A decision-making model for knowledge collaboration and reuse through scientific workflow

He, Longlong; Guo, Wei; Jiang, Pingyu

doi:10.1016/j.aei.2021.101345

Cited by 5 publications

(2 citation statements)

References 49 publications

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“…The main benefit of gathering asset MoL data is that it can feed data-driven decision-making tools and methods, representing a growing research field in maintenance service delivery [30,31]. He, Guo, and Jiang [32] affirm that the presence of a Decision-Support System (DSS) in a manufacturing environment is fundamental to support operations and activities along the product lifecycle. Despite this, in manufacturing companies, decisions are predominantly made by humans grounded on their expertise rather than on field data [33].…”

Section: Literature Backgroundmentioning

confidence: 99%

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

et al. 2022

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Data availability is changing the way companies make decisions at various levels (e.g., strategical and operational). Researchers and practitioners are exploring how product–service system (PSS) providers can benefit from data availability and usage, especially when it comes to making decisions related to service delivery. One of the services that are expected to benefit most from data availability is maintenance. Through the analysis of the asset health status, service providers can make informed and timely decisions to prevent failures. Despite this, the offering of data-based maintenance service is not trivial, and requires providers to structure themselves to collect, analyze and use historical and real-time data properly (e.g., introducing suitable information flows, methods and competencies). The paper aims to investigate how a manufacturing company can re-engineer its maintenance service delivery process in a data-driven fashion. Thus, the paper presents a case study where, based on the Dual-perspective, Data-based, Decision-making process for Maintenance service delivery (D3M), an Italian manufacturing company reengineered its maintenance service delivery process in a data-driven fashion. The case study highlights the benefits and barriers coming with this transformation and aims at helping manufacturing companies in understanding how to address it.

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Section: Literature Backgroundmentioning

confidence: 99%

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

et al. 2022

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“…Recently, a number of researchers have attempted to propose different collaboration and decisionmaking mechanisms. He et al propose a novel triple-deep workflow model for production decision support problem to realize the aim of product CTQS (i.e., lower cost, faster time to market, higher quality, and better service) with manufacturing intelligence [11]. Dai et al propose a multiagent-based computational framework for modeling decision-making and strategic interaction at the microlevel for smart vehicles in a smart world [12].…”

Section: Introductionmentioning

confidence: 99%

Delphi Collaboration Strategy for Multiagent Optical Fiber Intelligent Health Monitoring System

et al. 2022

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Optical fiber sensors are very attractive in mechanical structure intelligent health monitoring system due to some unique characteristics, such as immunity to electromagnetic interference and to aggressive environments, high sensitive and fast response, small physical dimension, excellent resolution and range, and so on. For improving the accuracy and reliability of the optical fiber intelligent health monitoring system in practical engineering application, the collaboration and decision-making strategy based on Delphi method for multiagent optical fiber intelligent health monitoring system is studied in this paper. The proposed system is mainly composed of optical fiber sensing agent, intelligent evaluation agent, and system collaborative decision-making agent. The intelligent evaluation agent is used to evaluate the health status of the monitored mechanical structures. Delphi method is used by the system collaborative decision-making agent to consult each intelligent evaluation agent. Meanwhile, the collaborative partner selection algorithm is used to select the intelligent evaluation agent participating in the collaboration, and the intelligent evaluation agent that does not participate in the decision-making is dynamically modified by the decision result. The experiment for an aircraft wing box as the typical engineering structure is carried out and the verification system is designed, the decision result is compared with that without dynamic correction of the evaluation result. The comparative results indicate that the evaluation accuracy and reliability of the monitored mechanical structural damage are improved significantly after multiple rounds of collaboration and decision making.

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Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning

Lü

Liu²,

Zhang³

et al. 2022

J. Shanghai Jiaotong Univ. (Sci.)

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A decision-making model for knowledge collaboration and reuse through scientific workflow

Cited by 5 publications

References 49 publications

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Data-Driven Decision Making in Maintenance Service Delivery Process: A Case Study

Delphi Collaboration Strategy for Multiagent Optical Fiber Intelligent Health Monitoring System

Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning

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