledge graph

Dong, Liang; Ren, Mengyang; Xiang, Zhongxia; Zheng, Pai; Cong, Jingchen; Chen, Chun‐Hsien

doi:10.1016/j.jclepro.2022.135270

Cited by 23 publications

(2 citation statements)

References 42 publications

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“…The construction and application of knowledge graphs are interdisciplinary fields that combine research achievements from various fields such as computer science, linguistics, and information science. With the continuous advancement ofAI technology, knowledge graphs will play an increasingly important role in intelligent information processing and knowledge discovery, providing people with richer, more accurate, and personalized information services [28].…”

Section: Basic Concepts Of Knowledge Graphsmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

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In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

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Section: Basic Concepts Of Knowledge Graphsmentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…The existing literature review reveals significant research gaps in supplier selection solutions, which primarily provide analytical insights and lack actionable decision support services which are crucial in real-world scenarios as summarised in Table 2.1. With manufacturing resilience as a key to achieve mass customization [124], the ability to reconfigure manufacturing processes efficiently is also essential in effectively managing disruptions [125].…”

Section: Product Family-oriented Supply Chain Managementmentioning

confidence: 99%

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Lim

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In today's increasingly dynamic business landscape, industries are faced with global headwinds, intensified competition, and increased volatility driven by factors such as increased demand for tailored products and solutions as well as geopolitical, economic, and climate disruptions.To address these challenges and remain competitive, the product lifecycle management (PLM) paradigm, which allows companies to be in control of their products and services across the lifecycle, has gained attention from industries. This cross-functional approach, enabled by Industry 4.0 information and communication technology (ICT) enablers such as digital twins (DT) and knowledge graphs (KGs), can elevate existing capabilities for customizability and manufacturing resilience. This thesis explores the role of DT and KG in enhancing PLM capabilities, integrating multiple lifecycle phases with emphasis on product design, manufacturing, and supply chain management (SCM). The adoption of these transformative technologies has the potential to enhance existing mass customization strategies such as the product family (PF) approach and Smart Product-Service Systems (Smart PSS), paving the way for intelligent decision support systems. Applicable to a wide range of industries, the use of DT systems with KG as a computational driver can support stakeholders in improving operational efficiencies, and responsiveness for disruption management. The main contributions of the thesis can be categorized into five research areas highlighted below:1) Establishing an environment-based context-aware DT system to enhance PF design and optimization. To overcome the challenges of identifying optimal product configurations in the planning phase and overcoming user requirement changes in the usage phase, a context-aware Abstract 10 DT system incorporating real-world environment aspects is proposed to aid PF reconfiguration and redesign. A novel benchmark and interacting mechanism automatically identifies feasible PF modules, reducing the need for costly and bias-prone expert recommendations, as demonstrated by a case study on tower crane planning and deployment.2) Developing DT-as-a-Service to advance service-oriented digital manufacturing. Based on the flexibility and versatility of DT in manufacturing processes, these systems are pivotal towards customized production systems. A four-tier technology stack is proposed to construct DT systems using a modular approach and leverages advanced computational methodologies and tools such as extended reality (XR) to support a wider range of shop floor manufacturing processes. Here, DT systems are used as a fundamental to enable Smart PSS and circular economy paradigms to drive sustainability and customizability efforts. These multi-faceted capabilities are featured in two use cases, additive manufacturing, and gearbox assembly line. 3) Enabling causal inference for maintenance operations using cognitive digital twins (CDTs).The intricate nature of large production systems results in challenges pertaining to the root ca...

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Challenges and Opportunities for the Design and Development of Intelligent, Sustainable and Resilient Personalized Product-Service Systems Towards Industry 5.0

Mourtzis,

Angelopoulos

2024

Data-Driven Decision Making for Product Service Systems

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A novel smart product-service system configuration method for mass personalization based on knowledge graph

Cited by 23 publications

References 42 publications

From data to insights: the application and challenges of knowledge graphs in intelligent audit

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Graph-enabled digital twins for intelligent product lifecycle management: a multi-dimensional approach to design, manufacturing, and supply chain transformation

Challenges and Opportunities for the Design and Development of Intelligent, Sustainable and Resilient Personalized Product-Service Systems Towards Industry 5.0

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