application of lean construction to reduce waste in highway projects

THE APPLICATION OF LEAN CONSTRUCTION TO REDUCE WASTE IN HIGHWAY PROJECTS

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SRIKKANT SHAH ( CP2008 ), CONSTRUCTION & PROJECT MANAGEMENT, CEPT UNIVESITY, BATCH’ 08-10

CHAPTER 1

INTRODUCTION


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CHAPTER 1 : INTRODUCTION

Construction is a key sector of the national economy for countries all around the world, as

traditionally it took up a big portion in nation’s total employment and its significant contribution to

a nation’s revenue as a whole. However, until today, construction industries are still facing

numbers of contingent problems that were bounded to beresolved since the past time. The

chronic problems of construction are well known such as Low productivity, poor safety, inferior

working conditions, and insufficient qualityand the phenomenon of the poor performance and

conditions inconstruction had long been witnessed and recorded by academics and practitioners

throughout the world.

1.0 GENERAL

Nowadays, increasing foreign competition, the scarcity of skilled labour and the need toimprove

construction quality are the key challenges faced by the construction industry.Responding to those

challenges imposes an urgent demand to raise productivity, qualityand to incorporate new

technologies to the industry. A lack of responsiveness can holdback growth, and to development of

the needed infrastructure for the constructionindustry and other key activities in the country.

With the lean construction paradigm, construction industry had started to be reviewed and

evaluated in the possibilities of implementing these new lean perspectives of production concepts in

the construction processes to optimize the overall construction performance on construction stage

as well as design stage. Performance improvement opportunities in construction can then be

addressed by adopting waste identification/ reduction strategies in the flow processes inparallel with

value adding strategies with the introduction of new management tools andwith proper trainings

and education programs. Unfortunately, these new leanconstruction concepts especially those on

wastes and values most of the times are notwell understood by construction personnel.

Particularly, waste is generally associatedwith waste of materials in the construction processes

while non-value adding activitiessuch as inspection, delays, transportation of materials and others

are not recognised as waste. As the result of that, the productivity of construction industry cannot

be fully optimized due to the narrow interpretation on the concept of wastecurrent adopted. In

this case, substantial education programs need to be arranged for allrelated parties involved in

order to implement the new process improvement strategiessuccessfully throughout the

construction process cycle.


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It is presumably that construction industries in india are facing the same generic(process-related)

problems/ wastes on construction activities which was also faced bytheir counterparts regardless

those in developed countries or developing countries.However, the main problem is the lack of

clear indicators on quantitative parameters to assess the extent of thoseproblems/ wastes to have

been impacted on the overall performance and productivity oflocal construction industries. To

date, there have not been many well-documentedquantitative studies and records on to process-

related problems/ wastes which arisen onconstruction site. As a result of that, the introduction of

the concepts and framework of new lean construction ideology are seen as an opportunity to

address the existing problems in local construction industry and utilising concepts and framework

of new lean construction ideology can then go further to formulate the extent of impactsof those

problems/ wastes on a more structured and quantitative basis.

1.1 NEED FOR STUDY

Prior to assess the severity of the process-related problems/ wastes which existed in

theconstruction processes for the local construction industries, the differentiate oftraditional and

new production/ construction concepts will have to be drawn prior tofurther investigation and

evaluation on any project performances. New measurementparameters such as waste, value, cycle

time or variability that was not covered undertraditional concepts are to be introduced into this

study as accordance to the leanconstruction ideologies and the subjects in this case; the local

construction personnelwill be subsequently examined with those new parameters to review the

level ofunderstanding and practicability in local construction industry compare to therequirements

and the concepts set forth by lean construction philosophy.

1. Examine the general perceptions of the local construction industry with the lean construction

principles of practices.

1.2 OBJECTIVES

2. Determine the degree of problems arisen from wastes identified in existing scenario and

practices in local construction industry.

3. Identify the source of wastes (classified under lean construction) & Study of the potential

project productivity improvements by reducing and eliminating the wastes as classified under

lean construction.


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1.3 METHODOLOGY

(a) Literature available for the past decade by the eminent researchers in the same field will be

carried out through the journals, research papers, government norms & regulations.

Literature review

(b) Data will be related to Lean Concepts. Data will be collected from 2 to 3 infrastructure

companies located in india.

Data Collection

(c) Data collected will be suitably analyzed and efforts will be made to apply the concepts &

compare the outcome of the companies.

Data analysis

(d) Based on above analysis a suitable conclusion will be formulated and scope for the future

work will be suggested.

Conclusion

This study is restricted to infrastructure companies dealing with highway projects in india.

1.4 SCOPE OF WORK

Literature review will be carried out till 1st week of February, 2010

1.5 RESEARCH PLAN

Data collection and data analysis will be completed by 4th week of march, 2010

Conclusion will be finalized at the 3rd week of april, 2010.


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CHAPTER 2

LITERATURE REVIEW


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2.0 INTRODUCTION

CHAPTER 2 : LITERATURE REVIEW

Construction industries worldwide have become notorious for under-performance in many

aspects such as quality, safety, productivity and product delivery to planned budgets, programmes

and client satisfaction. According to Adrain (1987), the construction industry in US has been

rated among the worst industries in term of productivity improvement. Koskela (1993) also

conducted a study to indicate the order of magnitude of non value-adding activities (waste) on

various partial studies carried out in Sweden and US. From Koskela’s data compilation, it has

shown that construction processes are characterised by high content of non value-adding activities

leading to low productivity as shown in Table 2.1

WASTE COST

Quality cost (non – conformance) 12% of total project cost

External quality cost 4% of total project cost

Lack of constructability 6 – 10 % of total project cost

Poor materials management 10 – 12% of total project cost

Excess consumption of material on site 10 % on average

Working time used for non-value adding

activities on site

Appr. 2/3rd of the total time

Lack of safety 6% of total project cost

TABLE 2.1 :- Waste in construction : compilation of existing data (koskela, 1992)

2.1 PROBLEMS IN CONSTRUCTION

The chronic problems of construction are well known: low productivity, poor safety, inferior

working conditions, and insufficient quality. (Koskela, 1993) However, most of the time, those

critical problems of construction were left unattended because people of the industry refrained to

believe or accept that there is a solution to those problems. According to Koskela (1992), the

incapability to improve the productivity level of construction projects is mainly perceived by

people in the industry as due to its peculiarities and special features: one-of-a-kind nature of


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projects, site production, and temporary multi-organisation. Most people concluded that its

fragmented nature, lack of co-ordination and communication between parties, adversarial

contractual relationships, and lack of customer focus inhibit the industry's performance.

Therefore, the organisation, planning, allocation and control of these resources, processes and

technologies are what finally determine the productivity that can be achieved.

Most of the early efforts involved new technology and process adoption from manufacturing

practices i.e. industrialisation, prefabrication and modularisation (new process adoption) and

computer integrated construction and automated construction (new technology adoption).

However, there have been no signs of major improvements to construction has resulting from both

trends of process dissemination and solutions as quoted by Koskela (2000). The main reasons

behind the failure of achieving any major improvements from both trends are mainly due to certain

key features between manufacturing and construction. A comparison with manufacturing shows the

key features, which distinguishes construction from manufacturing, is the extent of uncertainty

evident throughout the production phase as shown in Table 2.2

Start of manufacturing

production Start of construction in the field

What Highly defined Evolving as means refines ends How Highly defined. Operations plan is in

great detail based on many trails. Primary sequence of many tasks is inflexible and the interdependencies are documented and analyzed. Positions in process determine required skills

Partly defined but details un-examined.Extensive planning remains by hard logic but may change. Interdependencies due to conflicting measurements, shared resources, and intermediate products only partly understood. General craft skills to be applied in a variety of positions

Assembly objectives Produces one of a finite set of objects where details of what and how are known at the beginning of assembly

Make the only one. The details of what and how are not completely known at the beginning of assembly

Improvement strategy Rapid learning during the first units preparing for production line

Rapid learning during both planning and early sub-assembly cycles

TABLE 2.2 :- Context of manufacturing & construction production (koskela, 2000)


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2.2 CONCEPT OF NEW PRODUCTION PHILOSOPHY

The core of the new production philosophy is based on the conclusive understanding that all

production systems are constituted of 2 main activities: Conversions and Flows (waiting, moving,

and inspecting). In the new production paradigm, only conversion activities add value to the final

product whereas flow activities do not; value is determined under the value stream of the

customers with the satisfaction of their requirements and cost paid on the final product. Therefore,

the primary objectives for process/ performance/ productivity improvement under the flagship of

new production philosophy should be targeted separately. That can be done through the

improvement of flow activities by primarily focusing on reducing or eliminating them and on the

other hand, conversion activities should be focused on making them more efficient. This has

important implications for the design, control, and improvement of production processes, because

according to Koskela (1992), traditional production management paradigm sees the whole

process simply as a conversion of an input into an output that can be divided into sub-processes,

which are also conversion processes. All activities have been treated as though they were value-

adding conversions without separating from the flow processes.

Based on the understanding of the production process can be consists of both conversion and flow

activities, a generic process improvement plan based on new production philosophy can be derived

from the study of Enton (1994) on lean productivity of construction professions. The first step to

implement process improvement plan is by analysis and separation of conversions and flows

activities. For conversions activities identified, those activities should be channeled into the quality

cycles (Quality control, Quality assurance and Total Quality Management) to increase efficiency of

value added conversions. Whereas, for flow activities, the approach should be consists of way of

flows simplification (through Elimination, simplification and automation) in order to reduce or

eliminate non-value added flow activities.

The application of lean production philosophy to construction – or Lean Construction, as it has

been called by a group of collaborating researchers since 1993 (Koskela, 2000). Since then, the

enthusiasms over lean construction paradigm are intensified and widely accepted practitioners and

academics around the world under the belief that the implementation of Lean Construction will

dramatically improve construction performance and labour productivity.


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2.3 CONCEPT OF PRODUCTION A historical analysis carried out by Koskela (2000) has revealed that there are three concepts of

production where the conceptualization of production can be grouped based on the generation of

transformation-flow-value model of production theory or simply as TFV model.

TRANSFORMATION CONCEPT

The first core principle which has been used in conjunction with transformation concept stated

that: The transformation process can be decomposed into subprocesses, which also are transformation

process of breaking up the total transformation (production process) into much smaller and more

manageable transformations and eventually can be further breakdown into individual continual

tasks.

The second core principle of the transformation model is a general acceptance of independency

principle that the cost of the total process can be minimized through minimising the cost of each sub-

process. The key issue pertaining to this principle leads to the assumption that every sub-processes

of a total process are independent from each other and therefore cost minimisation can be applied

through focus on cost management in each operation, sub-process or department.

The third core principle formulated currently recommended that It is advantageous to insulate the

production process from the external environment through physical or organisational buffering. This

principle is related to the independence assumption from the second core principle as discussed

above and it reflects that the transformation process that is most important, and it is thus a

requisite to shield it from the erratic conditions in the environment.

FLOW CONCEPT

The flow view of production, firstly proposed by the Gilbreths (1922) in scientific terms, has

provided the basis for JIT and lean production. This view was firstly translated into practice by

Ford (1926). As a result, the flow view is embodied in JIT and lean production and the triumph of

the JIT and lean production has practically proven the power of this conception.

The new production concept of flow was emerged apparently from the erroneous view of

decomposition in the transformation model of production that is the intervals between

transformations, which happen to be non transformations activities. In flow concept, production is

viewed as a flow, where, in addition to transformation, there are waiting, inspection and moving


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stages. Production management equates to minimizing the share of non transformation stages of the

production flow, especially by reducing variability. In this context, flow model is looking beyond

transformation model by taking non-transformations activities into consideration as to improve

overall flow efficiency.

The first core principle of this flow concept is the introduction of time as resource in production

and therefore the main focus is in the amount of time consumed by the total transformation and its

parts by aiming for the production improvement at shortening of the total time of production.

The second core principle of the flow concept is that time is consumed by two types of activities in

the overall production flow which are transformation activities and non-transformation activities.

Gilbert (1922) categorised the non transformation activities as transfer, delay and inspection as

showed in Figure 1 and it is obvious that these non-transformation activities are unnecessary and

the less of them is better and best if there are none of them.

FIGURE 1 :- An exploration towards a production theory & its application to highway

construction (lauri, 2000)

VALUE GENERATION CONCEPT

The value generation view was initiated by Shewhart (1931) and further refined in the framework

of the quality movement but also in other circles. The value generation concept are formulated not

a same as transformation and flow concept by incorporating customer as the ultimate value

determinate to the production and argued that the goal of production is to satisfy customer needs.

This third concept of value generation concept views production as a means for the fulfillment of

customer needs. Production management equates to translating these needs accurately into a

design solution, and then producing products that conform to the specified design. It focus on

control of the transformation and flow, namely control for the sake of the customer and it is important to

highlights that the value generation concept does not focus on any particular aspect of physical


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production like transformation and flow model do but rather on its control in securing value

generated for the customer.

2.4 FLOWS IN CONSTRUCTION PRODUCTION

The production in construction is of assembly-type, where different material flows are connected

to the end product. In construction, there are 3 types of flows as suggested by Koskela (2000):

material flow (the transportation of components to the site for particular installation), location flow

(e.g. one particular trade goes through the different part of the building or construction site to get

their work done) and assembly flow (e.g. the sequential of works of assembly and installation).

There are at least seven resource flows that unite to generate the construction task as illustrated In

Figure 2. Many of these resource flows are of relatively high variability, and thus the probability of

a missing input is considerable.

Construction productions are subjected to more sources of variability and the insight gained is that

construction consists of assembly tasks involving a high number of input flows. Planning and

controlling production becomes very important and tasks and flows have to be considered in

parallel in production management because: “realization of tasks heavily depends on flows, and progress

of flows in turn is dependent on realization of task” (Koskela, 2000)

FIGURE 2 :- The preconditions for a construction task (koskela, 2000)


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2.5 CONSTRUCTION WASTE IN GENERAL

Waste in the construction industry has been the subject of several research projects around the

world in recent years. However, Most studies tend to focus on the waste of materials, which is only

one of the resources involved in the construction process. This seems to be related to the fact that

most studies are based on the conversion model, in which material losses are considered to be

synonymous of waste. Formosa, (2002) stated that many people in the industry have considered

waste are directly associated with the debris removed from the site and disposed of in landfills and

they suggested that the main reason for this relatively narrow view of waste is perhaps the fact that

it is relatively easy to see and measure. The main focus for those conventional material waste

studies in construction are seen to be restricted to physical waste or material waste in construction

and/ or the specific impacts due to the physical waste itself.

Formosa, (1999) in their earlier research paper entitled “Method for Waste Control in Building

Industry” had significantly grouped some researches and studies done by other researchers around

the world on the wastes in construction into 2 main aspects based on the impacts of the

construction waste namely :

1. Impacts on the environmental damage that result from the generation of waste material.

2. Economic aspects of waste in construction industry.

Some conclusions that were drawn from those conventional construction waste studies above such

as:

1. The waste of building materials is occasionally far higher than the nominal figures assumed

by the companies in their cost estimates.

2. There is a very high variability of waste indices from site to site. Furthermore, similar sites

might present different levels of wastes for the same material. This indicates that a

considerable portion of this wastage can be avoided.

3. Some companies do not seem to be concerned about material waste, since they do not

apply relatively simple procedures to avoid waste on site. None of them had a well-defined

material management policy, neither a systematic control of material usage.

4. The lack of knowledge was an important cause of waste. Most building firms did not know

the amount of waste they had.


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5. Most causes of waste are related to flaws in the management system, and have very little to

do with the lack of qualification and motivation of workers. Also, waste is usually the result

of a combination of factors, rather than originated by an isolated incident.

6. A significant portion of waste is caused by problems, which occur in stages that precede

production, such as inadequate design, lack of planning, flaws in the material supply system,

etc.

2.6 WASTE & VALUE LOSS IN CONSTRUCTION In search for the waste, loss of value and non value-adding activities in current construction

practices, Koskela (1992) has managed to present a few evidences from various partial studies

done by other researchers around the world apart from the material waste from conversion

activities. Koskela’s has been looking for the evidences of waste and value loss due to quality of

works, material management, non-productive time, safety and constructability.

• The waste & value loss in terms of quality cost has been 10 – 20% of the project cost. It

has also recorded the causes of these quality problems are 46% design-related, 22%

construction-related and 15% are related to material supply.

• The second factor that contributed to waste and value loss as compiled by Koskela is the

factor of constructability. It was found from a constructability report stating that projects

where constructability has been specifically addressed have reported 6 - 10% savings of

construction costs.

• Researchers have estimated that 10 – 12% savings in labour cost could be produced by

materials management systems. some researchers also reported that savings of 10% in

materials costs can be achieved from vendor cooperation in streamlining the material flow.

• It has been found that construction work flow consists of a lot of non value-adding activities

where they consume a high percentage of overall working time. All the estimation given

from the researches compiled by Koskela, the average distribution of working time used in

value-adding activities ranging around 30% to 40%. Oglesby and his co-author estimated

around 36% in 1989 while Levy in 1991 claimed that the average share of working time is

31.9 % in the United States.

• Another waste factor is lack of safety. safety-related costs are estimated to be 6 percent

of total project costs as reported by Levitt & Samelson.


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CONCEPT OF WASTE IN PRODUCTION ACTIVITIES

According to the new production philosophy, waste should be understood as any inefficiency that

results in the use of equipment, materials, labour, or capital in larger quantities than those

considered as necessary in the production of a building. Waste includes both the incidence of

material losses and the execution of unnecessary work, which generate additional costs but do not

add value to the product (Koskela 1992). Therefore, waste should be defined as any losses

produced by activities that generate direct or indirect costs but do not add any value to the

product from the point of view of the client.

The new production philosophy intend to look into and detail out the dimension of waste by

identifying non value-adding activities and introduce new measures to wastes such as additional

costs or opportunity costs especially due to time waste and value loss which very much invisible in

conversion model.

FIGURE 3 :- Performance improvement in conventional, quality and new production philosophy approaches.(koskela,2000)


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This means that there are 2 approaches to improving processes for new production philosophy

compared to conventional conversion view. One is to improve the efficiency of both value-adding

and non value-adding work, and the other is to eliminate waste by removing non value-adding

activities. Therefore, waste should be defined as any losses produced by activities that generate

direct or indirect costs but do not add any value to the product from the point of view of the

client.

The primary objectives for this new movement will be looking at value to the client and throughput,

the movement of information or materials to completion. Improvement results from reducing

waste that is the difference between the current situation and perfection, i.e., meeting customer

unique requirements in zero time with nothing in store.

2.7 WASTE CLASSIFICATION

Industry researchers and practitioners have acknowledged that there are many non-value adding

activities during the design and construction process and majority of those wasteful activities

consuming time and effort without adding value for the client. Since the beginning of a construction

project, Construction Managers have to deal with many factors that may negatively affect the

construction process, producing different types of waste (Serpell,1995). Waste includes both the

incidence of material losses and the execution of unnecessary work that generates additional costs

but does not add value to the product (Koskela, 1992). Moreover, some researchers, Alarcon

(1993), Koskela (1992) and Serpell, (1995) stated that waste in construction and

manufacturing include delay times, quality costs, lack of safety, rework, unnecessary transportation

trips, long distances, improper choice of management, methods or equipment and poor

constructability.

Regarding the possibility to control the incidence of waste, Formoso, (1999) commented that

there is an acceptable level of waste, which can only be reduced through a significant change in the

level of technological development. Based on the ratio of prevention investment cost over the cost

of waste itself, they have classified wastes into two general groups:

1. Unavoidable waste (or natural waste), in which the investment necessary to its reduction is higher

than the economy produced, The percentage of unavoidable waste in each process depends on the

company and on the particular site, since it is related to the level of technological development.


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2. Avoidable waste, when the cost of waste is significantly higher than the cost to prevent it. Waste

can also be classified according to its origin, i.e. the stage that the main root cause is related to.

Although waste is usually identified during the production stage, it can be originated by processes

that precede production, such as materials manufacturing, training of human resources, design,

materials supply, and planning as studied by Alarcon (1994), Womack and Jones, (1996),

Formoso, et al. (1999), Koskela (2000) and many others.

Shingo proposed the following waste classification whereby waste was classified by it nature, which

based on the Ohno’s framework of Toyota Production System:

1. Waste due to overproduction;

2. Waste due to wait periods;

3. Waste due to transport;

4. Waste due to system itself;

5. Waste due to stock;

6. Waste due to operation;

7. Waste due to defects;

Based on Shingo’s seven wastes, Formoso, (1999) went on to propose their main classification of

waste based on the analysis of some project sites they had carried out as shown below. It was

thought that the further classification will help managers to understand the different forms of waste,

why they occur and how to act in order to avoid them.

1. Overproduction: related to the production of a quantity greater than required or earlier

than necessary. This may cause waste of materials, man-hours or equipment usage. It usually

produces inventories of unfinished products or even their total loss, in the case of materials

that can deteriorate. An example of this kind of waste is the overproduction of mortar that

cannot be used on time.

2. Substitution: It is monetary waste caused by the substitution of a material by a more

expensive one (with an unnecessary better performance); the execution of simple tasks by

an over-qualified worker; or the use of highly sophisticated equipment where a much

simpler one would be enough.


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3. Waiting time: related to the idle time caused by lack of synchronisation and levelling of

material flows, and pace of work by different groups or equipments. One example is the idle

time caused by the lack of material or by lack of work place available for a gang.

4. Transportation: It is concerned with the internal movement of materials on site.

Excessive handling, the use of inadequate equipment or bad conditions of pathways can

cause this kind of waste. It is usually related to poor layout, and the lack of planning of

material flows. Its main consequneces are: waste of man hours, waste of energy, waste of

space on site, and the possibility of material waste during transportation.

5. Processing: related to the nature of the processing (conversion) activity, which could only

be avoided by changing the construction technology. For instance, a percentage of mortar is

usually wasted when a ceiling is being plastered.

6. Inventories: related to excessive or unnecessary inventories which lead to material waste

(by deterioration, losses due to inadequate stock conditions on site, robbery, vandalism),

and monetary losses due to the capital that is tied up. It might be a result of lack of resource

planning or uncertainty on the estimation of quantities.

7. Movement: concerned with unnecessary or inefficient movements made by workers

during their job. This might be caused by inadequate equipment, ineffective work methods,

or poor arrangement of the working place.

8. Production of defective products: it occurs when the final or intermediate product

does not fit the quality specifications. This may lead to rework or to the incorporation of

unnecessary materials to the building (indirect waste), such as the excessive thickness of

plastering. It can be caused by a wide range of reasons: poor design and specification, lack of

planning and control, poor qualification of the team work, lack of integration between design

and production, etc.

9. Others: waste of any nature different from the previous ones, such as burglary, vandalism,

inclement weather, accidents, etc.


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Some researchers have proposed some qualitative model by postulating the loss of productivity in

construction using categories of non-productive time. Researchers such as Borcherding in 1986

explained the loss of productivity in large and complex constructions using five categories of non-

productivities time as listed below:

1. Waste due to waiting or idle;

2. Waste due to travelling;

3. Waste due to slow work;

4. Waste due to ineffective work;

5. Waste due to rework

Borcherding’s five waste categories of non-productive time are found very much similar to the

categories of wastes of productive time proposed by Serpell (1995) derived from their case as

shown Figure 4 below:

FIGURE 4 :- Categories of wastes of productive time (Serpell. 1995)

However, they highlighted some limitations to the waste classification of nonproductive time. For

example the waste of time related to slow work is related to the efficiency of processes,

construction equipment and personnel. But it is difficult to measure it because it is first necessary

to know the optimal efficiency that can be achieved, which is not always possible.


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Instead of classifying the waste of productive time, Serpell, (1995) went a step further to

breakdown those wastes factors in relation of work categories. There are 3 types of work

categories as proposed:

1. Productive work (value-adding activities)

2. Contributory work (non value-adding activities but essential for conversion process): Those

contributory work which are classified as waste include transporting, instruction, measuring,

cleaning and others

3. Non-contributory work (non value-adding activities): Those non contributory work which are

classified as waste include waiting, idle time, travelling, resting, physiological needs, and rework.

There are also other categories of waste that have been mentioned in the literature, such as

accidents, working under sub-optimal conditions (Koskela 2000), design and products that do not

meet users’ needs. (Womack and Jones 1996) The main role of existing classification of waste is

to call the attention of people to most likely problems, since not all waste is obvious: it “often

appears in the guise of useful work.” (Shingo 1988).

2.8 KEY CONSTRUCTION WASTE CAUSES

After understanding the classification of waste, it is important to examine the type of possible

causes that lead to the occurrence of waste in construction process. This is very important because

just by knowing the waste itself just would help to monitor them but not reduce or eliminate them

from the process loops. To work out a continuous improvement strategy in reducing and

eliminating those wastes in construction processes, the origin of the waste itself has to be

identified.

A typical waste identification survey underlined a few examples of waste sources according to

different area of functions such as administration, use of resources and information systems. Several

potential sources of waste can be grouped under the particular area of functions and it can be

created to suit the need of particular projects such as the diagnostic survey developed by students

Francisco Lowener, Francisco Lira and Marcelo Beratto as documented by Alarcon (1994) listed

down the following potential sources of waste in their project:


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A. Administration

1. Unnecessary requirements

2. Excessive control

3. Lack of control

4. Poor planning

5. Bureaucracy

B. Use of resources

1. Surplus

2. Shortage

3. Misuse

4. Poor distribution

5. Poor quality

6. Availability

C. Information systems

1. Unnecessary

2. Defective

3. Late

4. Unclear

Serpell (1995) on the other hand identified several controllable causes of waste. Although his

study was mainly concentrated on wasted time but the classification of the causes to waste is found

rather structured and detailed compared to the previous listed in waste identification survey. They

divided the controllable wastes as identified from their research projects into three different

activities, which associate to flows, conversions, and management activities.

1. Controllable causes associated to flows The principal flow causes were as follow:

a) Resources

• Materials: Lack of materials at the work place; materials are not well distributed; inadequate

transportation means

• Equipment: Non availability; inefficient utilisation; inadequate equipment for work needs

• Labour: personal attitudes of workers; stoppage of work

b) Information

• Lack of information;

• Poor information quality

• Timing of delivery is inadequate


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2. Controllable causes associated to conversions

The following causes were identified:

a) Method

• Deficient design of work crews

• Inadequate procedures

• Inadequate support to work activities

b) Planning

• Lack of work space

• Too much people working in reduced space

• Poor work conditions

c) Quality

• Poor execution of work

• Damages to work already finished

3. Controllable causes associated to management activities

The following causes were identified:

a) Decision making

• Poor allocation of work to labour

• Poor distribution of personnel

b) Ineffective supervision/ control

• Poor or lack of supervision


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2.9 MODELLING WASTE & PERFORMANCE IN CONSTRUCTION

The introduction of new production philosophies in construction requires new measures of

performance (Koskela, 1992), such as waste, value, cycle time or variability. The shortcomings of

the traditional control systems, and models are unable or not appropriate to measure those new

performance elements but Alarcon (1993) suggested that some of the concepts developed in

previous research can be utilised in modeling new performance elements for construction required

for continuos improvement.

It is worthwhile to point out some of opinions of different researchers and authors related to the

extent of performance elements in the aspects of construction process. Among all, one of the most

classical opinions was from Sink (1985, as documented by Alarcon 1993). Sink has characterised

performance in a broad definition, as 7 criteria or elements on which management should focus its

efforts on: Those 7 criteria or elements are as explained below:

1. Effectiveness: A measure of accomplishment of the ‘right’ things:

• On time (timeliness),

• Right (quality),

• All the ‘right’ things (quantity), where ‘things’ are goals, objectives, activities and so forth,

2. Efficiency: A measure of utilization of resources. It can be represented as a ratio of resource

expected to be consumed divided by the resources actually consumed

3. Quality: A measure of conformance to specifications. In construction projects, quality has 2

dimensions:

• The first and overall one is that of the completed project functioning as the owner

intended

• The second concerns the many details involved in producing the results.

4. Productivity: Theoretically this is defined as a ratio between output and input and it is

primary measured in terms of cost. In the context of the construction industry, the output is

the structure or facility that is built or some components of it. The major input into the

construction process includes work force, materials, equipment, management, energy and

capital.


23


5. Quality of work life: A measure of employees’ affective response to working and living in

organizational systems. Often, the management focus is on insuring that employees are

‘satisfied’, safe and secure and so forth

6. Innovation: This is the creative process of adaptation of product, service, process or

structure in response to internal as well as external pressures, demands and changes, needs and

so forth

7. Profitability: A measure or a set of measures of the relationships between financial resources

and uses for those financial resources. Embarking with the new production philosophies,

Koskela (1992) has proposed some new measures as required for construction, to stimulate

continuous improvement such as:

1. Waste: Number of defects, rework, number of design errors and omissions,

number of change orders, safety costs, excess consumption of materials, etc;

2. Value: Value of the output to the internal customer;

3. Cycle time: Cycle time of main processes and sub processes;

4. Variability: Deviations from the target, such as schedule performance.

The problem of performance evaluation is a multi-attribute or multi-criteria one. Generally, the

evaluations of performance in construction are concentrated on few aspects only mainly on

profitability and productivity. Furthermore, different managers probably will use different

performance elements and some will have different weight for each individual measures. Therefore,

a model for evaluation or prediction must have the flexibility to include the individual organisational

objectives in the evaluation process. It also must have the ability to examine the effect of changes in

those objectives in the evaluation process.

2.10 RESEARCH GAP

From the study of literature review of last 15 years, it is found that all the researches on

lean construction has been done on housing & commercial complexes .

But , uptil now no eminant research has been carried out on highway & trasnsportation

sector.

So, efforts are made to analyze the lean construction principle in highway sector.


24


CHAPTER 3

DATA COLLECTION


25


CHAPTER 3 : DATA COLLECTION

3.1 METHOD OF RESEARCH

The purposes of this research thesis is to see whether the lean construction principles of waste

concepts have been well comprehended, accepted and adopted by the highway personnel especially

in waste recognition, reduction and elimination for the continuous improvement in construction

processes.

A quantitative research approach was adopted for this thesis requiring the development and

dissemination of a questionnaire survey. Due to the population of this research are virtually too

difficult to be quantified as the main targeted respondents would include all personnel who has

direct managerial experiences in construction field, the non-probability sampling methods will be

adopted in this research instead of probability sampling. Purposive sampling for specific groups or

types of respondents will be conducted by using expert sampling technique which involves the

assembling of a sample of managerial personnel with known experience and expertise in managing

construction field processes.

The thesis is conducted through structured questionnaires where those questionnaires were sent

to the particular “qualified” respondents. The respondents were approached through their

companies and firms namely L&T ECC, IRB infrastructures, BACKBONE enterprises, LASA INDIA,

MSK PROJECTS(I) LTD.etc.

This thesis was postulated around determining the general perceptions and actions of the

construction personnel against wastes in construction and the concept of nonproductive time or

wasted time were then integrated into the research process as the key element of lean

construction philosophy regarding flow concept. In this case, Waste in construction process is

classified into three main categories, which are direct conversion waste, non-contributory time

waste and contributory time waste. 19 waste elements are outlined consists of 9 direct conversion

wastes, 7 non-contributory time wastes and 3 contributory time wastes as shown in Table 3.1


26


In response to the examine the frequencies causes of wastes and their inter-relation with waste

elements, several waste causes factors were categorised into 5 main groups of cause factors which

are Management & Administration Factors (4 factors), People Factors (6 factors), Execution Factors

(6 factors), Material Factors (6 factors) and Information and Communication Factors (3 factors).

The entire breakdown of the waste cause factors is shown in Table 3.2

Table 3.1 : waste elements in 3 separate waste groups


27


Table 3.2 :- waste causes factor group


28


3.2 PROFILE OF RESPONDANTS

A randomly selected group of targeted respondents consists of those personnel who have a

commanding role in the highway project and resource management and extensive site experiences

were targeted as respondents for the sample survey. There has been a wide spectrum of personnel

with different position and job title, which had been responded to the survey and for the purpose

of analysis and comparison, the whole sample of respondents have been regrouped into 2 main

categories which are:-

1. Project management orientated group

2. Site operative management orientated group

Project management orientated group will feature those who have relatively more responsibilities

in overall project execution and resource management and not so much on site operative

management by its nature of job scope. Therefore, this group will involve personnel more on

planning, inter-coordinating and directing role in construction process and as for the sample

respondents for this research will include project managers, general managers, project schedulers/

planners, quantity surveyors.

Site operative management orientated group will feature those who have relatively more

responsibilities on the site operative management by its nature of job scope. The group will mainly

involve personnel in solving construction problems on site, more on intra-coordinating with

internal groups and trades, and as for the sample respondents for this research will include site

managers, site engineers, resident engineer and senior quality manager.

3.3 STRUCTURE OF QUESTIONNAIRE

The structure of questionnaires is divided into 5 main sections. (Refer sample questionnaire on

Appendix V) The first 2 sections of questionnaires are intended to examine the general perception

and acceptance of Lean Construction philosophy of local construction industries based on the

respondents’ waste concepts.


29


In this case, the respondents were asked to recognise 19 wastes elements and their personnel

experiences in controlling those waste elements during construction processes. There are 2-

options available for the respondents and there were required to answer whether the wastes

elements as listed is a waste or non-waste and whether they are controlled or not controlled

during the construction processes.

The third and fourth sections are intended to review the extent of waste problems in existing local

industry by ranking them in term of frequencies of occurrences and rate the likelihood of particular

waste sources/ causes in their construction practices where they work. For section 3, Respondents

were able to identify how frequently the waste occurred using 5 categories: (1) Never; (2) Very

Rare; (3) Seldom; (4) Frequent; and (5) Very Frequent and the respondents were provided with five

different scales from 1 (no significant effect variable) to 5 as (high detrimental effect variable);

For section 4, Respondents were asked to determine the likelihood of particular waste sources/

causes using 4 categories: (1) Most unlikely; (2) Unlikely; (3) Likely; (4) Most Likely and the

respondents were provided with five different scales from 1 (no significant likelihood) to 5 as (high

detrimental likelihood)

The fifth section is to examine the relevant sources of wastes as outlined in the fourth section to

have caused the particular construction wastes. The respondents were asked to identify the most

possible causes and other possible causes to the wastes elements in order to create a matrix table

between construction wastes and their sources of wastes.

3.4 SCORE ASSIGNMENT

Score assignment is a process of assigning values for each of the item and this is an important

process of conducting inferential analysis especially for correlation test using Pearson-r where

aggregation of points are required for this research. Score assignment for section 1 and 2, each

positive answer is assigned with 2 points and each negative answer is assigned with 1 point. Based

on the waste categories in Table 3.1, the maximum points for direct conversion wastes that can

be aggregated for each case is (2 X 9) equal to 18 points and the minimum of (1 X 9) equal to 9

points; Maximum points for non-contributory time waste is (2 X 7) or 14 points and minimum of (1

X 7) or 7 points whereas for maximum points for contributory time wastes is (2 X 3) or 6 points

and minimum of (1 X 3) or 3 points.


30


Score assignment for section 3 and 4 is based on the multiple-scale format. For section 3, points are

ranged from 1 to 5 and maximum points that can be aggregated for direct conversion wastes is (5

X 9) or 45 points, minimum of (1 X 9) or 9 points; for noncontributory time wastes, maximum that

can be achieved is (5 X 7) or 35 points and minimum of (1 X 7) or 7 points while maximum for

contributory time wastes is (5 X 3) or 15 points and minimum of (1 X 3) or 3 points. For section 4,

points are ranged from 1 to 4 but since correlation are not going to be tested in this section but

rather each item is going to be tested separately with One-way t-test for ranking purposes,

therefore not aggregation of points are required.

3.5 METHOD OF ANALYSIS

After all the primary data have been collected and processed, those data will then be analysed

according to the appropriate analysis methods. Analysis methods in this research are mainly divided

into 2 parts: (1) Descriptive analysis and (2) Inferential statistical analysis. Descriptive statistical

analysis is used to present the background profiles about the respondents and provide further

information for the inferential statistical analysis, besides that, the analysis on the descriptive data

about the waste recognition and waste control events in section 1 & 2 will also be conducted under

the same category. Inferential statistical analysis will be used to test certain research hypothesis,

type of analysis tools to be used include Coefficient Pearson r for correlation testing and one-way

t-test for frequencies ranking.

Inferential statistic analysis will use correlation Pearson-r to conduct testing on 9 hypotheses to see

whether any significant inter-relationship existed between understanding of wastes and actual

control practices of wastes in construction processes based on 3 cases of waste categories. The 9

hypotheses are:

Hypothesis 1: There is inter-relationship between construction’s direct conversion wastes

are been perceived with the tendency to control those wastes.

Hypothesis 2: There is inter-relationship between construction’s direct conversion wastes

are been perceived with the frequencies of occurrences of such wastes during the

processes.


31


Hypothesis 3: There is inter-relationship between the tendency to control those

construction’s direct conversion wastes with the frequencies of occurrences of such wastes

during the processes.

Hypothesis 4: There is inter-relationship between construction’s non-contributory time

wastes are been perceived with the tendency to control those wastes.

Hypothesis 5: There is inter-relationship between construction’s non-contributory time

wastes are been perceived with the frequencies of occurrences of such wastes during the

processes.


construction’s non-contributory time wastes with the frequencies of occurrences of such

wastes during the processes.

Hypothesis 7: There is inter-relationship between construction’s contributory time wastes

are been perceived with the tendency to control those wastes.

Hypothesis 8: There is inter-relationship between construction’s contributory time wastes

are been perceived with the frequencies of occurrences of such wastes during the

processes.


construction’s contributory time wastes with the frequencies of occurrences of such wastes

during the processes.

In every case above, the correlation Pearson-r will tell us the magnitude and direction of the

association between two variables. In SPSS, the outcomes of the Pearson-r analysis will provide us

three pieces of information: (1) the correlation coefficient, (2) the significance and (3) the number

of cases (N). The correlation coefficient is a number between +1 and -1. This number tells us about

the magnitude and direction of the association between two variables. The magnitude is the

strength of the correlation. The closer the correlation is to either +1 or -1, the stronger the

correlation. If the correlation is 0 or very close to zero, there is no association between the two

variables. The direction of the correlation tells us how the two variables are related.

One-Way t-test will be carried out basically to get the ranking on the frequencies of occurrence of

the wastes elements in section 3 and the likelihood of recognition certain wastes causes factors as

in section 4. In SPSS, the outcomes of the One-Way t-test analysis will provide us four pieces of

information: (1) the number of cases (N), (2) the mean value, (3) the standard deviation and (4) the


32


standard error means. The ranking will be done separately in a descending order from the greatest

magnitude of the mean value to the lowest mean value to differentiate the degree of frequencies

and likelihood from the less significant to the most significant as rated by the respondent of the

research.

The last part of the analysis will be involving the development of the Causes and Effects Matrix table

by combining all the inputs by the respondents in section 5 into the whole list of construction

wastes and waste causes table. From there, descriptive statistic analysis will take place in sorting out

the wastes causes factors and put them into 6 wastes factors as discussed previously and represent

the Matrix Table in Bar charts format for easy interpretation of the results.

3.6 COMPOSITION OF RESPONDANTS

The respondents for this research consists of 81 project and site management personnel with a

wide spectrum of positions ranging from project manager, project planner, general manager,

construction manager, resident engineer, quality engineer, highway engineer, site engineer &

manager. The composition of the respondent’s position are shown in the Figure 5.

FIGURE 5:- COMPOSITION OF RESPONDANTS POSITION

project manager

project planner

general manager

construction

manager

resident engineer

quality engineer

highway engineer

site engineer

Series 1 15 10 4 10 10 10 8 14

0

2

4

6

8

10

12

14

16

NO

'S


33


3.7 PROJECT DETAILS

CASE STUDY – 1 PROJECT:- 4 LANING OF ROAD FROM AHMEDABAD TO MALIYA ON SH – 7 & SH –

17 ON BOT BASIS.

PROJECT LENGTH ;- 180 KMS.

DURATION :- 30 MONTHS.

CONTRACT PRICE :- Rs. 1800 CR appx..

DATE OF START :- OCTOBER 2009.

CLIENT :- GSRDC(GUJARAT STATE ROAD DEVELOPMENT CORPORATION)

INDEPENDENT CONSULTANT :- BCEOM LTD.

DESIGN CONSULTANT :- LEA ASSOCIATES SOUTH ASIA PVT LTD.

EPC CONTRACTOR:- L & T ECC LTD.

CASE STUDY – 2

PROJECT:- 6 LANING FROM SURAT TO DAHISAR AS BOT (TOLL) PROJECT

PROJECT LENGTH ;- 239 KMS



DATE OF START :- 20TH FEBRUARY 2009.

DATE OF COMPLETION :- 19TH AUGUST 2011.

CLIENT :- NHAI(NATIONAL HIGHWAY AUTHORITY OF INDIA)

INDEPENDENT CONSULTANT :- INTERNATIONAL CONSULTING TECHNOLOGIES

PVT. LTD

PMC CONSULTANT :- FRISHMAN PRABHU PVT. LTD.

EPC CONTRACTOR:- IRB INFRASTRUCTURE DEVELOPERS LTD.


34


CASE STUDY – 3

PROJECT:- 4 LANING FROM NAGPUR TO HYDERABAD SECTION ON NH – 7.




DATE OF START :- 4TH OCTOBER 2009.

DATE OF COMPLETION :- 3RD OCTOBER 2010.

CLIENT :- NHAI(NATIONAL HIGHWAY AUTHORITY OF INDIA)

INDEPENDENT CONSULTANT :- AARVI ENCON PVT .LTD

EPC CONTRACTOR:- IRB INFRASTRUCTURE DEVELOPERS LTD.

CASE STUDY – 4

PROJECT:- 4 LANING OF ROAD ON SH – 31FROM LEBAD TO NAGDA




DATE OF START :- FEBRUARY 2009.

DATE OF COMPLETION :- AUGUST 2010.

CLIENT :- MPRDC (MADHYA PRADESH ROAD DEVELOPMENT CORPORATION)

INDEPENDENT CONSULTANT :- ARTEFACTS PROJECTS LTD.

TOTAL PMC :- STUP CONSULTANTS PVT. LTD

EPC CONTRACTOR:- PAN INDIA INFRASTRUCTURES PVT. LTD.


35


CASE STUDY – 5

PROJECT:- 6 LANING FROM BHOPAL TO VISHADA PACKAGE - 1 ON SH – 18.




DATE OF START :- OCTOBER 2009.

DATE OF COMPLETION :- MARCH 2011.

CLIENT :- MPRDC (MADHYA PRADESH ROAD DEVELOPMENT AUTHORITY)

INDEPENDENT CONSULTANT :- AARVI ENCON PVT .LTD

CONTRACTOR :- BACKBONE ENTERPRISES LTD.

CASE STUDY – 6

PROJECT:- 4 LANING OF ROAD FROM HALOL – GODHRA – SHAMLAJI ON STATE

HIGHWAY NO. 5 ON BOT (TOLL) BASIS.






CLIENT :- GSRDC (GUJARAT STATE ROAD DEVELOPMENT CORPORATION)

INDEPENDENT CONSULTANT :- BCEOM

DESIGN CONSULTANT :- LEA ASSOCIATES SOUTH ASIA PVT. LTD

EPC CONTRACTOR:- L&T ECC LTD.


36


CASE STUDY – 7

PROJECT:- 6 LANING OF ROAD FROM PIMPALGAON – NASIK – GONDE ON

NATIONAL HIGHWAY NO. 3 ON BOT (TOLL) BASIS.




DATE OF START :- MARCH 2010.

DATE OF COMPLETION :- SEPTEMBER 2012.

CLIENT :- NHAI (NATIONAL HIGHWAY AUTHORITY OF INDIA)

INDEPENDENT CONSULTANT :- SHELADIA ASSOCIATES PVT. LTD

THEME ENGINEERING SERVICES

PMC CONSULTANT :- LEA ASSOCIATES SOUTH ASIA PVT. LTD

EPC CONTRACTOR:- L&T ECC LTD.

CASE STUDY – 8

PROJECT:- DEVAS BHOPAL CORRIDOR PROJECT




DATE OF START :- DECEMBER 2008.

DATE OF COMPLETION :- NOVEMBER 2011.

CLIENT :- MPRDC (MADHYA PRADESH ROAD DEVELOPMENT CORPORATION)

INDEPENDENT CONSULTANT :- SAI CONSULTING ENGINEERS PVT. LTD

PMC CONSULTANT :- ESSEL GROUP.

EPC CONTRACTOR:- MSK PROJECTS (I) LTD.


37


CASE STUDY – 9

PROJECT:- 4 LANING OF ROAD FROM LUCKNOW – AYODHYA BYPASS ON

NATIONAL HIGHWAY NO. 28






CLIENT :- NHAI (NATIONAL HIGHWAY AUTORITY OF INDIA)

PMC CONSULTANT :- LEA ASSOCIATES SOUTH ASIA PVT. LTD

EPC CONTRACTOR:- HINDUSTAN CONSTRUCTION COMPANY LTD.

CASE STUDY – 10

PROJECT:- 4/6 LANING OF EXISTING 2 LANE CARRIAGEWAY ON NH – 5 FROM

BHADRAK TO BALASORE

PROJECT LENGTH ;- 62.64 KMS



DATE OF START :- DECEMBER 2008.

DATE OF COMPLETION :- NOVEMBER 2011.

CLIENT :- NHAI (NATIONAL HIGHWAY AUTHORITY OF INDIA)

INDEPENDENT CONSULTANT :- SAI CONSULTING ENGINEERS PVT. LTD

EPC CONTRACTOR:- BACKBONE ENTERPRISES LTD.


38


CHAPTER 4

DATA ANALYSIS


39


CHAPTER 4 : DATA ANALYSIS

4.0 INTRODUCTION

This chapter will present all the results obtained from the data analysis waste concepts and waste

causes factors in construction processes. Descriptive statistic analysis and inferential statistic

analysis will be utilised to present the results. The presentation of analysis of descriptive statistic

analysis will be conducted in the form of bar charts, pie charts and matrix tables to show the

distribution and frequencies of the particular variables. The presentation of analysis of inferential

statistic analysis will be done by using result outputs generated directly from SPSS 16. The

respondents for this research consists of 81 project and site management personnel with a wide

spectrum of positions ranging from project manager, project planner, general manager, construction

manager, resident engineer, quality engineer, highway engineer, site engineer & manager.

4.1 RESPONDANTS WASTE PERCEPTIONS & CONTROL ACTION

The descriptive analysis on the respondent’s waste perceptions and control actions will mainly

focusing on identifying the numbers of counts on wastes recognised and waste events controlled as

reckon by the respondents for 3 waste categories namely, direct conversion wastes, non

contributory wastes and contributory wastes.

Since the lean construction philosophy considered all those waste elements as tabulated in the

questionnaires as construction wastes which need to be reduced, eliminated or somehow

controlled, the degree of perceptions on wastes for the local construction industries eventually can

be verified by determining the numbers of positive counts on each of those wastes elements.

Besides that, an analysis over a matrix tables by crosstabbing both the waste concepts and waste

control actions will be carried out to study the frequencies of 4 different potential scenarios which

are anticipated to be occurred.


40


4.2 ANALYSIS ON DIRECT CONVERSION WASTE Under this category, there are 9 wastes elements. Those items are indexed as F, G, H, I, J, K, L, N,

S in the section A, B and C of the questionnaires. For the total of 81 respondents by calculation as

(9 X 81), it sums up a total of 729 overall counts of inputs. For construction waste recognition, all

the inputs are tabulated in Table 4.1 below and a total of 612 positive counts are recorded or

approximately 84% and it is shown a high recognition on the waste concepts for the elements

tested in this category.

This analysis concluded that a high recognition rate on direct conversion wastes by the

respondents. The breakdown of numbers of the waste elements recognised as wastes under this

direct conversion category are shown in Figure 6. The result shows that Item N: (Materials for

rework/ repair works/ defective work) is the most recognized construction wastes with 78 positive

counts while Item F: (Over-allocation/ Unnecessary equipment) is the least recognised construction

wastes with only 57 positive counts under the direct conversion waste category.

FIGURE 6 :- BREAKDOWN OF DIRECT CONVERSION WASTE

RECOGNITION CASES

F G H I J K L N S

NON WASTE 24 15 21 15 12 9 6 3 12

WASTE 57 66 60 66 69 72 75 78 69

0

10

20

30

40

50

60

70

80

90


41


F G H I J K L N S 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 3 2 2 2 1 2 2 2 2 2 4 2 2 2 2 2 2 2 2 2 5 1 1 1 1 2 2 2 2 2 6 2 2 2 2 2 2 2 2 2 7 2 2 2 2 2 2 1 2 2 8 1 1 1 2 1 1 2 1 2 9 1 1 1 2 2 2 2 2 2 10 1 1 1 2 1 1 1 2 2 11 2 2 2 2 2 2 2 2 2 12 2 2 2 1 2 2 2 2 2 13 2 2 2 2 1 2 2 2 1 14 2 2 2 2 2 2 2 2 2 15 2 2 2 1 2 1 2 2 2 16 2 2 2 2 2 2 2 2 2 17 2 2 2 2 2 2 2 2 2 18 2 2 2 2 2 2 2 2 2 19 1 2 1 2 2 2 2 2 2 20 2 2 2 2 2 2 2 2 2 21 2 2 2 2 1 2 2 2 1 22 2 2 1 1 2 2 2 2 2 23 2 2 2 2 2 2 2 2 2 24 1 2 2 2 2 2 2 2 2 25 2 2 2 2 2 2 2 2 1 26 1 1 1 2 2 2 2 2 2 27 1 2 2 2 2 2 2 2 2 28 2 2 2 2 2 2 2 2 2 29 2 2 2 2 2 2 2 2 1 30 2 2 2 1 2 2 2 2 2 31 2 2 2 2 2 2 2 2 2 32 1 1 1 1 2 2 2 2 2 33 2 2 2 2 2 2 2 2 2 34 2 2 2 2 2 2 1 2 2 35 1 1 1 2 1 1 2 1 2 36 1 1 1 2 2 2 2 2 2 37 1 1 1 2 1 1 1 2 2 38 2 2 2 2 2 2 2 2 2 39 2 2 2 1 2 2 2 2 2 40 2 2 2 2 1 2 2 2 1 41 2 2 2 2 2 2 2 2 2 42 2 2 2 1 2 1 2 2 2 43 2 2 2 2 2 2 2 2 2 44 2 2 2 2 2 2 2 2 2 45 2 2 2 2 2 2 2 2 2 46 1 2 1 2 2 2 2 2 2 47 2 2 2 2 2 2 2 2 2 48 2 2 2 2 1 2 2 2 1 49 2 2 1 1 2 2 2 2 2

1) NON WASTE 2) WASTE

LEGEND :-

F) Over allocation/unnecessary equipments on site

G) Over allocation/unnecessary materials on site

H) Over allocation/unnecessary workers on site

I) Unnecessary procedures & working protocols

J) Material loss/stolen from site during construction periods

K) Material deterioration/damage during construction periods

L) Mishandling or error in construction applications/installation

N) Material for rework/repair works/defective works

S) Accidents on site


42


50 2 2 2 2 2 2 2 2 2 51 1 2 2 2 2 2 2 2 2 52 2 2 2 2 2 2 2 2 1 53 1 1 1 2 2 2 2 2 2 54 1 2 2 2 2 2 2 2 2 55 2 2 2 2 2 2 2 2 2 56 2 2 2 2 2 2 2 2 1 57 2 2 2 1 2 2 2 2 2 58 2 2 2 2 2 2 2 2 2 59 1 1 1 1 2 2 2 2 2 60 2 2 2 2 2 2 2 2 2 61 2 2 2 2 2 2 1 2 2 62 1 1 1 2 1 1 2 1 2 63 1 1 1 2 2 2 2 2 2 64 1 1 1 2 1 1 1 2 2 65 2 2 2 2 2 2 2 2 2 66 2 2 2 1 2 2 2 2 2 67 2 2 2 2 1 2 2 2 1 68 2 2 2 2 2 2 2 2 2 69 2 2 2 1 2 1 2 2 2 70 2 2 2 2 2 2 2 2 2 71 2 2 2 2 2 2 2 2 2 72 2 2 2 2 2 2 2 2 2 73 1 2 1 2 2 2 2 2 2 74 2 2 2 2 2 2 2 2 2 75 2 2 2 2 1 2 2 2 1 76 2 2 1 1 2 2 2 2 2 77 2 2 2 2 2 2 2 2 2 78 1 2 2 2 2 2 2 2 2 79 2 2 2 2 2 2 2 2 1 80 1 1 1 2 2 2 2 2 2 81 1 2 2 2 2 2 2 2 2

TABLE 4.1 :- INPUTS OF CONSTRUCTION WASTE RECOGNITION FOR DIRECT CONVERSION WASTE


43


For construction waste events control, the total counts will calculated as (9 X 81) equal to 729Nos.

of inputs as tabulated in Table 4.2. A total of 531 positive counts are recorded appx. 73% and it

shows a slight drop in percentage on the waste control practices for the elements tested compared

to the construction waste recognized previously by the same set of respondents. In other words,

the respondents recognise the direct conversion wastes more than eventually control them.

However, the analysis result still shows that there are high control exercises on direct conversion

wastes as reported by the respondents. The breakdown of numbers of the waste elements

recognised as wastes under this direct conversion waste category are shown in Figure 7. From

chart in Fig. however it shows that Item F: (Overallocation/ Unnecessary equipment on site) have the

highest positive counts (69 Nos) on event controlled while Item S: (Accidents on site) is recorded as

the least event controlled with 39 Nos. of positive counts.

By cross tabbing of both Table 4.1 & 4.2 will result in a matrix table as show in Table 4.3 below.

This matrix table can be used to explain the inter-relationship between the direct conversion waste

concepts of the respondents with their actual control practices on construction processes. As

anticipated, there are 4 potential scenarios as observed, which are Case 1: Waste recognised and

controlled; Case 2: Waste not recognised and not controlled; Case 3: Waste recognised but not

controlled and Case 4: Waste not recognised but controlled.

From the analysis, it is found that Case 1 is the most occurrence scenario with 438 cases (60%)

followed by Case 3: 174 cases (23.86%), Case 4: 93 cases (12.75%) and finally Case 2: 24 cases

(3.3%). However, These results show that over half of the direct conversion construction wastes

have fully been recognised and controlled simultaneously but this analysis result is not very

convincing as there are still very high percentage of cases where wastes were partially recognised

and controlled and not recognised and controlled at all.


44


F G H I J K L N S 1 2 2 2 2 1 1 2 2 1 2 2 2 2 2 1 1 2 2 1 3 2 2 2 2 1 2 2 2 1 4 2 2 2 2 1 2 2 2 1 5 1 1 1 2 2 2 2 2 2 6 2 2 2 2 2 2 2 2 2 7 2 2 2 1 1 2 1 2 1 8 2 2 2 2 2 2 2 2 2 9 2 2 2 2 2 2 2 2 2 10 2 2 2 1 1 1 2 1 1 11 1 1 1 2 2 2 1 2 1 12 2 2 2 1 2 2 2 1 2 13 2 2 2 1 2 2 1 2 2 14 2 1 1 1 1 1 1 1 1 15 2 2 2 2 2 2 2 1 1 16 2 2 2 2 2 1 2 2 2 17 2 2 2 2 2 2 2 2 2 18 1 1 1 2 2 2 2 2 2 19 2 2 2 2 2 2 2 2 1 20 2 2 2 2 1 2 2 2 1 21 2 2 2 1 2 2 2 2 2 22 2 2 2 2 2 2 2 2 2 23 1 1 1 2 2 2 1 1 1 24 2 1 1 1 2 2 2 1 1 25 2 2 2 1 1 1 2 2 2 26 2 2 2 2 1 1 2 2 1 27 2 2 2 2 2 2 2 2 2 28 2 2 2 2 1 1 2 2 1 29 2 2 2 2 1 1 2 2 1 30 2 2 2 2 1 2 2 2 1 31 2 2 2 2 1 2 2 2 1 32 1 1 1 2 2 2 2 2 2 33 2 2 2 2 2 2 2 2 2 34 2 2 2 1 1 2 1 2 1 35 2 2 2 2 2 2 2 2 2 36 2 2 2 2 2 2 2 2 2 37 2 2 2 1 1 1 2 1 1 38 1 1 1 2 2 2 1 2 1 39 2 2 2 1 2 2 2 1 2 40 2 2 2 1 2 2 1 2 2 41 2 1 1 1 1 1 1 1 1 42 2 2 2 2 2 2 2 1 1 43 2 2 2 2 2 1 2 2 2 44 2 2 2 2 2 2 2 2 2 45 1 1 1 2 2 2 2 2 2 46 2 2 2 2 2 2 2 2 1 47 2 2 2 2 1 2 2 2 1 48 2 2 2 1 2 2 2 2 2 49 2 2 2 2 2 2 2 2 2

1) NOT CONTROL 2) CONTROL

LEGEND :-











45


TABLE 4.2 :- Inputs of construction waste event control for direct conversion waste

FIGURE 7 :- BREAKDOWN OF DIRECT

CONVERSION WASTE EVENT CONTROL CASES

F G H I J K L N S

NON CONTROL 12 18 18 24 30 21 18 18 42

CONTROL 69 63 63 57 51 60 63 63 39

0

10

20

30

40

50

60

70

80

90

NO

'S

50 1 1 1 2 2 2 1 1 1 51 2 1 1 1 2 2 2 1 1 52 2 2 2 1 1 1 2 2 2 53 2 2 2 2 1 1 2 2 1 54 2 2 2 2 2 2 2 2 2 55 2 2 2 2 1 1 2 2 1 56 2 2 2 2 1 1 2 2 1 57 2 2 2 2 1 2 2 2 1 58 2 2 2 2 1 2 2 2 1 59 1 1 1 2 2 2 2 2 2 60 2 2 2 2 2 2 2 2 2 61 2 2 2 1 1 2 1 2 1 62 2 2 2 2 2 2 2 2 2 63 2 2 2 2 2 2 2 2 2 64 2 2 2 1 1 1 2 1 1 65 1 1 1 2 2 2 1 2 1 66 2 2 2 1 2 2 2 1 2 67 2 2 2 1 2 2 1 2 2 68 2 1 1 1 1 1 1 1 1 69 2 2 2 2 2 2 2 1 1 70 2 2 2 2 2 1 2 2 2 71 2 2 2 2 2 2 2 2 2 72 1 1 1 2 2 2 2 2 2 73 2 2 2 2 2 2 2 2 1 74 2 2 2 2 1 2 2 2 1 75 2 2 2 1 2 2 2 2 2 76 2 2 2 2 2 2 2 2 2 77 1 1 1 2 2 2 1 1 1 78 2 1 1 1 2 2 2 1 1 79 2 2 2 1 1 1 2 2 2 80 2 2 2 2 1 1 2 2 1 81 2 2 2 2 2 2 2 2 2


46


F G H I J K L N S 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

Case 1 :- waste & control

Case 2 :- Nonwaste & non control

Case 3:- waste & non control

Case 4 :- non waste & control

LEGEND :-











47


41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81

TABLE 4.3 :- MATRIX TABLE FOR DIRECT CONVERSION WASTE


48


4.3 ANALYSIS ON NON CONTRIBUTORY TIME WASTE

Under this category, there are only 7 wastes elements indexed as A, B, C, D, E, M and O in the

section A, B and C of the questionnaires. For the total of 81 respondents by calculation as (7 X 81),

it sums up a total of 567 overall counts of inputs.

For construction waste recognition, all the inputs are tabulated in Table 4.4 below and a total of

387 positive counts are recorded or approximately 68% and it is still a high recognition on the

waste concepts for the elements tested in this category but it is relatively lower in percentage

compared to analysis carried out previously on direct conversion waste.

The breakdown of numbers of the waste elements recognised as wastes under this

noncontributory time waste category are shown in Figure 8. It is worthwhile to point out that

most of the respondents do not recognised Item O: (Time for workers’ resting during construction) as

construction as only 15 out of 81 respondents recognised it as construction waste. On the

opposite side, the most recognised construction waste under this non-contributory time waste

category is Item M: (Time for rework/ repair work/ defective works) which recorded a 75 positive

counts out of the maximum 81.

Same is the case for the controlled events which is tabulated in Table 4.5 & total of 465 counts

have been recorded or appx.82% and it shows an increase in percentage on the waste event

control for the elements tested compared to the construction waste recognised under this

category.

The breakdown of numbers of the waste elements recognised as wastes under this

noncontributory time waste category are shown in Figure 9. It shows that Item E:

(Waiting for the clarification and confirmation by client and consultants) is the least controlled waste

event where more than half of the respondents reported as waste event not being controlled. Item

B: (Waiting for equipment to be delivered on site) on the other hand recorded a perfect waste control

event.

Same as direct conversion waste analysis, the cross tabbing of both Table 4.4 & 4.5 for non-

contributory waste will result in a matrix table as show in Table 4.6. This matrix table can be used

to explain the inter-relationship between the non-contributory waste concepts of the respondents

with their actual control practices on construction processes. Again, 4 potential scenarios cases are

investigated for non-contributory time waste category.


49


A B C D E M O 1 1 2 2 2 1 2 1 2 2 2 2 2 2 2 1 3 2 1 1 1 1 2 1 4 2 1 1 2 2 2 1 5 2 2 2 2 2 2 1 6 2 2 2 2 1 2 2 7 2 2 1 2 2 2 1 8 2 2 2 2 2 1 1 9 2 2 2 2 2 2 1 10 1 1 1 1 2 1 2 11 2 2 2 2 2 2 1 12 2 2 2 2 1 2 2 13 1 1 2 2 1 2 1 14 2 2 2 2 2 2 1 15 1 2 2 2 2 2 1 16 2 2 2 2 2 2 2 17 1 1 1 1 2 2 1 18 2 2 2 2 1 2 1 19 2 2 2 2 2 2 1 20 2 1 1 2 2 2 1 21 1 1 1 2 2 2 1 22 2 2 2 2 1 2 2 23 2 1 1 1 1 2 1 24 2 2 2 2 2 2 1 25 2 2 2 2 2 2 1 26 2 2 2 2 1 2 1 27 2 1 2 2 2 2 1 28 1 2 2 2 1 2 1 29 2 2 2 2 2 2 1 30 2 1 1 1 1 2 1 31 2 1 1 2 2 2 1 32 2 2 2 2 2 2 1 33 2 2 2 2 1 2 2 34 2 2 1 2 2 2 1 35 2 2 2 2 2 1 1 36 2 2 2 2 2 2 1 37 1 1 1 1 2 1 2 38 2 2 2 2 2 2 1 39 2 2 2 2 1 2 2 40 1 1 2 2 1 2 1 41 2 2 2 2 2 2 1 42 1 2 2 2 2 2 1 43 2 2 2 2 2 2 2 44 1 1 1 1 2 2 1 45 2 2 2 2 1 2 1 46 2 2 2 2 2 2 1 47 2 1 1 2 2 2 1 48 1 1 1 2 2 2 1 49 2 2 2 2 1 2 2

LEGEND :-

A) Waiting for others to complete their works before the proceedings works can be carried out

B) Waiting for equipments to be delivered on site

C) Waiting for materials to be delivered on site

D) Waiting for skilled workers to be on site

E) Waiting for clarification & confirmation by client & consultants

M) Time for rework/repair works/defective works

O) Time for workers resting during construction

1)NON WASTE

2) WASTE


50


50 2 1 1 1 1 2 1 51 2 2 2 2 2 2 1 52 2 2 2 2 2 2 1 53 2 2 2 2 1 2 1 54 2 1 2 2 2 2 1 55 1 2 2 2 1 2 1 56 2 2 2 2 2 2 1 57 2 1 1 1 1 2 1 58 2 1 1 2 2 2 1 59 2 2 2 2 2 2 1 60 2 2 2 2 1 2 2 61 2 2 1 2 2 2 1 62 2 2 2 2 2 1 1 63 2 2 2 2 2 2 1 64 1 1 1 1 2 1 2 65 2 2 2 2 2 2 1 66 2 2 2 2 1 2 2 67 1 1 2 2 1 2 1 68 2 2 2 2 2 2 1 69 1 2 2 2 2 2 1 70 2 2 2 2 2 2 2 71 1 1 1 1 2 2 1 72 2 2 2 2 1 2 1 73 2 2 2 2 2 2 1 74 2 1 1 2 2 2 1 75 1 1 1 2 2 2 1 76 2 2 2 2 1 2 2 77 2 1 1 1 1 2 1 78 2 2 2 2 2 2 1 79 2 2 2 2 2 2 1 80 2 2 2 2 1 2 1 81 2 1 2 2 2 2 1

TABLE 4.4 :- INPUTS OF CONSTRUCTION WASTE RECOGNITION FOR NON CONTRIBUTORY TIME WASTE


51


A B C D E M O 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 4 2 2 2 2 1 2 2 5 2 2 2 2 2 2 2 6 2 2 2 2 2 2 2 7 2 2 2 2 1 2 2 8 2 2 2 2 2 2 2 9 2 2 2 2 2 2 2 10 2 2 2 2 1 2 1 11 2 2 2 1 2 2 1 12 2 2 2 2 1 1 1 13 2 2 1 1 1 2 2 14 1 2 1 1 1 1 1 15 2 2 2 2 1 1 2 16 2 2 2 2 2 2 2 17 2 2 2 2 2 2 2 18 2 2 2 2 2 2 1 19 2 2 2 2 1 2 2 20 2 2 2 2 2 2 2 21 2 2 1 1 1 2 2 22 2 2 2 2 1 2 2 23 2 2 2 2 2 1 1 24 2 2 2 1 1 2 2 25 2 2 2 2 1 2 1 26 2 2 2 2 2 2 2 27 2 2 2 2 1 2 2 28 2 2 2 2 1 2 2 29 2 2 2 2 2 2 2 30 2 2 2 2 2 2 2 31 2 2 2 2 1 2 2 32 2 2 2 2 2 2 2 33 2 2 2 2 2 2 2 34 2 2 2 2 1 2 2 35 2 2 2 2 2 2 2 36 2 2 2 2 2 2 2 37 2 2 2 2 1 2 1 38 2 2 2 1 2 2 1 39 2 2 2 2 1 1 1 40 2 2 1 1 1 2 2 41 1 2 1 1 1 1 1 42 2 2 2 2 1 1 2 43 2 2 2 2 2 2 2 44 2 2 2 2 2 2 2 45 2 2 2 2 2 2 1 46 2 2 2 2 1 2 2 47 2 2 2 2 2 2 2

LEGEND :-










52


48 2 2 1 1 1 2 2 49 2 2 2 2 1 2 2 50 2 2 2 2 2 1 1 51 2 2 2 1 1 2 2 52 2 2 2 2 1 2 1 53 2 2 2 2 2 2 2 54 2 2 2 2 1 2 2 55 2 2 2 2 1 2 2 56 2 2 2 2 2 2 2 57 2 2 2 2 2 2 2 58 2 2 2 2 1 2 2 59 2 2 2 2 2 2 2 60 2 2 2 2 2 2 2 61 2 2 2 2 1 2 2 62 2 2 2 2 2 2 2 63 2 2 2 2 2 2 2 64 2 2 2 2 1 2 1 65 2 2 2 1 2 2 1 66 2 2 2 2 1 1 1 67 2 2 1 1 1 2 2 68 1 2 1 1 1 1 1 69 2 2 2 2 1 1 2 70 2 2 2 2 2 2 2 71 2 2 2 2 2 2 2 72 2 2 2 2 2 2 1 73 2 2 2 2 1 2 2 74 2 2 2 2 2 2 2 75 2 2 1 1 1 2 2 76 2 2 2 2 1 2 2 77 2 2 2 2 2 1 1 78 2 2 2 1 1 2 2 79 2 2 2 2 1 2 1 80 2 2 2 2 2 2 2 81 2 2 2 2 1 2 2

TABLE 4.5 :- INPUTS OF CONSTRUCTION WASTE EVENT CONTROL FOR NON CONTRIBUTORY TIME WASTE


53


FIGURE 8 :- BREAK DOWN OF NON – CONTRIBUTORY TIME WASTE RECOGNITION CASES

FIGURE 9 :- BREAKDOWN OF NON – CONTRIBUTORY TIME WASTE CONTROL

A B C D E M O

NONWASTE 18 27 24 12 27 6 66

WASTE 63 54 57 69 54 75 15

0

10

20

30

40

50

60

70

80

90N

O'S

A B C D E M O

NON CONTROL 3 0 9 15 42 12 21

CONTROL 78 81 72 66 39 69 60

0

10

20

30

40

50

60

70

80

90

NO

'S


54


A B C D E M 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

LEGEND :-













55


41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81

TABLE 4.6 :- MATRIX DIAGRAM FOR NON – CONTRIBUTORY WASTE


56


4.4 ANALYSIS ON CONTRIBUTORY TIME WASTE

Under this category, there are only 3 wastes elements indexed as P, Q & R in the section A, B and

C of the questionnaires. For the total of 81 respondents by calculation as (3 X 81), it sums up a

total of 243 overall counts of inputs. For construction waste recognition, all the inputs are tabulated

in Table 4.7 below and a total of 30 positive counts are recorded or approximately 12% and this a

very low recognition on the waste concepts for the elements tested under this category compared

to analysis carried out previously on direct conversion waste and non-contributory time waste.

The breakdown of numbers of the waste elements recognised as wastes under this contributory

time waste category are shown in Figure 10. It is not surprising to see that all the 3 items

registered under contributory time wastes are recording significant negative counts, which

represent non waste recognition for the contributory time wastes. Very much different with the

first 2 waste recognition analysis, all 3 items are recording high negative counts that above 20

counts where Item P: (Time in supervising and inspecting the construction works) with the greatest

numbers of negative counts (26 Nos.) followed by Item R: (Time for transporting workers, equipment

and materials) – 23 Nos. and Item Q: (Time for instructions and communication among different tiers and

trades of workers) – 22 Nos.

Same is the case for the controlled events which is tabulated in Table 4.8 & total of 216 counts

have been recorded or appx.89% and it shows an increase in percentage on the waste event

control for the elements tested compared to the construction waste recognised under this

category. The breakdown of numbers of the waste elements recognised as wastes under this

contributory time waste category are shown in Figure 11. It shows that all the 3 items are having

high positive counts for waste event control where all 3 items of contributory time waste are

recording above 20 Nos. of positive counts lead by Item P: (Time in supervising and inspecting the

construction works) with 78 Nos.

Again, by cross tabbing of both Table 4.7 & 4.8 for contributory waste will result in a matrix table

as show in Table 4.9. Therefore, the inter-relationship between the contributory waste concepts

of the respondents with their actual control practices on construction processes can be explained

using this matrix table. 4 potential scenarios cases are to be investigated for contributory time

waste category.


57


P Q R P Q R 1 1 2 2 1 1 1 2 2 1 1 1 2 2 2 2 3 1 1 1 3 2 2 2 4 1 1 1 4 2 2 2 5 1 1 1 5 2 2 2 6 1 1 1 6 2 2 2 7 1 1 1 7 2 2 2 8 1 1 1 8 2 2 2 9 1 1 1 9 2 2 2 10 2 1 1 10 1 1 1 11 1 1 2 11 2 2 2 12 1 1 1 12 2 2 2 13 1 1 1 13 2 2 2 14 1 1 1 14 2 2 2 15 1 1 1 15 2 2 2 16 1 1 2 16 2 2 2 17 1 2 1 17 2 2 2 18 1 1 1 18 2 1 2 19 1 1 1 19 2 2 2 20 1 1 1 20 2 2 2 21 1 1 1 21 2 2 2 22 1 2 1 22 1 2 2 23 1 2 2 23 2 1 2 24 1 1 1 24 2 2 2 25 1 2 1 25 2 2 2 26 1 1 1 26 1 2 2 27 1 1 1 27 2 2 2 28 1 2 2 28 1 1 2 29 1 1 1 29 2 2 2 30 1 1 1 30 2 2 2 31 1 1 1 31 2 2 2 32 1 1 1 32 2 2 2 33 1 1 1 33 2 2 2 34 1 1 1 34 2 2 2 35 1 1 1 35 2 2 2 36 1 1 1 36 2 2 2 37 2 1 1 37 1 1 1 38 1 1 2 38 2 2 2 39 1 1 1 39 2 2 2 40 1 1 1 40 2 2 2 41 1 1 1 41 2 2 2 42 1 1 1 42 2 2 2 43 1 1 2 43 2 2 2 44 1 2 1 44 2 2 2 45 1 1 1 45 2 1 2 46 1 1 1 46 2 2 2 47 1 1 1 47 2 2 2 48 1 1 1 48 2 2 2 49 1 2 1 49 1 2 2

1) NON WASTE 2) WASTE


LEGEND :-

P) Time in supervising / inspecting the construction works

Q) Time for instructions & communication among different tiers & trades of workers

R) Time for transporting workers, equipments & materials


58


50 1 2 2 50 2 1 2 51 1 1 1 51 2 2 2 52 1 2 1 52 2 2 2 53 1 1 1 53 1 2 2 54 1 1 1 54 2 2 2 55 1 2 2 55 1 1 2 56 1 1 1 56 2 2 2 57 1 1 1 57 2 2 2 58 1 1 1 58 2 2 2 59 1 1 1 59 2 2 2 60 1 1 1 60 2 2 2 61 1 1 1 61 2 2 2 62 1 1 1 62 2 2 2 63 1 1 1 63 2 2 2 64 2 1 1 64 1 1 1 65 1 1 2 65 2 2 2 66 1 1 1 66 2 2 2 67 1 1 1 67 2 2 2 68 1 1 1 68 2 2 2 69 1 1 1 69 2 2 2 70 1 1 2 70 2 2 2 71 1 2 1 71 2 2 2 72 1 1 1 72 2 1 2 73 1 1 1 73 2 2 2 74 1 1 1 74 2 2 2 75 1 1 1 75 2 2 2 76 1 2 1 76 1 2 2 77 1 2 2 77 2 1 2 78 1 1 1 78 2 2 2 79 1 2 1 79 2 2 2 80 1 1 1 80 1 2 2 81 1 1 1 81 2 2 2

TABLE 4.7 TABLE 4.8

INPUTS OF INPUTS OF

CONSTRUCTION CONSTRUCTION

RECOGNITION WASTE CONTROL

CASES FOR CONTRIBUTORY TIME WASTE


59


FIGURE 10 :- BREAK DOWN OF CONTRIBUTORY TIME WASTE RECOGNITION

FIGURE 11 :- BREAKDOWN OF TIME WASTE CONTROL CASES

P Q R

NON WASTE 78 66 69

WASTE 3 15 12

0

10

20

30

40

50

60

70

80

90

P Q R

NON CONTROL 3 15 12

CONTROL 78 66 69

0

10

20

30

40

50

60

70

80

90

NO

'S


60


P Q R 1 2 3 4 5 6 7 8 9

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40

LEGEND :-

P) Time in supervising / inspecting the construction works

Q) Time for instructions & communication among different tiers & trades of workers

R) Time for transporting workers, equipments & materials






61


41 42 43

44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80

81

TABLE 4.9 :- MATRIX DIAGRAM

FOR CONTRIBUTORY TIME WASTE


62


4.5 INFERENTIAL ANALYSIS RESULTS

This section will discuss about the analysis results from inferential analysis. Statistic tools such as correlation Pearson-r and One-Way t-test will be utilised to test some hypotheses of the study and determine the frequency ranking of each particular event or case as rated by the respondents.

4.5.1 CORRELATION AMONG DIRECT CONVERSION WASTES CONCEPTS AND PERCEPTIONS, WASTE EVENT CONTROL AND FREQUENCIES OF WASTE EVENT OCCURRENCES There are 3 hypotheses to be tested under this direct conversion wastes category:

Hypothesis 1: There is inter-relationship between construction’s direct conversion wastes are been perceived (D_WASTE1) with the tendency to control those wastes (D_WASTE2) Hypothesis 2: There is inter-relationship between construction’s direct conversion wastes are been perceived (D_WASTE1) with the frequencies of occurrences of such wastes during the processes (D_WASTE3) Hypothesis 3: There is inter-relationship between the tendency to control those construction’s direct conversion wastes (D_WASTE2) with the frequencies of occurrences of such wastes during the processes (D_WASTE3)

These 3 hypotheses are to be tested together using correlation Pearson r, and the results show that there are not significant correlations among each other (refer Table 4.10) as the two-tail sig. value (K) is more than 0.05 for 3 cases tested. Hence Hypothesis 1, 2 and 3 is rejected.

HYPOTHESIS 1 TEST Variables D WASTE 1 D WASTE 2 -.145 K = .314> .05 HYPOTHESIS 2 TEST Variables D WASTE 1 D WASTE 3 -.040 K = .575>.05 HYPOTHESIS 3 TEST Variables D WASTE 2 D WASTE 3 - .056 K= .593> .05 Table 4.10 showing Correlation Pearson-r results summaries for hypothesis 1, 2 and 3 (Refer Appendix 1 for Correlation Pearson-r result outputs from SPSS 16.0)


63


4.5.2 CORRELATION AMONG NON-CONTRIBUTORY TIME WASTES CONCEPTS AND PERCEPTIONS, WASTE EVENT CONTROL AND FREQUENCIES OF WASTE EVENT OCCURRENCES Same as direct conversion wastes, there are 3 hypotheses to be tested under this noncontributory wastes category: Hypothesis 4: There is inter-relationship between construction’s non-contributory

time wastes are been perceived (NON_CON1) with the tendency to control those

wastes (NON_CON2)

Hypothesis 5: There is inter-relationship between construction’s non-contributory

time wastes are been perceived (NON_CON1) with the frequencies of occurrences of

such wastes during the processes (NON_CON3)


construction’s non-contributory time wastes (NON_CON2) with the frequencies of

occurrences of such wastes during the processes (NON_CON3)

These 3 hypotheses are to be tested together using correlation Pearson r, and the results show

that there are not significant correlations among each other (refer Table 4.11) as the two-tail sig.

value (K) is more than 0.05 for 3 cases tested. Hence Hypothesis 4, 5 and 6 is rejected.

HYPOTHESIS 4 TEST Variables NON CON 1 NON CON 2 .026 K = .224> .05 HYPOTHESIS 5 TEST Variables NON CON 1 NON CON 3 -.291 K = .470>.05 HYPOTHESIS 6 TEST Variables NON CON 2 NON CON 3 .325 K= .372 > .05

Table 4.11showing Correlation Pearson-r results summaries for hypothesis 4, 5 and 6 (Refer Appendix 1 for Correlation Pearson-r result outputs from SPSS 16.0)


64


4.5.3 CORRELATION AMONG CONTRIBUTORY TIME WASTES CONCEPTS AND PERCEPTIONS, WASTE EVENT CONTROL AND FREQUENCIES OF WASTE EVENT OCCURRENCES

Same as previous 2 wastes categories, there are 3 hypotheses to be tested under this contributory

wastes category:

Hypothesis 7: There is inter-relationship between construction’s contributory time

wastes are been perceived (CON1) with the tendency to control those wastes (CON2)

Hypothesis 8: There is inter-relationship between construction’s contributory time

wastes are been perceived (CON1) with the frequencies of occurrences of such wastes

during the processes (CON3)


construction’s contributory time wastes (CON2) with the frequencies of occurrences

of such wastes during the processes (CON3)

These 3 hypotheses are to be tested together using correlation Pearson r, and the results show that there are significant correlations between the way contributory time wastes have been perceived (CON1) with the tendency to control those wastes (CON2) as the 2-tail sig. value (K) signify the correlation is significant at 0.01 level or value K < 0.01 with a negative correlation (r = -.551) whereas the other 2 cases are tested non-significant with the 2-tail sig. value (K) is more than 0.05. (Refer Table 4.12) Hence Hypothesis 7 is accepted and hypothesis 8 and 9 is rejected. HYPOTHESIS 7 TEST Variables CON 1 CON 2 -.555 K = .004 < .05 HYPOTHESIS 8 TEST Variables CON 1 CON 3 -.223 K = .217>.05 HYPOTHESIS 9 TEST Variables CON 2 CON 3 .281 K= .185 > .05 Table 4.12 showing Correlation Pearson-r results summaries for hypothesis 7, 8 and 9 (Refer Appendix 1 for Correlation Pearson-r result outputs from SPSS 16.0)


65


4.6 RANKING ON FREQUENCY OF OCCURRENCE OF WASTE IN HIGHWAY PROCESSES The purpose of this analysis is to determine the frequency of occurrences of construction wastes as experienced by the respondents, the frequencies of occurrences for construction wastes are analysed by using one-way t-test to determine the mean values, standard of deviation and standard error mean and the mean of scores were listed as shown in Table 4.13. Construction waste variables N Mean Std.devi

ation Std. error mean

Waste categories

P3 Time in supervising & inspecting the construction works

81 4.00 .821 .091 Contributory time

E3 Waiting for clarification & confirmation by client & consultants

81 3.81 .776 .086 Non – contributory time

Q3 Time for instructions & communication among different tiers & trades of workers


A3 Waiting for others to complete their works before the proceeding works can be carried out


M3 Time for rework/repair works/defective works


N3 Materials for rework/repair works/defective works

81 3.33 .724 .080 Direct conversion

C3 Waiting for materials to be delivered on site


R3 Time for transporting workers, equipment & materials


B3 Waiting for equipment to be delivered on site


K3 Material deterioration/ damaged during construction periods


J3 Material loss / stolen from site during construction periods


L3 Mishandling or error in construction application/ installation


I3 Unnecessary procedures & working protocols


O3 Time for workers resting during construction


G3 Overallocation / unnecessary materials on site


D3 Waiting for skilled workers to be on site 81 2.66 .908 .100 Non – contributory time S3 Accidents on site 81 2.51 .691 .076 Direct conversion F3 Over allocation / unnecessary

equipments on site 81 2.44 .836 .093 Direct conversion

H3 Overallocation / unnecessary workers on site


TABLE 4.13 CONSTRUCTION WASTE VARIABLES RANKING


66


From the mean ranking results, it shows that time wastes categories regardless of contributory

time or non-contributory time wastes occurred at the top of the list compared to direct

conversion wastes. Therefore, it is recommended that for construction processes improvements, it

is eventually those contributory and noncontributory times waste variables that have to be given

more attentions and in real fact, most of them are related to process flows and sequences and this

can lead to lean construction’s tools and methods which are developed mostly to tackle those

wastes resulted from process flow inefficiencies.


67


4.7 RANKING ON LIKELINESS FOR SOURCES/CAUSES OF CONSTRUCTION WASTE The purpose of this analysis is to determine the respondent’s recognition of particular sources/

causes factors that cause construction wastes. Same as ranking for the frequencies of wastes

occurrences, the rating on these likelihood of waste sources/ causes factors as rated by the

respondents are analysed by using one-way t-test and the mean of scores were listed in as shown in

Table 4.14

Sources/ causes of construction waste N Mean Std.deviation

Std. error mean

Source/cause factor categories

E2 Late information & decision making 81 3.63 .557 .061 Information & communication factors

D2 Poorly scheduled delivery of material to site

81 3.37 .621 .069 Material factors

A1 Poor coordination among project participants

81 3.37 .621 .069 Management & administration factors

E3 Unclear information 81 3.26 .519 .057 Information & communication factors

A2 Poor planning & scheduling 81 3.26 .586 .065 Management & administration factors

D3 Poor quality of material 81 3.26 .703 .078 Material factors A3 Lack of control 81 3.23 .579 .065 Management &

administration factors D1 Delay of material delivery 81 3.22 .689 .076 Material factors E1 Defective or wrong information 81 3.11 .570 .063 Information &

communication factors B2 Inexperience inspectors 81 3.11 .500 .055 People factors D4 Inappropriate misuse of material 81 3.11 .790 .087 Material factors D6 Poor material handling on site 81 3.07 .666 .074 Material factors B3 Too few supervisors 81 3.03 .579 .064 People factors B5 Supervision too late 81 2.96 .697 .077 People factors B4 Uncontrolled sub – contracting practices 81 2.92 .607 .067 People factors B1 Lack of trades skills 81 2.92 .607 .067 People factors C3 Equipments shortage 81 2.92 .607 .067 Execution factors D5 Poor storage of material 81 2.92 .720 .080 Material factors C6 Poor site documentation 81 2.88 .570 .063 Execution factors C5 Poor site layout & setting out 81 2.85 .654 .072 Execution factors C4 Poor equipment choice/ineffective

equipment 81 2.81 .550 .061 Execution factors

A4 Bureaucracy 81 2.77 .570 .063 Management & administration factors

B6 Poor labour distribution 81 2.70 .660 .073 People factors C1 Inappropriate construction methods 81 2.66 .724 .080 Execution factors C2 Outdated equipment 81 2.51 .792 .088 Execution factors

TABLE 4.14 SOURCES/CAUSES OF CONSTRUCTION WASTE RANKING


68


As from the mean ranking result shows that Item E2: (Late information and decision making) is highly

regarded as the main contributory sources or causes to the construction wastes with the highest

mean value (3.63) and with a 0.26 from the second rank item D2: (Poorly scheduled delivery of

material to site).

Among the clusters of cause factors observed from Table 6.14, there are 3 categories of waste

sources/ causes factors are widely acknowledged as the key contributory factors to construction

wastes. Those categories included Information and Communication Factors, Management and

Administration Factors and Material Factors as most of the Cause factors captured under these 3

categories are rated with the mean value over 3.

Overall, the likelihood of recognising the items above as the sources/ causes of wastes that will

impact on the productivity of the projects, are still reasonably high as most of the mean value for

the items tested were clustering around the scale “3” value representing “likely as a sources/ causes

of wastes”. However, there are also some exceptions such as Item C1: (Inappropriate construction

methods) and Item C2: (Outdated equipment) both recorded a slightly low mean values of 2.67 and

2.52 respectively.

4.8 CAUSE & EFFECT MATRIX DIAGRAM

The purpose of this analysis to relate the particular sources or causes to the construction wastes

and this is to give us a better picture of what leads to the waste in construction processes as

suggested by the respondents feedback on this research. Figure 12 is the overall analysis on

Causes and Effects Matrix of the “Major cause” to the construction wastes based on 5 main causes

factors while Figure 13 is the Causes and Effects Matrix of the “Other causes” to the construction

wastes. (Refer overall Causes and Effects Matrix tables in Appendix 4 for a detailed understanding

on actual one to one relationship between wastes causes to wastes itself as per the data gather

from the respondents of this research)


69


FIGURE 12 :- CAUSE & EFFECT RELATIONSHIP FOR THE MAJOR CAUSES

0

10

20

30

40

50

60

70

A B C D E F G H I J K L M N O P Q R

major cause

not relevant

information & communication factors

material factors

execution factors

people factors

management & adminstration factors


70


FIGURE 13 :- CAUSE & EFFECT RELATIONSHIP FOR OTHER CAUSES

0

10

20

30

40

50

60

70

80

90

100

A B C D E F G H I J K L M N O P Q R

other cause

not relevant

information & communication factors

material factors

execution factors

people factors

management & adminstration factors


71


The matrix table provides a clearer insight into the types of causes factors that directly related

construction wastes, as we shall see that in Figure 12, Management and Administration Factors has

a relatively high counts numbers in causing the construction wastes items ranging from Item A to

Item I and Item Q, Materials Factors dominants over Item J & K and People Factors score higher in

Item M, N and O. By conducting this causes and effects matrix exercise, we can know that each

types of construction wastes has a different roots causes to the problems and it is important to

identify those particular causes to the problems in order to a proper corrective or preventive

actions can be carried to ensure continuous improvement in performance of construction activities.


72


CHAPTER 5

CONCLUSION


73


CHAPTER 5 : CONCLUSION

5.0 INTRODUCTION

This chapter concludes the whole study based on the findings. The tested hypotheses will be

related to the research objectives and further interpreted and conclusion on the achievement of

the research objectives. Some recommendations will also be drawn from the findings and the

limitations during the research.

5.1 RESEARCH FINDINGS The discussion on the findings of the research will be carried in 2 separate ways:

1. Relating the research findings to research objectives

2. Rewritten hypotheses and interpret the results

5.1.1 RESEARCH FINDINGS TO RESEARCH OBJECTIVES

• Research objective 1: General perceptions on construction wastes based on lean

construction principles.

From the research results, it is found that the general perceptions and lean concepts of local

construction personnel particularly on construction wastes and their tendency to control these

wastes are at an acceptable level. The local construction site personnel can identified most wastes

as outlined and the tendency of controlling those wastes is even higher than recognising the wastes

themselves. However, from the results, it also shows that the recognition over flow related

construction wastes is rather low compared to direct conversion wastes or physical wastes

especially those related to contributory time wastes. This signify that the local construction

personnel are still not fully comprehend the concepts of flows and non value-adding activities and

tends to included these contributory work as part and parcel of conversion process.

In fact, lean construction philosophy sees these contributory works as not adding any values to the

client even though they are sometimes necessary for the progress of the overall construction

processes. On the bright side, the research results also show a very high percentage on those

contributory works as being controlled during the construction processes signaling that those

contributory time wastes are actually well aware off those activities even though they being not

recognised as construction wastes.


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• Research Objective 2: Degree of problems arisen of the wastes identified

Based on the ranking of the event occurrences frequencies for waste events existed in construction

processes shows that the most frequent waste events occurred in construction activities are

actually flow related with both contributory time wastes and non-contributory time wastes were at

the top of the ranking list. On the other hand, many direct conversion wastes are recorded rather

low scores mostly in the range of “Seldom” and “Very Rare” occurrence events.

Eventually by breaking down the waste categories, it is made clear that the flow time wastes are the

prominent events that occurred in construction processes. Therefore, based on that information, a

better performance improvement strategies can be arranged to target at those flow related wastes

events, as those events are usually invisible or ignored by conventional construction management.

The construction processes can be further streamlined by reducing or eliminating those flow waste

elements by implementing the lean construction principles and practices such as employee

involvements, kanzan, JIT concepts etc at all level of construction processes.

• Research Objective 3 : Waste cause and effect relationship and potential improvement strategies

In this research, major sources of wastes are also been identified directly related to the respective

construction wastes from the wastes causes and effects matrix as shown in Appendix 4. From the

aggregated results shows that management and administrative factors are recognised as the

dominant sources of wastes for most of the cases while material factors and people factors are

more dominant for a few wastes types. If compared to the ranking of the likelihood for waste

factors to impact the construction productivity in general, information and communications factors

which are hardly seen as a dominant factor of any construction wastes types at the top of the

ranking list follow tightly by management and administrative factors. On the low side, the executive

factors and people factors scored relatively low in the ranking.

This is a very good exercise to point out the causes and effects relationship between the sources of

waste and waste itself for processes control, reengineering or redesign by targeting directly at the

respective sources of wastes for processes improvement. In most leaner construction organisition,

they usually practise this exercise in a survey called waste identification survey (WIS) through work

sampling practices in order to monitor and improve their flow performance from time to time

during their construction activities.


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5.1.2 HYPOTHESIS TESTING & INTERPRETATION OF RESULTS

From inferential statistical analyses in chapter 4, 9 hypotheses testing were conducted with

Pearson-r correlation. The results from the analyses had concluded following hypotheses.

1. There is no significant inter-relationship between construction’s direct conversion wastes

perceived with the tendency to control those wastes.

2. There is no significant inter-relationship between construction’s direct conversion wastes

perceived with the frequencies of occurrences of such wastes during construction.

3. There is no significant inter-relationship between the tendency to control direct conversion

wastes with the frequencies of occurrences of such wastes during construction.

4. There is no significant inter-relationship between construction’s noncontributory time wastes


5. There is no significant inter-relationship between construction’s noncontributory time wastes


6. There is no significant inter-relationship between the tendency to control non-contributory

time wastes with the frequencies of occurrences of such wastes during construction.

7. There is no significant inter-relationship between construction’s contributory time wastes


8. There is no significant inter-relationship between construction’s contributory time wastes


9. There is significant inter-relationship between the tendency to control contributory time

wastes with the frequencies of occurrences of such wastes during construction.

The non-significant over almost all the hypotheses tested in correlation testing shows that there

were not uniformity in the way the construction waste are recognised and controlled even with the

high recognition and control rates. Recognising particular construction wastes or frequencies of

occurrence of construction wastes on site by the construction personnel do not prompt them to

control them and vice versa, recognizing construction wastes are not prompt by the frequencies of

occurrence of those wastes during construction site. The construction wastes are treated very

subjectively from cases to cases and suggested that no proper doctrine or philosophy in supporting

for particular waste recognition and control mechanism.


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In the importance of continuous process and productivity improvement, having the correct

concepts and understanding and having the right attitudes to mitigate and control the flow and flow

related wastes are very essence. In this case, the worst scenario would be someone actually not

knowing what is the waste and therefore not put in any efforts to control it and letting the wastes

to repeat from time to time. There might be some other reasons for not recognising wastes and

not controlling them. Some would not treat it as a waste as those wastes are recoverable due to

defaults by others and some misunderstanding that wastes are necessary to avoid others bigger

wastes from happening. For example, as cited from the only 1 qualitative inputs from all 81

questionnaires received stated that waiting for clarification and confirmation by client and

consultants is not a waste because he/ she believed that it is important to wait for clarification and

confirmation “because lack of this will be more wastage (redo the task)”. From the results of cross-tab

matrix tables shows a relatively low percentage of that particular scenario and that should be a

good sign for the construction industries.

However, for the scenarios of knowing the wastes but not controlling them hit a rather high

numbers of cases and percentage especially in direct conversion wastes. This is particularly not a

good sign where those wastes are left behind the construction processes and hinder the full

potential of process improvement. The results to this might be abundant. One of the reason would

be the costs to control or improve the wastes might be higher that the cost of the wastes itself.

Besides that, the reasons of not control the wastes even the wastes are identified and recognised

perhaps is due to not sufficient tools and knowledge to control them and some might due to

misunderstanding during execution and not well trained personnel.


77


REFERENCES

1. Abdul Rashid Abdul Aziz and Abdul Aziz Hussin, (2003) “Construction Safety In Malaysia: A

Review Of Industry Performance And Outlook For The Future” in journal of Construction

Research, pp.146-148.

2. Alarcón, Luis F. (1993). “Modeling waste and performance in construction.” In Lean

Construction, Alarcón (Ed.), A.A. Balkema, Rotterdam, The Netherlands, 1997

3. Alarcón, Luis F. (1994). “Tools for the identification and reduction of waste in construction

projects.” In Lean Construction, Alarcón (Ed.), A.A. Balkema, Rotterdam, The Netherlands,

1997

4. Enton, David (1994). “Lean production productivity improvements for construction

professions” In Lean Construction, Alarcón (ed.), A.A. Balkema, Rotterdam, The Netherlands,

1997.

5. Formosa, Carlos T et al (2002) “Material waste in building industry: main causes and

prevention.” In Journel of Construction Engineering and Management, July/ August, pp. 316-318

6. Koskela, Lauri (1993).”Lean production in construction.” In Lean Construction, Alarcón (ed.),

A.A. Balkema, Rotterdam, The Netherlands, 1997.

7. Koskela, Lauri (1992). Application of new production philosophy to construction Tech. Rep. No

72, CIFE, Stanford, California

8. Koskela, Lauri (2000). An Exploration towards a production theory and its application to

construction, VTT Publication 408, Finland.

9. Taylor, T.W., (1913) “The principles of scientific management”, Harper and Brothers, New

York quoted in Formosa, Carlos T et al (2002) “Material waste in building industry: main

causes and prevention.” In Journal of Construction Engineering and Management, July/ August,

pp. 317

10. Womack, J and Jones, D. (1996). Lean Thinking. Simon & Schuster

WEBSITE:-

• www. Leanconstruction.org


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APPENDICES

APPENDIX - 1CORRELATION PEARSON R RESULTS FROM SPSS 16

CORRELATIONS :-

D WASTE 1 D WASTE 2 D WASTE 3 D WASTE 1 PEARSON CORRELATION SIG. ( 2 –TAILED) N

1.000 81

-.145 .314 81

-.040 .593 81

D WASTE 2 PEARSON CORRELATION SIG. ( 2 –TAILED) N

-.145 .314 81

1.000 81

-.056 .593 81

D WASTE 3 PEARSON CORRELATION SIG. ( 2 –TAILED) N

-.040 .593 81

-.056 .593 81

1.000 81

NON CON 1 NON CON 2 NON CON 3 NON CON 1 PEARSON CORRELATION SIG. ( 2 –TAILED) N

1.000 81

.026

.224 81

-.291 .470 81

NON CON 2 PEARSON CORRELATION SIG. ( 2 –TAILED) N

.026

.224 81

1.000 81

-.325 .372 81

NON CON 3 PEARSON CORRELATION SIG. ( 2 –TAILED) N

-.291 .470 81

-.325 .372 81

1.000 81

CON 1 CON 2 CON 3 CON 1 PEARSON CORRELATION SIG. ( 2 –TAILED) N

1.000 81

-.555 .004 81

-.223 .217 81

CON 2 PEARSON CORRELATION SIG. ( 2 –TAILED) N

-.555 .004 81

1.000 81

.281

.185 81

CON 3 PEARSON CORRELATION SIG. ( 2 –TAILED) N

-.223 .217 81

.281

.185 81

1.000 81


79


APPENDIX 2

ONE WAY T – TEST RESULTS FROM SPSS 16.0


80


APPENDIX 3

(A) SPSS DATA INPUT SHEET (WASTE CONCEPTS)

A1 B1 C1 D1 E1 F1 G1 H1 I1 J1 K1 L1 M1 N1 O1 P1 Q1 R1 S1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 2 2 2 1 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 2 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 1 1 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 2 2 2 1 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 2 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 1 1 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 1 1 1 1


81


2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 1 2 2 2 1 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 2 1 1 2 1 1 1 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 1 2 1 1 1 1 2 1 1 1 2 1 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 2 2 1 1 1 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 1 1 1 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 1 2 2 2 2 2 2 1 2 2 2 2 1 1 1 1 1 2 2 2 2 1 2 2 1 1 2 2 2 2 2 2 1 2 1 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 1 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 1 2


82


(B) SPSS DATA INPUT SHEETS (WASTE CONTROL EVENTS)

A2 B2 C2 D2 E2 F2 G2 H2 I2 J2 K2 L2 M2 N2 O2 P2 Q2 R2 S2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 1 1 1 1 1 1 2 2 2 1 2 1 1 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 1 1 1 2 1 2 1 2 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 1 1 1 1 1 1 2 2 2 1 2 1 1 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1


83


2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 1 1 1 2 1 2 1 2 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 1 1 1 1 1 1 2 2 2 1 2 1 1 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 1 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 1 1 1 2 1 2 1 2 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2


84


(C) SPSS DATA INPUT SHEETS (FREQUENCIES OF

OCCURRENCE)

A3 B3 C3 D3 E3 F3 G3 H3 I3 J3 K3 L3 M3 N3 O3 P3 Q3 R3 S3 3 3 3 2 5 4 4 4 4 3 4 4 3 3 3 3 3 2 4 3 3 2 2 4 3 3 2 4 2 2 3 4 4 3 5 4 3 2 3 3 4 3 4 3 3 3 3 3 3 3 3 3 3 4 4 4 2 4 3 3 3 4 3 4 4 4 4 4 3 4 4 3 5 4 3 3 4 3 4 3 3 3 3 3 3 2 2 2 3 3 2 3 3 4 2 4 4 4 3 3 3 4 3 3 4 4 4 4 4 5 5 5 3 3 3 1 2 1 4 1 2 2 2 4 4 5 4 3 2 5 5 2 3 2 2 2 1 2 3 3 2 1 1 2 2 2 2 2 4 4 2 1 4 3 3 3 3 2 2 2 2 3 3 2 3 3 3 4 3 3 2 3 4 2 3 4 3 4 3 2 4 3 3 3 4 2 4 3 3 3 5 4 5 3 3 3 3 2 4 3 4 3 4 2 2 3 4 3 3 5 3 3 4 4 3 3 1 3 2 2 2 3 3 3 5 5 4 2 3 3 4 4 4 3 3 2 3 3 3 2 3 2 3 3 3 3 3 4 2 3 2 5 2 4 2 4 3 5 5 5 5 3 2 2 4 3 4 4 4 3 5 3 3 3 3 4 3 4 4 4 5 5 4 5 3 4 5 5 4 4 1 2 2 3 3 3 2 3 3 4 5 5 5 2 3 3 2 2 3 1 1 1 1 2 2 2 4 4 3 4 4 1 2 4 4 4 2 4 1 2 1 3 3 3 3 3 3 2 4 3 4 3 4 3 3 3 3 3 3 4 3 4 4 3 4 4 3 4 4 3 3 3 4 4 4 5 2 2 3 5 3 3 3 4 4 3 5 4 3 3 4 4 4 4 4 3 4 2 4 3 3 3 3 4 2 4 3 4 3 4 3 2 1 3 1 3 2 2 3 3 3 2 3 3 4 4 4 1 3 4 5 3 4 3 2 2 2 4 3 4 4 3 4 3 4 4 2 4 2 3 2 3 2 3 2 3 2 1 2 3 3 4 4 3 3 3 4 2 3 2 5 3 2 2 4 3 3 3 3 3 2 3 4 3 2 3 3 3 2 4 2 3 3 3 4 4 4 3 3 3 4 4 2 2 5 3 3 3 4 2 4 3 3 4 4 3 3 4 3 4 4 4 3 3 3 3 2 5 4 4 4 4 3 4 4 3 3 3 3 3 2 4 3 3 2 2 4 3 3 2 4 2 2 3 4 4 3 5 4 3 2 3 3 4 3 4 3 3 3 3 3 3 3 3 3 3 4 4 4 2 4 3 3 3 4 3 4 4 4 4 4 3 4 4 3 5 4 3 3 4 3 4 3 3 3 3 3 3 2 2 2 3 3 2 3 3 4 2 4 4 4 3 3 3 4 3 3 4 4 4 4 4 5 5 5 3 3 3 1 2 1 4 1 2 2 2 4 4 5 4 3 2 5 5 2 3 2 2 2 1 2 3 3 2 1 1 2 2 2 2 2 4 4 2 1 4 3 3 3 3 2 2 2 2 3 3 2 3 3 3 4 3 3 2 3 4 2 3 4 3 4 3 2 4 3 3 3 4 2 4 3 3 3 5 4 5 3 3 3 3 2 4 3 4 3 4 2 2 3 4 3 3 5 3 3 4 4 3 3 1 3 2 2 2 3 3 3 5 5 4 2


85


3 3 4 4 4 3 3 2 3 3 3 2 3 2 3 3 3 3 3 4 2 3 2 5 2 4 2 4 3 5 5 5 5 3 2 2 4 3 4 4 4 3 5 3 3 3 3 4 3 4 4 4 5 5 4 5 3 4 5 5 4 4 1 2 2 3 3 3 2 3 3 4 5 5 5 2 3 3 2 2 3 1 1 1 1 2 2 2 4 4 3 4 4 1 2 4 4 4 2 4 1 2 1 3 3 3 3 3 3 2 4 3 4 3 4 3 3 3 3 3 3 4 3 4 4 3 4 4 3 4 4 3 3 3 4 4 4 5 2 2 3 5 3 3 3 4 4 3 5 4 3 3 4 4 4 4 4 3 4 2 4 3 3 3 3 4 2 4 3 4 3 4 3 2 1 3 1 3 2 2 3 3 3 2 3 3 4 4 4 1 3 4 5 3 4 3 2 2 2 4 3 4 4 3 4 3 4 4 2 4 2 3 2 3 2 3 2 3 2 1 2 3 3 4 4 3 3 3 4 2 3 2 5 3 2 2 4 3 3 3 3 3 2 3 4 3 2 3 3 3 2 4 2 3 3 3 4 4 4 3 3 3 4 4 2 2 5 3 3 3 4 2 4 3 3 4 4 3 3 4 3 4 4 4 3 3 3 3 2 5 4 4 4 4 3 4 4 3 3 3 3 3 2 4 3 3 2 2 4 3 3 2 4 2 2 3 4 4 3 5 4 3 2 3 3 4 3 4 3 3 3 3 3 3 3 3 3 3 4 4 4 2 4 3 3 3 4 3 4 4 4 4 4 3 4 4 3 5 4 3 3 4 3 4 3 3 3 3 3 3 2 2 2 3 3 2 3 3 4 2 4 4 4 3 3 3 4 3 3 4 4 4 4 4 5 5 5 3 3 3 1 2 1 4 1 2 2 2 4 4 5 4 3 2 5 5 2 3 2 2 2 1 2 3 3 2 1 1 2 2 2 2 2 4 4 2 1 4 3 3 3 3 2 2 2 2 3 3 2 3 3 3 4 3 3 2 3 4 2 3 4 3 4 3 2 4 3 3 3 4 2 4 3 3 3 5 4 5 3 3 3 3 2 4 3 4 3 4 2 2 3 4 3 3 5 3 3 4 4 3 3 1 3 2 2 2 3 3 3 5 5 4 2 3 3 4 4 4 3 3 2 3 3 3 2 3 2 3 3 3 3 3 4 2 3 2 5 2 4 2 4 3 5 5 5 5 3 2 2 4 3 4 4 4 3 5 3 3 3 3 4 3 4 4 4 5 5 4 5 3 4 5 5 4 4 1 2 2 3 3 3 2 3 3 4 5 5 5 2 3 3 2 2 3 1 1 1 1 2 2 2 4 4 3 4 4 1 2 4 4 4 2 4 1 2 1 3 3 3 3 3 3 2 4 3 4 3 4 3 3 3 3 3 3 4 3 4 4 3 4 4 3 4 4 3 3 3 4 4 4 5 2 2 3 5 3 3 3 4 4 3 5 4 3 3 4 4 4 4 4 3 4 2 4 3 3 3 3 4 2 4 3 4 3 4 3 2 1 3 1 3 2 2 3 3 3 2 3 3 4 4 4 1 3 4 5 3 4 3 2 2 2 4 3 4 4 3 4 3 4 4 2 4 2 3 2 3 2 3 2 3 2 1 2 3 3 4 4 3 3 3 4 2 3 2 5 3 2 2 4 3 3 3 3 3 2 3 4 3 2 3 3 3 2 4 2 3 3 3 4 4 4 3 3 3 4 4 2 2 5 3 3 3 4 2 4 3 3 4 4 3 3 4 3 4 4 4 3


86


(D) SPSS DATA INPUT SHEETS ( SCORE AGGREGATION )

D WASTE 1

D WASTE 2

D WASTE 3

NON CON 1

NON CON 2

NON CON 3 CON 1 CON 2 CON3

18 15 26 11 13 23 5 4 8 17 15 33 13 14 24 3 6 12 17 16 29 9 14 21 3 6 12 18 16 24 11 13 24 3 6 12 14 15 16 13 14 20 3 6 10 18 18 28 13 14 27 3 6 13 17 14 29 12 13 25 3 6 12 12 18 25 12 14 27 3 6 10 15 18 30 13 14 24 3 6 10 12 13 34 9 12 22 4 3 10 18 13 25 13 12 21 4 6 10 17 16 23 13 11 22 3 6 14 16 16 32 10 11 27 3 6 9 18 10 26 13 8 17 3 6 8 16 16 17 12 12 13 3 6 14 18 17 21 14 14 22 4 6 15 18 18 27 9 14 26 4 6 9 18 15 21 12 13 25 3 5 11 16 17 33 13 13 24 3 6 11 18 16 30 11 14 29 3 6 12 16 17 21 10 11 29 3 6 11 16 18 22 13 13 23 4 5 12 18 12 31 9 12 23 5 5 11 17 13 21 13 12 18 3 6 10 17 15 21 13 12 21 4 6 10 15 15 25 12 14 21 3 5 10 17 18 28 12 13 21 3 6 12 18 15 26 11 13 23 5 4 8 17 15 33 13 14 24 3 6 12 17 16 29 9 14 21 3 6 12 18 16 24 11 13 24 3 6 12 14 15 16 13 14 20 3 6 10 18 18 28 13 14 27 3 6 13 17 14 29 12 13 25 3 6 12 12 18 25 12 14 27 3 6 10 15 18 30 13 14 24 3 6 10 12 13 34 9 12 22 4 3 10 18 13 25 13 12 21 4 6 10 17 16 23 13 11 22 3 6 14


87


16 16 32 10 11 27 3 6 9 18 10 26 13 8 17 3 6 8 16 16 17 12 12 13 3 6 14 18 17 21 14 14 22 4 6 15 18 18 27 9 14 26 4 6 9 18 15 21 12 13 25 3 5 11 16 17 33 13 13 24 3 6 11 18 16 30 11 14 29 3 6 12 16 17 21 10 11 29 3 6 11 16 18 22 13 13 23 4 5 12 18 12 31 9 12 23 5 5 11 17 13 21 13 12 18 3 6 10 17 15 21 13 12 21 4 6 10 15 15 25 12 14 21 3 5 10 17 18 28 12 13 21 3 6 12 18 15 26 11 13 23 5 4 8 17 15 33 13 14 24 3 6 12 17 16 29 9 14 21 3 6 12 18 16 24 11 13 24 3 6 12 14 15 16 13 14 20 3 6 10 18 18 28 13 14 27 3 6 13 17 14 29 12 13 25 3 6 12 12 18 25 12 14 27 3 6 10 15 18 30 13 14 24 3 6 10 12 13 34 9 12 22 4 3 10 18 13 25 13 12 21 4 6 10 17 16 23 13 11 22 3 6 14 16 16 32 10 11 27 3 6 9 18 10 26 13 8 17 3 6 8 16 16 17 12 12 13 3 6 14 18 17 21 14 14 22 4 6 15 18 18 27 9 14 26 4 6 9 18 15 21 12 13 25 3 5 11 16 17 33 13 13 24 3 6 11 18 16 30 11 14 29 3 6 12 16 17 21 10 11 29 3 6 11 16 18 22 13 13 23 4 5 12 18 12 31 9 12 23 5 5 11 17 13 21 13 12 18 3 6 10 17 15 21 13 12 21 4 6 10 15 15 25 12 14 21 3 5 10 17 18 28 12 13 21 3 6 12


88


(E) SPSS DATA INPUT SHEETS ( WASTE SOURCES/CAUSES)

A1

A2

A3

A4

B1

B2

B3

B4

B5

B6

C1

C2

C3

C4

C5

C6

D1

D2

D3

D4

D5

D6

E1

E2

E3

4 4 4 3 3 3 4 3 3 3 3 3 3 3 3 4 3 3 4 4 4 3 3 2 3 3 4 4 2 2 3 2 2 2 3 3 2 2 3 3 3 3 3 2 2 2 3 2 4 3 3 3 3 3 2 2 2 2 2 2 3 2 3 3 2 2 4 3 2 2 2 2 2 3 3 4 3 3 2 3 4 3 2 2 2 2 3 3 3 2 3 2 2 3 2 3 2 3 4 3 3 3 2 2 3 3 3 3 4 2 2 4 3 3 3 3 4 4 4 2 1 2 2 4 4 2 2 2 2 3 3 2 3 3 2 2 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 2 2 3 3 2 3 3 2 2 3 3 2 2 3 3 2 4 2 2 3 3 3 4 3 3 3 3 3 3 3 4 4 1 1 3 2 4 3 4 4 3 4 4 4 3 4 4 3 3 3 3 3 3 4 3 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 3 3 4 4 - 4 4 4 3 4 2 3 3 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 3 4 4 3 4 4 4 4 4 3 4 4 3 3 3 3 4 4 3 3 3 3 4 3 4 4 3 3 2 3 2 3 2 3 3 3 4 4 3 3 3 4 4 4 4 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 4 4 4 4 3 4 4 4 4 4 3 3 3 3 3 4 3 3 3 3 2 3 3 3 3 3 4 4 4 3 3 3 4 3 4 4 4 3 3 3 3 4 4 3 2 2 3 3 3 3 4 4 4 4 4 4 4 4 3 2 2 4 2 2 3 3 3 3 2 3 2 2 2 2 2 2 2 3 3 3 3 3 3 2 4 3 3 3 3 2 2 3 2 3 2 3 3 3 3 4 3 4 4 3 3 4 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 3 4 3 3 4 4 3 3 4 3 4 3 3 3 4 2 3 3 3 2 3 3 3 3 3 3 3 3 4 3 3 4 4 4 4 4 3 4 4 4 3 4 4 3 3 4 3 4 3 3 3 3 4 3 4 4 4 3 3 3 4 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 2 2 3 3 4 3 3 4 3 3 3 3 3 3 2 3 2 2 4 3 3 2 3 4 3 3 3 3 3 3 4 3 3 3 2 2 3 3 3 2 3 2 2 2 3 3 2 4 3 3 4 2 2 3 4 3 3 3 3 3 3 3 3 2 3 1 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 4 3 3 3 2 3 3 3 3 3 2 1 3 2 2 3 4 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 2 2 3 3 3 3 3 4 4 4 3 3 3 4 3 3 3 3 3 3 3 3 4 3 3 4 4 4 3 3 2 3 3 4 4 2 2 3 2 2 2 3 3 2 2 3 3 3 3 3 2 2 2 3 2 4 3 3 3 3 3 2 2 2 2 2 2 3 2 3 3 2 2 4 3 2 2 2 2 2 3 3 4 3 3 2 3 4 3 2 2 2 2 3 3 3 2 3 2 2 3 2 3 2 3 4 3 3 3 2 2 3 3 3 3 4 2 2 4 3 3 3 3 4 4 4 2 1 2 2 4 4 2 2 2 2 3 3 2 3 3 2 2 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 2 2 3 3 2 3 3 2 2 3 3 2 2 3 3 2 4 2 2 3 3 3 4 3 3 3 3 3 3 3 4 4 1 1 3 2 4 3 4 4 3 4 4 4 3 4 4 3 3 3 3 3 3 4 3 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 3 3 4 4 - 4 4 4 3 4 2 3 3 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 3 4 4 3 4 4 4 4 4 3 4 4 3 3 3 3 4 4 3 3 3 3 4 3 4 4 3 3 2 3 2 3 2 3 3 3 4 4 3 3 3 4 4 4


89


4 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 4 4 4 4 3 4 4 4 4 4 3 3 3 3 3 4 3 3 3 3 2 3 3 3 3 3 4 4 4 3 3 3 4 3 4 4 4 3 3 3 3 4 4 3 2 2 3 3 3 3 4 4 4 4 4 4 4 4 3 2 2 4 2 2 3 3 3 3 2 3 2 2 2 2 2 2 2 3 3 3 3 3 3 2 4 3 3 3 3 2 2 3 2 3 2 3 3 3 3 4 3 4 4 3 3 4 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 3 4 3 3 4 4 3 3 4 3 4 3 3 3 4 2 3 3 3 2 3 3 3 3 3 3 3 3 4 3 3 4 4 4 4 4 3 4 4 4 3 4 4 3 3 4 3 4 3 3 3 3 4 3 4 4 4 3 3 3 4 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 2 2 3 3 4 3 3 4 3 3 3 3 3 3 2 3 2 2 4 3 3 2 3 4 3 3 3 3 3 3 4 3 3 3 2 2 3 3 3 2 3 2 2 2 3 3 2 4 3 3 4 2 2 3 4 3 3 3 3 3 3 3 3 2 3 1 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 4 3 3 3 2 3 3 3 3 3 2 1 3 2 2 3 4 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 2 2 3 3 3 3 3 4 4 4 3 3 3 4 3 3 3 3 3 3 3 3 4 3 3 4 4 4 3 3 2 3 3 4 4 2 2 3 2 2 2 3 3 2 2 3 3 3 3 3 2 2 2 3 2 4 3 3 3 3 3 2 2 2 2 2 2 3 2 3 3 2 2 4 3 2 2 2 2 2 3 3 4 3 3 2 3 4 3 2 2 2 2 3 3 3 2 3 2 2 3 2 3 2 3 4 3 3 3 2 2 3 3 3 3 4 2 2 4 3 3 3 3 4 4 4 2 1 2 2 4 4 2 2 2 2 3 3 2 3 3 2 2 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 2 2 3 3 2 3 3 2 2 3 3 2 2 3 3 2 4 2 2 3 3 3 4 3 3 3 3 3 3 3 4 4 1 1 3 2 4 3 4 4 3 4 4 4 3 4 4 3 3 3 3 3 3 4 3 3 2 2 2 3 3 3 3 2 3 3 3 3 3 3 4 3 3 3 3 3 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 3 3 4 4 - 4 4 4 3 4 2 3 3 2 2 2 2 2 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 3 4 4 3 4 4 4 4 4 3 4 4 3 3 3 3 4 4 3 3 3 3 4 3 4 4 3 3 2 3 2 3 2 3 3 3 4 4 3 3 3 4 4 4 4 4 4 3 3 4 4 3 4 4 4 4 4 4 4 3 4 4 4 4 3 4 4 4 4 4 3 3 3 3 3 4 3 3 3 3 2 3 3 3 3 3 4 4 4 3 3 3 4 3 4 4 4 3 3 3 3 4 4 3 2 2 3 3 3 3 4 4 4 4 4 4 4 4 3 2 2 4 2 2 3 3 3 3 2 3 2 2 2 2 2 2 2 3 3 3 3 3 3 2 4 3 3 3 3 2 2 3 2 3 2 3 3 3 3 4 3 4 4 3 3 4 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 2 3 4 3 3 4 4 3 3 4 3 4 3 3 3 4 2 3 3 3 2 3 3 3 3 3 3 3 3 4 3 3 4 4 4 4 4 3 4 4 4 3 4 4 3 3 4 3 4 3 3 3 3 4 3 4 4 4 3 3 3 4 4 3 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 4 2 2 3 3 4 3 3 4 3 3 3 3 3 3 2 3 2 2 4 3 3 2 3 4 3 3 3 3 3 3 4 3 3 3 2 2 3 3 3 2 3 2 2 2 3 3 2 4 3 3 4 2 2 3 4 3 3 3 3 3 3 3 3 2 3 1 3 3 3 2 2 3 3 3 3 3 3 3 3 3 3 4 3 3 3 2 3 3 3 3 3 2 1 3 2 2 3 4 3 3 3 3 3 3 4 3 3 3 3 3 3 3 3 3 3 2 3 2 3 3 3 3 2 3 2 2 3 3 3 3 3


90


APPENDIX 4

CAUSE & EFFECT MATRIX TABLES

A1 A2 A3 A4 B1 B2 B3 B4 B5 B6 C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6 E1 E2 E3 F A 24 21 15 6 B 36 9 3 9 3 6 C 24 9 3 12 15 3 D 3 15 15 15 3 3 3 6 3 E 24 9 6 3 21 3 F 15 15 9 6 6 3 G 30 12 6 9 3 3 H 27 15 21 I 12 18 12 12 6 J 3 3 3 3 3 3 33 9 3 K 3 6 3 3 3 6 3 27 9 L 6 6 6 9 6 6 6 3 12 M 3 3 15 9 9 6 6 3 9 N 3 3 12 6 3 21 15 O 3 27 18 3 12 P 12 6 9 3 15 3 3 3 9 Q 9 21 3 3 12 3 3 9 R 9 3 15 3 6 6 3 3 3 3 3 6

MAJOR CAUSE


91


APPENDIX 4

CAUSE & EFFECT MATRIX TABLES

A1 A2 A3 A4 B1 B2 B3 B4 B5 B6 C1 C2 C3 C4 C5 C6 D1 D2 D3 D4 D5 D6 E1 E2 E3 F A 9 15 15 3 3 3 3 3 3 3 3 3 6 3 6 9 3 B 12 3 6 3 3 9 3 9 9 3 3 3 3 C 15 6 6 3 3 6 15 6 3 12 3 3 D 15 9 9 3 3 6 12 3 E 3 3 3 9 3 3 3 3 3 6 15 18 F 6 6 3 3 3 3 3 3 3 3 3 G 6 3 3 6 12 3 6 3 3 3 H 9 3 3 3 3 15 3 3 I 3 3 3 6 6 3 3 J 3 6 3 3 9 9 K 3 6 9 3 6 6 3 9 9 L 3 6 6 3 3 6 3 3 3 9 3 6 M 3 9 9 3 9 3 3 3 9 12 3 3 N 3 3 3 3 3 3 3 6 3 3 O 3 6 3 6 9 3 3 3 P 3 6 3 3 3 6 3 Q 3 6 3 3 3 3 3 3 3 R 9 3 6 3 6 9 6 3 3 3 3

OTHER CAUSES

Date post:	10-Apr-2015
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application of lean construction to reduce waste in highway projects

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