Dynamic Modeling of Machinery Replacement Problems
Chapter One
THE IMPORTANCE OF THE STUDY
Replacement investment is one of the overheads competing for scarce financial resources of any industry. A replacement investment should therefore be justified. An effective machinery replacement policy can be put in place to achieve this purpose. In this study, a model will be developed that will assist industry managers to make decision on replacement date when maintenance is no longer advisable. Planned replacement will reduce or perhaps eliminate unnecessary downtime arising from forced shutdowns. Total Process Reliability (TPR) is one of the methods of realizing a planned replacement of machines. Total Process Reliability (TPR) views every maintenance events as an opportunity to upgrade manufacturing processes.
Bloch et al, (2006) advise that process plants should be reliability-focused instead of repair-focused. Process plants that are repair focused have trouble surviving because they place emphasis on parts replacement and have neither time nor the inclination to make systematic improvements. They hardly identify the reason for parts failure and do not implement the type of remedial action that discourages the recurrence of failures. Reliability-focused plants, on the other hand, view every maintenance event as an opportunity to upgrade. Whenever cost justifies, this upgrade is achieved by adhering more closely to smarter work processes, following better procedures, selecting superior components (not parts), implementing better quality controls, using more suitable tools and choosing a suitable replacement date. These measures may reduce downtime and maximize machinery uptime.
CHAPTER TWO
LITERATURE REVIEW
Replacement refers to a situation in which machinery is worn out and cannot physically perform its intended function and must be exchanged for another machinery. Replacement may also arise in case of upgrade, the existing equipment may be functionally alright, but output needs to be increased.
Replacement/Refurbishment has been an age long practice especially when such machinery is not providing the required services. Even the manual farm tools and equipment are either repaired to improve on the services rendered or replaced completely due to failure to provide services at all.
In modern day industries where plant and machinery are required for the production of goods and services, the replacement of machinery is common due to deterioration which gives rise to facility failures.
In this chapter, brief discussion of certain terms will be presented. It is expected that the discussion of these terms will put this work in proper focus and simultaneously promote a better understanding of machinery replacement practice.
The terms to be discussed are as follows:-
- Deterioration (Depreciation)
- Probability Distribution
- Simulation
- Monte Carlo Method
- Random Numbers
- Replacement Models
DETERIORATION (DEPRECIATION)
Deterioration is defined as the loss of value (American Society of Appraisers [ASA], 2000). Machinery deterioration is therefore the loss of value of the machinery from all causes. Deterioration may be curable or incurable. The types of deterioration include the following:-
- Physical deterioration
- Functional Obsolescence
- Economic Obsolescence (External Obsolescence)
Physical Deterioration
Physical deterioration is the loss of value or usefulness of a plant or machinery due to the using up or expiration of its useful life caused by wear or tear, exposure to harsh environment, physical stresses and similar factors.
Generally, physical deterioration may be caused by age, wear and tear, fatigue, stress, exposure to harsh environment and lack of maintenance. The inability of a plant or machinery to perform at design capacity may be a measure of physical deterioration. Caution should, however, be exercised in determining whether or not the inability to meet the design capacity is a function of the plant/machinery or a function of the production schedule. The former denotes physical inability while the latter denotes economic obsolescence.
Functional Obsolescence
Functional Obsolescence is the loss in value or usefulness caused by inefficiencies or inadequacies of the plant/machinery when compared to more efficient or less costly replacement machinery that new technology has developed.
Functionally obsolescence is the impairment of functional capacity, inadequacies or changes in the state of the art that affect the machinery rendering it incapable to adequately perform the function it was initially designed to undertake. Generally, functional obsolescence may be caused by lack of utility, change in design, efficiency and technology change.
CHAPTER THREE
METHODOLOGY
The methodology adopted in this study will be discussed first before the development of the replacement model. Issues raised in this discussion will be very significant in model development and solution technique.
It was established in the previous chapter that deterioration is a major factor in facility failure and indeed machinery replacement decisions. This is true because deterioration affects the total and maintenance costs, the salvage value and indeed the replacement date of any equipment.
Cognizance of the effect of deterioration on machinery replacement is yet to be fully appreciated. Emphasis had usually been placed on the minimization of total and maintenance costs as well as the maximization of profit to arrive at the optimal replacement date. Values for deterioration are usually assumed or at best determined by methods that are highly subjective.
Even the popular failure analysis (Sachs; 2007) like the Component Failure Analysis (CFA), Root Cause Investigation (RCI) and the Root Cause Analysis (RCA) are not only subjective, they are also expensive. Some of the replacement models even exclude the salvage value in the build-up of cost. Yet it is the value that is directly affected by deterioration.
In this study, the methodology adopted comprises:
- The analytical process
- The experimental process
CHAPTER FOUR
MODEL DEVELOPMENT AND SOLUTION
The objective of machinery replacement models has always been to find a replacement date that will minimize total maintenance and operating costs and maximize profit.
Some models exclude salvage (resale) value in the cost build-up while most models trivialize the effect of deterioration on the resale value and indeed in the build-up of costs. Even in the cases where the effect is considered, values of deterioration are assumed or at best, determined by methods that are highly subjective and expensive at times.
The development of the model and solution (results) follow below.
CHAPTER FIVE
DISCUSSION OF RESULTS
Details of the result of the model are discussed in this chapter. The discussion will assist in the conclusions and recommendations. The discussion is based on the observations derived from the results of the new model.
PERCENTAGE DEVIATION.
The percentage deviation between the measured and predicted total costs for the machines in this study is generally low (0.09-19.28%). This may be an indication of the quality of the field data as well as the reliability of the new model.
CHAPTER SIX
CONCLUSIONS AND RECOMMENDATIONS
CONCLUSIONS
The study is introduced in chapter one which includes the objectives, relevance and contribution of the study to knowledge. The methodology adopted for the study is briefly mentioned.
The literature review of chapter two addresses issues related to machinery replacement. The origin of the principles and earlier application of some of these principles are mentioned. An overview of the relevance of these principles or methods to this study is highlighted. In addition, various existing replacement models, their limitations and objectives are discussed.
Details of the methodology adopted in the study are presented in chapter three. The Analytical and Experimental processes are part of the methodology of the study. The Dynamic programming method is the solution technique adopted for the study.
The model is developed in chapter four. The assumptions and features of the model are also highlighted. The model is calibrated and verified with field data from three Industries namely Construction, Pharmaceuticals and Plastics. Finally the model is compared with other models and found to predict results that are satisfactory. The results are presented in tables and graphs.
The results of the model are discussed in details in chapter five. The effects of inflation and deterioration rates on relevant variables are highlighted. The variation of these variables with time is also mentioned.
The summary of the findings of the study follows.
SUMMARY OF FINDNGS
- The new model is simple, reliable and operational.
- It is amenable to review and adjustment.
- It is amenable to solution techniques like optimization, dynamic programming and economic lot size inventory control method.
- The results of the new model are comparable to those of other models like the Walker’s (1994) model.
- The new model appears to be suitable for machines with small economic life. However, with sufficient cost data the new model will also be applicable to the replacement problems of machines with long economic life.
- The new model can address the replacement problems of some industries like construction, pharmaceutical, plastic and transport services. The application of the model can be extended to other industries provided that sufficient cost data are available.
- The results of the model depend on regular preventive maintenance and timely corrective maintenance of the machines. Proper maintenance record with costs should be kept.
- It is also assumed that the machines will be operated by trained operators with proper work ethics for handling the machines while in service to avoid unnecessary downtime arising from forced shutdowns. The latter impacts negatively on the maintenance costs and indeed the total cost.
- The simplicity in the application of the model may lead to savings in terms of cost and time. In fact, the use of Monte Carlo simulation to generate values for deterioration rates eliminates the cost incurred through failure analysis.
- It is expected that the model will assist industry managers to make effective machinery replacement decisions, since the model predicts machinery replacement date that reflects real life situation.
- Maximum total cost K(T) may suggest refurbishment.
RECOMMENDATION FOR FURTHER STUDY
To improve on the model, further study in the following area is encouraged and recommended.
- Generate random numbers under other probability distributions like the normal distribution and poisson distribution.
- The application of the economic lot size inventory control method as the solution technique.
- Introduction of parameters like capital allowance, taxation rate and book value of the existing machine in the development of the model.
- Obtain failure data and analyze same to determine the failure mode which gives the shape parameter that may lead to a replacement policy.
- Other statistical tests in addition to the regression analysis should be used to test the model’s performance.
REFERENCES
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