System Analysis: A Potential Technique to Research Construction Productivity Management 1
Er.Sanna Ratnavel1 ,Divya T.M.2 ,Dr. G.Chitra3, Nancy Deborah D.4 Chief Executive, Infrastructure Systems Consultant, Sceba Consultancy Services, Madurai, India (
[email protected]) 2 PG scholar, Thiagarajar College of Engineeering, India (
[email protected]) 3 Associate Professor, Thiagarajar College of Engineering, India (
[email protected]) 4 Assistant Professor, Sri Krishna College of Technology, Coimbatore, India (
[email protected]) production per unit of input is termed as productivity. Productivity can also be defined as an economic measure of output of a worker, machine or an entire national economy in the creation of goods and services to produce wealth [2]. A company that most minimizes input and maximizes output has the highest productivity. Productivity in general is a total concept that addresses the key elements of competition i.e. innovation, cost, quality and delivery. It must be viewed as value adding in addition to optimizing the cost and quality of construction [4].
Abstract — Productivity is an essential performance measurement tool. In developing countries like India, the construction sector is highly unorganized in terms of man, money and material, making it a poorly managed sector in the present scenario. Recent researches are mainly focused on Data Analysis techniques like Likert Scale, Relative importance Index. The results produced by these methodologies give ranking of the key factors that influence the issue. But, a next dimension of work is required to help analyze the productivity measures, which must encompass the whole problem and lead to solutions with less effort, less cost and more efficiency. That is, the focus of ‘parts’ to ‘whole’ must be changed to ‘whole’ to ‘parts’. One such potential approach is Systems thinking and Systems Analysis done by applying system methodologies. This approach considers all the elements of the system in which the construction productivity is a part of, which would lead to the identification of the inter-relationships, interdependencies and interactions within the elements. Thus, a productive measure of better perspective can be suggested. For better understanding a system analysis was carried out on Construction Labour Productivity using Interpretive Strucctural Modeling (ISM), a system methodology and compared with Likert Scale Analysis, The outcome proves the suitability of systems thinking in construction sector. Thus the system of the systems approach of a construction productivity management consists predominantly of the respondents’ attributes, system methodologies (ISM, Neural Network, Fuzzy Logic), and the interpreter’s intelligence.
For any complex problem under consideration, a variety of factors may be related to the issue or problem. However, the direct and indirect relationships between the factors describe the situation far more accurately than the individual factor taken into isolation [11], [13]. This perspective of looking at the problem is offered by Systems thinking. A system is a constitution of elements along with the interrelationships, inter relations and inter dependencies among them. The process starts with certain system-related data, ideas, skills; and /or knowledge residing in the various participants, and ends with an enhanced understanding of the system by the participants, individually and collectively. In order to establish the importance of any number of elements from within and outside the system, expert opinions and field studies are required. Data analyzing techniques are hence implemented for such purposes.
Keywords— Systems thinking, ISM, Construction Management, Productivity, Labour productivity
The basic mathematical entity common to the tools to represent the system is a structural model. A structural model is simply a collection of elements and their relationships.
1. INTRODUCTION To understand productivity, we must have an insight on the general definition of Productivity. The output of any aspect of 1
Graphically these models are represented by a set of nodes with some or all the nodes connected by lines [7]. Interpretive Structural Modeling (ISM) is one such structural modelling tool which has an added advantage compared to other structural modelling with respect to its factor analysing capacity and ease of handling.
nature. Respondents are asked to indicate their level of agreement with a given statement by way of an ordinal scale, ranging through 1(lower limit) to 9(upper limit) [2]. Each level is assigned a numeric value or coding, usually starting at 1 and incremented by one for each level. Most commonly seen as a 5point scale ranging from ―Strongly Disagree‖ on one end to ―Strongly Agree‖ on the other with ―Neither Agree nor Disagree‖ in the middle; however, use of 7 or 9 point scales adds an additional granularity. Sometimes a 4-point scale is used to produce a forced choice measure where no indifferent option is available.
The construction industry labour system is a large section suffering from poor working conditions and adverse terms of work. The present landscape of the unorganised sector becomes evident to be synonymous with the kaleidoscope of unregulated, poorly skilled and low-paid workers. While defining an unorganised sector we can say that it is a part of the workforce which has not been able to organise in pursuit of a common objective because of constraints such as; casual nature of employment, ignorance and illiteracy, small size of establishments with low capital investment per person employed, scattered nature of establishments, superior strength of the employer etc [10], [12].
Each specific question can have its response analyzed separately, or have it summed with other related items to create a score for a group of statement. This is why Likert scales are sometimes called summative scales. Individual responses are normally treated as ordinal data because although the response levels do have relative position, we cannot presume that participants perceive the difference between adjacent levels to be equal [2].
2. OBJECTIVE The primary objective is to analyze the procedure and characteristics of the decision making methodologies available to evaluate a system and to choose the most accountable method that conceptualizes it.
Relative Importance Index is one of the supplementing methods to analyze the outcomes of Likert scale responses and regression methods. The relative importance of each factors is determined from the responses using the forthcoming formula [2],[8].
The secondary objective is to establish the interactions, inter-relationships and the inter dependencies among the factors of the Labour Productivity system in India using Interpretive Structural Modeling.
∑ ∑ Where: Wi = the rating given to each factors by the respondents ranging from 1 to n Xi = the percentage of respondents scoring I = the order number of respondents. n = maximum value of response in Likert Scale
3. METHODOLOGIES The following methodologies are selected such that they are used to evaluate data from questionnaire surveys. The data consists of subjective opinions of respondents on various factors influencing Labour Productivity.
Therefore, higher the value of RII, higher will be the rank of the factors that were analysed.
A. Likert Scale Likert scale is a psychometric response scale primarily used in questionnaires to obtain a participant‘s preferences or degree of agreement with a statement or set of statements. It is a non-comparative scaling technique and are onedimensional (only measure a single trait) in
Some of the strengths of Likert scale analysis are that it is simple to construct, likely to produce a highly reliable scale, Easy to read and complete for participants 2
Some of the weaknesses are the central tendency bias, acquiescence bias, social desirability bias, lack of reproducibility of the respondents; and the validity may be difficult to demonstrate.
reachability matrix from SSIM. For this, SSIM is converted into the initial reachability matrix by substituting the four symbols (i.e., V, A, X or O) of SSIM by 1s or 0s in the initial reachability matrix. The rules for this substitution are as shown in Table II:
B. Interpretive Structural Modeling TABLE II. SUBSTITUTION OF SYMBOLS IN REACHABILITY MATRIX
ISM is a tool which permits identification of structure within a system [12]. It was first introduced by J.N. Warfield in 1974. Interpretive structural modeling (ISM) is a wellestablished methodology for identifying relationships among specific items, which define a problem or an issue.
SSIM Symbol V A O X
In this technique, a set of different directly and indirectly related elements are structured into a comprehensive systematic model. The method is interpretive in that the group‘s judgment decides whether and how items are related; it is structural in that, on the basis of the relationship, an overall structure is extracted from the complex set of items; and it is modeling in that the specific relationships and overall structure are portrayed in a digraph model. Therefore, ISM develops insights into collective understandings of these relationships [12].
X X
x x
0 1 0 1
Step 4: Conical matrix: Conical matrix is developed by clustering factors in the same level across the rows and columns of the final reachability matrix. The drive power of a factor is derived by summing up the number of ones in the rows and its dependence power by summing up the number of ones in the columns. Next, drive power and dependence power ranks are calculated by giving highest ranks to the factors that have the maximum number of ones in the rows and columns, respectively.
TABLE I. STRUCTURAL SELF INTERACTION MATRIX SYMBOLS ji
1 0 0 1
Step 3: Level partitions: From the final reachability matrix, for each factor, reachability set and antecedent sets are derived. The reachability set consists of the factor itself and the other factor that it may impact, whereas the antecedent set consists of the factor itself and the other factor that may impact it. Thereafter, the intersection of these sets is derived for all the factors and levels of different factor are determined. The factors for which the reachability and the intersection sets are the same occupy the top level in the ISM hierarchy. Once the top-level factor is identified, it is removed from consideration. Then, the same process is repeated to find out the factors in the next level. This process is continued until the level of each factor is found. These levels help in building the diagraph and the ISM model.
Step 1: Structural Self-Interaction Matrix (SSIM): Keeping in mind the contextual relationship for each factor and the existence of a relationship between any two factors (i and j), the associated direction of the relationship is questioned. The following four symbols are used as given in Table I to denote the direction of relationship between two factors (i and j): Based on the contextual relationships, the SSIM is developed.
ij
ji
Following these rules, the initial reachability matrix is prepared.
The various steps involved in ISM modelling are as follows [11].
SSIM Symbol V A O X
ij
Step 5: Digraph: From the conical form of reachability matrix, the preliminary digraph including transitive links is obtained.
Step 2: Reachability Matrix: The next step in ISM approach is to develop an initial 3
MICMAC analysis: Matrice d‘Impacts croisesmultiplication appliqúe an classment (crossimpact matrix multiplication applied to classification) is abbreviated as MICMAC. Based on the drive power and dependence power, the factors, have been classified into four categories i.e. autonomous factors, linkage factors, dependent and independent factors.
series of judgments based on pairwise comparisons of the elements. iii. The consistency of the judgments is checked by confirming with the respondent for the transitive relation of the factors that were already judged. iv. These judgments are synthesized to yield a set of overall priorities for the hierarchy by calculating the normalized principal Eigen Vector of the matrix formed. v. A final decision based on the results of this process is arrived at.
i. Autonomous factors have weak drive power and weak dependence power. They are relatively disconnected from the system, with which they have few links, which may be very strong. ii. Linkage factors have strong drive power as well as strong dependence power. These factors are unstable in the fact that any action on these factors will have an effect on others and also a feedback effect on themselves. iii. Dependent factors have weak drive power but strong dependence power. iv. Independent factors have strong drive power but weak dependence power.
Fig.1: Hierarchy for Analytic Hierarchy Process But there is sufficient evidence to suggest that the recommendations made the AHP should not be taken literally. In matter of fact, the closer the final priority values are with each other, the more careful the user should be. An apparent remedy is to try to consider additional decision criteria which, hopefully, can assist in drastically discriminating among the alternatives [6].
A factor with a very strong drive power, called the ‗key factor‘ falls into the category of independent or linkage factors. C. Analytical Hierarchy Process The Analytic Hierarchy Process (AHP) is a multi-criteria decision-making approach used to solve complex decision problems and was introduced by Saaty. It uses a multi-level hierarchical structure of objectives, criteria, sub criteria, and alternatives. The pertinent data are derived by using a set of pairwise comparisons. These comparisons are used to obtain the weights of importance of the decision criteria, and the relative performance measures of the alternatives in terms of each individual decision criterion. If the comparisons are not perfectly consistent, then it provides a mechanism for improving consistency [6], [8].
4. COMPARATIVE EVALUATION The questionnaire survey and the evaluation technique for Likert scale are rather simple compared to the other methodologies. In case of Interpretive Structural Modeling and Analytical Hierarchy Process, pair-wise comparison leads to voluminous number of questions. With respect to reliability and Consistency, Interpretive Structural Modeling and Analytical Hierarchy Process have an upper hand for its ability to check for consistency. Likert scale does not have an inbuilt system to check consistency, hence, the judgmental capacity of the respondent and the perception of the weights and evaluation criteria are unclear. But Likert scale has a higher level of reliability for its frequent and wide usage in the construction industry.
The procedure for using the AHP can be summarized as: i. The problem is modelled as a hierarchy containing the decision goal, the alternatives for reaching it, and the criteria for evaluating the alternatives as shown in fig. 1 ii. The priorities among the elements of the hierarchy are established by making a
The order of methodologies with respect to its accuracy would be, Analytical Hierarchy Process, Interpretive Structural Modeling and Likert scale. Meanwhile, the precision and 4
Motivated labours usually are more enthusiastic and initiative. They work harder and respond faster to instructions. Their pace is, moreover, associated with a greater sense of pride, satisfaction, and responsibility, thus they typically achieve more, in comparison with demotivated or discouraged labourers [1].
accuracy of any methodology also lies in the nuances of the questions being posed and the mental maturity of the respondents. The understandability of the methods and its grading system by the respondents becomes equally important for the accountability of the methodology. In such cases Interpretive Structural Modeling has simpler questioning procedures while elements are compared with one another, while Analytic Hierarchy Process, and Likert scale requires a distinctive weighing and grading by the respondents which requires familiarity with the process.
Communication between labours and supervisors is an essential element in the labour system. The ability of the supervisor to make understand the labour of the specificities of the work at site is important in terms of wastages, safety.
Also, an outcome for any system for its productivity in the construction industry, must rank the factors, identify the inter-relations and the inter dependencies among them and establish the key nodes which has to be concentrated in order to improve the scenario. In this regard, Interpretive Structural Modeling provides a digraph representation of the key elements and its interconnectivity. 5.
Poorly trained and unskilled labours are commonly characterized with low and faulty outputs coupled with unjustifiably high inputs. To the contrary, experienced labours possess sound intellectual abilities, practical solutions to encountered obstacles, and high technical and motor skills, all of which lead to higher productivity, lower cost of labour, and better quality of finished outputs [1], [5].
APPLYING SYSTEMS THINKING TO LABOUR PROUDIIVITY FACTORS
Proper planning of activities corresponds to the scheduling of activities, management of men and material at site, housekeeping, formwork setting and scaffolding works.The wrong use of construction methods aside from slowing down productivity of workers could gravitate to rework with the waste of material and human resources [9].
Around 56 to 60 factors influencing labour productivity in the construction industry was identified based on primary and secondary data collection, which is through extensive literature review and opinions from experts of the field. These factors can be familiarly categorized as Labour attribute, Management, Environmental, societal, legal, Ergonomic, Safety, Technology, and Economical.
Lack of supervision encourages labours, especially those who are under the direct employment method, to engage in unproductive activities, take frequent unscheduled breaks, wait idle, or even leave the job sites during working hours to attend to personal matters[1]. This element is more prominent in large scale projects where the current supervisor must oversee several projects at once [2].
The factors are given a number as follows: F1 – Labour Experience F2 – Motivation and incentives F3 – Communication between labour and supervisor F4 – Training of the labour F5 – Non-planning of construction activities F6 – Method of working F7 – Supervision F8 – Insecure feeling of labour at site F9 – Payment delay F10 – Labour inattentiveness
Insecure feeling to the labour while working at heights, improperly fixed scaffoldings, lack of safety equipment and other unsafe conditions affects the labour‘s efficiency at work. Payment delay is a factor that leads to extensive absenteeism among the labours, in turn leads to distrust on the employer. It greatly occurs when the project economy is not favorable to the employer. Labour inattentiveness is a factor leading to loss in labour productivity. It relates to the labour‘s attitude in general, unfulfillment
Labour experience is an element corresponding to a labour‘s age, circumstances of prior projects related to a specific activity at site [1]. 5
of his basic needs dissatisfaction with the supervisor or the construction activity in general or due to unusual illness when at site.
D P2
A group of field experts, namely, consultants, site engineers, supervisors, contractors were asked to determine the contextual relationship between the listed labour productivity system elements. Based on the majority opinion, the Structural Self Interaction Matrix (SSIM) is formulated which is shown in Table III. Using the symbol conversion, the reachability matrix is formed as shown in Table IV. Further analysis is done based on the methodology discussed earlier. The reachability set, antecedent set and intersection set of the factors are identified and the levels of the factors are represented in Table V. TABLE III Structural Self Interaction Matrix F F F F F F F F F F1 1 2 3 4 5 6 7 8 9 0 F1 X O V A V V O V O V X X O O V A O A V F2 X O V V A O A V F3 X O V O V O O F4 X V A V A O F5 X A V A O F6 X V O V F7 X O O F8 X V F9 X F1 0
F1 F2 F3 F4 F5 F6 F7 F8 F9 F1 0
4
5
1
5
8
1
6
1
6
DP1 – Driving Power; DP2 – Dependence Power
The contextual statement taken for the pair wise comparison is ―Element ‗i‘ influences element ‗j‘ with respect to labour productivity‖.
TABLE IV
2
TABLE V.
LEVEL PARTITION
Antecede nt set
Reachabili ty set
Inte rsec tion set
F1
1,3,5,6,8,1 0
1,4
1
5
F2
2,3,6,10
2,3,7,9
2,3
3
F3
2,3,5,6,10
1,2,3,7,9
2,3
4
F4
1,4,6,8
4
4
6
F5
5,6,8
1,3,5,7,9
5
3
F6
6,8
1,2,3,4,5,6, 7,9
6
2
F7
2,3,5,6,7,8 ,10
7
7
5
F8
8
1,4,5,6,7,8
8
1
F9
2,3,5,6,9,1 0
9
9
5
F1 0
10
1,2,3,7,9,1 0
10
1
Leve l
REACHABILITY MATRIX
F F F F F F F F F F 1 2 3 4 5 6 7 8 9 1 0 1 0 1 0 1 1 0 1 0 1 0 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1
D P1 6 4 5 4 3 2 7 1 6 1
Fig. 2: Digraph - Direct Links in the Labour Productivity system
We can now represent the findings of the ISM analysis in a diagrammatic manner called a digraph. In Fig. 2, we can see that there are direct links of factors from one level to another 6
and Fig. 3 shows other significant links which are inter-relating and linking between factors which are not in the precedent or succeeding level.
position in the system as per the analysis, which falls in Level 6 and its characteristic being Autonomous. Labour Experience (F1), Supervision (F7), Payment delay (F9) are independent factors which have high driving power and low dependence power falls in the next level, i.e. Level 5. Communication between labour and supervisor (F3) is a factor that occupies level 4 and it shares all the characteristics of the system. Incentives and motivation (F2) and non-planning of activities (F5) are in level 3, method of working (F6) is in level 2. Finally, insecure feeling of labours and labour inattentiveness are at the lower most level which is Level 1. TABLE VI. S. No
Fig. 3: Digraph - Significant Links in the Labour Productivity System
1
The next step in the process is to identify the characteristics of the various factors within the system. This is done by the use of MICMAC analysis. Fig. 3 represents the MICMAC analysis of the factors with the driving power of the factors on the abscissa and dependence power as the ordinate.
2 3 4 5
MICMAC ANALYSIS
Dependence Power
10 Depend ent
8 6
Linka
2, 8
6
1, 6 3, 5
4
5, 5
7
4, 4
2
Independe nt 6, 2 4, 1 6, 1
Autonom ous
0 0
2
Driving4Power
6
7, 1
8
8
9
Fig. 4: MICMAC Analysis 6.
10
SUMMARY OF THE ANALYSIS
Factor Insecure Feeling of labour Labour inattentiveness Method of working Incentives and motivation Non planning of construction activities Communicatio n between labour and supervisor Labour Experience Supervision Payment Delay Labour training
Fact or No.
Le vel
Factor Type
F8
1
Depende nt
F10
1
F6
2
F2
3
F5
3
F3
4
F1
5
F7
5
F9
5
F4
6
Depende nt Depende nt Autonom ous Depende nt, autonom ous Equally likely Independ ent Independ ent Independ ent Autonom ous
OBSERVATIONS AND DISCUSSIONS Through MICMAC analysis we could say that the linkage factors, independent factors, autonomous factors and dependent factors are the most critical elements of the system in descending order. The linkage and independent have high driving powers such that by rectifying them, the productivity can be easily enhanced. In our case, the factors which fall
The analysis of the elements of the labour productivity system in the construction industry is summarized in Table VI. From the Level Partition step we could observe the following. Labour Training (F4) is the factor occupying the most important 7
under linkage category are none, but we have as many as 3 factors under independent category, namely, supervision, labour experience and payment delay; coincidentally, fall on the same level. Looking out for the next important factors which are autonomous are labour training, incentives and motivation, and non-planning of construction activities which are in Level 1 and Level 3 respectively. Hence, the labour training element gets more importance compared to the other factors.
[3] [4]
[5]
[6]
The digraph shows the relationships of the factors. Among the identified key factors from the level partitioning and MICMAC analysis, the inter linkages are scarce. We see that Labour experience is influenced by labour training, which is an autonomous factor and occupies a high level in the system. The next most critical factors such as motivation to workers and nonplanning of workers do not affect the independent factors of the system.
[7]
[8]
7. CONCLUSION Modeling an unorganized sector like the labour system is not a simple task. An appropriate methodology is necessary to identify and establish the key factors that influence the system. Hence, the most sought after methodologies like Likert scale, Analytical Hierarchy Process, and Interpretive Structural Modeling are taken for a comparative analysis.
[9]
[10]
Interpretive Structural Modeling turns out to be more relevant and suitable to study a system due to its comparatively simpler methodology, relativity to the real world, and practicality which makes it easier for the respondents and the analyzers. The analysis points out that labour training is one of the key factors in the labour system which can enhance labour productivity tremendously.
[11]
[12]
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