Optimizing Maintenance and Replacement Decisions

answers the question of “What type of maintenance action needs to be taken?” The issue of when to perform the recommended maintenance action that will produce the best results possible remains to be addressed. Taking a longer-term perspective, we have to make decisions on asset replacement in the best interests of the organiza-tion and determine the resource requirements of the maintenance operaorganiza-tion that will meet business needs in a cost-effective manner. The optimization of these tactical decisions is the important issue addressed in the top of the “continuous improve-ments” layer of the maintenance excellence pyramid shown in Figure 1.1.

Traditionally, maintenance practitioners in industry are expected to cope with maintenance problems without seeking to operate in an optimal manner. For exam-ple, many preventive maintenance schemes are put into operation with only a slight, if any, quantitative approach to the scheme. As a consequence, no one is very sure of just what the best frequency of inspection is or what should be inspected, and as a result, these schemes are cancelled because it is said that they cost too much. Clearly, some form of balance between the frequency of inspection and the returns from it is required (e.g., fewer breakdowns because minor faults are detected before they result in costly repairs). In the subsequent chapters of this book, we will examine various maintenance problem areas, noting the conflicts that ought to be considered and illustrating how they can be resolved in a quantitative manner to achieve optimal or near-optimal solutions to the problems. Thus, we indicate ways in which mainte-nance decisions can be optimized, wherein optimization is defined as attempting to resolve the conflicts of a decision situation in such a way that the variables under the control of the decision-maker take the best possible values. Because the qualifier best is used, it is necessary to define its meaning in the context of maintenance. This will be covered in Section 1.8 of this chapter.

14 Maintenance, Replacement, and Reliability

Asset managers who wish to optimize the life cycle value of the organization’s human and physical assets must consider four key decision areas, which are shown as columns in Figure 1.4. The first column deals with component replacement; the second column deals with inspection activities, including condition monitoring; the third column deals with replacement of capital equipment; and the last column cov-ers decisions concerning resources required for maintenance and their location.

Figure 1.4 forms the framework for Chapters 2 to 5. These chapters are devoted to the construction of mathematical models that are appropriate for different problem situations. The purpose of these mathematical models is to enable the consequences of alternative maintenance decisions to be evaluated fairly rapidly to determine opti-mal decisions in relation to an objective. The problem areas covered are as follows:

Chapter 2: Component Replacement Decisions

This chapter covers the determination of replacement intervals for equip-ment (the operating costs of which increase with use), the interval between preventive replacements of items subject to breakdown (also known as the group or block policy), and the preventive replacement age of items subject to breakdown.

Chapter 3: Inspection Decisions

This chapter covers the determination of inspection frequencies for com-plex equipment used continuously, fault-finding intervals for protective devices, and CBM decisions.

Database (CMM/EAM/ERP System)

Optimizing Equipment Maintenance and Replacement Decisions

Component 5. Software: SMS and

OREST 3. FFI’s for protective

devices

Time value of money

(Discounted cash flow) Queuing theory simulation

FIGURE 1.4 Key areas of maintenance and replacement decisions.

Introduction 15

Chapter 4: Capital Equipment Replacement Decisions

This chapter is concerned with determining the replacement intervals for capital equipment (the utilization pattern of which is fixed), replacement intervals for capital equipment (the utilization pattern of which is variable), and replacement policy for capital equipment taking into account techno-logical improvements.

Chapter 5: Maintenance Resource Requirements

This chapter discusses problems relating to the determination of the mix of equipment to be installed in a maintenance workshop, the right size and composition of a maintenance crew, the extent of use of subcontracting opportunities, and lease or buy decisions.

In Chapters 2 to 5, we use a common format to present each decision optimization model. First, we provide a statement of the decision problem. A model that represents the essence of the decision problem is then presented. This is followed by a numeri-cal example to illustrate the use of this model. To avoid unnecessary complications in developing the analytical model, various assumptions are made that, in practice, may not be applicable in some situations. Because the assumptions used in constructing the model are clearly stated, it is hoped that the reader will then be able to extend the simple model to fit specific problems. To this end, we provide comments on further extension of the model to represent more details of the reality, if deemed necessary. For each model, with a few exceptions, we also provide one or more real-world application examples.

Some of these decision optimization models are made available as software pro-grams that can be downloaded from the publisher’s web site. In such cases, use of the software program in decision analysis is explained with at least one illustrative example.

Appendices 1 through 6 are included in the book to give a brief introduction to cer-tain basic concepts and tools that must be understood before we can proceed to deter-mine optimal maintenance procedures. Because uncertainty abounds in the area of maintenance (e.g., uncertainty about when equipment will fail), knowledge of statistics and probability is required. An introduction to relevant statistics is given in Appendix 1.

Modeling the risk of failure is a crucial step in optimizing replacement of components that are subject to failure. Weibull analysis, a powerful tool for modeling such risks, is introduced in Appendix 2. Maximum likelihood estimator is another widely used approach to estimating distribution parameters, but it involves computationally intensive operations. This approach is introduced in Appendix 3. Appendix 4 introduces Markov chains, which is an important tool used in creating the decision model for optimization of condition-based maintenance decisions. The concept of knowledge elicitation used for estimating the parameter of a distribution when data are sparse is presented in Appendix 5. Appendix 6 deals with the present value concept. When making replacement decisions for capital equipment, we take account of the fact that the value of a sum of money to be spent or received in the future is less than that if it is spent or received now. The present value concept is used to cover this fact. Although applications of the tools featured in each chapter are highlighted in Chapters 2 to 5, an expanded list of such applications is provided in Appendix 7. It serves to illustrate the breadth of actual applications that use the models or procedures presented in the book, or their extensions.

16 Maintenance, Replacement, and Reliability

Optimizing maintenance and replacement decisions needs good quality and timely data. This need is depicted as the foundation of the framework shown in Figure 1.4. Such data are typically maintained in the database of the CMMS, EAM, or enterprise resource planning (ERP). Readers interested in discussions of CMMS, EAM, and ERP in the context of PAM are referred to articles published on Web sites such as www.plant- maintenance .com and www.reliabilityweb.com.