Cost and quality of service analysis of production systems based on the cumulative downtime

Faria, José; Nunes, Eusébio P.; Matos, Manuel

doi:10.1080/00207540802068631

Cited by 12 publications

(4 citation statements)

References 52 publications

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“…Based on this table, probability of system failure can be changed. Considering the consequences estimated by a cost C f [7], then the process risk is F(T)*C f , where T represent the maintenance interval.…”

Section: Using the Modelmentioning

confidence: 99%

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Measuring and managing operational risk in industrial processes

Sousa

Nunes²,

Lopes³

2015

FME Transaction

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The risk of not achieving process outcomes in key processes should be identified and reduced to achieve better results. The identification of risks involves defining the possible events that compromises the achievement of process outcomes and also the consequences of the occurrence of these events. This paper proposes a methodology to minimize operational/technical risk across different processes or departments, minimizing the possibility of spending excessive resources in a given process while other processes pose bigger risks to the organisation or considered system. The probability of occurrence of the events that hinder the achievement of outcomes can be influenced by some technical, administrative or management actions. The methodology is tested through a case study of a CNC milling section on a furniture manufacturing process. The reliability of a key item, electrospindles, is determined and the influence of maintenance activities on such reliability and therefore on risk is analysed.

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Section: Using the Modelmentioning

confidence: 99%

“…This cost also depends on the size of stock (buffers or work-in-process). A thorough analysis of such costs is presented in [7].…”

Section: Introductionmentioning

confidence: 99%

Measuring and managing operational risk in industrial processes

Sousa

Nunes²,

Lopes³

2015

FME Transaction

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“…Manufacturers try to cope with this increased complexity of their production processes by focusing on their core competencies so that the in-house production depth can be decreased. Faria et al 2010). Possibilities of cheap procurement have arisen in recent years due to a reduction in transportation costs.…”

Section: Motivationmentioning

confidence: 99%

Pro-active Dynamic Vehicle Routing

Ferrucci¹

2013

Contributions to Management Science

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“…In terms of the random characteristics of the production task, Li et al 10 proposed a calculating method of the probability of customer demand satisfaction to analyze the running state of manufacturing systems. Faria and Matos 11 proposed the nonlinear relationship between the probability of successfully accomplishing a mission and the cumulative production downtime of machines. These studies analyzed the performance of manufacturing systems from the perspective of task completion probability.…”

Section: Introductionmentioning

confidence: 99%

Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network

Han

et al. 2017

Proceedings of the Institution of Mechanical Engineers, Part O:

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Multi-state-oriented mission reliability modeling is the premise of intelligent scheduling and predictive maintenance for the multi-station manufacturing system. Previous studies on reliability modeling for manufacturing system could only provide a static reliability model based on the basic reliability of the components of manufacturing systems, which cannot support reliability-oriented production scheduling and preventive maintenance effectively. To resolve this dilemma, a multi-state-oriented mission reliability modeling for multi-station manufacturing system is proposed. First, the mapping relationship between the produced product reliability and mission reliability of the manufacturing system is proposed as the basis for modeling, and the connotation of mission reliability is elaborated by analyzing the polymorphisms of the multi-station manufacturing system. Second, a graphical representation to improve the state transparency named as Quality State Task Network is proposed based on production data by integrating the variability of task-demands propagation as well as the multi-state in material quality and machine performance. Third, the mission reliability modeling method based on the Quality State Task Network is proposed. Finally, a case study of cylinder-head manufacturing system has been applied to validate the proposed model.

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Cost and quality of service analysis of production systems based on the cumulative downtime

Cited by 12 publications

References 52 publications

Measuring and managing operational risk in industrial processes

Measuring and managing operational risk in industrial processes

Pro-active Dynamic Vehicle Routing

Mission reliability modeling for multi-station manufacturing system based on Quality State Task Network

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