Exploration on the Application of Multifactrorial

Asian Research Consortium Asian Journal of Research in Social Sciences and Humanities Vol. 6, No. 3, March 2016, pp. 86-101. ISSN 2249-7315 A Journal Indexed in Indian Citation Index

Asian Journal of Research in Social Sciences and Humanities www.aijsh.com

Exploration on the Application of Multifactrorial Evaluation Model in Performance Appraisal System Dr. Sheik Manzoor A.K.* *Assistant Professor, Department of Management Studies, Anna University, Chennai, India. DOI NUMBER-10.5958/2249-7315.2016.00031.9

Abstract In the contemporary competitive milieu, performance management of human capital determines the success or otherwise of any organization. The key ingredient in the pivotal performance management is performance appraisal system (PAS). But all the existing conventional PAS are fraught with perils like linguistic vagaries, psychological skewness technical pitfalls, et al, thus making them not only devoid of its intended aristocratic course but also leading to aberrations in human relations, the core propellant of any organization. This paper seeks to explore to ring in a non conventional PAS using distinctive, supplemental recuperative criteria apart from employing MEM to effectively cope up with appraisal grades articulated in obscure linguistic expression. It vouches the supremacy of this model over the conventional PAS using a sample size of 100 employees of a chemical company.

Keywords: 5 MEM, PAS, Weighting factors, Grouping of employees, fuzzy. ________________________________________________________________________________

Introduction It has been acknowledged across all realms that performance appraisal constitutes the nucleus in making the human resources achieve par excellence in ascending an organization towards the zenith of financial success. Managers are aware of the need of an objective PAS devoid of biasedness. Employees are also conscious of the significance of unprejudiced evaluation system and the role it plays in their pecuniary compensation and advancement in their career path. Performance appraisal is habitually done on an annual or semi-annual basis.

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Manzoor A.K. (2016). Asian Journal of Research in Social Sciences and Humanities, Vol. 6, No.3, pp. 86-101.

Though PAS has evolved over a period of time still it is plagued by hazards like linguistic vagaries, psychological skewness, technical pitfalls, etc. The increasing awareness and appreciation of fuzzy set theory have made some researchers apply it and fuzzy logic to the PAS.

Research Gap No such attempt has been made in the chemical industry till now. This research endeavours to design and apply MEM to the PES in a chemical company, and prove its supremacy over the conventional evaluation system followed in the company.

Objectives 1. To develop a PES using MEM for Assessing staff performance move precisely based on specific performance appraisal criteria advocated by the company 2. To establish the supremacy of the new model over the conventional one used by the company

Review of Literature Performance Appraisal using Performance Appraisal Criteria’s Performance Appraisal tends to improve the work performance, communication expectations, determining employee potential and aiding employee counseling. An effective performance appraisal program will assist the company in achieving its goals and objectives. Not only will training needs be acknowledged and addressed during a performance appraisal evaluation, but unseen talent can be discovered as well Ashima Aggarwal (2013) 1,. As plotted by C. C. Yee, and Y.Y.Chen (2010)2, it is important for the organizations to maintain the talented knowledge workers, since the world began to shift towards knowledge based capitalism. Therefore it is always essential to determine and promote the most qualified applicants because valuable human proficiency is the main source of viable advantage for the organizations. Thus, the construction of performance appraisal criteria is an important necessity towards performance appraisal and it is also essential to assess the employee‟s performance across various aspects more precisely. Various techniques or methods have been used by human resource management experts to evaluate the performance of an employee. According to Fred C. Lunenburg (2012) 3, Performance appraisal is defined as the systematic observation and evaluation of employees‟ performance. Some of the most commonly used performance appraisal methods include the judgmental approach, the absolute standards approach, and the results-oriented approach. As summarized by Vicky G (2002)4, some of the appraisal methods include ranking; trait scales; critical incident; narrative; and criteria-based. Terrence, H. M. and Joyce, M (2004)5 mentioned few other methods including management-by-objectives (MBO), work planning and review, 360 o appraisal and peer review. With all the existing systems, it is necessary to realize that different organization might use different appraisal system in evaluating staff performance. Since all the techniques mentioned above has their own advantages and disadvantages, most organizations might mix and match

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different techniques for their own performance appraisal system that can fulfill their organizational needs.

Multifactorial approach in Performance Appraisal According to C. C. Yee, and Y.Y.Chen (2010) 2, Multifactorial appraisal model follows a systematic step in determining a staff‟s performance, and therefore, it creates a system of appraisal which is able to consistently produce reliable and valid results for the appraisal process. This system can be used by other organizations when the aspects to be evaluated and weightage for each of these aspects are defined in the system beforehand. As outlined by GMeenakshi (2012)6, Multifactorial evaluation model is used in assisting high-level management, to appraise their employees. This system can be used as a diagnostic & assessment tool to increase employee participation & to demonstrate a commitment to their workforce. 360 degree feedback determines relationship between strategic plan/vision of institute &performance expectations. It increases focus on customer service & reinforce continuous process improvement programs. Team based culture can be developed for attaining organizational objectives.

Application of Fuzzy Based Method Fuzzy based method has been applied into several performance appraisal systems and other application where several authors have proved that fuzzy set theory could be successfully used to solve multiple criteria problems. Jing. R.C et al (2007)7 developed a computer-based group decision support system-fuzzy group decision support system (FGDSS) for the assessment of military officer with respect to criteria and the importance weights of criteria are given by decision makers in linguistic terms. Fuzzy group decision support system (FGDSS) includes three ranking methods (intuition ranking, Lee and Li‟s fuzzy mean/spread and metric distance). Based on the findings of their work, the application of fuzzy set theory in FGDSS is said to be able to assist decision maker to make better decisions and to provide more transparent information under different fuzzy circumstances and alternatives. Tutmez et. al. (2007)8 had used Multifactorial fuzzy approach and evaluated performance measurements and stone properties for the sawability classification of building stones. Using three different decision functions, the sawing performances of diamond saws were classified into three categories: excellent, good and poor. It is possible to evaluate the sawability and select a suitable diamond saw for a new building stone by only some stone properties testing using the developed fuzzy classification system. This study has suggested that information can reasonably obtain and sawability classification is reasonable and acceptable. The measurement of aerodynamic forces and moments acting on an aircraft model is important for the development of wind tunnel measurement technology to predict the performance of the full scale vehicle. Fuzzy logic approach is found as efficient for the representation, manipulation and utilization of aerodynamic characteristics. Altab Hossain et. al. (2011) 9, presented an adaptive approach based on the use of fuzzy logic for the prediction of lift coefficient for the 88


aircraft model. In comparison to other predictive modeling techniques, fuzzy models have the advantage of being simple (rule base and membership functions) and robust. In this study, according to evaluation criteria of predicted performances of developed fuzzy knowledge-based model was found to be valid. Fuzzy logic could be used successfully to model situations in which people make decisions in an environment that is so complex that it is very hard to develop a mathematical model. Such situations for example often occur in the field of traffic and transportation when studying the work of dispatchers or modeling choice problems. Fuzzy logic is shown to be a very promising mathematical approach for modeling traffic and transportation processes characterized by subjectivity, ambiguity, and uncertainty and imprecision. The basic premises of fuzzy logic systems are presented as well as a detailed analysis of fuzzy logic systems were developed by Amrita Sarkaret. al. (2012)10, to solve various traffic and transportation planning problems. Emphasis is put on the importance of fuzzy logic systems as universal approximators in solving traffic and transportation problems. Fuzzy logic systems provide two other very important advantages. They can use existing linguistic knowledge very successfully, and they treat uncertainty in an appropriate manner. The results obtained from the study show that fuzzy set theory and fuzzy logic present a promising mathematical approach to model complex traffic and transportation processes that are characterized by subjectivity, ambiguity, uncertainty and imprecision. From earlier works it is proved that, the benefits from the fuzzy logic will be more accurately assessed as the number of successful practical applications of the fuzzy logic in traffic control and transportation planning increases. Many researches dealt with the problem of induction motors fault detection and diagnosis. The major difficulty is the lack of an accurate model that describes a fault motor. Hence M. Zeraouliaet. al. (2005)11 presented a method of using fuzzy logic to interpret current sensors signal of induction motor for its stator condition monitoring. This study revealed that high diagnosis accuracy is obtained when the current signals are correctly processed and input them to a fuzzy decision system. In the field of medical diagnosis, Mokhtar Beldjehem (2011) 12 proposed to learn a compact fuzzy medical knowledge base through a cognitively-motivated granular hybrid neurofuzzy or fuzzy-neuropossibilistic model appropriately crafted as a means to automatically extract fuzzy weighted production rules. The result of the study revealed that the application of fuzzy relational calculus helped in effective human decision making and clinical problem-solving. GAO Guifeng et. al.(2006)13, carried out the synthetic appraisal with the method of AHP and fuzzy evaluation theory, and verified the validity and rationality by practical example. The evaluation results can provide the related safety management measures to the managers for improving the safety of the highway tunnel traffic system. The fuzzy subclass sheet of subjection degree can be gained according to the experts‟ experience and the probability distribution theory directly. Thus based on the literature review it is clear that fuzzy set theory would be a good concept to be used in the development of the performance appraisal system. Therefore

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performance appraisal system using Multifactorial evaluation model is used in this paper since the application of fuzzy set theory helps in reliable decision making process.

Research Methodology The current research is of exploratory nature. An existing MEM has been modified and augmented with extra factors and additional appraisal criteria appended to suit the needs of the company. Data collected from supervisors/ appraisal teams for a sample size of 100 employees have been used to evaluate the proposed MEM. Apart from the application of fuzzy set theory, % age analysis and ttest have been used to achieve the objectives of the research.

Performance Appraisal Model (i)

Modus Operand of Performance Appraisal

Employees are assigned tasks at the beginning of the year .At periodic intervals they are supposed to report on the progress of the task assigned to them. At the end of the year they are consolidated to evaluate the annual performance of the employees (ii)

Criteria for appraising Staff and Verbal Grade Scale

Five aspects have been contemplated, each containing multiple sub criteria. 

Operational Output (Aspect 1): It attempts to gauge the volume, quality, effectiveness of the output of the staff and his punctuality.



Intelligence and dexterity (Aspect 2): It seeks to size up his intelligence, dexterity and communication skill exhibited by the staff in his work place and adherence to rules.



Traits (Aspect 3): This encompasses traits exercised by the staff like discipline innovativeness, spirit of co-operation, self relevance



Staff’s contribution (Aspect 4): It considers the staff‟s contribution to the company he is serving and to the community state and country.



Safety And Health Measures (Aspect 5): It takes into account the employees‟ awareness of probability of accidents hazards arising from harmful exposures, techniques of responding in the event of contingencies, knowledge of handling safety equipments

A staff‟s performance will be assessed against each sub criteria on a scale of 1 to 10, with 1 indicating poor performance and 10 designating very high performance. The verbal grade and the scale applicable for each aspect.

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VERBAL GRADES Very High High Moderate Low Very Low

SCALE 9-10 7-8 5-6 3-4 1-2

Proposed Application of Multifactorial Evaluation System Target Respondents The performance appraisal data for this study are collected from the supervisors/appraisal team for a sample size of 100 employees and this data is used to evaluate the proposed Multifactorial evaluation model. The sample size includes only the knowledgeable employees such as Quality auditors, Chemical engineers and Research and development personnel‟s. Convenience sampling is used in this study, and the sample is drawn from a population which is readily available and convenient and the sample composition is given in the following table.

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Gender Male Female Age group Below 25 years 26 - 35 years 36 - 45 years Above 45 years Marital Status Single Married Qualification BE/B.Tech B.Sc Diploma Designation Type Operational Executive Working Experience 0-2 years 2-4 years 4-6 years Above 6 years

Percentage 63 37 Percentage 52 34 10 4 Percentage 54 46 Percentage 50 26 24 Percentage 72 28 Percentage 41 25 13 21

Sample Composition Data Collection Process Prior to the actual implementation of the proposed system, questionnaires were prepared and distributed to the human resource section to evaluate the efficiency and usefulness of the system. Closed ended questions along with the rating scale method were used so that the respondent can rate the employees based on the given number of options in the rating scale These questionnaires were circulated to the supervisors/appraisal team of the organization. The performance of each employee was observed by the appraisers and rated by using rating scale method.

Multifactorial Performance Evaluation Model The proposed application of Multifactorial Evaluation Model (C. C. Yee and Y.Y.Chen, 2010) 2 in the performance appraisal system is a combination of five Multifactorial evaluation models based on the research organization. Each model represents the aspect to be evaluated in the performance appraisal. In the Multifactorial evaluation system, Ui is the factors to be evaluated in each aspect whereas D(Ui) is the result of staff‟s performance in a particular aspect.

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The performance appraisal model can be illustrated with the example of one employee. The first evaluation model uses U1, that is, the factors in Aspect 1 (Operational Output) as its input. The subcriteria for this aspect will be used as the basic factors under this aspect which are: f1 = volume of the output produced by the employee, f2 = Quality of the output produced by the employee, f3= Punctuality of the employee and f4 = Effectiveness of the output. Therefore, F = {f1, f2, f3, f4}

The verbal grades used for the appraisal are: e1 = Very High, e2 = High, e3 = Moderate, e4 = Low, and e5 = Very Low Therefore, E = {e1, e2, e3, e4, e5}

Single Factor Evaluation Vector 

The weightage for each verbal grade is determined by the Management/Appraisal team based on the importance of the verbal grade. The weightage differs from organization to organization and it is based on the importance provided to each categories/aspects.



As an example, the weightage for the Aspect1 “Operational Output” factor f1 are, 10% for Very High, 40% for High, 30% for Moderate, 10% for Low, and 10% for Very Low, thus, the single-factor evaluation vector R1(U) is R1(U) = {0.1, 0.4, 0.3, 0.1, 0.1}



Similarly, the single-factor evaluation vectors for f2, f3 and f4 are as follows: R2(U) = {0.2, 0.4, 0.2, 0.2, 0.0} R3(U) = {0.4, 0.3, 0.2, 0.1, 0.0} R4(U) = {0.2, 0.4, 0.3, 0.1, 0.0} 93


R5(U) = {0.3, 0.4, 0.2, 0.1, 0.0} 

From the above, the following evaluation matrix can be constructed:

R1(U) R2(U) R(U)= R3(U) R4(U) R5(U)

0.1 0.2 = 0.4 0.2 0.3

0.4 0.4 0.3 0.4 0.4

0.3 0.2 0.2 0.3 0.2

0.1 0.2 0.1 0.1 0.1

0.1 0.0 0.0 0.0 0.0

Weighting Factor Weighting factor is denoted by W(U). W(U) represents the appraiser‟s rating towards a staff (in the rating scale of 1-10) for all the sub criteria in a particular aspect. As an example, the appraiser‟s rating for weight vector corresponding to the five factors in all aspects for Employee1 are W1(U) = {0.3, 0.23, 0.2, 0.27} W2(U) = {0.27, 0.23, 0.2,0.3} W3(U) = {0.29, 0.24, 0.26, 0.21} W4(U) = {0.31,0.19,0.35,0.15} W5(U)={0.27,0.24,0.18,0.3}

Staff’s Performance in each Aspect Multiplication of matrices W(U) and R(U) is based on the min-max composition of fuzzy relations, where the resulting evaluation is in the form of a fuzzy set D(u) = [d 1, d2, d3, d4,d5].Since the aspect of Operational Output has five verbal grades, that is, E = {e 1, e2, e3, e4, e5} which would be involved in the performance appraisal system, thus, the resulting evaluation is in the form of a fuzzy set D(u) = [d1, d2, d3, d4,d5]as shown below D(U) = W1(U) . R(U)

= 0.3

0.23 0.2

0.27 .

= 0.2

0.3 0.3

0.2 0.1

0.1 0.2 0.4 0.2 0.3

0.4 0.4 0.3 0.4 0.4

0.3 0.2 0.2 0.3 0.2

0.1 0.2 0.1 0.1 0.1

0.1 0.0 0.0 0.0 0.0

Calculation of D(U) by using fuzzy based Method 

The values of D(U) are calculated through the following steps . Here „^‟denotes the operations min and „v‟ represent the operation max. 94


d1 = (w1 ^ r11) v (w2 ^ r21) v (w3 ^ r31) v (w4 ^ r41) v (w5 ^ r51) = (0.3 ^ 0.1) v (0.23 ^ 0.2) v (0.2 ^ 0.4) v (0.2 7^ 0.2) v (0.0 ^0.0) = 0.1 v 0.2 v 0.2 v 0.2 d1= 0.2

d2 = (w1 ^ r12) v (w2 ^ r22) v (w3 ^ r32) v (w4 ^ r42) v (w5 ^ r52) = (0.3 ^ 0.4) v (0.23 ^ 0.4) v (0.2 ^ 0.3) v (0.27 ^ 0.4) v (0.0 ^ 0.4) = 0.3 v0.23 v0.2 v0.27v0.0 d2= 0.3 d3 = (w1 ^ r13) v (w2 ^ r23) v (w3 ^ r33) v (w4 ^ r43) v (w5 ^ r53) = (0.3 ^ 0.3) v (0.23 ^ 0.2) v (0.2 ^ 0.2) v (0.27 ^ 0.3) v (0.0 ^ 0.2) = 0.3 v0.2 v0.2v0.27v0.0 d3= 0.3 d4 = (w1 ^ r14) v (w2 ^ r24) v (w3 ^ r34) v (w4 ^ r44) v (w5 ^ r54) = (0.3^ 0.1) v (0.23 ^ 0.2) v (0.2 ^ 0.1) v (0.27 ^ 0.1) v (0.0 ^ 0.1) = 0.1 v0.2 v 0.1 v 0.1v0.0 d4= 0.2 d5 = (w1 ^ r15) v (w2 ^ r25) v (w3 ^ r35) v (w4 ^ r45) v (w5 ^ r55) = (0.3 ^ 0.1) v (0.23 ^ 0.0) v (0.2 ^ 0.0) v (0.27 ^ 0.0) v (0.0 ^ 0.0) = 0.1 v 0.0 v 0.0 v 0.0 v 0.0 d5= 0.1 

The largest components of D(U) are d2 = 0.3, and d3 =0.3 occurring simultaneously.



Referring to the verbal grades, E = {Very High, High, Moderate, Low, Very Low}, the analyzed staff‟s performance in terms of Operating Output obtained a rating somewhere between “High” and “Moderate”.



However, by applying the principle of the biggest subjection degree as mentioned by Guifeng, G. et. al (2006)13, the staff‟s performance in terms of Operating Output is “High”. 95




The same method of calculation can be applied to U2, U3, U4 and U5



As an example for Employee 1, the staff‟s performance against 

U2 is “Good”



U3 is “Good”



U4 is “Very Active”



U5 is “High”.

An Employees overall average rating (OAR) is calculated as follows: OAR = (Aspect 1 * 40) + (Aspect 2 * 25) + (Aspect 3 * 20) + (Aspect 4 * 10) + (Aspect 5 * 5) Aspect Aspect 1 & 5

Aspect 2 & 4

Aspect 3

Verbal Grades Very High High Moderate Low Very Low Excellent Good Moderate Weak Very Weak Very Active Active Moderately Active Less Active Not Active

Weighting for Each Aspect 1 0.8 0.6 0.4 0.2 1 0.8 0.6 0.4 0.2 1 0.8 0.6 0.4 0.2

Verbal Grades and Scale for Aspects 1,2,3,4 and 5 Based on the above table, the analyzed staff‟s performance in terms of Operational Output obtained a rating of “High”. As a result, 0.8 would be the weighting for Aspect 1. Meanwhile, according to what have been computed by using the Multifactorial evaluation model, the staff has been rated as “Good” or the weighting of 0.8 in terms of intelligence and dexterity. As for the aspect of Traits, the staff‟s performance is “Good” or the weighting of 0.8 would be selected. As for the staff‟s contribution, the staff has gained a “Very Active” performance or the weighting of 1for this aspect. For the final aspect of Safety and Health Measures, the staff‟s performance is “High”. Thus, the rating and weighting for each aspect is as summarized in table.

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Aspect Aspect 1 (Operational Output) Aspect2 (Intelligence and dexterity) Aspect3 (Traits) Aspect4 (Staff‟s contribution) Aspect5 (Safety and Health Measures)

Rating High Good Good Very Active High

Weighting 0.8 0.8 0.8 1 0.8

Table: Summarized Rating and Weightage of the Five Aspects for Employee1 Therefore, the employee‟s overall average rating (OAR) is OAR = (0.8* 40) + (0.8 * 25) + (0.8 * 20) + (1 * 10) + (5*0.8) OAR= 82 The rating obtained by the same employee without Multifactorial evaluation model is 77 (average of individual scores in all aspects by traditional rating method). Thus it is clear that by giving certain categories of evaluation on increased weightage, the employees count for more points in the overall score. This in turn gives employees higher motivation to excel in areas that are important to business. In the same way, OAR is calculated for all the employees who are taken into consideration and it is compared with the average rating by traditional rating method using statistical tools.

Grouping of Employees Employees are grouped based on the benchmark provided by the organization. As a result, according to the calculation of OAR for employee 1 and by referring to the table below, the staff would be categorized in the “Medium Performer(s)” group. OAR Above 85% Less but 70% Less but 60%

than 85% more than than 70% more than

Below 60%

Group High Performer

Remarks/Comments High Incentive, Certificate of Appreciation, Eligible for 'Best Performer' Award

Medium Performer

Medium Incentive, Counseled to improve their performance in the coming year

Average Performer

Advised to develop their performance in the coming year, The employee should go to training sessions& workshops

Low Performer

Corrective action might be taken towards the employee, The employee should regularly report his / her work improvement to his / her evaluators in a stated period

Table 7: Summarized Rating and Weightage of the Five Aspects Similarly, the overall average rating for all 100 employees are calculated by using Multifactorial evaluation model and these are grouped by using Percentage analysis method. Thus the sample data are evaluated using the proposed model. 97


Data Analysis and Interpretation Analysis based on the Grouping of Employees Average Rating by Multifactorial Evaluation Model Above 85% Less than 85% but more than 70% Less than 70% but more than 60% Below 60%

Employee Group

Employees

Percentage

High Performer Medium Performer Average Performer Low Performer

8 35 44 13

8 35 44 13

Table : % Analysis for Grouping of Employees 







From the overall average rating received by all the employees through Multifactorial evaluation model, we could classify 8% of the employees as „High Performer‟ and they are qualified for following rewards/honor 

High Incentive.



A certificate of appreciation.



Qualified for “Best Service Award”.

35% of the employees are grouped as „Medium Performer‟ and they are eligible for the following comments 

Medium Incentive



Advised to develop their performance in the coming year

44% of the employees are grouped as „Average Performer‟ and following are the comments 

Advised to improve their performance in the coming year



The employees should attend training sessions & workshops

13% of the employees are grouped as „Low Performer‟ and following are the remarks 

Corrective action might be taken towards the staff.



The employees should regularly report his / her work progress to his / her evaluators in a stated period

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Analysis of Compared Rating Scores of Employees Hypothesis A Null Hypothesis H0: MEM is not superior to normal rating method Alternate Hypothesis H1: MEM is superior to normal rating method Tool used The rating scores (one group of units) received by the employees through normal rating method and Multifactorial evaluation model are tested by using t-test.

Assessment Method

Mean

N

Standard Deviation

t

p value

Multifactorial Evaluation model Normal Rating method

64.72 69.46

100

9.408 9.419

Degree of freedom

15.978

99

0.000

Table 9: Testing of Statistical significance of rating scores (one group of units) by normal rating and Multifactorial Model From the above testing, the value of p is found to be 0.00. Since the p value is less than 0.05, null hypothesis (HA0) is rejected and the alternate hypothesis (H A1) is accepted Thus, we conclude that MEM is superior to normal voting method

Conclusion The research has contributed to the development of a non-conventional performance evaluation system using five multifactorial evaluation models for assessing the knowledgeable employees‟ performance more precisely in the hitherto untapped chemical industry. It has also statistically established its supremacy over the traditional evaluation system. The paper has thus contributed towards widening the boundaries of the existing literature to encompass successful application of fuzz set theory in the form of five MEM to chemical industry. The model can be replicated by other companies after suitable modifications to reflect their operational environment in terms of aspects to be evaluated and weightage to be assigned to each aspect.

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