Fuzzy Comprehensive Evaluation Enterprise Performance Management

Transcription

1 Fuzzy Comprehensive Enterprise Performance Management Model Based on AHP Hunan Vocational College of Railway Zhuzhou, Hunan province, China Abstract Performance management is one important element in human resource management. More and more enterprises pay much attention to and actively implement the performance evaluation. But when evaluating, the personal relations will be considered. At the same time, many evaluation indexes are qualitative indexes which are not easily quantified. It will lead to the performance evaluation system without effects. In this research, we combine Analytic Hierarchy Process (AHP) with fuzzy comprehensive evaluation method. he weight coefficient of performance index can be determined by AHP. We will use fuzzy comprehensive evaluation method to evaluate the result of AHP, and finally establish enterprise performance management model. After analyzing the examples, we have found the combination is workable and can evaluate scientifically and objectively. It can be regarded as the effective method to classify the employee performance assessment. Keywords: Performance ; AHP; Fuzzy Comprehensive ; Enterprise 1. Introduction In 21st century, the economy develops very fast. When the foreign enterprises and capitals entering China, they bring a lot of technologies and management experiences which promote the Chinese enterprises [1-3]. At the same time, more and more enterprises realize that human resource plays a very important role in operation and development [4]. In the human resource management, we can use performance evaluation to assess the working behavior and working result of the employee. he performance evaluation also can supervise the improvement of the employee quality. At the same time, it can find out the problems and disadvantages, which can improve the production and management of the enterprise [5-7]. he performance evaluation should be away from the subjective factors and strengthen the objectiveness, regularity and operability. At present, more enterprises think highly of the performance evaluation and begin to implement it [8]. But the effect is not favorable. Because of the traditional impact, the personal relations will be considered which will lead to little effect. So the scientific and accurate performance evaluation has great significance to the development of Chinese enterprises [9-11]. In this article, we use AHP to determine the weight coefficient of performance indexes. We will use fuzzy comprehensive evaluation method to evaluate the result of AHP, and finally establish enterprise performance management model so as to improve the objectiveness, regularity and operability of the performance evaluation [12-15]. It can be regarded as the effective method to classify the employee performance assessment. 2. he heory of Fuzzy Comprehensive Model Based on AHP he model of fuzzy comprehensive model based on AHP (Analytic Hierarchy Process) is made from two parts which are AHP and fuzzy comprehensive evaluation [16]. Simply speaking, as the core of the model, AHP regards evaluating object or problem as a system. According to the nature of the problem and the overall target, it separates the problem into different elements and assembles the elements according to different layers [17]. After finding element the weight proportion in the unity, one multiple layer structure system is formed, which can methodize the problems. As the comprehensive evaluation method, according to the membership grade of the weight projected level indexes got by AHP, Fuzzy determines the property of the evaluated objects. Fuzzy comprehensive evaluation is implemented based on AHP [18, 19]. he two coordinate together and improve the accuracy and reliability of the model. International Journal of Digital Content echnology and its Applications(JDCA) Volume7,Number6,March 2013 doi: /jdcta.vol7.issue

2 2.1. AHP Determines the Weight. AHP uses qualitative and quantitative systematical analysis methods. his method can make systemize, quantify and model the complicated problems. hat is to say, we can separate the problem into several elements and then resolve them into more accurate, detailed and quantitative small elements which are indexes [20]. We should determine the weights according to the significance of elements in the same layer. hen the weights connect the layers together forming a statistical model with multiple targets and layers. he basic steps are as follows, Establish multilayer hierarchical structure and form a target tree figure. AHP model includes 3 layers which are high layer, middle layer and low layer. Please see Fig. 1. he high layer is the target layer which analyzes the overall target of AHP; the middle layer is also called constraint layer which includes some main factors influencing the overall target; the low layer is also called measurement layer which includes final measures solving problems. hey are all quantitative indexes. Fig. 1. Model structure of AHP Construct comparison judgment matrix to calculate the weights. We use Saaty s weight method. First we should compare the indexes and provide the scores which are shown in able 1. According to the scores, we establish judgment matrix to get the weights of the indexes. he approximate weight of each index is W = m a a (1) i i1 i2aim We make the approximate weights have moralization procession according to the following formula Wi W i = (2) m W hen each branch vector is the weight he Significance Calibration a ij i=1 able 1. he score standard of each layer in AHP he Relative Importance 1 Equally important 3 Somewhat important 5 Basically important 7 Actually important 9 Definitely important 2,4,6,8 he intermediate value of adjacent degree of importance If the importance proportion of element i and element j is a ij, the importance proportion of Reciprocal element j and ii is a 1/ a 3) Consistency ext After calculating the normalization weight coefficient, we should check whether the relative weight coefficient is logic. First we should calculate the consistency index CI λ - = max m CI (3) m -1 i ji ij 540

3 1 m λ = max λ i / m (4) n m i=1 λ = a (5) i j=1 ijw j / Wi Calculate the random consistency proportion CI CR (6) RI In this formula, m is the number of the sub-goals in the tested layer. max is the maximum latent root; i is the latent root of the sub-goal pair comparison judgment optimal matrix of the layer; RI is the same order average random consistency index which is shown in able 2. able 2. he value of average random consistency index Exponent Number RI When the random consistency proportion CR is less than 0.1, we believe that the consistency of the judgment matrix is favorable. Use product method and calculate the combined weight of the low layer (solution layer). Combined weight is the coefficient got by multiplying the weighted indexes in different layers Fuzzy Comprehensive Based on obtaining the weights of evaluated indexes by AHP, we need to implement the index evaluation to the objects. Fuzzy comprehensive evaluation is to assess the belonging grade or categories and make decisions based on many evaluation factors by implementing fuzzy set theory [21]. Because the dividing the number is fuzzy, the adapted fuzzy mathematical method make the objects evaluation reasonable. he detailed calculating process is as follows. Ensure the evaluation index set X ( x1, x2,, x n ) (7) 2) Determine the tested index weight set connected closely with the tested objects according to AHP. W ( 1, 2,, n ) (8) 3) Determine the evaluation set. Y ( y1, y2,, y n ) (9) 4) Determine the standard membership grade of the evaluation set u. u ( u1, u2,, u m ) (10) 5) Establish fuzzy evaluation matrix of the tested objects R. r11 r 12 r1 m r21 r 22 r 2m R rn1 r n2 rnm (11) 6) Calculate fuzzy comprehensive membership grade set B B R u (12) 7) Calculate overall comprehensive membership grade U A B (13) Comprehensive membership grad U is the overall score of tested object. So we can make comparatively objective evaluations to many tested objects. 541

4 3. he Establishment of Fuzzy Comprehensive Enterprise Performance Management Model Based on AHP 3.1. Choose Indexes and Establish Hierarchical Structure When evaluating the employees, the evaluation indexes are different as to different positions. In this article the managers are selected to be evaluated. After reading the references home and abroad and consulting the enterprise management experts, we decide to evaluate the morality, ability, performance and development nurture. We will evaluate every aspect in different indexes. he established hierarchical structure of performance evaluation indexes is shown in able 3. able 3. he hierarchical structure of performance evaluation indexes Layer A Layer B Layer C Responsibility C1 Morality B1 Clean and Fair C2 Behavior C3 Leadership C4 B2 Operational C5 Coordination C6 Employee Recognition C7 Performance Performance B3 Development Nurture B4 Work Plan Implementation Status C8 Work Plan Achievement Rate C9 Management Innovation C10 alents raining Program Implementation Status C11 alents raining Achievement Rate C12 raining Innovation C Establish Pair Comparison Judgment Matrix and Calculate the Weights of Indexes Use Saaty s weight method to get all levels evaluation system judgment matrix and indexes weights. Please see able 4 to able 8. able 4. he judgment matrix and weight of first level evaluation system Development Value of Morality Performance Index Nurture Weight Morality Performance Development Nurture able 5. he judgment matrix and weight of second level evaluation system(morality) Index Responsibility Clean and Fair Behavior Value of Weight Responsibility Clean and Fair Behavior able 6. he judgment matrix and weight of second level evaluation system () Index Leadership Operation Coordinatio n Employee Recognition Value of Weight Leadership Operation Coordination Employee

5 Recognition able 7. he judgment matrix and weight of second level evaluation (Performance) index Work Implementation Achievement Management Value of Status Rate Innovation Weight Work Implementation Status Achievement Rate Management Innovation able 8. he judgment matrix and weight of second level evaluation(development Nurture) Implementation Achievement raining Index Status Rate Innovation Value of Weight Implementation Status Achievement Rate raining Innovation Consistency est By using formula 3 to formula 6, calculate the random consistency proportion CR values of one first level matrix and four second level matrixes. he results are in able 9. able 9. Results of Consistency est Matrix CR Consistency Results First Level Matrix Satisfied Second Level Matrix B Satisfied Second Level Matrix B Satisfied Second Level Matrix B Satisfied Second Level Matrix B Satisfied 3.4. Using product method, we calculate the combined weights of the indexes in the low layer. he result is shown in able 10. Layer A Performance able 10. he value of combined weight of the indexes in low layer Weight of Indexes in Weight of Indexes in Layer C Combined Weight Layer B Responsibility(0.577) Morality(0.054) Clean and Fair(0.312) Behavior(0.111) Leadership (0.507) (0.546) Operation (0.228) Coordination (0.212) Employee Recognition(0.053) Performance(0.3 02) Development Nurture(0.098) Work Plan Implementation Status(0.318) Work Plan Achievement Rate(0.597) Management Innovation(0.085) alents raining Program Implementation Status(0.292) alents raining Achievement Rate (0.605) raining Innovation (0.103) From the ranking list of weights, the ability occupies a maximum proportion in second level indexes B and working performance ranks the second. In third level indexes C, responsibility, leadership 543

6 ability, work plan achievement rate and talents training achievement rate occupy a larger proportion. he performance indexes of the managers involve the task performance indexes and peripheral performance indexes. he nature of the work of the managers will be considered in peripheral performance indexes. he leadership ability of the managers and the project operation with the core of coordination can describe the reality of the performance objectively. So in the performance evaluation indexes of the managers, the peripheral performance indexes occupy a large proportion. he traditional performance evaluation method can not provide the quantitative peripheral performance indexes. Even if we use grade fraction method to provide the fractional value, it will be pale because there is no basis to provide the value, which is totally the subjective judgment. Because the method has problems, people are stimulated to adjust and obtain the benefit. If the performance result can t make others convinced, the behavior is too formalized. Because the evaluation standard and evaluation indexes are fuzzy of this kind of indexes, we should take fuzzy comprehensive evaluation method to deal with them, which can reduce the interruption of the subjective factors. And then a accurate performance evaluation result can be got Using Fuzzy Comprehensive to Assess the Objects In this article, we evaluate the managers in Company X. We randomly select 7 persons from the high, middle and common employees as the evaluators by using stratified random sampling method. And then we score them. After the score conversion, the 7 evaluators provide the accurate scores which are shown in able 11. able 11. he scores of the examinees provided by 7 evaluators Indexes Z1 Z2 Z3 Z4 Z5 Z6 Z7 C C C C C C C C C C C C C In the scores, 85 points or above means excellent; points means good; points means pass; 60 points or below means failed. We sort out the scores according to the scores, the number of the samples in each grade is shown in able 12. Indexes able 12. he score grade of Examinee s performance Grade Excellent Good Pass Failed C C C C C C C C C C C C

7 C According to the scores, we use fuzzy comprehensive evaluation to evaluate. he Indexes System: X ( Morality,, Performance, Development Nurture) 2)he Weight Determination of the Assessed Indexes Set by Using AHP According to the above result, the weight of the assessed indexes set in this article is W = ( 0.054,0.546,0.302,0.098) 3) Y:Determination of Assessed Grade: Y = (Excellent, good, pass, failed) 4) u:he Determination of Standard Membership Degree of Set u (1/excellent,0.8/good,0.6/pass,0.1/failed) When calculating, we select u ( 1,0.8,0.6,0.1) 5) he Establishment of Fuzzy Matrix R of Assessed Objects From able 12, we can get the judgment matrixes of morality B1, ability B2, performance B3, and development nurture B4 which are shown below, 0/8 5/8 2/8 0/8 3/8 4/8 0/8 0/8 0/8 3/8 4/8 0/8 2/8 3/8 2/8 0/8 1/8 2/8 4/8 0/8 B1 0/8 4/8 3/8 0/8 B2 B3 1/8 3/8 3/8 0/8 B 4 2/8 4/8 1/8 0/8 1/8 2/8 4/8 0/8 2/8 4/8 1/8 0/8 2/8 2/8 3/8 0/8 2/8 4/8 1/8 0/8 2/8 4/8 1/8 0/8 6) Calculating Fuzzy Comprehensive Membership Degree u 1 0/8 5/8 2/8 0/ /8 4/8 0/8 0/ /8 3/8 2/8 0/8 u1 Bu 1 0/8 4/8 3/8 0/ u2 B2u 0.6 1/8 2/8 4/8 0/ /8 4/8 1/8 0/ /8 4/8 1/8 0/ /8 3/8 4/8 0/ /8 2/8 4/8 0/ u3 B3u 1/8 3/8 3/8 0/ u4 B4u 2/8 4/8 1/8 0/ /8 2/8 3/8 0/ /8 4/8 1/8 0/ ) Calculating Overall Membership Degree U Calculate the overall membership degrees of B1, B2, B3 and B4 which are U1, U2, U3 and U4 respectively U1 1u , 0.312, U2 2 u ,0.228,0.212, U3 3u ,0.597,

8 0.625 U4 4 u ,0.605, Finally, calculate the overall comprehensive membership degree U u ( U1, U2, U3, U4) U u (0.054, 0.546, 0.302, 0.098) (0.039, 0.36, 0.192, 0.068) According to the Membership Degree Maximizing Principle, the Result can be got. Analyzing the result U, we can get u is maximum; so performance evaluation result of the managers is good. 4. Conclusion In this article, we obtain the final performance result of the corresponding grade to the maximum membership grade in analyzing the case of Company X performance evaluation. his method belongs to the application of primary AHP fuzzy comprehensive evaluation model, which is easily operated. But the lost information is much and sometimes the result may be unreasonable. So we should test the final result of the evaluation to secure the accuracy of the result. Although AHP fuzzy comprehensive evaluation model has problems theoretically and practically, the basic method can explore a new way to make decision. In this article, we use this model and some mathematical theories in the human resource management, which proves that the model is suitable for the performance management. he shortage of the theory has not caused errors in performance evaluation, which has not affected the effectiveness of the result. he theory of AHP fuzzy comprehensive evaluation needs is improving. Although it has shortage in theory system and practice, the shortage will not influence the position and significance of AHP fuzzy comprehensive evaluation model. At present, AHP has been regarded as the simple and effective multiple targets decision-making method in operation field and it application field is being enlarged. he decision support system which is called Expert Selection with the basic method of AHP is commercialized. his system is welcome abroad. More and more people are study the theory and research method. AHP-fuzzy model plays an increasingly important role in social economy as a practical decision-making tool. 5. References [1] homas,landers,michael H."he virtual warehousing concept", ransportation research Part, no. 36, pp , [2] Schary Philip B, James Coakley. "Logistics Organization and the Information System", he International Journal of Logistics Management, no. 2, pp , [3] Scottcharles,Roy Westbrook."New strategic ools for supply chain Management", International Journal of Logistics and Logistics Management, no. 2, pp , [4] Wang hongli, "Representation and ransfer of State of Qualitative Simulation in QSIM Based on Cloud Model", JDCA, Vol. 7, No. 1, pp. 1 ~ 10, 2013 [5] Yun Peng, Hongxin Wan, "Web ext Clustering and Algorithm Based on Fuzzy Set", JDCA, Vol. 7, No. 1, pp. 11 ~ 18, 2013 [6] Rongrong Fu, Kangfeng Zheng, ianliang Lu, Dongmei Zhang, Yixian Yang. Biologically Inspired Anomaly Detection for Hierarchical Wireless Sensor Networks.Journal of Networks, Vol 7, No 8 (2012), [7] Chuiwei Lu, Zhengbing Hu. A Fuzzy Search Algorithm for Structured P2P Network Based on Multi-dimensional Semantic Matrix.Journal of Networks, Vol 7, No 2 (2012), [8] Carsten Homburg.Improving Activity-Based Costing Heuristics by High level Cost Drivers", European Journal of Operations Research, no. 157, pp ,

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