Thesis Abstract

Abstract
Machinery replacement is a critical decision-making process in various industries to optimize operational efficiency and minimize costs. Dynamic modeling of machinery replacement problems involves analyzing complex systems to determine the optimal timing for replacing existing machinery with new, more efficient equipment. This research focuses on developing a dynamic model that incorporates key factors such as equipment deterioration, maintenance costs, technological advancements, and economic factors to provide decision-makers with valuable insights. The dynamic modeling approach allows for the consideration of changing variables over time, enabling a more realistic representation of the machinery replacement problem. By utilizing mathematical modeling techniques and simulation tools, this research aims to evaluate different replacement strategies and their impact on the overall performance of the system. The model accounts for uncertainties in factors such as equipment reliability, market conditions, and maintenance costs to help decision-makers make informed choices. Furthermore, the research explores the trade-offs between the initial investment in new machinery and the long-term savings in operational costs. By quantifying the benefits of timely machinery replacement, organizations can avoid costly breakdowns, reduce downtime, and improve productivity. The dynamic model provides a framework for assessing the optimal replacement schedule that balances the upfront capital expenditure with the expected returns from improved efficiency and reduced maintenance expenses. Additionally, the research considers environmental factors by evaluating the impact of machinery replacement on energy consumption, emissions, and sustainability. By analyzing the lifecycle costs and environmental footprint of different replacement scenarios, decision-makers can make environmentally conscious choices that align with corporate sustainability goals. The dynamic modeling approach facilitates the identification of environmentally friendly replacement strategies that not only benefit the bottom line but also contribute to a greener operating environment. Overall, this research contributes to the field of machinery replacement by offering a comprehensive dynamic modeling framework that integrates technical, economic, and environmental considerations. The model provides decision-makers with a systematic approach to evaluating machinery replacement decisions and optimizing the performance of industrial systems. By leveraging the dynamic modeling capabilities, organizations can make strategic investments in new machinery that enhance competitiveness, profitability, and sustainability in the long run.

Thesis Overview

INTRODUCTION

 

Replacement of machinery is not widely discussed in Engineering Literatures. Yet it is a common occurrence in industries requiring the use of plant and equipment for the production of goods and services. Even where it is discussed, the emphasis is usually placed on minimization of total, maintenance and operating costs as well as the maximization of profit without recourse to the uncertainty resulting from the method of determining deterioration. Cognizance of the effect of deterioration on the resale value of equipment and indeed on machinery replacement date is yet to be fully appreciated. Values of deterioration are usually assumed or at best determined by methods that are highly subjective and sometimes expensive like the popular failure analysis (Sachs, 2007). Some replacement models exclude resale value in the build-up of cost. Yet it is the value that is directly affected by deterioration. This missing link will be supplied by this study.

 

1.1   OBJECTIVE

The following are the objectives of this study:

1. To reduce the error arising from the subjective methods of determining deterioration by generating random numbers using  Monte Carlo simulation  under the uniform probability distribution to produce values for deterioration.
2. To incorporate economic parameters like the inflation rate and the rate of return on replacement investment in the model.

• To develop machinery replacement model with emphasis on the effect of deterioration on the salvage (resale) value and indeed on the replacement date.

1. To employ the dynamic programming method in the enumeration to obtain the optimal replacement date.
2. To calibrate and verify the model with field data obtained from some industries as well as compare the results of the model with those of existing models for reliability and operationability.

 

1.2   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.

 

1.3   THE METHODOLOGY:

This study lays emphasis on the effect of deterioration on salvage (resale) value and indeed on the replacement date of machinery. It also recognizes the inherent errors in the existing method of determining deterioration. These methods are highly subjective and sometimes expensive (Sachs, 2007). To reduce these errors or perhaps eliminate them the values of deterioration will be treated as stochastic variables and generated as random numbers under the uniform (rectangular) probability distribution.

The cost minimization model that will be developed will have three main cost components namely the purchasing cost, the maintenance cost and the salvage (resale) value.

Solution of the salvage value component (function) of the model is expected to produce the initial resale date that may be used to start an enumeration process. The dynamic programming technique will be adopted in the enumeration process.

The model will incorporate economic parameters like the inflation rate and the rate of return on replacement investment.

The model will then be tested with field data to verify the reliability of its results, for possible review and adjustments.

In summary, the methodology of this study consists of two parts namely the analytical and the experimental processes

1. Analytical Process: In the analytical process the following steps are taken:
2. Values of deterioration are generated as random numbers under the uniform probability distribution.
3. The initial replacement date is obtained from the derivative of the salvage value component of the model.

iii.     The dynamic programming technique is employed as the enumeration process for obtaining the optimal replacement date starting perhaps with an initial resale date obtained from the solution of the salvage value component of the model.

1. Experimental Process: Field data obtained from different industries like the construction, pharmaceutical and plastic companies are used to test the model with the view to verifying the reliability of the results for possible review and adjustments.

Finally, results from the model will be compared with those of existing models for possible advantages in terms of operationability, reliability and perhaps savings.

 

 

1.4   THE CONTRIBUTION TO KNOWLEDGE

This study develops a model that considers the effect of deterioration, inflation and rate of return on replacement investment on total cost and indeed on the replacement date of machinery. Thus the results of the model are intended to give answer as to whether to replace or refurbish existing machinery as well as indicate when replacement is optimal. The study therefore complements as well as improves on the existing knowledge of machinery replacement and assists industry managers in making replacement decisions especially when maintenance or refurbishment is no longer advisable. In this regard, the contribution of the study should be viewed in the following areas.

1. Verification of the Salvage value to test its suitability to our industrial environment.
2. The incorporation of economic parameters like the rate of return on replacement investment and inflation rate in the development of the new model

• The reduction of error inherent in the subjective methods of determining deterioration by generating values under the uniform probability distribution to represent deterioration.

1. The development of cost minimization model with emphasis on the effect of deterioration on the salvage value.
2. The application of dynamic programming method as the solution technique to the new model. Dynamic programming is good for problems with overlapping sub-problems and optimal substructure and therefore has an advantage over other solution techniques like the cumbersome forward algorithm adopted by some existing models.
3. The calibration and verification of the model with field data and comparing its results with those of existing models for reliability and operationability.