Replacement Analysis of Aging Equipments

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REPLACEMENT ANALYSIS OF PLANT AND MACHINERY

A THESIS

submitted by

VAIBHAV KAPOOR

in partial fulfilment of the requirements

for the award of the degree of

MASTER OF TECHNOLOGY

in

CONSTRUCTION TECHNOLOGY AND MANAGEMENT

BUILDING TECHNOLOGY AND CONSTRUCTION MANAGEMENT DIVISION

DEPARTMENT OF CIVIL ENGINEERING

INDIAN INSTITUTE OF TECHNOLOGY MADRAS

MAY 2008


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CERTIFICATE

This is to certify that the project entitled Replacement Analysis of Plant and

Machinery being submitted by Vaibhav Kapoor to the Indian Institute of Technology

Madras , in partial fulfilment of the requirements for the award of the degree of Master of

Technology in Civil Engineering , is a record of bona fide work carried out by him. The

contents of the thesis have not been submitted and will not be submitted to any other

institute or University for the award of any degree or diploma.

GUIDE

Dr. K. AnanthanarayananAssociate Professor,

Department of Civil Engineering

Indian Institute of Technology Madras.

HOD

Dr. K. RajagopalProfessor & Head,

Department of Civil Engineering,

Indian Institute of Technology Madras.

Place: Chennai

Date: 1 st May 2008


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ACKNOWLEDGEMENTS

The thesis as we see today in its present form is an outcome of persistent efforts and a

great deal of dedication and has drawn intellectual support from the various sources. It is

therefore almost impossible to express adequately the debts; I owe to many persons who

came up with ideas and implementations to make this a success.

I take this opportunity to thank Dr. K. Ananthanarayanan , Associate Professor, Dept of

Civil Engineering , for the invaluable guidance and frequent suggestions, incorporated

together with long hours of his precious time to help me during every course of my thesis.

His suggestions helped me to maintain a good quality of work. I am unable to find words

to express my heartfelt gratitude towards him.

I extend my sincere thanks to Mr. G. Balasubramanian of P&M Department, Larsen &

Toubro Ltd for giving me an opportunity to take up this project and complete it

successfully. My regular visits to him have always contributed to steer things in the right

and practical direction.

I would like to thank Mr. T. Chandrasekaran , RPLM (Bangalore), Larsen &Toubro Ltd

and his team in Bangalore, specially Mr. S. Gandhi and Mr. Jose Thomas for providing

the required data and arranging my visits to various sites in the region.

I would like to express my deep sense of gratitude to Dr. K.N. Satyanarayana, Prof and

Head of Lab, BTCM division for rendering valuable suggestions and laboratory facilities.

Last but not the least, I would like to thank all those friends who lent me a helping hand

at the hour of need.

Vaibhav Kapoor


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ABSTRACT

Keywords : Construction Equipment, Replacement Analysis, Equipment Replacement,

Equipment Economics, Equipment Cost Modelling.

Replacement analysis involves the use of mathematical models and analysis to decide

how to provide a service currently being provided by some existing asset, traditionally

called the defender. Other assets, traditionally called challengers, could replace the

defender now and improved challengers may also be available in the future. The

decisions to be made are whether to replace the defender now and, if not now then when.

In order to make these decisions, replacement analysis also examines decisions regarding

replacements at future times.

In seeking to help a decision maker, this study performs two tasks. First, it creates a

mathematical description of the equipment costs defining the economic behaviour of the

equipment; this description is called a cost model. Second, the study performs

mathematical manipulations on the model in order to extract the model's implications.

This step, which we call decision analysis or decision modelling, may include methods

such as proving implications of the model, finding the optimal solution to the model,

simulating the model on a computer, and so forth. The results obtained through this

research should assist a decision maker in making a more informed decision on

equipments replacement but by no means it can replace him/her as replacementdecisions cannot be made on economic consideration alone.


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CONTENTS

Page

CERTIFICATE i

ACKNOWLEDGEMENT ii

ABSTRACT iii

CONTENTS iv

LIST OF FIGURES vii

LIST OF TABLES ix

CHAPTER 1 INTRODUCTION

1.1 Background 1

1.2 Need For The Study 2

1.3 Objective 3

1.4 Scope 3

1.5 Methodology 4

1.6 Thesis Outline 4

CHAPTER 2 LITERATURE REVIEW

2.1 Research Done Till Now 6

2.2 Strategic Factors 8

2.3 Articles Classification 11

2.4 Remarks 11


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CHAPTER 3 COST MODELLING

3.1 Introduction 13

3.2 Description of Factors 14

3.3 Inter Relationships Between Factors 20

3.4 Cost Terms 22

3.5 Regression Modelling 24

3.5.1 Introduction 24

3.5.2 Linear Regression Modelling 26

3.5.3 General Linear Data Model 293.5.4 Regression Diagnostics 30

3.5.5 Data Collection And Sample Regression 32

3.6 Cost Modelling Automation Program 39

CHAPTER 4 DECISION MODELLING

4.1 Introduction 43

4.2 Simple Model 44

4.3 Alternative Model 45

4.4 Model Algorithm 49

CHAPTER 5 DESCRIPTION OF WEBSITE

5.1 The Home Page 50

5.2 Security Feature 51

5.3 Adding a New Equipment to the Database 52

5.4 Equipment Database 55


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5.5 Replacement Analysis 57

5.6 Results 58

5.7 Changing External Data 60

5.7.1 Economic Factors 61

5.7.2 Labour Factors 62

5.7.3 Energy Factors 63

CHAPTER 6 CONCLUSIONS

6.1 Summary 65

6.2 Limitations 66

6.3 Suggestions for Further Improvement 67

REFERENCES 68


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LIST OF FIGURES

Page

Figure 2.1 Typical relationships in replacement analysis models 10

Figure 2.2 Expanded model of relationships in replacement analysis 11

Figure 3.1 Inter-relationship between replacement factors 20

Figure 3.2 Typical cash flow diagram of an equipment 23

Figure 3.3 Illustration of linear regression on a data set 27

Figure 3.4 Prod. Vs Cumulative Hours for Batching Plant(BP) at RMC 2 33

Figure 3.5 Power Units Vs Cumulative Hours for BP at RMC 2 34

Figure 3.6 Maintenance Cost Vs Cumulative Hours for BP at RMC 2 35

Figure 3.7 Spares Cost VS Cumulative Hours for BP at RMC 2 36

Figure 3.8 Daily wage rate model for Bangalore Region 37

Figure 3.9 Labour Costs Vs Cumulative Hours of use for BP at RMC 2 37

Figure 3.10 Black Box Representation of Regression Procedure 38

Figure 3.11 Procedure for selecting the best fit regression model 40

Figure 3.12 Snapshot of a sample operating cost history file 41

Figure 4.1 Cash Flow Diagram (CFD) of a single replacement cycle 44

Figure 4.2 CFD for defender and challenger for two replacement cycles 46

Figure 4.3 Flow chart for replacement decision model 48

Figure 5.1 Home page of Equipment Replacement Analysis (ERA) 49

Figure 5.2 Login page of ERA web tool 50

Figure 5.3 Add New Equipment page of ERA web tool 51


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Page

Figure 5.4 New Equipment Confirmation page of ERA web tool 52

Figure 5.5 Cost History Upload page of ERA web tool 53

Figure 5.6 Cost Model Plot of ERA web tool 53

Figure 5.7 Database Page of ERA web tool 54

Figure 5.8 Database Search page of ERA web tool 55

Figure 5.9 Record Display page of ERA web tool 55

Figure 5.10 Replacement Analysis page of ERA web tool 56

Figure 5.11 Results page of ERA web tool 58Figure 5.12 External Factors page of ERA web tool 59

Figure 5.13 Economic factors page of ERA web tool 60

Figure 5.14 Labour factors page of ERA web tool 61

Figure 5.15 Energy factors page of ERA web tool 62


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LIST OF TABLES

Page

Table 3.1 Elements of Deterioration 25

Table 3.2 Details of the RMC plants visited for data collection 32

Table 3.3 Results of production regression modelling for BP at RMC 2 33

Table 3.4 Results of energy regression modelling for BP at RMC 2 34

Table 3.5 Results of maintenance regression modelling for BP at RMC 2 35

Table 3.6 Results of spares cost regression modelling for BP at RMC 2 36

Table 4.1 Limiting values of k and l 47


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CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

Any equipment that is used in construction industry (or other) has two life periods

associated with it A physically limited working life which is fixed by the manufacturer

and a cost-limited economic life which is associated with the economic conditions in

which the equipment operates. The physical life depends on the manufacturers

competence to produce a quality product and to an extent on the working conditions of

the equipment. Thus nothing much can be done about it once the equipment is bought and

it will continue to function until its physical life is over. Economic life, on the other hand

is that milestone which may reach before the physical life of the equipment but after

which the equipment is financially unviable to run. The equipment may still be fit to

operate but it starts losing money instead of earning it. The obvious question which then

comes to the mind is when to call it a day? Most of these decisions of replacing the

current equipment with a new one, in the industry, are based on past experiences,

productivity, owning & operating costs, major repairs, availability and cost of spares,

technological obsolescence and sometimes even on emotional attachments with the

equipment!

The central concept of replacement analysis is concerned with finding the correct balance

between falling trade-in value and rising repair and maintenance costs of the equipment

so that the overall cost to the firm of owning and using the equipment is as small as it can

be. Some equipments are replaced often and with equipments of a more or less similar

kind. They are usually replaced before they are completely worthless and the problem


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continually arises of deciding just how quickly such assets should be turned over. If they

are held until practically worn out physically some costs, such as repairs, become very

high. On the other hand trade-in value falls rapidly in the early years of equipments life

and much more slowly later.

1.2 NEED FOR THE STUDY

By conducting several interviews with personnel dealing with replacement of equipments

in the Indian construction equipment industry it was found out that a rigorous economic

analysis is never carried out before taking the replacement decision. Top and middle levelmanagers often rely on experience and gut feeling while making the judgment. Decision

taken in such a manner may at times prove to be correct to a desired level of accuracy but

has no scientific proof supporting it. When such a process of making decision is analyzed

for a huge organisation having several people taking decisions for corresponding

equipments is adopted, it results in the process becoming a completely arbitrary one. No

single system exists with the contractors and equipment owners in the Indian construction

industry that accurately processes the range of available inputs and makes a decision

based on such an analysis. The motivation behind this research is to make this process

more accurate and consistent by taking into account all the quantitative and qualitative

parameters associated with the equipment and analyzing them before making a final

decision on replacement so that it would ensure an economically beneficial decision,

saving a lot of money to the contractor (equipment owner).


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1.3 OBJECTIVE

The objective of the project is to refine and improve the decision making process by

applying the replacement models to construction equipments.

This objective addresses the absence of a comprehensive decision making tool which

takes into account all the major factors contributing to replacement of construction

equipments. The factors are Direct Cost considerations (Acquisition, Operational &

Maintenance Costs), Obsolescence & Technological Advancements, Equipment

Productivity, Maintenance Periods, Market Forces (Tax rates & Inflation) and Working

Condition.Thus a more precise objective is to enhance the existing process of replacing construction

equipments by incorporating all of the aforesaid factors.

In order to make the results of this research fruitful to the industry a web application will

be developed that will help taking replacement decisions.

1.4 SCOPE

Replacement analysis of equipments is a vast topic which can be broadly categorized into

two domains: Replacement based on failure of the equipment and based on economic

considerations. Replacement based on failure focuses on developing failure prediction

models that forecast the time of physical failure of the equipment based on past failure

records. This kind of replacement analysis is beyond the scope of this research which

focuses on developing a replacement decision model based on economic considerations

alone. The study aims to arrive at a replacement decision by maximizing the net profits

(or minimizing the costs) associated with the equipment.


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1.5 METHODOLOGY

The methodology adopted to accomplish the project objective is:

Collecting maximum information and increase knowledge on various

contributing factors, mostly through journal articles

Selecting model equipment that is important from economic point of view on

which the model has to be applied

Strategically combining all these contributing factors to obtain an optimized

model for replacement of the selected equipment

Validating the result by means of opinions from experts working in the field

of construction equipment.

Refining the model if necessary and generalizing it

Converting the mathematical models into user friendly online web-tool to

make the decision making process swift.

1.6 THESIS OUTLINE

This thesis comprises of five chapters followed by references and appendices. A brief

outline of these chapters is given below.

Chapter 2 Details of the work done in similar fields over the past years are reviewed in

this chapter.


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Chapter 3 deals with the cost modelling using regression analysis techniques. Data from

sample equipment is taken to manually model the operating cost of the equipment. An

algorithm to automate the modelling process is described.

Chapter 4 describes the decision model used to take the final decision on replacing the

equipment.

Chapter 5 covers the description of online software tool from the result of the study,

namely Online Equipment Replacement Analysis in detail.

Chapter 6 summarizes the research outcome and elaborates on limitations and

suggestions for further improvement.


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CHAPTER 2

LITERATURE REVIEW

2.1 RESEARCH DONE TILL NOW

Most of the studies of the replacement processes have been done at the Machinery and

Allied Products Institute (MAPI) and National Centre for Education and Research in

Dynamic Equipment Policy. Operations Research Society has extended application of the

theory to phenomenon not previously treated and later extended the theory itself.

Replacement processes fall into two categories depending on the life pattern of the

equipment involved; i.e., whether the equipment deteriorates or becomes obsolete (i.e.,

becomes less efficient as compared to other equipments in the market) because of

introduction of new developments, or does not deteriorate but is subjected to failure or

death.

For deteriorating items, the problem consists of balancing the cost of new equipment

against the cost of maintaining efficiency on the old and/or that due to loss of efficiency.

Although general solutions to this problem have been obtained, models have been

developed and solutions have been found for various sets of assumptions about the

conditions of the problem.

Grantt has solved the replacement problem assuming that a. there will be no new more

efficient equipment made available before replacement, b. the value of money remains

constant over the useful life of the equipment, and c. annual operating costs do not

decrease. Terborghs model assumes a constant rate of technological improvement. He

computes the past rate of obsolescence and projects it into the future. He also uses a


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predicted price of new equipment in the future but does not take into account the possible

errors in the predictions.

Dean has criticized the use of fixed discount rate (as employed by Grant) to compute the

cost of investment. He employs a method which involves a comparison of alternative

investments. Consequently, in his model investment costs change with business

conditions and opportunities.

The underlying mathematics of replacement processes has a relatively long history. The

problem has attracted the attention of various prominent mathematicians, statisticians,

economists and actuaries. Among them are Blackwell, Brown, Chung and Pollard, Chungand Wolfowitz, Doob, Feller, Karlin and Preinreich.

Following the work of Alchian at RAND, Bellman has applied the functional equation

technique of the theory of dynamic programming to replacement problem. For a given

output of equipment and its cost of upkeep as a function of time, under the assumption

that replacement is possible only at specified times and that delivery of equipment id

immediate, Bellman has found a policy which maximizes the overall discounted return.

By 1986, Meredith was able to collect some 38 articles on the topic of Justifying New

Manufacturing Technology into one volume. Canada had compiled a bibliography of

113 articles. The diagnoses varied. Maybe it was the pay back criterion or maybe it was

unreasonably high rate of return requirements. May be we had just forgotten that man

shall not make decisions by economic analysis alone. After much debate, a few major

themes emerged as evidence by the direction of research articles since 1986. It was not

what the replacement analysis included that was the problem. It was what they didnt

include.


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One simplification of replacement models that was attacked was the single machine

performance. There was a growing recognition of the interdependencies of the separate

equipment items in a manufacturing or service industry system. Multiple machine

replacement analysis was formulated to address this concern. Another concern was that

modern technology produced some cost savings in categories of overhead and indirect

expenses that are commonly overlooked in application of replacement models. Examples

are savings resulted from reduced inventory, improved design/production interface,

simplified scheduling procedures, lower scrap rates, and reduced rework. The typical

replacement models certainly did not prevent these costs from being included, but it wasthe responsibility of the analyst to determine what these cost savings would be. Models

were later developed that explicitly include some of these factors.

2.2 STRATEGIC FACTORS

Three major levels of decisions in an organization are often distinguished: strategic,

tactical, and operational. Traditionally, replacement decisions have been treated as

tactical decisions, that is, they carry out a strategic decision to provide a function for the

organization for a fairly long period of time. The replacement decision is not treated as a

strategic decision since the need for the function is not questioned, and it is not treated as

an operational decision since replacement decisions are made relatively infrequently and

affect the firm for a long period. When the challenger differs dramatically from assets

available in the past, the traditional treatment of the replacement decision as a tactical

decision is not appropriate. In these situations, the challenger is not simply another way

to provide the same function as the defender, rather, it embodies a different mix of


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functions, providing some new functions and eliminating some functions the defender did

provide. Acquiring such a challenger is a strategic replacement decision. The introduction

of computerized manufacturing systems is an example of a strategic replacement

situation that many companies face now.

Another major recognition was that changes in production technology are often strategic

moves more so than tactical moves. Rather than being driven by cost savings, they are

being driven by competitive considerations. In todays market place, being able to produce

a product faster, or with higher quality or with greater variety has a distinct advantage.

These advantages are generally not incorporated into the economic replacement models.The dominant approach to addressing the strategic factors is to consider them as non-

economic criteria through such techniques as multi-attributive analysis or expert system

technology.

Azzone and Bertele address strategic factors from a strictly economic perspective

(although not considering deterioration and the timing of replacements). They note that

the most suitable manufacturing system for the firms strategic position is the most

profitable system. That is, if a product or a system has a competitive advantage, then it

can command either a higher price or a greater market share, meaning a larger demand.

Higher prices result in higher profits directly, but higher demand has both a direct and an

indirect effect. Directly, it results in more revenue and indirectly, it may lead to lower per

unit production cost as a result of higher equipment utilization and other economies of

scale. Since costs and revenues are cash flows, then discounted cash flow techniques can

be used to assess the strategic factors.


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Figure 2.1 shows the relationship typically modelled in replacement analysis. To

formulate a replacement decision, one would compare the profits of two or more

machines for which cash flows are estimated. Often the demand and price are treated as

given and independent of item of equipment being used. Sometimes this allows the

revenues and the profits portion of the model to be left out and selection of equipment

based on the minimum cost.

Figure 2.1 Typical relationships in replacement analysis models

In evaluating replacement decisions that involve a change in technology, the models used

should be extended to incorporate the competitive advantages of the more advanced

machines being considered. Thus a link is needed between machine characteristic and

demand and price. If the production quantity is allowed to respond to the changes in

demand, there is a link between the demand and the operating costs via utilization.

Incorporating the significance of system interdependencies, as mentioned earlier, an

expanded model of replacement relationships is shown in Figure 2.2.


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Figure 2.2 Expanded model of relationships in replacement analysis

2.3 ARTICLES CLASSIFICATION

The details of literature survey are presented below:

Articles related to Fundamental Concepts: 26

Articles related to Obsolescence: 12

Articles related to Productivity Issues: 4

Articles related to OR issues: 13

Articles related to Equipment Maintenance: 7

Articles related to Tax & Inflation: 6

Articles related to Other Categories: 29

2.4 REMARKS

The outcome of the literature survey is that there are a lot of articles addressing

contributing factors in great depths where every author has tried to expand the particular


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dimension by presenting their respective models but there are very few works which

address all these dimensions in totality. None has combined all of this existing knowledge

to make more generic models. Also none of them has tried to apply all of these factors to

Construction Equipments.


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CHAPTER 3

COST MODELLING

3.1 INTRODUCTION

Cost modelling of the equipment is perhaps the biggest gap that exists between the theory

and the practice of replacement decision. It is impossible to take any decision on

replacement unless we know the true costs associated with the equipment. Representing

all the costs of the equipment into mathematical equations is thus a very important pre-

requisite for taking the decision. How else can one expect to choose between the two

options if one does not know which one is cheaper?

Net cost of equipment can be represented by a cost model in form of the following

equation:

Net Cost = Owning Cost + Operating Cost +Maintenance Cost Revenue

In order to successfully represent all the costs of equipment into sets of mathematical

equations several brainstorming sessions were carried out to list the factors influencingthe equipment costs. These sessions included experts from the industry and academia. As

a result an exhaustive yet generalized list of factors affecting the equipment costs was

arrived at. The list is presented below:

Direct Acquisition and Salvage Costs

Deterioration of the equipment

Technological Obsolescence

Productivity


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Utilization

Inflation

Tax Regime

Maintenance

Availability of New Equipment

Government Policies

Working Conditions

3.2 DESCRIPTION OF FACTORS

The factors influencing the equipment costs are described as follows:

Direct Acquisition and Salvage Costs : These costs consist of the price at which

the equipment was purchased, its current market value, its salvage value after

scrapping the equipment. Costs such as insurance, road tax, duties and levies are

also included under this category. Several factors under this category react

differently to time. While purchase price of the equipment keeps on increasing

with time, its current market value gets decided by demand supply gap, its

deterioration, availability. Similarly, the salvage value generally has a falling

trend with time. These factors contribute directly towards the owning costs and

are easily measurable.


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Deterioration of the equipment : Table 3.1 shows the effect of deterioration both in

absolute and relative terms.

Table 3.1: Elements of Deterioration

Deterioration

Actual (internal) Opportunity (external)

degradationCost

obsolescence

Market

obsolescence

Increasing

use cost

Decreasing

capacity

Decreasing

sale value

Increasing

operating cost

as compared

to newer

equipment

Decreasing

revenue potential

as compared to

newer equipment

Pure replacement: fixed output level and priceStrategic replacement: output level

and price a function of technology

As the equipments cumulative production increases, it keeps on deteriorating and

loses its productivity. Good maintenance level can slow down the rate at which this

productivity is lost but the past data of a vast majority of equipments surveyed reveals

that the decline is always positive unless a very major overhaul takes place. Even in

the case of major overhauls or increased maintenance the deterioration leads to an

increase in costs under these headings. So, no matter under which header we book the

costs, deterioration will always lead to its increase. Since measuring deterioration is a

tough task and definition of deterioration varies for different equipment under


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consideration, productivity is chosen as its indicator. Fall in productivity of the

equipment is directly proportional to the increasing deterioration and will be

modelled accordingly. Deterioration also leads to decrease in resale value of the

equipment. Another important point to be considered here is that deterioration is

equipment specific factor and is independent of the market forces.

Technological Obsolescence : This factor has the same effect as that of the

deterioration described above apart from the fact that it is an external factor

governed by market. Also, the technological improvements get better with time

instead of worsening as with deterioration. The equipment manufacturers

continuously strive to bring in technological advanced equipments into the

market. All technological improvement may not necessarily improve the

productivity of the equipment but may decrease some overheads or maintenance.

In the short term these advanced equipments generally have a very small level

of advancement which adds to the usability of the machine but when seen in

intermediate and long terms trends, they tend to improve the productivity.

Another significant effect of technological advancement is the decrease in market

price of the current equipment. As contractors are inclined to buying the latest

equipment in the market, the demand supply dynamics in the market pushes the

price of the obsolete equipment downwards. It can also be generalized that a

newer model has a higher purchase price than its predecessor. Whether the

increased cost of purchase of the new equipment and decrease in resale value of

the present equipment is justified by the improvement in the new equipments


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productivity is debatable and one of the very important questions this research

aims to answer.

Productivity : Equipment productivity is the most important factor which decides

its behaviour with time. All equipments lose their productivity with time and all

class of equipments gain productivity as the time passes. These two effects are

explained by the above two factors of deterioration and technological

advancements. When talking over a lifetime of single equipment the productivity

generally shows a parabolic trend where it increases first and then falls. The

increase can be attributed to the learning curve effect and getting used to

operating the equipment. The fall is due to degradation. Interestingly, productivity

is the sole measurable factor which branches out to other measurable factors

rather than converging on them. This provides a very convenient way of

modelling the equipment costs. Costs such as fuel, power, labour, etc. Have a

great correlation with productivity and thus an accurate forecast of productivity

can enable us to accurately forecast other dependent factors. There is also a word

of caution here as a poor correlation between productivity and time can give

wrong implications about the various costs that depend on the productivity of the

concerned equipment.

Utilization : Equipment utilization is also a key factor that influences the cost of

the equipment. Utilization does not follow a trend with either time or production.

It however has some correlation with equipment maintenance. Better maintained

machines tend to have good utilization levels than ill maintained ones. Utilization

thus is generally a result of tactical decisions taken at the site. General


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intermediate trends in utilization of the equipment get reflected in its productivity

but it is difficult to model the short term or long term trends. While any attempt to

model the short term trends is thwarted by prevailing conditions and requirements

at site which may change on daily and sometimes even hourly basis. Long term

trends get disturbed by movement of equipments to various sites and locations. In

this study it was decided to take utilization into account but at fixed levels. This

can be justified as utilization is an external factor and hence will contribute

equally towards the old and newer equipments. Internal component of utilization,

dependent on the specific equipment due to varied maintenance levels will getadjusted in productivity modelling.

Inflation : This factor was included to make sure that the equipment economics

does not get separated from the broader business environment under which the

equipment operates. Inflation has been a key economic factor for a long time in

this county and has recently regained the limelight. The sky rocketing prices of

steel and oil have lead to an unprecedented increase of industrial good prices. This

has also affected the construction equipment industry in many ways. The direct

implication is that both purchase as well as resale prices of the construction

equipments have increased substantially in the past 5 years. Also, owing to

devastating prices of crude oil, the contractors through-out the world have started

to demand more efficient equipments from the manufacturers. This has also lead

to an increase in purchase price of the equipments due to technological

advancements. In this study, inflation is considered as an umbrella factor which

influences all other factors. It must be added here that inflation does not follow a


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generalized trend with time. The inflation modelling is thus beyond the scope of

this research. The results of such a modelling carried out by market pundits will

be used in the study.

Tax Regime : Tax is another umbrella factor which gets decided by the external

agencies but gives some control in terms of the way the contractor calculates

equipment depreciation. The tax bracket under which the contractors company

falls is fixed by the government and all the taxes must be applied before reaching

the final profit after tax figure or PAT.

Maintenance : As a famous saying goes A stitch in time can save nine.

Maintenance is a very essential indirect contributor to the net equipment costs. It

is generally seen that a minor increase in maintenance (preventive) spending can

substantially decrease the breakdown time of the machine and can hence increase

the revenue generation. The repair costs however follow an increasing trend with

time which is due to the degradation of the machine with time.

Availability of New Equipment : One may decide to replace an aging equipment

with a new equipment in the market but the availability of the equipment with the

supplier and the time lag between placing the order and delivery can fiddle with

the entire decision model. This lag needs to be taken into account before making

the final decision. Also, another point to be considered is that if the lag is too long

such that the market dynamics as well as the equipments internal cost dynamics

change within the lag period, the model has to be reformulated.


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Government Policies : Sometimes the government may impose a heavy duty on

imported construction equipments which otherwise are very lucrative to replace

with and at other times may provide incentives to import the equipments from a

friendly nation. There are other factors too which may provide a boon or a setback

for certain type of equipments. These factors generally depend on the prevailing

socioeconomic and political conditions. Such factors thus can never be modelled

in sets of mathematical equations and have to be considered differently. These

factors however get indirectly represented in the cost model.

Working Conditions : Working environment of equipment is another indirect

factor that is difficult to measure but significantly influences the other factors. It

includes factors such as labour unions, climatic conditions, site equipment

management, geographical location, etc.

All of the above mentioned factors contribute towards one or more terms in the right

hand side of the cost equation.

3.3 INTER-RELATIONSHIPS BETWEEN FACTORS

Many of these factors are inter-related and hence modelling of one factor implicitly

models the other factors. The inter relationship of various factors is shown in figure 3.1.


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Figure 3.1: I

The solid straight lines in fig

terms whereas the dotted c

factors. This research will fo

cost terms. The internal beh

consequence on the final co

argument is that in most o

contributing to the equipmen

It was also observed that all

of mathematical equations. policies on construction

availability can be modelled

incorporation of these model

ter-relationship between replacement factors

ure 3.1 show the direct relationship of these fac

rved lines represent the indirect relationship

cus on modelling the direct implications of the

viour and reactions of various factors on each

solidated cost model. Another point in support

the cases we can measure only the individu

t cost and not the direct factors.

of the above mentioned factors cannot be repre

For example it is almost impossible to modequipment. Similarly, although working co

by compromising the generalization aspect of

s would reduce its usefulness. These three fact

21

ors with cost

between the

actors on the

other bear no

of the above

al cost terms

sented in sets

l governmentditions and

the research,

ors were thus


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dropped off from the initial list. It should also be noted here that although these factors

are not considered for cost modelling their indirect effect on the model in terms of

productivity, tax regimes and technological obsolescence cannot be ignored. One has to

take care that although some of the factors may seem very easy to model but their

modelling can get very frustrating due to the lack of data.

3.4 COST TERMS

As already detailed, most of the factors described in the previous section are not directly

measurable for any given equipment but contribute to one of the terms in equation (3.1).Figure 3.1 depicts these contributions. A better way is to arrive at the equipments cost

model by estimating these measurable terms after understanding the implications of these

factors on various costs. This would enhance the modelling process. Thus our approach

would be to model these terms on time and productivity. The terms on the right hand side

of the equation (3.1) also have contributing factors as described as follows:

Owning Costs: This is obtained from the current-market-value of the equipment

and its salvage value. These are non-recurring costs that are beared at the

beginning and at the end of the equipment life. Costs such as insurance, road tax,

duties and levies are also included under this category.

Operating Costs: This includes the recurring costs that occur throughout the

production life of the equipment. It typically includes labour costs, power/energycharges, consumable costs, etc. Operating costs are very much correlated with the

equipments productivity which in turn is correlated with equipment run time.


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Maintenance Cost:

and cost of minor re

to their random natur

Revenues: Revenue i

the equipment in a gi

turn is dependent on

taken as constant. Th

reducing the tax bene

The challenge thus is to mo

model can be consolidated.

shown in figure 3.2.

Figure 3.

aintenance cost includes the cost of preventive

airs. Major overhauls are not incorporated in t

.

s the negative cost. It includes the revenue ge

en period. Revenue is dependent on the produc

demand. For simplicity of comparison, the dem

is constant can however be varied. Revenue is

fits and incorporating the depreciation benefits.

del these terms in the cost equation so that a

This is represented by the following cash flo

: Typical cash flow diagram of an equipment

23

maintenance

his study due

nerated from

tion which in

and has been

calculated by

omplete cost

diagram as


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As mentioned previously, in order to take a replacement analysis decision for the given

equipment we need to compare them in the same future time period. This calls for

forecasting their costs. Regression Analysis also known as regression modelling is one of

the most widely used techniques for analyzing and forecasting such data. Before fitting

the past data into sets of equations to predict the future costs, a brief insight into

regression analysis is required.

3.5 REGRESSION MODELLING

3.5.1 IntroductionRegression analysis is a technique used for the modelling and analysis of numerical data

consisting of values of a dependent variable (response variable) and of one or more

independent variables (explanatory variables). The dependent variable in the regression

equation is modelled as a function of the independent variables, corresponding

parameters ("constants"), and an error term. The error term is treated as a random

variable. It represents unexplained variation in the dependent variable. The parameters

are estimated so as to give a "best fit" of the data. Most commonly the best fit is

evaluated by using the least squares method, but other criteria have also been used.

Data modelling can be used without there being any knowledge about the underlying

processes that have generated the data; in this case the model is an empirical model.

Moreover, in modelling knowledge of the probability distribution of the errors is not

required. Regression analysis requires assumptions to be made regarding probability

distribution of the errors. Statistical tests are made on the basis of these assumptions. In


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regression analysis the term "model" embraces both the function used to model the data

and the assumptions concerning probability distributions.

Regression can be used for prediction (including forecasting of time-series data),

inference, and hypothesis testing and modelling of causal relationships. These uses of

regression rely heavily on the underlying assumptions being satisfied. Regression

analysis has been criticized as being misused for these purposes in many cases where the

appropriate assumptions cannot be verified to hold. One factor contributing to the misuse

of regression is that it can take considerably more skill to critique a model than to fit a

model!Underlying assumptions before performing the analysis

The sample must be representative of the population for the inference prediction.

The dependent variable is subject to error. This error is assumed to be a random

variable, with a mean of zero. Systematic error may be present but its treatment is

outside the scope of regression analysis.

The independent variable is error-free. If this is not so, modelling should be done

using Errors-in-variables model techniques.

The predictors must be linearly independent, i.e. it must not be possible to express

any predictor as a linear combination of the others.

The errors are uncorrelated, that is, the variance-covariance matrix of the errors is

diagonal and each non-zero element is the variance of the error.

The variance of the error is constant (homo-scedasticity). If not, weights should

be used.


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The errors follow a normal distribution. If not, the generalized linear model

should be used.

3.5.2 Linear Regression Modelling

In linear regression, the model specification is that the dependent variable, yi is a linear

combination of the parameters (but need not be linear in the independent variables ). For

example, in simple linear regression there is one independent variable, xi, and two

parameters, 0 and 1:

straight line:

In multiple linear regression, there are several independent variables or functions of

independent variables. For example, adding a term in xi2 to the preceding regression

gives:

parabola:

This is still linear regression as although the expression on the right hand side is

quadratic in the independent variable xi, it is linear in the parameters 0, 1 and 2.

In both cases, i is an error term and the subscript i indexes a particular observation.

Given a random sample from the population, we estimate the population parameters

and obtain the sample linear regression model:

The term ei is the residual,

.


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One method of estimation is Ordinary [Least Squares]. This method obtains

parameter estimates that minimize the sum of squared residuals, SSE:

Minimization of this function results in a set of normal equations, a set of

simultaneous linear equations in the parameters, which are solved to yield the

parameter estimators, . See regression coefficients for statistical properties of

these estimators.

In the case of simple regression, the formulas for the least squares estimates are

and

where is the mean (average) of the x values and is the mean of the y values.


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Figure 3.3: Illustration of linear regression on a data set

See linear least squares(straight line fitting) for a derivation of these formulas and a

numerical example. Under the assumption that the population error term has a

constant variance, the estimate of that variance is given by:

This is called the root mean square error (RMSE) of the regression. The standard

errors of the parameter estimates are given by


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Under the further assumption that the population error term is normally distributed,

the researcher can use these estimated standard errors to create confidence intervals

and conduct hypothesis tests about the population parameters.

3.5.3 General Linear Data Model

In the more general multiple regression model, there are p independent variables:

The least square parameter estimates are obtained by p normal equations. The

residual can be written as

The normal equations are

In matrix notation, the normal equations are written as

yXXX ''

Thus the matrix consisting of regression c oefficients is given as

yXXX ')'( 1

(3.1)

Again, SSE in matrix notation can be written as

yXy ''

ySSE

The regression sum of squares, SSR is

n

ySSR

n

ii

2

1'

yX


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Also, Total Sum off squares is

SST = SSE + SSR

or

n

y ySST

n

ii

2

1'

y

Pearson coefficient R is given as

SST SSR

R 2(3.2)

In general, R 2

always increases when a regressor is added to the model, regardless of

the value of the contribution of that variable. Therefore, it is difficult to judge whether

an increase in R 2 is really telling us anything important. Here an adjusted R 2 is used

which is independent of the number of regressor. It is given as:

1

)(1

2

nSST

pnSSE

R Adj (3.3)

where p is the number of regressor. For all practical purposes, the value of this

adjusted R 2 will be used in the regression modelling.

3.5.4 Regression Diagnostics

Once a regression model has been constructed, it is important to confirm the goodness

of fit of the model and the statistical significance of the estimated parameters.

Commonly used checks of goodness of fit include the R-squared, analyses of the

pattern of residuals and hypothesis testing. Statistical significance is checked by an F-

test of the overall fit, followed by t-tests of individual parameters.


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Interpretations of these diagnostic tests rest heavily on the model assumptions.

Although examination of the residuals can be used to invalidate a model, the results

of a t-test or F-test are meaningless unless the modelling assumptions are satisfied.

The error term may not have a normal distribution. See generalized linear model.

The response variable may be non-continuous. For binary (zero or one) variables,

there are the probit and logit model. The multivariate probit model makes it

possible to estimate jointly the relationship between several binary dependent

variables and some independent variables. For categorical variables with more

than two values there is the multinomial logit. For ordinal variables with more

than two values, there are the ordered logit and ordered probit models. An

alternative to such procedures is linear regression based on poly-choric or poly-

serial correlations between the categorical variables. Such procedures differ in the

assumptions made about the distribution of the variables in the population. If the

variable is positive with low values and represents the repetition of the occurrence

of an event, count models like the Poisson regression or the negative binomial

model may be used


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3.5.5 Data Collection and Sample Regression

As per the research methodology a model equipment had to be chosen for which the

above regression analysis can be done to obtain the cost model. After discussions with

industry experts a set of four Concrete Batching Plants in Bangalore were chosen. All

these plants belonged to L&T Concrete and operated for their Ready Mix Concrete

(RMC) Plants in and around the city of Bangalore. The reasons for choosing Concrete

Batching Plant in RMC industry were:

Concrete Batching Plants are one of the most expensive piece of equipment class

in the construction industry and hence the relative benefits of forming a

replacement strategy for these equipments would be much larger than say a

welding machine

Larsen & Toubro, one of the beneficiaries of this research has a large number of

aging Concrete Batching Plants

RMC industry has predictable levels of demand and utilization

Concrete Batching Plants are similar to a factory production environment and thus

has clearly defined costs and revenues

Site visits were conducted on four of these plants and all operating cost, productivity and

labour report data was collected. Unfortunately, all the data was in hard copy format and

had to be keyed in a spreadsheet program (Microsoft Excel 2007) before analysis can be

done.


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Details of the visited plants are summarized in the table 3.2:

Table 3.2: Details of the RMC plants visited for data collection

Asset Code Model

Purchase Date

(yyyy-mm-dd) Location

13260049 CP 30, Schwing 2000-06-01 RMC 2, Bangalore



A spreadsheet template was created in which the formulas was set to evaluate the

regression coefficients and adjusted R 2 value based on the equations described in the

previous section. Regression analysis was carried out by choosing cumulative hours of

run as regressor variable and operating costs as dependent variables. The analysis was

done without the use of matrix notation. This made the entire process very tedious and a

need for automation was realized. The model with best adjusted R 2 was chosen and

forecast data for future months was prepared. The process was repeated for all

components of operating and maintenance costs. The results of the cost modelling for

concrete batching plant in RMC 2 are presented in the charts and tables that follow. The

notation for the equation is as follows:

y: Corresponding operating cost

x: Cumulative hours of production


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Figure 3.4: Production Vs

Table 3.3: Result of prod

Best Fit:

umulative Hours for Batching Plant at RMC 2

ction regression modelling for Batching Plant

34

, Bangalore

t RMC 2


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Figure 3.5: Power Units Vs

Table 3.4: Result of en

Best Fit

Cumulative Hours for Batching Plant at RMC

rgy regression modelling for Batching Plant at

Bangalore

35

, Bangalore

RMC 2,


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Figure 3.6: Maintenance

Table 3.5: Result of maint

Best Fit

Cost Vs Cumulative Hours for Batching Plant a

Bangalore

enance regression modelling for Batching Plant

36

t RMC 2,

at RMC 2


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Figure 3.7: Spares cost VS

Table 3.6: Result of spar

Best Fit

Cumulative Hours for Batching Plant at RMC 2

s cost regression modelling for Batching Plant

37

, Bangalore

t RMC 2


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In order to determine the lab

of the workers in the given

years was used to prepare th

Figure 3.8:

The result of this model was

costs.

Figure 3.9: Labour costs

our cost, it is important to model the increase i

area. Daily wage data for skilled labourers fo

forecast model.

aily wage rate model for Bangalore Region

fed to the labour cost model to accurately forec

s Cumulative hours of use for Batching Plant

Bangalore

38

n daily wage

r the past 10

ast the labour

t RMC 2,


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Once all the operating costs were modelled, the costs were consolidated to yield the Total

cost after any given cumulative run hours h.

3.6 Cost Modelling Automation Program

Software procedures were written in order to automate the entire process of cost

modelling. As the aim was to prepare a web based application, PHP (Hypertext

Preprocessor) language was chosen to write these procedures. A user interface was

created where in an excel file was uploaded to the web server. The interface

automatically detects the run time of the equipment, production quantity and associatedoperating costs depending on the field labels. The various operating costs get regressed

against cumulative run time and production. The best fit coefficients of regression

obtained by regressing each individual operating cost against the regressors are

temporarily stored in the matrix. The number of data points is also determined

automatically and the past data is fed into the regression function. When looked as a

black box the regression procedures take the equipments historical data in form of an

excel file as input and outputs regression coefficients along with the adjusted R 2 value.

This black box representation is shown in figure 3.10 as:

Figure 3.10: Black Box Representation of Regression Procedure


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The regressors and variables are sent to various standard regression procedures and the

model with best adjusted R 2 is chosen. The standard regression procedures to which the

input is fed are:

Simple Linear Modelling

Polynomial Modelling (2 nd degree)

Logarithmic Modelling

Exponential Modelling

Power Modelling

Flow chart in figure 3.11 explains the procedure for selecting the best fit out of the above

regression models. The regressors and dependent variables are sent to the entire array of

probable regression procedures namely linear, polynomial, logarithmic, exponential and

power. The regression analysis is performed in these procedures and their output is

adjusted R 2 value. Another procedure determines the maximum of the adjusted R 2 value

and records the type of regression procedure in X. The procedure X is then fed with

regressors and dependent variables to yield the best fit coefficients. These coefficients are

stored for further use in the program.

These coefficients are later used to formulate the entire cost model. A sample excel file

is uploaded on the web server and regressed against the regressor variables as shown in

figure 3.12.


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Figure 3.11: Procedure for selecting the best fit regression model


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Figure 3.12: Snapshot of a sample operating cost history file


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CHAPTER 4

DECISION MODELLING

4.1 INTRODUCTION

Traditional Replacement Decision Models have focussed on deciding whether the

defending equipment needs to be replaced by the challenger in todays date or not. The

basic idea behind these models is that if they suggest keeping the defending equipment

for one more period, one should repeat the analysis in next decision period with current

data prevailing at that time. Very few models have addressed the question "If not now,

then when?" ie if outcome of a replacement decision is negative (do not replace) today

then when will it get positive (replace). Put in simple words, at what time can we replace

the equipment? The answer to this question can lay a replacement strategy for the

equipment owner. This question however is a bit hard to answer as it involves accounting

the effect of future replacement on prevailing economic environment of the equipment.

As previously mentioned, the aim of this study is to formulate a replacement policy for

the equipment giving exact time (or cumulative run hours) after which it must be replaced

with the defending equipment. We know that traditional replacement analysis techniques

cannot answer the questions raised above nor can they formulate such a policy. To make

the replacement decision more comprehensive, a better model had to be developed.

Inspiration for developing this elaborate decision model has come from works of Waddell

and Christer. The model proposed by Waddell expands on the classical replacement

theory proposed by Churchman, Ackoff and Russell in their Operations Research

publication.


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4.2 SIMPLE MODEL

The new model adequately considers most of the owners questions about time related

costs. It assumes that the equipment is to be used for some activity that has a finite

duration and that the equipment may be replaced a number of times and then salvaged at

the end of the activity. The final duration poses no practical limitation since it can be

specified as long as we want. The only consideration in this regard is that we should

expect the same cost behaviour from the equipment as exhibited in the previous years

from which cost model is derived. We know that this will never be true and hence for

sake of practicality we will reduce the number of replacements to one. This will make themodel more practical. Furthermore, it is always the first replacement we are interested in.

However while developing the model we will not restrict ourselves to single replacement

cycle.

Let us start by developing the model from the classical cost equation:

),,/}()()({)( k r P Ar t S dt r t f P t C k k

o

t (4.1)

Where

P = Current market price of the equipment

S(t)= Resale price after k years

f(t)= Total operating cost per unit time at age given age t

k=time after which replacement will be made

r=discount factor taking into account company MARR and inflation

),,/( k r P A = factor to amortize the present worth of all costs on monthly basis

Here C(t) represents the cost per unit time for single replacement cycle. The cash flow

diagram for such a replacement cycle is shown in figure 4.1.


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Figure 4.1: Cash flow diagram of a single replacement cycle

This has to be expanded to at least another replacement cycle so that the current decision

will be influenced by the future conditions. It has been already mentioned that a third,

fourth or subsequent replacement cycle will not be considered due to rapidly changing

market dynamics and limitations imposed by extrapolation of the regression model

beyond certain limits.

4.3 ALTERNATIVE MODEL

An alternative criterion function to the classical cost function explained above is

proposed as follows:

),,/)}()()('()()({)( 21' l k r P Ar t S dt r t f P r r t S dt r t n f P t C l

l

o

t k k k

o

t

Where

P1 = Current market price of the equipment

P2 = New market price of the equipment after k years

S(t)= Resale price after k years


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f(t)= total operating cost per unit time at age given age t (derived from the cost

model)

f(t)= Challengers total operating cost per unit time at age given age t

k=time after which first replacement will be made

l=time after which second replacement will be made

r=discount factor taking into account company MARR and inflation

(A/P,r,k+l)= factor to amortize the present worth of all costs on monthly basis

The above equation represents the total discounted cost per unit time of operatingequipment currently n months old for a further k months, replacing it, and then operating

the new equipment for a further l months before replacing again. There are, therefore,

two replacements involved over a period of (k+l), and both k and l are selected to

minimize this function. It is to be noted that the only parameter of operational interest is

k, the time to replace the current operating plant. A second cycle is introduced into the

function to represent the ongoing nature of the requirement for the plant and to influence

k accordingly. Any number of subsequent replacement cycles, m say, could have been

selected and incorporated into the criterion function. Only one has been selected since the

prevailing economic situation deems it desirable to keep the period over which economic

forecasts are required, namely (k+m.l) as short as possible. The cash flow diagram for

such a replacement cycle is shown in figure 4.2.


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Figure 4.2: Cash Flow diagram for defender and challenger for two replacement cycles

Now the function C(t) represents the total amortized cost of two consecutive

replacement cycles. The decision model constitutes the selection of k and l for which

C(t) is minimum. This can be represented as.

Replacement Age = k,l for MIN(C(t))

That is: Replacement Age = k,l for

MIN( ),,/)}()()('()()({)( 21' l k r P Ar t S dt r t f P r r t S dt r t n f P t C l

l

o

t k k k

o

t )

A trivial but unacceptable solution for this problem is k = . The above equation can be

solved with the help of differential calculus for defined function f(t) and f(t). However,

in the present scenario these functions are themselves a variable. There is no information

about the precise function characteristic which gives rise to too many permutations and

combinations.

Another method of solving the above criterion is through iterations. The function can be

iterated for all possible values of k and l, and extracting their value at which cost function


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converges on the minimum value. In order to reduce the iteration time, a smart system

was developed where in one can specify the minimum time for which the new equipment

would be kept. This would shrink the range for variable l and reduce the total number of

iterations. The above provision is also practically justified as there is always a tendency

to keep the equipment for at least 2 or 3 years before selling it off. This method can thus

remove this warming up period from the iterations. The limiting value of k and l are

provided in the table below:

Table 4.1: Limiting values of k and l

Minimum Limit Maximum Limit

k (defenders replacement) 0 Physical Equipment Life

l (challengers replacement) Minimum age to keep Physical Equipment Life


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4.4 MODEL ALGORITHM

The flow chart for the decision model is presented in figure 4.3.

Figure 4.3: Flow chart for replacement decision model


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CHAPTER 5

DESCRIPTION OF WEBSITE

Based on the cost modelling and decision modelling described in the previous two

chapters, a web based online replacement analysis application was made. The purpose of

this application was to provide the user with a quick economic analysis before making the

final replacement decision. Following few pages will describe the features and functions

of the website.

5.1 THE HOME PAGE

Figure 5.1: Home Page of Equipment Replacement Analysis Web Application


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5.2 SECURITY FEATURE

The website is completely protected against anonymous logins. Only users with pre

registered ids can enter and use the website. These ids can be registered on request of the

users. It is planned to transfer the control for handling the ids to P&M department in the

headquarters. The reasons for creating authenticated logins are:

Various users will be able to track the equipments under their command

Users will be able to share the cost models of various equipments

Automatic generation and expansion of the equipment database.

Security

Figure 5.2: Login Page of Equipment Replacement Analysis Web Application


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5.3 ADDING A NEW EQUIPMENT TO THE DATABASE

A user-friendly interface has been created to quickly add new equipments to the database.

Only the data relevant from replacement point of view is collected. User will have to

enter the unique asset code of the equipment followed by its model description. If the

equipment model is not available in the given list it can be swiftly added by clicking the

Not Available? link on the page. Rest of the data entry is self explanatory in nature.

Figure 5.3: Add New Equipment Page of Equipment Replacement Analysis Web

Application


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It has to be noted here that Current price and Scrap price can be rough estimates of prices

at which current equipment will be sold in the market today and after its physical life

respectively.

Once the new equipment has been added, one can choose to upload the operating cost

data in form of an excel file. This upload has been previously explained in section 3.xx.

Alternatively the user can choose to skip this step. Note that cost data has to be made

available before a user can perform replacement analysis of given equipment.

Figure 5.4: New Equipment Confirmation Page of Equipment Replacement Analysis

Web Application

The cost data can be loaded by simply clicking the browse button and pointing towards

the file containing the operating cost history.


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Figure 5.5: Cost History Upload Page of Equipment Replacement Analysis Web

Application

As soon as operating cost history is uploaded, the system plots the best-fit trends to let

the user know of the fall/rise in various parameters with respect to cumulative hours of

use. This plot is for informative purpose only but a user can detect if the trends are

grossly deviated from those expected.

Figure 5.6: Cost Model Plot of Equipment Replacement Analysis Web

Application


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5.4 EQUIPMENT DATABASE

An equipment database is created in which equipments of various manufacturers and

suppliers can be added. The expansion of the database is kept in mind and therefore an

exhaustive search is provided to find the desired equipment for the analysis. Equipment

can be found either by entering its unique asset code or by specifying its Category,

Supplier, Model Number or state in which it is operating. This would help not only to

find the equipment of interest but would also show other similar equipments which can

be used as challengers.

Figure 5.7: Database Page of Equipment Replacement Analysis Web Application


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As shown in the figure, when a search on all batching plants available in state of

Karnataka was performed the search resulted in listing three plants. Each of these plants

(assets) can be clicked to get their complete information. The following snapshots depict

the same.

Figure 5.8: Database Search Page of Equipment Replacement Analysis Web Application

Figure 5.9: Record Display Page of Equipment Replacement Analysis Web Application


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5.5 REPLACEMENT ANALYSIS

The web page performing the actual analysis can be accessed from two different ways.

One can either click on Replace this equipment while searching a given equipment or

he/she can directly click the Replace link on the website. While the defending

equipment gets automatically selected in the former case, it has to be explicitly chosen in

the later.

The website has been made user friendly and a lot of smart functions are added in it. One

of those is that as soon as defending equipment is selected, the options available as

challenger automatically gets modified in the Available Challenger dropdown boxmenu.

Figure 5.10: Replacement Analysis Page of Equipment Replacement Analysis Web

Application


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Here one can choose to either replace the current defending equipment with same new

equipment or to replace it with other new equipment in the class. One can also choose the

expected level of production load in the near future. The system gives three options: low,

average and high having average as the default value. When a lower or higher value is

selected, the models are adjusted accordingly. This eradicates the need for modelling the

production demand as demand is an external factor completely independent of equipment

economics. Thus it will remain the same for an old or a new equipment.

The minimum number of years that the new equipment after replacement would be kept

can also be chosen on this page. As already mentioned this not only reduces thecomputation time by reducing the number of iterations but also take care of the warm up

period of the equipment when its operating costs are generally high due to learning curve

effect.

5.5 RESULTS

After clicking the Analyze button on the web page, all the iterations described in the

previous chapter are performed. The relative amortized cost of owning and operating the

defending equipment with respect to challenger are calculated for each month beginning

from today and these costs are plotted as shown in the next snapshot. The algorithm

suggests replacing the equipment after the period when this relative amortized cost is a

minimum. The plot is an interactive one and the cost of replacing the equipment after nth

month is shown on moving the mouse cursor over the replacement line. The results page

also shows the number of iterations used to arrive at the result. This gives the fare idea on

time taken to carry out the analyses which can vary from 2 to 10 seconds.


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Figure 5.11: Results Page of Equipment Replacement Analysis Web Application


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5.7 CHANGING EXTERNAL DATA

One can easily change the external factors which influence the replacement decision.

These factors are categorized as:

Economic Factors

Labour Factors

Power/ Energy Factors

Figure 5.12: External Factors Page of Equipment Replacement Analysis Web Application

Only administrator-users have the privileges to enter this area and make the changes.

These factors work as a constant for all analysis purpose irrespective of class or location

of the equipment. If a user desires any change in these factors he/she can issue a request

to the administrator-users. It is to be noted here that by administrator-users we mean all


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those users who are given this privilege. Each of these factors has been briefly described

below:

5.7.1 Economic Factors

It provides to alter the Company MARR (minimum attractive rate of return) or more

specifically MARR for the Plant & Machinery business unit of Larsen & Toubro, the

corporate tax rate (flat rate) under which the company comes and the prevailing inflation

rate in the economy. Due to ongoing dynamics in the Indian economy the inflation rate is

touching close to MARR for most of the construction and equipment operationcompanies, making the role of inflation in the current research more relevant practically.

Figure 5.13: Economic factors Page of Equipment Replacement Analysis Web

Application


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5.7.2 Labour Factors

Labour Factor sheet allows the administrator-users to create a wage rate model for skilled

and semi-skilled labourers in a given state. One can enter the average annual wage rates

of skilled and semi-skilled labourers separated by a comma and starting from base year to

the current year. On updating this record a unique model for the state is generated and

stored in the database. Please note that a minimum of past five years data is required to

create a state specific wage model rate. In absence of a state wage model, the daily wage

model is derived from the Nation average of daily wage rates taking 2000 as the base

year.

Figure 5.14: Labour factors Page of Equipment Replacement Analysis Web Application


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The daily wage model is needed in accurately modelling the labour costs associated with

the equipment. If a labour cost is included in the uploaded excel file at the time of model

creation, it will supersede the labour model created from daily wage rate models.

5.7.3 Energy Factors

Similar to the Labour Factor sheet, the Energy factor sheet allows the administrator-users

to create a energy rate model for kWh and kVA rates in a given state. One can enter the

average annual rates for kWh and kVA separated by a comma and starting from base year

to the current year. On updating this record a unique model for the state is generated andstored in the database. Please note that a minimum of past five years data is required to

create a state specific energy rate model. In absence of a state energy model, the energy

model is derived from the Nation average of energy rates taking 2000 as the base year.

Figure 5.15: Energy factors Page of Equipment Replacement Analysis Web Application


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The energy model is needed in accurately modelling the power costs associated with the

equipment. If a direct power cost instead of power unit consumption is included in the

uploaded excel file at the time of model creation, the model would be superseded.


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CHAPTER 6

CONCLUSIONS

6.1 SUMMARY

As a part of developing replacement analysis strategy a framework for creating definite

cost models for diverse construction equipments has been developed. This framework can

help in creating specific cost models for any capital equipment. In order to validate the

framework, operating costs models for concrete batching plants are developed with the

framework to determine their optimal replacement age. As an alternative to the standard

net present value (NPV) criterion for selecting the replacement periods for capital

equipments, a criterion function is developed which relates the amortized discounted cost

over multiple replacement cycles. In view of the current market dynamics, the

replacement cycles were limited to two.

The proposed procedure can assist with the short term decision problem of when to

replace the current operating plant or equipment of a given age. Input to the model

consists of a forecast of the discount factor over fairly near future and an estimate of

operating costs based upon historic data. The estimate is obtained from a rigorous

regression analysis performed on the historic operating cost data. Operating costs relate

specifically to the replacement problem in that only those thought likely to influence the

replacement decision are incorporated. Factors such as tax allowances, regional

developments and technological improvements are readily encompassed within the

proposed model. The resulting web application for carrying out replacement analysis on

aging equipment automates the tedious process full of complex mathematical operations.

On top of that it makes the decision making process a collaborative one allowing the


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users to share the specific equipment models with each other thereby further improving

the decision.

6.2 LIMITATIONS

Several limitations were encountered before the start of the research, during its course

and after its completion. Although most of them were taken care of as the research

progressed, those remaining are listed below:

1. This research focuses only on the economic aspect of replacement analysis. It

professes that an equipment should be replaced when it becomes economicallyunviable to run. Other intangible factors which may influence the replacement

decision have been neglected by this study.

2. The cost model is completely dependent on the historic data and future

predictions are made by extrapolating the current model. This approach assumes a

constant uncertainty which is same as that existing in the available historic data.

3. The accuracy of the cost model formulated by historic data is as good as the

accuracy of the data provided. Feeding erroneous data to the system can lead to

unexpected trends.

4. Although some of the factors (section 3.2) play a very important role in taking a

replacement decision, they could not be included due to sheer uncertainty and

complexity surrounding them.


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6.3 SUGGESTIONS FOR FURTHER IMPROVEMENT

In order to overcome the remaining limitations, following points of suggestions are listed.

These have to be dealt with at research as well as at implementation levels:

1. Research in the area of modelling the intangible factors that influence the

replacement decision would enhance the cost model. The factors such as

availability of new equipment, government policies and improving chances of

bagging a contract by using newer equipments can be given a cost implication.

2. A better modelling of prevailing and expected market conditions like price

inflations, rise/fall in scrap value, major technological advancements, etc can provide an uncertainty model which would eventually overcome the limitation of

constant uncertainty. This can be achieved by an exhaustive market research in

construction equipments.

3. On the implementation front, the data collection process has to be fully automated

to eradicate any human intervention or manipulation. Also, the data collection

should be synchronized with the live data obtained from various equipments.

4. The number of prospective replacement cycles can be increased in future as

against two replacement cycles used in this study. This would ask for overcoming

the existing constraints both in software as well as in hardware resources.


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REFERENCES

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productivity." Journal of Construction Engineering and Management , 128(4), 357-361.

Burns, P. (1980). "Replacement Analysis." Management Accounting , 58(3), 34.

Cassady, C. R., Maillart, L. M., Bowden, R. O., and Smith, B. K. "Characterization of

optimal age-replacement policies." Anaheim, CA, USA, 170-5.

Chand, S., McClurg, T., and Ward, J. (2000). "Model for parallel machine replacement

with capacity expansion." European Journal of Operational Research , 121(3), 519-531.

Chang, P.-T. (2005). "Fuzzy strategic replacement analysis." European Journal of Operational Research , 160(2), 532-559.


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Cheevaprawatdomrong, T., and Smith, R. L. (2003). "A paradox in equipment

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