Characterisation of collaborative decision making processes

Seguy, Anne; Noyes, Daniel; Clermont, Philippe

doi:10.1080/0951192x.2010.506654

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…Drift diagnosis process is a daily activity of collective decision-making for human operators, see e.g. [10], [11]. In this scenario, using a DSS can help human operators to make the "best" decision in production control process and, thus to perform the "best" operation of the production industry.…”

Section: Introductionmentioning

confidence: 99%

An approach of decision support system for drift diagnosis in cyber-physical production systems

Arama

Villeneuve

Merlo

et al. 2022

2022 IEEE International Systems Conference (SysCon)

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Despite the development and application of new digital solutions in the production industry, the human operator is still essential in the production chain monitoring and control processes. In this context, some activities can be crucial for the human operator like, for example, drift diagnosis in production control process. It requires attention and experience and can be assisted by Decision Support System (DSS) to guide operators in decision-making in industrial production process control. Drift diagnosis process is a challenging problem in this context and artificial intelligence technologies are promising to tackle this issue. In this paper, we propose a new approach of DSS for drift diagnosis. The proposed approach is built upon a literature review on drift concept, drift detection methods and failure diagnosis approaches. This multi-model approach is designed to address all the diagnostics tasks of production systems and is based on Machine Learning (ML) algorithms to model the behavior of production systems, a knowledge-based model to integrate human experiences and a data-driven model to combine historical data from sensors. When the drift occurs, the proposed DSS can help human operator to determine drift causes and to suggest corrective actions. This article also provides guidelines about the design of a decision support system to support human operators in complex decision activities.

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Section: Introductionmentioning

confidence: 99%

An approach of decision support system for drift diagnosis in cyber-physical production systems

Arama

Villeneuve

Merlo

et al. 2022

2022 IEEE International Systems Conference (SysCon)

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“…In addition, SMMEs are being flooded with big data due to the dramatic evolution of advanced information technology and IoT (Shafiq et al , 2016; Kuik et al , 2016; Cochran et al , 2016; Zheng et al , 2015; Zheng, 2011; Seguy et al , 2010; Turban, 2010). Building a model-driven DSS for a CMN is relatively prevalent to achieve minimum operating costs under collaborative environment.…”

Section: Introductionmentioning

confidence: 99%

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Kuik

Diong

2019

IMDS

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Purpose The purpose of this paper is to present the model-driven decision support system (DSS) for small and medium manufacturing enterprises (SMMEs) that actively participates in collaborative activities and manages the planned obsolescence in production. In dealing with the complexity of such demand and supply scenario, the optimisation models are also developed to evaluate the performance of operations practices. Design/methodology/approach The model-driven DSS for SMMEs, which uses the optimisation models for managing and coordinating planned obsolescence, is developed to determine the optimal manufacturing plan and minimise operating costs. A case application with the planned obsolescence and production scenario is also provided to demonstrate the approach and practical insights of DSS. Findings Assessing planned obsolescence in production is a challenge for manufacturing managers. A DSS for SMMEs can enable the computerised support in decision making and understand the planned obsolescence scenarios. The causal relationship of different time-varying component obsolescence and availability in production are also examined, which may have an impact on the overall operating costs for producing manufactured products. Research limitations/implications DSS can resolve and handle the complexity of production and planned obsolescence scenarios in manufacturing industry. The optimisation models used in the DSS excludes the variability in component wear-out life and technology cycle. In the future study, the optimisation models in DSS will be extended by taking into the uncertainty of different component wear-out life and technology cycle considerations. Originality/value This paper demonstrates the flexibility of DSS that facilitates the optimisation models for collaborative manufacturing in planned obsolescence and achieves cost effectiveness.

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“…When a CBM activity needs to be planned in any running schedule, two alternatives may be adopted to update this latter: generating a new schedule or altering the predictive schedule to adapt it to new conditions. As (Seguy, Noyes, & Clermont, 2010), we consider that the first alternative might in principle be better for maintaining optimal solutions, but these solutions require prohibitive computation times. Moreover, frequent schedule regeneration can result in instability and lack of continuity in detailed plant schedules, leading to additional production and maintenance costs attributable to what has been termed ''shop floor nervousness" (Dorn, Kerr, & Thalhammer, 1993).…”

Section: Introductionmentioning

confidence: 99%

Considering human resource constraints for real joint production and maintenance schedules

Bouzidi-Hassini

Tayeb

Marmier

et al. 2015

Computers & Industrial Engineering

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This study deals with the problem of scheduling jobs and preventive maintenance (PM) activities in production workshops jointly in order to reduce failure occurrence. PM activities can be planned at fixed periods (time-based PM) or in response to a signal provided from a machine's captor after anomaly detection (condition-based PM). Maintenance activities' planning depends on different constraints like human resources, spare parts availability and past operating duration of the machine. Proposed related works evaluate possible arrangements of both production and maintenance activities according to different criteria (makespan (Cmax: completion date of the last operation on the last machine), machine's failure risk, jobs' lateness, etc.). However, maintenance activities are frequently considered as production jobs. In fact, maintenance activities present different constraints and cannot be planned in the same manner as production ones. In addition to that, most of current works deal with the time-based maintenance planning even so condition-based maintenance is qualified as more realistic and more economic than the former. This work discusses a new approach to integrate the scheduling of production and maintenance operations. Proposed approach takes explicitly into account human resources availability and skills when updating integrated production and maintenance schedules. It is based on multi-agent systems for modeling the production workshop.

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Characterisation of collaborative decision making processes

Cited by 12 publications

References 25 publications

An approach of decision support system for drift diagnosis in cyber-physical production systems

An approach of decision support system for drift diagnosis in cyber-physical production systems

A model-driven decision approach to collaborative planning and obsolescence for manufacturing operations

Considering human resource constraints for real joint production and maintenance schedules

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