ABSTRACT
Engineering utilities are confronted with myriad of challenges that include aging infrastructure, high failure rate etc. To enhance expectation of reliability, reduce cost and coping effectively with uncertainties, it is very crucial to optimize maintenance performance with respect to cost and schedule. The methodology focuses on failure rate of one of the most important equipment within Akwa Ibom International Airport, development of maintenance strategy to reduce these failures and reduce their likelihood of occurrence. Maintenance-Cost-Schedule-Variance Model developed as an important tool to analyze the performance of maintenance activities. This model measures the maintenance performance progress and helped in identifying the critical activities thereby, bringing the maintenance activities on schedule within budget. This thesis shows the Maintenance-Cost-Schedule-Variance application on a real time maintenance project at Akwa Ibom International Airport. It helped in identifying the critical areas, forecast and predict the future performance of maintenance by the use of statistical techniques. It can provide an important contribution in cost management of a maintenance project as it inspires the management team to pay more attention to cost, schedule and progress with more intensity and optimizes the maintenance programme.
TABLE OF CONTENTS
Cover page
Title page i
Declaration ii
Dedication iii
Certification iv
Acknowledgement v
Table of Contents vi
List of Tables x
Abstract xi
CHAPTER 1: INTRODUCTION
1.1 Background Information 1
1.2 Aim and Objectives of Study 3
1.3 Problem Statement 3
1.4 Scope of Study 4
1.5 Justification 5
CHAPTER 2: LITERATURE REVIEW
2.1 Maintenance Optimization 6
2.2 Models on Optimization of Preventive Maintenance Policies 7
2.2.1 Considering corrective maintenance (CM) from cost point of view 8
2.2.2 Considering corrective maintenance as a minimum failure 10
vi
2.2.3 Simultaneous consideration of corrective & preventive maintenance 11
2.3 Earned Value Management 12
2.4 History of Earned Value Management 13
2.5 Terminologies in Earned Value Management 14
2.5.1 Planned value 14
2.5.2 Actual cost 14
2.5.3 Earned value 15
2.6 Review of Protagonist Literature on Earned Value Management 16
2.7 Review of Objectionist Literature on Earned Value Mangement 18
2.8 Review of Extensionist Literature on Earned Value Management 19
2.9 Review on Empirical Project Management Literature 19
CHAPTER 3: MATERIALS AND METHODS
3.1 Materials 22
3.1.1 Source of data 22
3.2 Methods 25
3.2.1 Exponential function 25
3.2.2 Replacement analysis 34
3.2.3 Economic service life 36
3.3 Model Formulation and Development 37
3.3.1 Planned maintenance value (PMV) 37
3.3.2 Actual cost of maintenance (ACM) 37
vii
3.3.3 Earned maintenance value (EMV) 37
3.3.4 Percentage completed planned maintenance (%CPM) 38
3.3.5 Percentage completed actual maintenance (%CAM) 38
3.3.6 Maintenance cost variance (MCV) 38
3.3.7 Maintenance cost variance percentage (MCV%) 38
3.3.8 Maintenance cost performance indicator (MCPI) 39
3.3.9 To complete maintenance cost performance indicator (TCMCPI) 39
3.3.10 Maintenance schedule variance (MSV) 39
3.3.11 Maintenance schedule variance percentage (MSV%) 40
3.3.12 Maintenance schedule performance indicator (MSPI) 40
3.3.13 To complete maintenance schedule performanace indicator (TCMSPI) 41
3.3.14 Maintenance budget at completion (MBAC) 41
3.3.15 Maintenance estimate to complete (METC) 41
3.3.16 Maintenance estimate at completion (MEAC) 41
3.3.17 Maintenance variance at completion (MVAC) 42
CHAPTER 4:RESULTS AND DISCUSSION
4.1 Results 43
4.1.1 Cash flow analysis 43
4.1.2 Opportunity cost analysis 46
4.1.3 Unequal service life analysis 47
4.1.4 Analysis for economic service life
4.1.5 Marginal analysis 56
4.1.6 Cost of capital recovery 60
4.1.7 Sinking fund 62
4.2 Application of Cost-Schedule-Variance-Model 62
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion
5.1.1 Contributions to knowledge 66
5.2 Recommendations 66
References 68
Appendices 73
LIST OF TABLES
3.1 Generating plant data sheet 23
3.2 Operations & Maintenance cost per year 24
4.1 Cash flow results for generating plants 45
4.2 Opportunity cost result for generating plants 46
4.3 Unequal service life for generating plants 50
4.4 Economic service life for generating plants A1 and A2 51
4.5 Economic service life for generating plants B1 and B4 52
4.6 Economic service life for generating plants B2 and B3 52
4.7 Economic service life for generating plants C1 and C2 53
4.8 Economic service life for Mikano generating plants VOR1 and VOR2 53
4.9 Economic service life for Jubaili Bros generating plant 54
4.10 Economic service life for Cummins generating plant at maintenance workshop 54
4.11 Economic service life for Cummins generating plant at airport police station 55
4.12 Economic service life for Perkins generating plants at the admin building 56
4.13 Economic service life for FG Wilson generating plants at governor’s lodge
(defender) 56
4.14 Economic service life for the challenger (Perkins generating plants) 57
4.15 Economic service life for the challenger (FG Wilson generating plants) 58
4.16 Economic service life for the challenger (generating plants C1 and C2) 59
4.17 Cash flow and opportunity cost comparison 60
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND INFORMATION
Maintenance is defined as an activity to arrest, reduce or eliminate device deteriorations while maintenance optimization is defined as a method aimed at finding the optimal balance between preventive maintenance and corrective maintenance with respect to objectives. Therefore, the purpose of maintenance is to extend equipment lifetime, increase asset value, equipment conditions and avoid costly consequences of failures. Maintenance can also be seen as an investment in availability and reliability of equipment, this will further increase the profit for industries particularly manufacturing industries etc.
Maintenance had been a significant activity in industrial practice. According to Halasz et al. (1999) on the cost of maintenance across eleven Canadian industry sectors “….in addition to every dollar spent on new machinery, an additional fifty-eight cent is spent on maintaining existing equipment. This amounts to repair costs of approximately fifteen ($15) billion dollars per year”. As a result, maintenance optimization becomes necessary.
Basically, the problem of maintenance optimization can be expressed as follows; Consider a system that is susceptible to failure, rather than running the system to failure, one can make a plan to carry out a preventive replacement at the high-risk condition to avoid costly failure. Also, one may consider at failure epoch to decide, if it will cost effective to repair the system or to replace the system with a brand new one. The aim is to optimize the system performance based on a given criterion such as:
1. Average cost.
2. Discounted cost.
3. Total net profit criterion.
When optimizing maintenance schedule over a time period the aim is to minimize the number of maintenance occasions and spare parts needed as well as the time the equipment will be out of use due to maintenance repairs, thereby minimizing the cost of maintenance and loss of production. An optimal maintenance schedule can lessen the cost of keeping the equipment operating as well as increasing its reliability.
There are different theories for when and why maintenance is carried out. Corrective maintenance is when a component is replaced or repaired when a failure had occurred. This simple method of maintenance is, however, the most expensive method to use. Preventive maintenance is when a component is replaced prior before failure occurs using predictive life approach. To make this theory cost effective, however requires adequate historical and/or measured data concerning the wear of a component or the system to calculate an expected life of the component or the system. An advantage of preventive maintenance is that the reliability and availability of the equipment are improved. Opportunistic maintenance is a combination of preventive and corrective maintenance. When a component/system fails, a decision is taken on whether or not to perform maintenance on the other components or system while the equipment is opened for maintenance. This may increase the time until another component or system fails or need maintenance which can save the cost of extra maintenance occasion.
To obtain the tangible mathematical model, one needs to identify all the contents in the above conceptual model, such as:
1. Deterioration dynamics.
2. Cost structure.
3. Information level.
4. Available maintenance options, etc.
Moreover, an adequate interpretation of the model is required for a real-life application. Throughout the years, the significance of maintenance activities and of maintenance management has developed remarkably. The prevalent mechanization and automation have reduced the number of production personnel, increased the capital used in production equipment and structures. As a result, the duties of employees working in the maintenance department unit have grown, as well as costs. In refineries, as an example, it is not exceptional that the maintenance and operations departments/units are the largest and each comprises about thirty (30%) percent of the total workforce.
Furthermore, next to energy costs, maintenance expenditure can be the largest part of the operational budget in industries. This research will focus on maintenance planning optimization and its applications. Maintenance planning in an industry is important to maintain a high availability of production and optimizing maintenance schedules can substantially improve the availability as well as decrease the total cost for performing maintenance.
1.2 AIM AND OBJECTIVES OF STUDY
The aim of this study is to:
1. Evaluate the performance of power equipment network maintenance facilities in some airports in Nigeria. The specific objectives are to:
2. Formulate and develop a model that will track maintenance activities, control cost and schedule.
3. Evaluate the economic service life of the power equipment.
1.3 PROBLEM STATEMENT
The enormous part of the operating cost in industry comes from maintenance of equipment and machines. The cost of maintenance in Akwa Ibom International Airport had been a cause for concern by the management of the Airport. Maintenance cost was estimated to have risen from three (N3M) million naira per month in 2009 to eight (N8M) million naira in 2017, this representing an increase of two hundred and sixty-six point six, seven percent (266.67%). It is projected that one-third of these costs are due to ineffectual management of maintenance. Therefore, large savings can be made by increasing the efficiency of maintenance operations in this airport.
1.4 SCOPE OF STUDY
The study scope is at Akwa Ibom International Airport Uyo, Akwa Ibom State one of the recently built and actively operating, equipped with the state of the art navigational facilities such as Instrument landing system (ILS), which aid in safe land/take-off of aircraft even under intense adverse weather conditions, localizer and Non-direction Beacon which gives the alignment of the aircraft to runway on approach and the bearing respectively. These and many facilities at the airport are being powered by the supposed secondary power system. Twenty-six (26) units of generating plants are in operations. This research will tend to focus on finding the optimal solution to reoccurring generating plants failure rate and to minimize their maintenance cost in other for the management of the airport to deliver quality, safe and flawless service delivery.
1.5 JUSTIFICATION
The airport being an important component of an aeronautical infrastructure requires high safety standards. The level of safety can only be accomplished by effective and efficient maintenance of all the elements comprising an airport.
Maintenance which is also defined as measures to restore or maintain the operational performance as well as measures to check and evaluate the current function of an element. It is believed that findings from the research would help the management of Akwa Ibom International Airport to make gainful decisions in the quest to reduce high equipment failure rate and to minimize maintenance cost.
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