EMPLOYEE PERFORMANCE APPRAISAL SYSTEM

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EMPLOYEE PERFORMANCE APPRAISAL

SYSTEM

Employees are the most valuable and dynamic assets of an organiza-tion. This chapter proposes a new performance appraisal system. It mea-sures an employees qualitative meamea-sures in fuzzy parameters to assess the performance of an employee in an organization. This method comprises of collection of fuzzy appraisals from immediate supervisors, converts lin-guistic appraisals into fuzzy numbers and derives a performance evaluation score of the employee. Uniqueness of this new fuzzy performance appraisal system is, it is more object oriented, informative, accurate and unbiased.

6.1

Introduction

Employee appraisal system is an essential factor in enhancing the qual-ity of the work input, motivates employees to strive harder, and presents a rationale for promotions and increments and also in the growth of an or-ganization. Periodical employees performance appraisal in an organization helps a management to identify its strengths and weaknesses.

In any organization, performance appraisal aims to identify current sta-tus of their work force. Any standard appraisal system consists of collection of data, extracts information, converts information into a real number called performance score. This score is compared to decide on an employees con-tribution. To evaluate an employees individual contribution with regard to the company’s objectives , it is essential to have an accurate unbiased ap-praisal system.

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To decide on the performance level of an employee, employers use fol-lowing factors: motivation of an employee, professional ethics, code of con-duct, interpersonal skills, knowledge in respective field, skills to achieve a goal, target achieving attitude, time management, contribution to team tar-gets, continuous development in knowledge, participation in training pro-grams, innovative thinking, and problem solving techniques. As these fac-tors are fuzzy in nature a fuzzy performance appraisal method is more suit-able.

Performance appraisal system depends on the nature of the industry, an organization belongs to. It mostly relates to the product output of a company or the end users of an organization. Performance expected from of an em-ployee of a super market is different from the performance of a scientist in science research lab. In an examination performance of a student expected in a written test varies from performance expected in a project presentation. Therefore, even within a company, performance expected from employees is not the same from all. It varies according to the nature of work, designa-tion and sector of an organizadesigna-tion. In a University, faculties of school are people who directly contact, educate and contribute to students knowledge. Thus, performance of a faculty is vital both for students and school, and must be measured for positive reinforcement to faculty knowledge and un-derstanding. Fuzzy concept gives a wide chance to measure, evaluate, and analyze these fuzzy factors.

In 1965, Zadeh [1, 94, 95] introduces Fuzzy set as a multi-valued logic, that allows intermediate values to be defined between conventional evalua-tions like true/false, yes/no, high/low, etc. Fuzzy theory easily formulates notions like rather tall or very fast and maintains fuzzy nature of data to a possible extent. In order to apply a more human-like way of thinking in programming of computers [96] fuzzy theory is more important.

This dissertation classifies performance appraisal system into three stages. First stage is to collect appraisals of an employee from supervisors or end users in a standard form; second stage converts linguistic appraisal or ob-jective type of appraisals into a crisp number and finally grades employees

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according to their appraisals. So an effective fuzzy performance appraisal system depends on a method to collect assessments, assessment strategy, converts assessments into useful fuzzy or crisp information and grade or rank procedures to assess employees and their effectiveness with respect to organizational expectations.

This part discusses about various evaluation systems on students per-formance in an examination. This idea helps to improve the process of evaluating employees performance appraisal. Jauch and Glueck [97] de-veloped measures of research output both objective and subjective in order to identify which one is effective to evaluate research performance. They discussed relative effectiveness of different measures to evaluate research performance of professors.

Biswas [98] discuss about grading method of students performance in an examination. He introduce Fuzzy Evaluation Method (FEM) in which they use six objective points following uniform distribution, to evaluate each an-swer of a student. It is a computer based fuzzy approach, where it uses a vector valued marking system. Later Biswas [98] generalizes FEM and introduces Generalized Fuzzy Evaluation Method (GFEM) in which they adopt a matrix-valued marking. GFEM evaluates each answer of a question from four different aspects and assigns a mark. Even though it observes a students knowledge more objectively, in every stage they determine sepa-rate grades for each question applying a rounding off to the next by grade. This rounding off results in an error at each step of evaluation and thereby causes a very huge error in the final total grade.

Various appraisal systems are available in literature. They differ in pur-pose, implementation procedure and output expectations. Chen and Lee [99] propose two methods for appraisal on objectives. They eliminate draw-backs in the Biswas [98] method to some extent. Cheng and Yang [100] present an approach for evaluation, based on similarity measure between interval-valued fuzzy sets. But it results in a huge computation process and an evaluator takes more time to fill in grade sheets. Echauz and Vachtse-vanos [101] discuss about difficulties associated with translating a set of crisp scores into letter-grades.

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Mesak and Jauch [102] have developed a model by which college and university administrators might evaluate performances of major compo-nents of a faculty work: research, teaching, and service. They determine overall faculty performance as a function of teaching achievements, search achievements, and service achievements under a sub-criterion re-lated to those achievements. Ellington and Ross [103] propose a teaching evaluation scheme to assess university teachers. This scheme accounts for a teaching skills profile that enables an academic staff to undertake self-rating with respect to a set of basic criteria for effective performance in teaching and other activities related to it.

Agrell and Steuer [104] propose a multi-criteria evaluation system for individual faculty member’s performance, which consists of five criteria which are research output, teaching output, external service, internal ser-vice, and cost. Their aim is to identify promotional candidates, reveal un-derlying problems in managerial consistency, and they suggest categoriza-tions for faculty groupings. Meho and Sonnenwald [105] analyze relation-ship between citation ranking and peer evaluation to assess faculty research performance with a multi-criteria approach. They use two sources of peer evaluation data: citation content analysis and book review content analysis. This study presents many subjective and objective criteria on the area of re-search performance and concludes that citation ranking can provide a valid indicator for comparative evaluation of senior faculty research performance. Sproule [106] mentions reporting errors, inadequate sample size, and accuracy errors of data as reasons of under determination of teaching per-formance by student evaluations. Sproule [106] suggests that committees in universities, whose mission is a justification related to reappointment, pay, and compensation. Otherwise their decision rule becomes invalid, unreli-able, and flawed. Paulsen [107] says that purpose of evaluating teaching effectiveness can be grouped as formative and summative. Formative eval-uation tries to provide informative feedback to assist faculty in improving effectiveness but summative evaluation tries to help managerial decisions related to pay incentive, awards, honors, etc.

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Weistroffer et al., [108] propose a structure to model a faculty perfor-mance appraisal that considers both quality and quantity of faculty outputs in the areas of teaching, scholarship, and service. They identify the crite-ria related to measuring quantity of performance outputs and assign quality weights. Adnan and Minwar [109] propose a framework for empirical eval-uation using fuzzy logic for performance appraisal systems. Huberty [110] consider factors such as instruction skills, research capacity, service inten-sion, and administration activities for evaluation of a faculty.

In a problem of faculty performance evaluation, evaluation attributes are generally multiple and often structured in multilevel hierarchies. Addition-ally, since judgements from experts are usually vague rather than crisp, a judgement in terms of fuzzy sets has the capability of representing vague data. Two multi-attribute evaluation methods, AHP and TOPSIS, can han-dle and solve this problem by integrating fuzzy set theory. Jinget al., [111] demonstrate that fuzzy set theory successfully solves multiple criteria per-formance appraisal systems.

Deutsch and Malmborg [112] shows that a fuzzy representation is useful to compare alternative collections of performance measures. They present an example of performance evaluation of university professors. After that Honet al., [113] propose a multi-attribute method based on fuzzy weighted average. Moonet al., [114] propose a methodology using fuzzy set theory and electronic nominal grouping technology for multi-criteria assessment in group decision making of promotion screening. Yee and Chen [115] pro-pose a model to evaluate staff performance of an Information and Commu-nication Technology (ICT) based on specific performance appraisal criteria. They use multi factorial evaluation model to appraise employees.

This chapter introduces a new FPAS. This chapter aims to build a sys-tematic model for a fuzzy performance appraisal system. With integration to algorithmic approach, following FPAS can give more information to a manager, and help to analyze strength and weakness of an organization.

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6.2

Preliminaries

This section explains term of performance appraisal system. Also it re-views the existing performance appraisal system, limitations, grading pro-cedures, and various errors. It explains the method to convert a linguistic term into fuzzy numbers and also the various methods of conversion scales available.

6.2.1 A review on performance appraisal methods

Criteria for measuring subjects differ among organizational behavior and research requirements. It is important to ensure that such methods do not neglect any potential aspect of an employee and reduce ambiguity re-lated to individual judgment as their value changes during transformation to crisp numbers. This section briefs strengths and shortcomings of some existing appraisal methods.

6.2.2 Existing appraisal methods

Graphic rating scale method By this method, a supervisor assesses posi-tion of his subordinate between a minimum and maximum of a rating scale accepted by organization common assessment system. These methods have limitations due to moderation, severity, and leniency rating scale errors; which results in inaccurate appraisals.

For Example a sample graphic rating scale appraisal form is given below.

Table 6.1: Graphic Rating Scale Method

Parameter Poor Fair Satisfactory Good Excellent Quantity of work

Quality of work Attitude: Dependability:

Cooperation:

Forced choice distribution method According to forced choice distribu-tion method a supervisor assesses his subordinates with respect to a predetermined distribution. Usually they follow normal or uniform

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distributions. This approach forces an assessor to place a certain num-ber of subordinates at bottom level of a grade or at top of grade. It becomes unavoidable even if all employees performances are not sig-nificantly different. Particularly if the number of employees in an or-ganization is a small group with high performance capacity then this method fails.

For example a manager evaluates as follows:

Employee follows directions a. always b. usually c. seldom. Does not waste time a. Mostly b. usually c. minimal.

Management objectives method Management objectives method of appraisal maps performance of an employee to management objec-tives. In a performance appraisal form supervisors maps an employ-ees contribution towards management objectives and compares his tar-get achievements, with other faculties. Sometime this method fails to maintain a positive working environment in a branch and/or at a de-partmental level. It takes more time in meetings and in turn brings down production time.

For example an appraisal form of a investment firm is shown below: Growth percentage attained a. 100 b. 75 c. 50 d. 25 e. 0

No of days taken to reach the goal a. 30 b. 20 c. 10 d. 5 e. 1 Revenue growth percentage QoQ a. 200 b. 100 c. 50 d. 25 e. 0

Essay type method By essay type of assessment method, supervisors pre-pare descriptive reports on his subordinates that may be structured or unstructured. This method fails in some cases due to lack of structure, in-discriminatory nature of reports and failure to keep an on-going log. Structure of a report differs with sectors, designations within sectors, and objectives in overall performance. Final assessment report of an employee gives very less information about his performance and his abilities. It becomes difficult to link with promotions, incentives, and training needs.

For example a sample report is given below:

Sincere, Hardworking and semi skilled employee. Satisfies the cus-tomer at service. Reacts to the complaints with bias most of the times.

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Character is good. Slow in adapting to the new challenges in task. Tar-get achievement is not satisfactory. Meeting the company objective is satisfactory. Cooperation in the team is fair.

Behavioral check list method Behavioral check list method focuses on be-haviors of an employee. It checks all important dimensions of perfor-mance of a job, critical incidents of effective and ineffective behavior, checking of behavioral dimensions and rating to appropriate perfor-mance dimensions. This method of appraisal follows a uniform struc-ture, which becomes easy for a manager to get more information about an employee. But behavior is a fuzzy factor. Assessment using crisp numbers is not a suitable one; using fuzzy numbers will enhance its efficiency.

For example a checklist for computer salespersons might include a number of statements like the following:

Is able to explain equipment clearly.

Keeps abreast of new developments in technology. Tends to be a steady worker.

Reacts quickly to customer needs Processes orders correctly.

Multi-rater method This method is also known as “360 degree assess-ment”. This system takes feedbacks of an employee from all pos-sible sources which come in contact with an employee on his job. Feedback sources include co-workers, managers and supervisors, cus-tomers, peer staff and self-appraisal. This provides a complete assess-ment of an individual from multiple sources and by multiple persons. Feedback in Multi-rater method is objective type and it restricts as-sessor in giving feedback on performance of an employee, which is a main setback in this system.

For example to evaluate the marketing manager for the firm, 360 eval-uation is best, because this individual would affect all of the groupssub-ordinates, customers, peers, the organization, and himself or herself. So, it is important to evaluate the specific situation and use the number of methods necessary to get an accurate assessment of the individual.

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This chapter aims to integrate, improve, and propose a new FPAS which can help an organization on following objectives:

i To improve the structure of an appraisal method which is more com-mon to organizations.

ii To identify exact strength and weakness of an employee with respect to Organizational Mission and Vision (OMV).

iii To categorize an employees with respect to companies OMV and link with incentives, promotions, and training needs.

iv To minimize bias error in appraisal system.

v To give more information on fuzzy factors by introducing fuzzy num-bers into appraisal system.

Construction of fuzzy number membership function depends on context of applications. Conversion of linguistic terms to fuzzy numbers plays an important role, when an application intakes linguistic inputs. Chen and Hwang [24] propose a numerical approximation system to systematically convert linguistic terms to their corresponding fuzzy numbers. It contains eight conversion scales.

The principle of converting a linguistic term is to pick a figure that con-tains all verbal terms given by an assessor about an employee. Determi-nation of number of conversion scale in a system is intuitive. Miller [116] proves that “seven plus or minus two” represents the greatest amount of in-formation an observer can give about an object on the basis of an absolute judgement.

6.3

Main Result

This section explains assumptions in an employee performance appraisal system and proposes a new method to compare performance of an employee in an organization. RSM method is applied to compare fuzzy numbers and uses fuzzy linguistic terms to observe an employees strength and weakness with respect to organizational goals. The advantages of existing methods are carried and improved in the proposed method of employee performance

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appraisal system.Suppose that there are n questionsqi ;i = 1, 2, 3, . . . nin a performance appraisal form and it evaluates m independent objectivesRj,

j = 1, 2, 3 . . . m.Here Ek denoteskth supervisor and k denotes number of supervisors ;k = 1, 2, 3, . . . krate one employee andWrdenoterthemployee and r denotes number of employees;r = 1, 2, 3 . . . r of an organization.

This method assumes that all supervisors are assessing their subordi-nates during every assessment period and supervisors record their assess-ment for every employee and for each objective in terms of linguistic terms; like, always sincere, more hard working, most of the times punctual, 90% of the production/target achieved, good in relations, etc. An assessor is not forced to fix terms (crisp) and they are structured in their objectives. If ac-tual terms are different, then they can be matched into equivalent seven plus or minus two terms in standard conversion scale. Miller [116] proves that seven plus or minus two represents the greatest amount of information an observer can give about an object on the basis of an absolute judgement.

˜ark

ij denotes fuzzy assessment of an employeeWr, assessed by an expert

or supervisorEk, on OMV j, from question i of an appraisal form. Different types of conversion scales of a linguistic term into a fuzzy number are pro-posed by Bass and Kwakerneak [28], Efstathiou and Ton [117], Kerre [50], and Wenstop [118].

6.3.1 Implementation procedure

Main stakeholders of an organization defines OMV in initial stage. Then department supervisors and managers derive goals of each department or division of organization to reach OMV. They divide responsibilities and targets among employees of organization based on their skill and qualifica-tions.

During the probation period a rater communicates to an employee about the desired outcomes, Key Performance Indicators (KPI) and performance standards expected. So each employee’s target in an organization directly or indirectly links to OMV. This defines blue print of a FPAS.

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Based on the blue print, each employee records their achievements, crit-ical incidents, and motives to count for their actions at regular intervals. Every employer assesses all his subordinates and reports to an appraiser at regular intervals. Appraiser uses FPAS to derive a ranking of an employee and reports to stake holders.

FPASS helps top level management to identify strength and weakness of an employee and have a detailed report map to OMV. RSM is used to rank the employee performance, which is a fuzzy factor. At the end of every assessment period, it provides more information to Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis, quality assurance, skill suffi-ciency, knowledge retention, training needs analysis, Pareto chart, incentive calculations, and pink slip preparation.

6.3.2 Fuzzy Performance Appraisal System by Score

This section proposes a new FPAS called FPASS. This method consists of three phases. In first phase a rater collects appraisals from supervisors, peers, and clients about an employee. Appraisal form contains essay type questions; for which supervisors, experts, peers and clients give their feed-back on performance of an employee. They express their satisfaction level, and evaluate performance expected from them. FPASS questionnaires give options for supervisors to express their satisfaction level in verbal terms. There are no prefixed terms, like good, average and poor.

In second phase, the rater converts all linguistic terms under an ob-jective with an appropriate conversion scale into a fuzzy number. Con-version scales of a linguistic term into a fuzzy number are proposed by Bass and Kwakerneak [28], Efstathiou and Ton [117], Kerre [50], Wenstop [118]. This dissertation follows Cagman and Gokbulu [119], Law [120] and Hwang [24] methods of conversion.

Third phase inputs fuzzy numbers into blue print and derives fuzzy weights or fuzzy appraisals of an employee. Blue print describes targets, skills, expertise, or KPI of an employee to achieve OMV. It gives appro-priate weightage to skills required to perform task ‘i’ , appraised by the performance appraisal questionnaire.

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An employee knows about the structure of the questionnaire well in ad-vance before an appraisal cycle. Usually he gets a sample of this question-naire during his probation or mentor ship period. During the mentorship period he gets a suitable training or knowledge enhancement programme to develop this KPIs to perform better. The rater’s information from feedback forms about each employee in term of fuzzy number can be summarized in following matrix (6.1). Scale for each linguistic term for a particular objective is taken as one.

˜ Eijkr =  ˜akr ij . . . ... ... ...  (6.1) Blue print of an appraisal form for an employee or employees performing similar type of job is given by matrix (6.2). This blue print changes with respect to section of an organization and designation with in a department. This blue print represents the expected performance from an employee dur-ing a period of performance appraisal cycle.

P =  pij . . . ... ... ...  (6.2) with n  i=1 m  j=1 pij = 100. (6.3)

Now the rater derives weighted fuzzy appraisals of an employee by each supervisor or experts with respect to a common blueprint which is given in following matrix (6.4).

This fuzzy matrix represents weighted appraisal in terms of fuzzy num-bers. This matrix preserves distinct feedbacks of a supervisor between two employees of the same cadre in fuzzy form. The rater does not approximate a supervisors feedback. So, the information is more accurate and fuzzy fac-tors are preserved without bias or approximation error. This removes the halo effect and horn effect of an appraisal system.

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˜ Mkr ij =  ˜akr ij pij = ˜mkrij . . . ... ... ...  (6.4) The Matrix (6.5) represents each employees total fuzzy appraisals on their attainment on objective ‘i’. Objectives of an appraisal form relates directly or indirectly to OMV. From Matrix (6.5) the rater understands the impor-tance of an employees’ contribution to OMV. This helps him to place a talented and skilled employee according to their needs. This is an input for training analysis and SWOT analysis.

˜ Mjkr =  ˜bkr j =ni=1m˜krij . . . ... ... ...  (6.5) Matrix (6.6) pools averages across supervisor, experts, peers and clients. This is a strength of FPASS method. This pooling average reduces cen-tral tendencies effect, leniency and strictness errors of an appraisal system. Also final fuzzy weight do not depend on a single appraisal form, it removes spillover effect, Fear of losing subordinates and spoiling relations, Good-will effects. The average fuzzy score across supervisors, experts, peers and clients are given in matrix (6.6).

˜ Mjr=  ˜ Cr j = k1 k k=1˜bkrj . . . ... ... ...  (6.6) To prepare incentives, promotions and pink lists, the rater computes fuzzy appraisal of each employee by using the Equation (6.7). This helps a HR head to get an information about overall importance of an employee to an organization. ˜ wr = m j=1˜crj m (6.7)

Score of a fuzzy number w˜r computes by using ranking procedure given in Section 2.3. To rank fuzzy numbers FPASS adopts Sudhagar and Gane-san [91, 93] procedure. This final score is an useful information to HR and to all stake holders of an organization.

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6.4

Numerical Example

Example 6.1. Suppose that an organization expects an employee to com-plete four objectives of their OMV. Let the total number of employees doing the similar job relates to these four objectives during same period of a per-formance appraisal cycle is 10. LetD1,D2,D3,. . ., D10denotes employees. Structure of appraisal form suits to department objective, including qualita-tive and quantitaqualita-tive measurements areR1,R2,R3 andR4.

Appraisal questionnaire contains four questionsQ1,Q2,Q3 andQ4 with weights 10, 20, 30 and 40. OMV links to skill measures R1, R2, R3 and

R4 weights 28, 22, 25 and 25. Object oriented weight structure of question-naire and OMV weight structure expected from an employee is shown in following blue print ‘ P’

Table 6.2: Blue print of appraisal questionnaire P R1 R2 R3 R4 Weights Q1 2 3 4 1 10 Q2 6 2 8 4 20 Q3 8 5 7 10 30 Q4 12 12 6 10 40 Weights 28 22 25 25 100

The fuzzy linguistic assessment of 10 employees by one supervisor is given in Table 6.3 - 6.12. For computational convenience and for better understanding of proposed method, this example assumes that there is only one supervisor; whereas, proposed method can be applied to cases with multiple experts, supervisors, clients and end users.

Table 6.3: Experts feedback for employee-1

D1 R1 R2 R3 R4

Q1 M L L ML

Q2 M L L MH

Q3 VL M L E

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Table 6.4: Experts feedback for employee-2 D2 R1 R2 R3 R4 Q1 M L M N Q2 VL H L ML Q3 MH H H VH Q4 VL H L E

Table 6.5: Experts feedback for employee-3

D3 R1 R2 R3 R4

Q1 L H H ML

Q2 VL H M H

Q3 L H H VL

Q4 L L L E

Table 6.6: Experts feedback for employee-4

D4 R1 R2 R3 R4

Q1 VH H M E

Q2 H H H VH

Q3 M H M E

Q4 VL L H N

Table 6.7: Experts feedback for employee-5

D5 R1 R2 R3 R4

Q1 VH H M VH

Q2 H H H VH

Q3 M M M M

Q4 VL H H VL

Table 6.8: Experts feedback for employee-6

D6 R1 R2 R3 R4

Q1 VL H H VL

Q2 L M M N

Q3 VH L L ML

Q4 VH H H VH

Table 6.9: Experts feedback for employee-7

D7 R1 R2 R3 R4

Q1 VH L L H

Q2 H H H VL

Q3 VL H M E

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Table 6.10: Experts feedback for employee-8 D8 R1 R2 R3 R4 Q1 VH H L L Q2 H L L MH Q3 M L H ML Q4 VL H H VH

Table 6.11: Experts feedback for employee-9

D9 R1 R2 R3 R4

Q1 VH M M N

Q2 H L L MH

Q3 M L M H

Q4 VL H L ML

Table 6.12: Experts feedback for employee-10

D10 R1 R2 R3 R4

Q1 VH M H H

Q2 H H L MH

Q3 M L L H

Q4 VL M M E

Convert linguistic terms into fuzzy number by using conversions scales. In this case, supervisor uses linguistic terms, Very Low (VL), Low (L), Medium(M), Medium to High(MH), High(H) and Very High(VH) forR1. According to standard conversions scales [28, 50, 117–121], Scale-5 is suit-able forR1. R2 uses terms Low(L), Medium(M), High(H). Scale-2 is suit-able forR2 andR3. R4 uses Excellent (E) and None/Not applicable (N) in addition to above linguistic terms. So scale-8 fits intoR4.

Calculation of fuzzy weights for an employee-1 by rater-1 is given in fol-lowing Table 6.13. For simplicity, this example considers only one rater for an employee. A general procedure proposed in this chapter accommodates more than one rater and considers their averages for a better assessment.

Table 6.14 gives weighted assessment of an employee-1, according to blue print P of Table 6.2. Table 6.15 helps informs to perform SWOT anal-ysis of Employee-1. In case, if an employee undergoes360, pooling av-erages across people will help a rater to perform SWOT analysis about an employee. This pooling averages reduces bias and rating errors.

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Table 6.13: Fuzzy numbers conversion for employee-1 D1 R1 R2 R3 R4 Q1 (0.4,0.5,0.5,0.6) (0,0,0.2,0.4) (0,0,0.2,0.4) (0.3,0.4,0.4,0.5) Q2 (0.4,0.5,0.5,0.6) (0,0,0.2,0.4) (0,0,0.2,0.4) (0.5,0.6,0.6,0.7) Q3 (0,0,0.1,0,2) (0.2,0.5,0.5,0.8) (0,0,0.2,0.4) (0.9,1,1,1) Q4 (0.1,0.2,0.2,0.3) (0,0,0.2,0.4) (0,0,0.2,0.4) (0,0,0,0.1)

Table 6.14: weighted assessment of employee -1

D1 R1 R2 R3 R4 Q1 (0.8, 1, 1, 1.2) (0, 0,0.6,1.2) (0,0,0.8,1.6) ( 0.3,0.4,0.4,0.5) Q2 (2.4,3,3,3.6) (0,0,0.4,0.8) ( 0,0,1.6,3.2) (2,2.4,2.4,2.8) Q3 (0,0,0.8,1.6) (1,2.5,2.5,4) (0,0,1.4,2.8) ( 9,10,10,10) Q4 (1.2,2.4,2.4,3.6) (0,0 ,2.4,4.8) (0,0,1.2,2.4) (0,0,0,1) Total (4.4,6.4,7.2,10) (1,2.5,5.9,10.8) ( 0,0,5,10) (11.3,12.8,12.8,14.3)

Table 6.15: Employee -1 contribution to objectives Objective Fuzzy Evaluation Score Expected

R1 (4.4,6.4,7.2,10) 28

R2 (1,2.5,5.9,10.8) 22

R3 (0,0,5,10) 25

R4 (11.3,12.8,12.8,14.3) 25

Applying procedure given in Section 6.3, gives fuzzy weights of em-ployees as shown in Table 6.16. Also Table 6.16 illustrates fuzzy perfor-mance appraisal of employees. Computation of appraisal from Table 6.15 for employee-1 is as follows:

4.4 28

+

221

+

250

+

11.325

4

= 0.1636

6.4 28

+

2.522

+

250

+

12.825

4

= 0.214

7.2 28

+

5.922

+

255

+

12.825

4

= 0.309

10 28

+

10.822

+

1025

+

14.325

4

= 0.455

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Table 6.16: Final fuzzy weights, Score value and grading of employees Employee fuzzy weightsWi Score value Rank

D1 (0.164,0.214, 0.309, 0.455) 0.784 10 D2 (0.404,0.507, 0.622, 0.709) 1.518 4 D3 (0.283, 0.4, 0.49,0.597) 1.198 9 D4 (0.386,0.499,0.589,0.687) 1.462 5 D5 (0.384,0.55,0.629,0.737) 1.549 3 D6 (0.441,0.57,0.669,0.758) 1.648 1 D7 (0.424,0.55, 0.655, 0.73) 1.594 2 D8 (0.368, 0.474, 0.575, 0.682) 1.422 6 D9 (0.291, 0.41, 0.493, 0.639) 1.245 8 D10 (0.329, 0.461, 0.527, 0.687) 1.362 7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 D1 D2 D3 D4 D5 D6 D7 D8 D9 D10

Figure 6.1: Fuzzy Performance Appraisal Scores

Figure 6.1 represents the fuzzy weights of the employees performance. In the above procedure, scale of assessment for skills is determined by vari-ation of employees performance, but not defined by a supervisor. This reduces leniency or severity error in assessing employees. These fuzzy weights are ranked by RSM given in Section 2.3. This rating process can be easily run by a performance appraisal system programme. Periodical ap-praisal and improvement in performance can be identified by manager and raters for quick and timely decisions.

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When performance involves employees working in more than one site or multinational organizations, it is easy to compare employees KPIs. This is a useful input for inplant training analysis, incentive allocations, promotion criteria, and service termination decisions. Proposed FPAS can be easily programmed and appraisal can be completed periodically. Appraisal period may vary according to organization as semester or project period or calender year. This algorithm can be extended for multi-level and multi-disciplinary systems with a suitable blue print.

6.5

Conclusion

Fuzzy performance appraisal algorithm is a better method for evaluat-ing employee’s performance with fewer errors. Strength of the proposed method is, supervisors are not forced to limited distributions and their ap-praisal was captured in their own linguistic term.

Performance distribution depends on individual employees characteris-tics and ability but not predefined by PAS. Also performance appraisal score depends on more than one expert or clients’ appraisal. This reduces bias error and loosing Goodwill and Spoiling relation errors. By following a regular appraisal cycle, it reduces spillover effect also.

Another advantage of the proposed FPASS compare to existing method is, it is easy to do map OMV and an employees contribution. Traditional ex-isting methods converts performance appraisal of an employee into a single numerical value, and ranks them to compare employee’s performance. If a particular vision or mission of an organization is found to be unachieved, it is difficult to track reasons for it. It is not easy to identify, which employee underperformed in achieving the respective goal.

By the newly proposed method, from Matrix (6.6), a company can iden-tify contributions of an rth employee to company’s jth goal/objective. By comparing from Matrix (6.6), the company can analyze against the defined objectives and employee’s performance during that period. This helps an organization to take decision rapid decisions to avoid delay loss.

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Various objectives of company and employees contribution in each ob-jective is observed and recorded in proposed FPASS. This is a very use-ful piece of information for raters and managers for future expansions and employees recruitment criteria. Proposed FPAS is limited to an individual employee appraisal only; it cannot be applied for a team appraisal. In the proposed fuzzy performance appraisal system, by introducing K supervi-sors, error involved in rating scale method gets reduced but at the same time it increases central tendency error. These are some limitations of the pro-posed FPASS method.

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