34 CHAPTER 3 ANALYTICAL HIERARCHY PROCESS, EXTENDED BROWN AND GIBSON MODEL AND QUALITY FUNCTION DEPLOYMENT COMBINED MODEL 3.1 INTRODUCTION Majority of the service organizations still uses production measures as performance dimensions. A multi-dimensional analysis with cost, time and service quality as performance measures in the services sector is rarely found in the literature. Hence a multi-dimensional service performance management model has been proposed, aided by Analytical Hierarchy Process (AHP), Extended Brown-Gibson (EBG) model and Quality Function Deployment (QFD), in this chapter. The proposed model integrates cost, time and service quality as service performance metrics. This chapter explores the ways and means of using AHP for service quality measurement; EBG model for service performance measurement and QFD for redesigning the existing service processes. The developed model has been illustrated using a case study from automobile repair shops. 3.2 AHP-EBG-QFD COIMBINED MODEL The proposed AHP-EBG-QFD combined model (Figure 3.1) consists of two phases, namely, service performance measurement and service performance improvement. The two phases are discussed in the following sections.
35 Identify service performance dimensions by conducting brainstorming sessions with customers and service managers Classify service dimensions into 1. Objective factors (cost and time dimensions) 2. Service quality factors (qualitative dimensions) Service Performance Measurement Conduct a structured survey at the work place at predetermined intervals 1. Calculate the objective factor measures in terms of time and cost dimensions 2. Evaluate the service quality factors using Analytical Hierarchy Process (AHP) Calculate Service System Performance Measure (SSPM) using Extended Brown-Gibson (EBG) model YES Satisfied with SSPM Value NO Identify service characteristics in order to meet the customer requirements Develop House of Quality (HoQ) Service Performance Improvement Determine the optimum set of service requirements to be included in the new service design Implement the new service design Review the implementation plan Figure 3.1 AHP-EBG-QFD combined model
36 3.2.1 Service Performance Measurement performance: The following six steps have been proposed to measure the service (i) Identifying the service performance determinants and classifying them into objective and service quality factors (ii) Conducting a structured survey at the workplace at predetermined time intervals (iii) Evaluating the Objective Factor Measure (OFM) (iv) Evaluating the Service Quality Measure (SQM) (v) Calculating the Service System Performance Measure (SSPM) (vi) Analysing the SSPM and making decisions about the service design process These steps are discussed in detail in the following sections. Step1 : Identifying the service performance determinants and classifying them into objective and service quality factors. A pilot survey is conducted among the focus groups, customers and service managers in order to identify the service performance dimensions. These dimensions are classified as objective and service quality factors. Data pertaining to objective and service quality factors can be recorded using a suitable questionnaire.
37 (a) Objective factors An organization s performance has been measured in terms of cost and time dimensions, which form the objective factors. The cost dimensions can be classified into effective and ineffective costs. Similarly, the time factor can also be classified into effective and ineffective time. The various objective parameters have been classified as follows: i. Effective and Ineffective cost Effective Cost (EC): It involves the costs which are to be maximized, e.g., profit / revenue. Ineffective Cost (IEC): It involves the costs which are to be minimized, e.g., cost of error in service. ii. Effective and Ineffective time Effective Time (ET): All productive time has been considered as Effective time, e.g., time to service a vehicle. Ineffective Time (IET): Non-productive time has been brought under Ineffective time, e.g., time to rectify the error in service. (b) Service quality factors A pilot survey is conducted among the customers and the service quality factors which influence the performance of the organization are identified. The service quality factors are also identified through a brainstorming session with the focus groups. Using ranking systems, the key parameters from customers perspective have been identified.
38 Step 2 : Conducting a structured survey at the workplace A structured survey has to be conducted with a help of a suitable questionnaire at predetermined time intervals. The data pertaining to objective factor measures are collected at the workplace in terms of time and cost dimensions. The data pertaining to service quality are collected from the customers. Step 3 : Evaluating the Objective Factor Measure (OFM) Let m be the number of competitors whose performance is to be measured. The OFM for i th competitor has been measured in terms of Cost and Time Effectiveness (CTE). OFM i of i th competitor has been provided by the equation (3.1) (Punniyamoorthy and Vijaya Ragavan 2003): OFM i CTEi m i 1 1 CTE i (3.1) equation (3.2): The CTE of the competitor i has been obtained through the following CTE EC i i m i1 1 EC IEC m i i1 1 IECi where, EC i = Effective cost of competitor i IEC i = Ineffective cost of competitor i ET i = Effective time of competitor i IET i = Ineffective time of competitor i i 1 ET i m i 1 1 IET ET i m i i 1 1 IETi 1 (3.2)
39 Step 4 : Evaluating the Service Quality Measure (SQM) AHP is one of the most popular multi-criteria decision making tools for formulating and analysing decisions. In the proposed model, the SQM required for EBG model has been evaluated using AHP. The following steps are involved in AHP: i. Identifying the service quality factors which influence the decision. ii. Grouping the service quality factors based on their interdependence, as criteria, sub criteria and sub-sub criteria. iii. Formulating a hierarchical structure, i.e. the objective function arranged in the top level, criteria, with sub criteria and sub-sub criteria in the intermediate level and alternatives at the lower levels. iv. Constructing a pair wise comparison matrix A for each level. In the pair wise comparison matrix, values ranging from 1 to 9 and their reciprocal values are assigned. The factors in a row are compared with the factors in a column and the comparison value is given in the crossing cell. When the factor in a row is stronger (more significant) than the factor in a column, then the crossing cell is strong and its corresponding cell, which compares the latter with the former, takes a reciprocal value and is weak. Service managers of the organizations are involved in evaluating the criteria and the sub-criteria. The following 9-point scale has been used for the pair wise comparison:
40 1-Equally preferred 2-Equally to moderately preferred 3-Moderately preferred 4-Moderately to strongly preferred 5-Strongly preferred 6-Strongly to very strongly preferred 7-Very strongly preferred 8-Very to extremely strongly preferred 9-Extremely preferred v. Determining the maximum Eigen value (λ max ) and its corresponding Eigen vector using the following equation (3.3) A x W = λ max x W (3.3) Here, A λ max W = Observed matrix of pair wise comparison = Largest Eigen value of A = Principal Eigen vector (a measure of relative importance weightage of the criteria or sub criteria or the alternative) vi. Determining the Consistency Ratio (CR), the ratio between consistency index and the random index using the following equation (3.4) where, CR = CI / RI = (λ max n)/ (n-1) (3.4) CI = Consistency index of A RI = Random index of A n = Order of the matrix A
41 From Table 3.1, random index value corresponding to n has been determined. When the CR value is 10% or less, the matrix is accepted as consistent. Table 3.1 Random index Order of matrix 1 2 3 4 5 6 7 8 9 10 (n) Random Index 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49 vii. A comparison matrix B needs to be constructed by comparing the alternatives with respect to each of the factors at the lowest level of the hierarchy. Using the survey results from the customers, the comparison matrix B can be arrived as described in step (iv). The steps (vi) and (vii) need to be again carried out in order to check the consistency of matrix B. viii. Arriving at SQM measure using matrix A and matrix B. The SQM for i th competitor evaluated with respect to j service quality criterion can be arrived through the following equation (3.5) local weight of the competitor with respect to criterion j from matrix B SQM i local weight of criterion j from matrix A (3.5) j Step 5 : Calculating the Service System Performance Measure (SSPM) In EBG approach, both the objective factors and the service quality factors have to be converted into consistent and dimensionless indices to measure the SSPM. SSPM of i th competitor can be arrived through the following equation (3.6)
SSPM i = α (OFM i ) + (1- α) SQM i (3.6) 42 where, α = objective factor weight 1- α = service quality factor weight 0<α <1 Step 6 : Analysing the SSPM and making decisions about service design process When the evaluated SSPM value has been satisfactory, the measurement process needs to be continuously repeated after a predetermined or suitable interval and further improvement opportunities need to be analysed. When SSPM value falls below the satisfactory level, redesigning the existing services becomes essential. 3.2.2 Service Performance Improvement In the second phase, QFD has been used in order to improve the service performance. The whole QFD procedure uses a series of matrices called House of Quality (HoQ), to express the linkages between inputs and outputs of different phases of development (Hauser and Clausing 1988). In the redesigning process, HoQ needs to be developed and the optimum set of service requirements needs to be determined. Building the first HoQ consists of five basic steps: i. Identifying the customer requirements. ii. Identifying the service design requirements.
43 iii. iv. Relating the customer requirements to the service design requirements. Conducting an evaluation of competing service providers. v. Evaluating the service design requirements and development of targets. The new service strategies have to be deployed further and the implementation plans need to be reviewed periodically in order to improve the service performance. 3.3 CASE STUDY- I To demonstrate the applicability of the proposed model in service organizations, a case study has been carried out in eight leading identical car repair shops namely unit A, unit B, unit C, unit D, unit E, unit F, unit G and unit H at two cities namely Erode and Coimbatore (located in the southern part of India). 3.3.1 Service Performance Measurement using AHP and EBG Model Step1 : The various objective factors in car repair shops have been identified through brainstorming sessions with service managers of the repair shops and are listed in Table 3.2.
44 Table 3.2 Objective factors affecting performance Dimensions Factors Effective Cost (EC) Total profit /Total Revenue Average service cost Customers /employees training cost Post service follow- up and Service due follow up cost Ineffective Cost (IEC) Cost incurred due to technician s poor workmanship / improper diagnosis Replaced component failure Loss of revenue due to non-retainment of existing customers Effective Time (ET) Time spent on vehicle while attending the repair Time spent for explanation (about repairs / about charges) Time spent for advice after the service Time spent on post service follow-up & Service due follow up Ineffective Time (IET) Time spent in re-repair due to faulty service Waiting time of the customer after the promised time Waiting time due to non-availability of spare parts The problem of the selection/building of criteria is a most important issue for service providers, as it is important whether these criteria of service quality can effectively evaluate and improve service quality. Based on Behara et al (2002) and Brito et al (2007) service quality parameters, a pilot survey is conducted among the customers and focus group in order to identify the important service quality factors. The attributes are ranked and for further study only eight parameters are considered. The parameters are as follows:
45 1. Promptness of service advisor in attending the customer (SQ 1 ) 2. Understanding the problem in the vehicle (SQ 2 ) 3. Attention to modifications demanded by the customer (SQ 3 ) 4. Mechanics trustworthiness (SQ 4 ) 5. Timely delivery of vehicle (SQ 5 ) 6. Value for money service (SQ 6 ) 7. Ability to fix the problem in the first visit (SQ 7 ) 8. Quality of service done (SQ 8 ) Step 2 : Structured surveys have been conducted in the repair shops to collect the data pertaining to objective factor measures (Appendix 1). The data from the eight repair shops (units A-H) have been tabulated below (Table 3.3). The data pertaining to service quality have been collected from the customers through a suitable questionnaire (Appendix 2). These data have been provided as input to AHP matrix. Table 3.3 Cost and time data for case study-i Unit Average cost (INR)/day Average time (minutes)/day Effective Ineffective Effective Ineffective A 2500 450 294 98 B 3000 540 258 69 C 1750 350 350 95 D 2000 305 290 47 E 2750 300 450 70 F 750 60 298 60 G 600 75 229 80 H 2500 566.67 331 90
46 Step 3 : CTE of an alternative has been converted into dimensionless indices to obtain the objective factor measure. A sample calculation related to Unit A is shown below: CTE A = [2500 / (2500 + 3000 + 1750 + 2000 + 2750 + 750 + 600 + 2500)] + [450 (1/ 450 + 1/540 + 1/350 + 1/305 + 1/300 + 1/60 + 1/75 + 1/566.67)] -1 + [294 / (294 + 258 + 350 + 290 + 450 + 298 + 229 + 331)] + [98 (1/98+1/69 + 1/95 + 1/47 +1/70 + 1/60 + 1/80 + 1/90)] -1 = 0.4163 The CTE for the remaining units (B to H) are as follows: 0.4638, 0.4082, 0.5061, 0.5557, 0.6844, 0.5363 and 0.4291. The objective factor measure for Unit A is calculated as follows: OFM A = 0.4163 / (0.4163 + 0.4638 +0.4082 +0.5061 + 0.5557 + 0.6844 + 0.5363 + 0.4291) = 0.1041 OFM for the remaining units (B to H) are as follows: 0.1160, 0.1021, 0.1265, 0.1389, 0.1711, 0.1341 and 0.1073. Step 4 : AHP has been used in evaluating the SQM required for the EBG model. The following steps have been used: 1. The service quality parameters are grouped and a hierarchical structure has been constructed as shown in Figure 3.2. The objective function i.e., the best service system is arranged in the top level with the criteria in the intermediate level and alternatives (Unit A, Unit B Unit H) at the lower levels.
47 Service Quality Criteria Best service performance system SQ 1 SQ 2 SQ 3 SQ 4 SQ 5 SQ 6 SQ 7 SQ 8 Unit A Unit B Unit C Unit D Unit E Unit F Unit G Unit H Figure 3.2 Hierarchical structure for AHP calculations 2. In the car repair shops under consideration, a group of experts has been involved in determining the judgemental comparison value required by the independent cells in the comparison matrices. Table 3.4 provides the normalized comparison matrix A gathered from the expert group. Table 3.4 Comparison matrix (A) for service quality parameters Service quality parameter SQ 1 SQ 2 SQ 3 SQ 4 SQ 5 SQ 6 SQ 7 SQ 8 Eigen vector SQ 1 1 1/7 1 1 1/3 1 1/7 1/7 0.0344 SQ 2 7 1 5 9 5 9 1 1 0.2623 SQ 3 1 1/5 1 3 1 5 1/5 1/5 0.0648 SQ 4 1 1/9 1/3 1 1/5 1 1/9 1/7 0.0267 SQ 5 3 1/5 1 5 1 5 1/5 1/3 0.0848 SQ 6 1 1/9 1/5 1 1/5 1 1/9 1/9 0.0248 SQ 7 7 1 5 9 5 9 1 1 0.2623 SQ 8 7 1 5 7 3 9 1 1 0.2395
48 From Table 3.4, W = [0.0344, 0.2623, 0.0648, 0.0267, 0.0848, 0.0248, 0.2623, 0.2395] T λ max = 8.3717 Consistency index (CI) = (λ max -n)/ (n-1) Order of the matrix A (n) = 8 Consistency index (CI) = (8.3717-8)/ (8-1) = 0.0531 Consistency ratio (CR) = CI / RI = 0.0531/1.41 = 0.037 matrix is consistent. Since the consistency ratio is less than or equal to 10% or 0.1, the 3. The repair shops (Unit A, Unit B Unit H) are compared with respect to all the service quality factors (SQ 1, SQ 2,., SQ 8 ) at the lowest level of the hierarchy using customers' opinion as shown in Tables 3.5 to 3.12. The largest eigen vector, λ max and CR are mentioned below each table. Table 3.5 Comparison matrix (B 1 ) with respect SQ 1 Unit A B C D E F G H Eigen Vector A 1 1/5 1/3 1/9 1/5 1/5 1/3 1/3 0.0250 B 5 1 3 1/5 1 1 1 1 0.1026 C 3 1/3 1 1/9 1/3 1 1 1 0.0599 D 9 5 9 1 5 5 7 7 0.4479 E 5 1 3 1/5 1 1 3 3 0.1352 F 5 1 1 1/5 1 1 1 1 0.0896 G 3 1 1 1/7 1/3 1 1 1 0.0698 H 3 1 1 1/7 1/3 1 1 1 0.0698 λ max = 8.4203 CR = 0.0404
49 Table 3.6 Comparison matrix (B 2 ) with respect SQ 2 Unit A B C D E F G H Eigen Vector A 1 1/5 1 1/9 1/5 1/5 1 1 0.0356 B 5 1 3 0.2 1 1 5 3 0.1330 C 1 1/3 1 1/7 1/3 1/3 3 1 0.0513 D 9 5 7 1 5 5 9 9 0.4413 E 5 1 3 0.2 1 1 5 3 0.1330 F 5 1 3 0.2 1 1 5 3 0.1330 G 1 1/5 1/3 1/9 1/5 1/5 1 1 0.0313 H 1 1/3 1 1/9 1/3 1/3 1 1 0.0410 λ max = 8.3682 CR = 0.0354 Table 3.7 Comparison matrix (B 3 ) with respect SQ 3 Unit A B C D E F G H Eigen Vector A 1 1/5 1 1/9 1/3 1/3 1 1 0.0395 B 5 1 5 1/5 1 1 3 3 0.1357 C 1 1/5 1 1/9 1/5 1/5 1 1 0.0359 D 9 5 9 1 5 5 9 9 0.4556 E 3 1 5 0.2 1 1 3 3 0.1252 F 3 1 5 0.2 1 1 3 3 0.1252 G 1 1/3 1 1/9 1/3 1/3 1 1 0.0413 H 1 1/3 1 1/9 1/3 1/3 1 1 0.0413 λ max = 8.235 CR = 0.0226
50 Table 3.8 Comparison matrix (B 4 ) with respect SQ 4 Unit A B C D E F G H Eigen Vector A 1 1/5 1/3 1/9 1/9 1/5 1/5 1/7 0.0202 B 5 1 1 1/5 1/3 1 1 1 0.0812 C 3 1 1 1/7 1/5 1 1 1/3 0.0596 D 9 5 7 1 1 5 5 3 0.3254 E 9 3 5 1 1 3 3 1 0.2262 F 5 1 1 1/5 1/3 1 1 1 0.0812 G 5 1 1 1/5 1/3 1 1 1 0.0812 H 7 1 3 1/3 1 1 1 1 0.1244 λ max = 8.3284 CR = 0.0316 Table 3.9 Comparison matrix (B 5 ) with respect SQ 5 Unit A B C D E F G H Eigen Vector A 1 1/7 1/3 1/9 1/5 1/5 1/3 1 0.0270 B 7 1 5 1/3 1 1 3 5 0.1666 C 3 1/5 1 1/7 1/3 1/3 1 1 0.0502 D 9 3 7 1 5 5 7 9 0.4143 E 5 1 3 1/5 1 1 1 3 0.1136 F 5 1 3 1/5 1 1 1 3 0.1136 G 3 1/3 1 1/7 1 1 1 3 0.0791 H 1 1/5 1 1/9 1/3 1/3 1/3 1 0.0353 λ max = 8.3825 CR = 0.037
51 Table 3.10 Comparison matrix (B 6 ) with respect SQ 6 Unit A B C D E F G H Eigen Vector A 1 1/7 1/3 1/9 1/5 1/5 1/5 1/7 0.0215 B 7 1 3 1/3 1 1 1 1 0.1214 C 3 1/3 1 1/7 1 1 1 1/3 0.0652 D 9 3 7 1 5 5 5 3 0.3782 E 5 1 1 1/5 1 1 1 1 0.0946 F 5 1 1 1/5 1 1 1 1 0.0946 G 5 1 1 1/5 1 1 1 1/3 0.0838 H 7 1 3 1/3 1 1 3 1 0.1403 λ max = 8.338 CR = 0.033 Table 3.11 Comparison matrix (B 7 ) with respect SQ 7 Unit A B C D E F G H Eigen Vector A 1 1 1 1/9 1/5 1/5 1 1/5 0.0359 B 1 1 1 1/9 1/3 1/3 1 1/3 0.0413 C 1 1 1 1/9 1/3 1/5 1 1/3 0.0395 D 9 9 9 1 5 5 9 5 0.4556 E 5 3 3 1/5 1 1 3 1 0.1252 F 5 3 5 1/5 1 1 3 1 0.1357 G 1 1 1 1/9 1/3 1/3 1 1/3 0.0413 H 5 3 3 1/5 1 1 3 1 0.1252 λ max = 8.235 CR = 0.0226
52 Table 3.12 Comparison matrix (B 8 ) with respect SQ 8 Unit A B C D E F G H Eigen Vector A 1 1/5 1 1/5 1/3 1/5 1 1/5 0.0316 B 5 1 3 1/5 1 1 5 1 0.1193 C 1 1/3 1 1/9 1/3 1/3 1 1/3 0.0363 D 9 5 9 1 7 5 9 7 0.4640 E 3 1 3 1/7 1 1 3 1 0.0979 F 5 1 3 1/5 1 1 3 1 0.1097 G 1 1/5 1 1/9 1/3 1/3 1 1/3 0.0346 H 5 1 3 1/7 1 1 3 1 0.1062 λ max = 8.3367 CR = 0.032 Step 4 : The SQM has been arrived from the principal eigen vector of the comparison matrix A and individual factor comparison matrices (B 1 to B 8 ). The calculation related to unit A is shown below. SQM A = (0.0344 * 0.0250) + (0.2623 * 0.0356) + (0.0648 * 0.0395) + (0.0267 * 0.0202) + (0.0848 * 0.0270) + (0.0248 * 0.0215) + (0.2623 * 0.0359) + (0.2395 * 0.0316) = 0.0331 Table 3.13. SQM for all the units has been calculated and is presented in the
53 Table 3.13 SQM for case study-i Units A B C D E F G H SQM 0.0332 0.1060 0.0463 0.4352 0.1148 0.1289 0.0440 0.0851 Step 5 : In the automobile repair shops, the weightage given to the service quality factor measure has been more important than the objective factor measure. After consultation with various service managers, it has been concluded to have the value of α as 0.4. Using equation (3.6), the SSPM for all the units has been calculated as shown below. SSPM A = (0.4*0.1041) + (1-0.4)*0.0332 = 0.062 SSPM B = (0.4*0.1160) + (1-0.4)*0.1060 = 0.110 SSPM C = (0.4*0.1021) + (1-0.4)*0.0463 = 0.069 SSPM D = (0.4*0.1265) + (1-0.4)*0.4352 = 0.312 SSPM E = (0.4*0.1389) + (1-0.4)*0.1148 = 0.124 SSPM F = (0.4*0.1711) + (1-0.4)*0.1289 = 0.146 SSPM G = (0.4*0.1341) + (1-0.4)*0.0440 = 0.080 SSPM H = (0.4*0.1073) + (1-0.4)*0.0851 = 0.094 Step 6 : From SSPM, it has been found that the service performance of unit A has been lower in comparison with the remaining units. Hence in order to improve the service performance of unit A, the services offered have to be redesigned with the help of QFD. 3.3.2 Service performance improvement using QFD The services offered by unit A has to be improved and QFD has been employed to facilitate this process. QFD procedure deals with building HoQ. Building the first HoQ for unit A consists of the following five steps:
54 i. Identifying the customer requirements The HoQ matrix starts with identifying the customer requirements. This process has been carried out during service quality measurement in the EBG model. From the absolute weight column in the HoQ matrix (Figure 3.3), it has been very clear that the prioritized customer requirements are in the following order: understanding the problem in the vehicle (SQ 2 ); ability to fix the problem in the first visit (SQ 7 ); quality of service done (SQ 8 ); timely delivery of vehicle (SQ 5 ); attention to modifications demanded by the customer (SQ 3 ); promptness of service advisor in attending the customer (SQ 1 ); mechanics trustworthiness (SQ 4 ); value for money service (SQ 6 ). ii. Identifying the service design requirements The QFD team (service managers and service engineers) identifies service design requirements that are most needed to fulfill the customer requirements. The service design requirements identified are as follows: trained service executive at the reception (DC 1 ); trained service mechanic (DC 2 ); rewards and recognition scheme to employees (DC 3 ); service reporting (DC 4 ); man power planning (DC 5 ); use of genuine parts for service (DC 6 ); rechecking of complaints at the time of service completion and delivery (DC 7 ); response to customer feedback (DC 8 ). iii. Relating the customer requirements to the service design requirements The customer requirements are related to the service design requirements through the central matrix construction. The central matrix provides the degree of influence between each service design requirement and each customer requirement. The degree of relationship has been defined by placing various symbols as shown in Figure 3.3.
55 iv. Conducting an evaluation of competing service providers The customer competitive assessment in the house of quality provides a good way to determine whether the customer requirements have been met. It also indicates areas to be concentrated in the next design. It contains an appraisal of where an organization stands relatively to its major competitors in terms of each requirement. The assessment values are obtained from the AHP results. v. Evaluating the service design requirements and development of targets In order to meet the customer requirements, the service organization has to prioritize the service design requirements and fix the targets for each service design requirement. Prioritized service requirements are in the order: trained service mechanic (DC 2 ); service reporting (DC 4 ); trained service executive at the reception (DC 1 ); rechecking of complaints at the time of service completion and delivery (DC 7 ); rewards and recognition scheme (DC 3 ); response to customer feedback (DC 8 ); use of genuine parts for service (DC 6 ); man power planning (DC 5 ). The targets to meet these requirements have been identified and deployed further by QFD team. The targets for the prioritized service DCs are as follows: expertise training once in two months; intensive training in service reporting procedures; job card preparation training once in six months; establishing a fool-proof mechanism for re-checking the customer complaints; implementing proper performance appraisal procedure; initiating corrective actions based on feedback analysis; implementing proper purchase procedure and retaining the talented pool and proper allocation of resources. The strategies for redesigning the services are further deployed and the implementation plans are reviewed for continuous performance improvement.
Service Design Characteristics Customer Requirements DC 1 DC 2 DC 3 DC 4 DC 5 DC 6 DC 7 DC 8 SQ 1 0.025 0.103 0.060 0.448 0.135 0.090 0.070 0.070 0.448 0.034 SQ 2 Δ 0.036 0.133 0.051 0.441 0.133 0.133 0.031 0.041 0.441 0.262 SQ 3 Δ Δ Δ 0.040 0.136 0.036 0.456 0.125 0.125 0.041 0.041 0.456 0.065 SQ 4 0.020 0.081 0.060 0.325 0.226 0.081 0.081 0.124 0.325 0.027 SQ 5 Δ 0.027 0.167 0.050 0.414 0.114 0.114 0.079 0.035 0.414 0.085 SQ 6 Δ 0.022 0.121 0.065 0.378 0.095 0.095 0.084 0.140 0.378 0.025 SQ 7 Δ 0.036 0.041 0.040 0.456 0.125 0.136 0.041 0.125 0.456 0.262 SQ 8 0.032 0.119 0.036 0.464 0.098 0.110 0.035 0.106 0.464 0.240 UNIT UNIT UNIT UNIT UNIT UNIT UNIT UNIT 6.95 8.22 5.96 7.66 1.74 2.78 6.12 3.46 A B C D E F G H Absolute weight TARGETS Job card preparation training once in six months Expertise training once in two months Proper performance appraisal procedure Intensive training in service reporting procedures Retaining the talented pool and proper allocation of resources Implementing proper purchase procedure Establishing a fool-proof mechanism for rechecking Initiating corrective actions based on feedback analysis Customer competitive assessment ( from AHP ) +9 - Strong +3 - Medium +1 Δ - Weak Target value Absolute weight (from AHP matrix) Prioritized customer requirements Figure 3.3 House of Quality 56
57 Improving performance is a never ending process and the organization should exceed the expectations of customers to increase its goodwill and gain potential future business. Hence, the whole process of measuring and redesigning the service process needs to be continuously monitored and the implementation plans have to be reviewed at regular intervals. 3.4 SCOPE AND LIMITATIONS OF AHP-EBG-QFD COMBINED MODEL The scope of AHP-EBG-QFD combined model is found to be: The model provides a means to measure the current performance of an organization. The model provides an opportunity to operationalise the relationship among cost, time and service quality dimensions. An important contribution of this model is that it combines both qualitative and quantitative dimensions for service performance measurement. Both the objective factors and service quality factors have been converted into consistent and dimensionless indices to measure the service system performance measure (SSPM). The model provides a means of identifying the performance improvement measures and provides the target for the same. The following limitations are identified while using AHP-EBG-QFD based model in the case study:
58 Since the human assessment on service quality attributes is always subjective and imprecise, conventional AHP seems to be inadequate in capturing decision maker s requirements explicitly. In building HoQ, using crisp values for assessing the importance of customer needs, degree of relationship between customer needs and design requirements, and degree of relationship among the design requirements are often vague or imprecise. 3.5 CONCLUDING REMARKS An integrated AHP-EBG-QFD combined model to enhance the performance management process has been proposed in this chapter. The case study presented has demonstrated the applicability of the model to support services of automobile repair shops. The case study proves the usability of EBG model for the Performance Management process. From the SSPM, the performance of service organizations has been analyzed and services offered are redesigned using QFD whenever necessary. The case study has led to identify the scope and limitations of AHP- EBG-QFD combined model. In order to overcome the limitations, fuzzy AHP- EBG-Fuzzy QFD combined model has been proposed. The details are presented in the next chapter.