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Indian Construction Industry
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Indian Construction Industry is one of the fastest growing Construction Industry internationally and the second largest employer in India. It accounts for about 11% of India's GDP. As the construction industry advances, it is characterized by the major challenges such as time and cost overruns, low productivity, poor safety, insufficient quality, lack of skilled manpower. These are associated with considerable amount of waste present in the construction sites. A better way to reduce these waste is by implementing lean principles. Most of the Indian construction organizations are yet to address this issue. Lean Construction is a way to design production systems to minimize waste of materials, time, and effort in order to generate the maximum possible amount of value. It focuses on elimination of waste, cycle time reduction, variability reduction and continuous improvement. Therefore it is important to understand the concept of waste and to identify the critical waste variables and its root causes. Waste or non value-adding activities can be defined as activities that consume time, resources or space but do not add project value.
Interpretative Structural Modeling (ISM) is a method used when it is desired to utilize systematic and logical thinking to approach a complex issue and then to communicate the results to others. This method was first introduced by J. Warfield in1973. It is used for the hierarchical modeling of elements. The main objective of this study is to prepare the hierarchical modeling of the waste in Indian construction industry. For this thirteen waste variables were identified and questionnaires were prepared. A contextual relationship “leads to” was chosen to identify the interacting position of each waste variable for analysis. Almost 8 responses from different construction officials were obtained and the reachability matrix was developed. Using this, the digraph was prepared to indicate the hierarchical modeling of the waste variables. The dependence power and the driving power of the waste variables were determined inorder to perform the MICMAC analysis. MICMAC analysis was performed to analyse the driver power and dependence power of each element. Then the waste variables were classified into autonomous, independent, linkage and dependent clusters.

The objective of the study is to identify the wastes in involved in the construction industry and to prepare a hierarchical structure of the wastes. The study also aimed to find out the interrelationship of the waste variables and to determine the driving power and dependency power of the same.

The study was limited to data collected within the Indian construction industry. Responses obtained were limited and the responses were from the medium construction firms in Kerala.

This report based on the study conducted includes seven chapters. Chapter 1 gives a brief introduction about the study. The second chapter deals with the literature review conducted. The details of waste in the construction industry are given in the Chapter 3. A detailed description of the Interpretative Structural Modeling (ISM) is given in the Chapter 4. Chapter 5 describes the methodology and about the data collection. The results of the study are included in Chapter 6. The conclusions drawn based on the results obtained are included in seventh chapter.

Literature review was conducted in order to find out the wastes involved in the construction industry. The details of the Interpretative Structural Modeling (ISM) were also obtained from the literature review. The wastes were identified based on the literature review.
Ramaswamy K. P. (2010) investigated the incidence of waste in Indian Construction Industry. The objective of the study was to identify and investigate the relative importance of waste and waste cause variables which can affect construction performance in building projects according to the experience-based judgment and perceptions of contractor companies in India. Forty variables that related to waste activities identified were then separated into waste variables and waste cause variables. The data collected was analysed using the concept of relative importance index to determine the rank order and the criticality of the variables. Based on the perception of respondents, idle workers, waste of raw materials on site, labour waiting for materials, labour travelling empty handed and labour transporting materials were identified as the top five critical waste variables. The main reasons for the nonproductive time were found to be poor planning and scheduling, lack of adequate supervision, lack of skilled labour, inappropriate construction methods, unbalanced crew, poor site layout etc. The top five critical waste cause variables identified were poor planning and scheduling, lack of skilled labour, design changes, inappropriate construction methods, and poor coordination among project participants.

K. P. Ramaswamy and Satyanarayana N. Kalidindi (2009) studied waste in Indian Building Construction Projects. The paper focused mainly on building projects. Six ongoing projects were taken up for the study and the wastes identified were quantified in terms of project cost by collecting data through direct observations, records and using tools such as work sampling. Material scrap waste for selected materials was quantified using reconciliation data. The waste due to excess inventory was quantified from the records maintained in stores. Labour and equipment inefficiency was quantified through work sampling technique. The results showed that waste due to non value added activities by labour and equipment was much higher compared to material waste generated in the sites. The total cost of waste calculated as percentage of project cost, for the items studied, varied from 5.38% to 14.70% among the projects studied without including the cost of quality deviations.

K. C. Iyer and Mohammed Sagheer (2010) studied on the Hierarchical Structuring of PPP Risks using Interpretative Structural Modeling. The paper suggested the use of Interpretative Structural Modeling (ISM) to prepare a hierarchical structure as well as the interrelationships of public-private partnership project risks that would enable the decision makers to take appropriate steps. ISM along with MICMAC analysis provided a useful hierarchy of risks whose individual relationships were unambiguous but group relationships were too complex to organize intuitively and can help practitioners better understand risk dependencies and prioritize risk-mitigation efforts. The study identified 17 risks encountered during the development phase of PPP project in Indian road sector and found that 14 risks were weak drivers and weak dependents. The paper also recommended that the analysis can be extended for risk analysis in other infrastructures such as railways, seaports, airports etc.

Alwi.S, Hampson. K. and Mohamed.S. (2002) studied the factors influencing contractors’ performance in Indonesia. This paper reveals that waste can significantly affect the business performance and productivity of contracting organizations. The study aimed to investigate the incidence of non value-adding activities within contracting organizations in Indonesia, focusing on non-residential building and infrastructure projects. Data was collected through questionnaires and personal interviews targeting 99 respondents from 46 different contracting organizations. Statistical analyses were performed to rank the importance of waste variables and waste causes variables for different categories of organizations. The findings from survey conducted suggest that repair on finishing works, delays to schedule and waiting for materials were identified as the key variables, whereas design changes, lack of labourers’ skill and slowness in making decisions were identified as the key waste causing variables of non value-adding activities. It also recommended that contractors should maintain detailed records of all events which occur on-site in relation to the incidence of waste in order to minimize the same.

Sugiharto Alwi, Keith Hampson and Sherif Mohamed (2002) investigated the non value-adding activities in Australian construction projects. The study was a part of an on-going research project aimed to determine the incidence of non value-adding activities occurring within contracting organisations in Australia. A quantitative approach was adopted for this research utilising the results of a questionnaire survey involving 53 variables that related to non value-adding activities. The variables were then separated into 2 classifications: waste variables (22 variables) that contributed to a reduction in the value of construction productivity and waste causes variables (31 variables) that could be defined as factors producing waste. Results showed that there was no significant difference between respondents’ perceptions towards waste. Waiting for instruction was found to be the most important variable and poor quality site documentation, weather, unclear drawings, poor design, design changes, slow drawing revision and distribution and unclear specifications were identified as key waste cause variables of non value-adding activities. This paper recommended that investigation into the incidence of non value-adding activities was needed during construction to improve performance and enhance customers satisfaction.

Sadi. A. Asaf, Mohammed Al-Khaliland and Muhammed Al-Hazmi (1995) studied the causes of delay in large construction projects. The paper outlined the main causes of delay in large building projects in Saudi Arabia and their relative importance. A survey of randomly selected sample of 24 contractors, 15 architectural/engineering (A/E) firms and nine owners were undertaken .The survey include 56 causes of delay and the degree of importance were collected. The delay factors were grouped into nine major groups. It was found that contractors, A/Es and owners generally agree on the individual delay factors. The financing group of delay factors was ranked the highest and the environmental factors the lowest by all the parties.

The construction industry in almost all the countries is subjected to the incidence of waste. These wastes causes delay of the projects which will cause time and cost overruns, low productivity, poor safety, insufficient quality, lack of skilled manpower. The identification of the predominant wastes is important to minimize the same. Interpretative Structural Modeling (ISM) helps to the prepare a hierarchical structure as well as the interrelationships of wastes. So it can be applied to model the waste variables. MICMAC analysis helps to analyse the dependence and driving power of the variables. It is identified from the literature review that waste modeling using ISM has not been done so far. Hence this project aims to model waste using ISM.



According to lean construction philosophy, waste has a broader meaning which includes both the incidence of material losses and the execution of unnecessary works by man and machine, which generate additional costs but do not add value to the product. All the activities that produce costs, direct or indirect, and time, resources or require storage but do not add value or progress to the product can be called non-value- adding activities or wastes. These activities detrimentally affect the performance of construction projects. The identification of the factors that produce waste, their causes, and a measurement of their level of importance, would provide useful information that would allow management to act to reduce their negative effects in advance. In this study an attempt has been made to find out the waste that is predominant in the Indian construction industry. Variables that are related to non- value adding activities are separated into two categories such as waste variables and waste cause variables. Waste variables are that factors that contribute to a reduction in the value of construction productivity and waste cause variables are the factors produce waste.


Waste was classified into materials, poor quality, labour and equipment inefficiency. Material wastes were classified into waste of raw materials on site, excess inventory on site, material overproduction. Quality wastes include the repair on foundation works, structural work, formwork, finishing works. The wastes associated with equipment were classified into equipment breakdown, idle equipment, low productivity. The waste variables associated with labour were categorized into idle workers, slow/ineffective work, travelling empty handed, waiting for equipments and materials to arrive, accidents on site, delays to schedule. In this study 13 waste variables are identified based on the literature review. They are
1. Repair
Repair is defined as an activity that must be redone or altered. Repair includes variations and it can occur any time and within any activity during construction Repair works on finishing works, structural works, formwork, foundation work are considered to be the wastes. These are caused by the lack of skilled labour, use of inappropriate construction methods, poor planning and scheduling, slow drawing revision and distribution, design changes. Poor site quality documentation also affects the control of quality costs.
2. Waste of raw materials on site
The waste of raw materials, loss of damaged materials on site is mainly due to poor material handling in the site, lack of skilled labourers, use of inappropriate construction methods.
3. Too much of inventory on site
Excess inventory on the site is mainly due to the delay of material delivery to site, variability in lead time of materials. This leads to unnecessary storage, handling and financial cost.
4. Material Overproduction
Material overproduction is mainly due to the delay of material delivery to site, variability in lead time of materials. This leads to unnecessary storage, handling and financial cost.
5. Equipment Breakdown
Equipment breakdown lead to waiting of the labourers for the work to be finished and thus productivity decreases. This is mainly due to the lack of supervision.
6. Idle equipment
Idle equipment is the one which is not using at the time of operation. This will also leads the work to be unfinished affecting the productivity.
7. Low productivity of the equipment
This waste also leads to delays of schedule which affects the productivity. This will cause financial and time overruns. This is mainly due to the lack of supervision and poor equipment choice.

8. Idle worker
The workers who are not doing any job even after the work is being assigned to them. This is caused due to the lack of adequate supervision and inclement weather conditions.
9. Slow/Ineffective work
This is due to the unskilled labour, lack of adequate supervision, lack of motivation and thus leads to lower productivity.
10. Travelling empty handed
This activity is non productive which is due to poor site layout and poor distribution of labour. Motion takes time and does not add value to the product.
11. Waiting for materials and equipment to arrive
Waiting refers to the periods of inactivity that occur because the preceeding activity did not deliver on time or finish completely. Waiting waste increases cycle time during which no value added activity is performed. This is because of the lack of management practices and poor site layout.
12. Accidents on site
Accidents on site cause low productivity and it is due to lack of proper inspection and negligence.
13. Delays to schedule
Projects can be delayed for a large number of reasons, usually impacting project cost and schedule. This is also caused by adverse weather conditions, lack of skilled labour, poor planning and scheduling, delay of material delivery to site, design changes, slow decision making, unclear specifications, inappropriate construction methods etc.


Interpretative Structural Modeling (ISM) is a computer assisted learning process which was first proposed by J. Warfield in 1973. This is a process that transforms unclear, poorly articulated mental models of systems into well defined models useful for many purposes. ISM is a process intended for use when it is desired to utilise systematic and logical thinking to approach a complex issue and then communicate the results to others. ISM uses expert’s practical experience and knowledge to decompose a complicated system into several sub systems and construct a multilevel structural model. This method is found to be cheap, time-saving, easily understandable and fully implementable. It helps in presenting a hierarchical model showing the interdependencies between the factors.
4.2.1 Formation of Reachability Matrix
The fundamental concept of the process are an “element set” and a “contextual relation”. The element set is identified within some situational context and the contextual relation is selected as a possible statement of relationship among the elements in such a manner that is contextually significant for the purpose of enquiry. An equivalent binary matrix which is known as reachability matrix is prepared with the elements are the contents of the index set for the rows and columns of the matrix and the relation is indicated by “1” or “0”. From the reachability matrix through an iterative process redundant relationships between elements are eliminated and the final level of relationships between elements are established in a hierarchical form. This form is known as diagraph or directed graph. The diagraph is then converted to a structural model, inspected and revised to capture the user’s best perceptions of situations. The driving power and the dependency power is also identified. The driving power is identified by the summation of “1” in the corresponding row and the dependency power is obtained by the summation of “1” in corresponding column.

Partitioning The Reachability Matrix
The reachability matrix obtained is then partitioned by deriving the reachability set and the antecedent set to obtain the ISM hierarchy. The reachability set for each element represent a set of elements upon which the element has an impact. This is identified from the entry 1 in different cells of the horizontal row of the reachability matrix corresponding to the current element. The antecedent set represents the set of elements which have an impact on the current element. For any element, if the reachability set is a complete subset of antecedent set, that elements are taken out and assigned a particular level. This is the first iteration. In Table 4.2 reachability set for element 1 includes 1, 3, 6 and it is a complete subset of the antecedent set. So the elements 1, 3, 6 are taken out and placed in Level I. The process is repeated till all the elements are exhausted and their respective levels are obtained.

Development of Digraph
A digraph will be able to pictorially interpret the contextual relationship between each of these elements and their hierarchies. For preparing the diagraph, the elements move out during the first iteration appear at Level I and are placed at the top of the hierarchy. The elements that appear in Level II are placed just below the top level. The process is repeated and the interrelationship between the elements can be obtained by corresponding entries in the triangular matrix.
4.2.4 Degree of Relationship between Elements
MICMAC analysis can be done to ascertain the degree of relationship between the elements. This was first developed by J. C. Dupperin and M.Godet. The objective of MICMAC analysis is to analyse the driving power and dependency power of each element. MICMAC explores influence and dependence between issues and classifies them into independent, relay/linkage, dependent and autonomous clusters. Fig. 4. 1 shows an example for MICMAC analysis. From the reachability matrix, the driver power and dependence power of each element is obtained. Then each element is plotted as a point using the conventional X-Y coordinate system with the dependence power along the X axis and driving power along the Y axis.

The questionnaire was prepared and data was collected from the officials who are involved in the construction industry in Kerala. A contextual relationship “leads to” was choosen to identify the interacting position of each waste variable for analysis. Then a 13×13 matrix (reachability matrix) of the identified waste elements with 1 for Yes and 0 for No for paired comparison between each pair of element was developed. Eight were received and from the data obtained, for each elements the reachability matrix was prepared. The driving and dependence powers were also determined. The experience of the construction officials were classified into less than 5 years, 5-10 years, 10- 15 years, greater than 15 years.


This chapter deals with the results obtained from the study which includes the analysis of reachability matrix, partitioning of the reachability matrix, digraph analysis and the MICMAC analysis.
A 13×13 reachability matrix is shown in Table 6. 1. This is prepared on the basis of the responses obtained. The driving power as well as the dependency power of the thirteen waste variables are obtained
In the first iteration (Table 6. 2) the waste variables waste of raw materials on site (2), idle equipment (6), low productivity of equipment (7), idle worker (8), slow/ ineffective work (9), travelling empty handed (10), waiting for equipment or materials to arrive (11), delays to schedule (13) were taken out and placed in the first level. Similarly waste variables repair (1), too much of inventory (3), material overproduction (4), equipment breakdown (5) were taken out and placed in the second level in iteration II (Table 6.3). The waste variables accidents on site (12) was placed in the third level.

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