Details
Firmly based on the results of real-world research in physical asset management, the book focuses on data-driven tools for asset management decisions. It provides a solid foundation for various tools that can be used to optimize a variety of key maintenance/replacement/reliability decisions. It presents cases that illustrate the application of these tools in a variety of settings, such as food processing, petrochemical, steel and pharmaceutical industries, as well as the military, mining, and transportation (land and air) sectors.
The book includes models relating to spare-parts provisioning, condition-based maintenance, and replacement of equipment with varying levels of utilization.
| Author: | Jardine and Tsang |
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| Published: | 2013 |
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| Format: | Hardback |
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| Pages: | 364 |
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| Table Of Contents: | Introduction
From Maintenance Management to Physical Asset Management
Challenges of PAM
Improving PAM
PAS 55—A Framework for Optimized Management of Physical Assets
Reliability through the Operator: TPM
Reliability by Design: RCM
Optimizing Maintenance and Replacement Decisions
The Quantitative Approach
Data Requirements for Modeling
References Component Replacement Decisions
Introduction
Optimal Replacement Times for Equipment Whose Operating Cost Increases with Use
Stochastic Preventive Replacement: Some Introductory Comments
Optimal Preventive Replacement Interval of Items Subject to Breakdown (Also Known as the Group or Block Policy)
Optimal Preventive Replacement Age of an Item Subject to Breakdown
Optimal Preventive Replacement Age of an Item Subject to Breakdown, Taking Account of the Times Required to Carry Out Failure and Preventive Replacements
Optimal Preventive Replacement Interval or Age of an Item Subject to Breakdown: Minimization of Downtime
Group Replacement: Optimal Interval between Group Replacements of Items Subject to Failure: the Lamp Replacement Problem
Further Replacement Models
Case Study on Project Prioritization, Trend Tests, Weibull Analysis, and Optimizing Component Replacement Intervals
Spare Parts Provisioning: Preventive Replacement Spares
Spare Parts Provisioning: Insurance Spares
Solving the Constant-Interval and Age-Based Models Graphically: Use of Glasser’s Graphs
Solving the Constant-Interval and Age-Based Models Using OREST Software
References
Inspection Decisions
Introduction
Optimal Inspection Frequency: Maximization of Profit
Optimal Inspection Frequency: Minimization of Downtime
Optimal Inspection Interval to Maximize the Availability of Equipment Used in Emergency Conditions, Such as a Protective Device
Optimizing CBM Decisions
References
Capital Equipment Replacement Decisions
Introduction
Optimal Replacement Interval for Capital Equipment: Minimization of Total Cost
Optimal Replacement Interval for Capital Equipment: Maximization of Discounted Benefits
Optimal Replacement Interval for Capital Equipment Whose Planned Utilization Pattern Is Variable: Minimization of Total Cost
Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Finite Planning Horizon
Optimal Replacement Policy for Capital Equipment Taking into Account Technological Improvement: Infinite Planning Horizon
Software for Economic Life Optimization
References
Maintenance Resource Requirements
Introduction
Queuing Theory Preliminaries
Optimal Number of Workshop Machines to Meet a Fluctuating Workload
Optimal Mix of Two Classes of Similar Equipment (such as Medium/Large Lathes) to Meet a Fluctuating Workload
Rightsizing a Fleet of Equipment: An Application
Optimal Size of a Maintenance Workforce to Meet a Fluctuating Workload, Taking Account of Subcontracting Opportunities
The Lease or Buy Decision
References
Appendices:
Statistics Primer
Weibull Analysis
Maximum Likelihood Estimator
Markov Chains
Knowledge Elicitation
Time Value of Money: Discounted Cash Flow Analysis
List of Applications of Maintenance Decision Optimization Models
Ordinates of the Standard Normal Distribution
Areas in the Tail of the Standard Normal Distribution
Values of Gamma Function
Median Ranks Table
Five Percent Ranks Table
Ninety-Five Percent Ranks Table
Critical Values for the Kolmogorov–Smirnov Statistic (dα) Answers to Problems
Index |
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