Improvement of decision making grid model for maintenance management in Small and Medium Industries

Tahir, Zulkifli; Burhanuddin, M. A.; Ahmad, Abd Rahman; Halawani, Sami M.; Arif, Fahmi

doi:10.1109/iciinfs.2009.5429791

Cited by 17 publications

(12 citation statements)

References 8 publications

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“…1)]. This stage involves the application of clustering analysis over a distance interval that allows for equal and robust criteria measurements for the two selected rankings of factors that most impact the RTS subsystem [43]. For maximum X max and minimum values X min , the ranges of the three criteria for the two ranking factors as indicated in step 3(b) are estimated as follows:…”

Section: Processing Procedures For the Proposed Hybrid Modelmentioning

confidence: 99%

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

et al. 2021

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Railway transport system (RTS) failures exert enormous strain on end-users and operators owing to in-service reliability failure. Despite the extensive research on improving the reliability of RTS, such as signalling, tracks, and infrastructure, few attempts have been made to develop an effective optimisation model for improving the reliability, and maintenance of rolling stock subsystems. In this paper, a new hybrid model that integrates reliability, risk, and maintenance techniques is proposed to facilitate engineering failure and asset management decision analysis. The upstream segment of the model consists of risk and reliability techniques for bottom-up and top-down failure analysis using failure mode effects and criticality analysis and fault tree analysis, respectively. The downstream segment consists of a (1) decision-making grid (DMG) for the appropriate allocation of maintenance strategies using a decision map and (2) group decision-making analysis for selecting appropriate improvement options for subsystems allocated to the worst region of the DMG map using the multi-criteria pairwise comparison features of the analytical hierarchy process. The hybrid model was illustrated through a case study for replacing an unreliable pneumatic brake unit (PBU) using operational data from a UK-based train operator where the frequency of failures and delay minutes exceeded the operator’s original target by 300% and 900%, respectively. The results indicate that the novel hybrid model can effectively analyse and identify a new PBU subsystem that meets the operator’s reliability, risk, and maintenance requirements.

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Section: Processing Procedures For the Proposed Hybrid Modelmentioning

confidence: 99%

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

et al. 2021

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“…The DMG model has been used and reported by various researchers such as Fernandez et al [8], Burhanuddin [9], Aslam-Zainudeen, and Labib [10], Shain et al [11], and Tahir et al [12]. All literature have reported considerable saving in terms of minimising downtime and significant maximisation of productivity without having to acquire any additional capacity.…”

Section: The Decision Making Grid (Dmg) Modelmentioning

confidence: 99%

Learning from failures through feedback to design

Labib

2013

Safety and Security Engineering V

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The paper attempts to define the concept of learning from failures through feedback to design. This is demonstrated through case studies where analytical tools have been incorporated to facilitate problem structuring and analysis. We provide a framework on how to analyze case studies of learning from major disasters. We then provide examples of tools and techniques that are capable of analyzing the rare event of high impact.

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“…Raw data was collected for whole year and later recommendations were given. Tahir et al [10] implemented DMG with tri-quadrant clustering method and used an intelligent module in maintenance decision support system to identify maintenance strategies in SMI's. It was found out that not only there was reduction in the cost and machine downtime but also there was better reliability on daily operations and maintenance management.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Machine Failure Using DMG and Weibull Analysis in an Automotive Industry: A Case Study

Shah

Khurshid

Haq

et al. 2021

Mehran Univ. res. j. eng. technol.

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In manufacturing or production setup, maintenance cost is one of the major portions of overall operating expenditure. It can vary between 15 to 60 percentage of overall cost for various industries including food related industries, iron, steel and other heavy industries. Such a high cost directly impacts manufacturing setup, profitability and sustainability in long run. In most of the industries, ineffective maintenance management can result in loss of capital and inefficient human resource deployment. This in turn affects the plants’ ability to manufacture quality products that are competitive in the market. Various maintenance management strategies including Operate to Failure (OTF), Design Out Maintenance (DOM), Skill Level Upgrade (SLU), ConditionBased Monitoring (CBM) and Fixed Time Maintenance (FTM) are used in industries for maximizing productivity. In recent years, Computerized Maintenance Management System (CMMS) has become an integral part of most of the industries so its importance and characteristics cannot be understated. While CMMS cannot live standalone, it requires some decision-making techniques to be equipped with. These techniques range from Failure Mode and Effect Analysis (FMEA) to Decision Making Grid (DMG). In this paper, concept of DMG has been applied to an automotive parts Manufacturing Industry in conjunction with Weibull analysis. Parallels are drawn between the results of DMG and Weibull analysis.

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Improvement of decision making grid model for maintenance management in Small and Medium Industries

Cited by 17 publications

References 8 publications

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

Practical Demonstration of a Hybrid Model for Optimising the Reliability, Risk, and Maintenance of Rolling Stock Subsystem

Learning from failures through feedback to design

Forecasting Machine Failure Using DMG and Weibull Analysis in an Automotive Industry: A Case Study

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