Omega 29 (2001) 171–182
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An application of the AHP in vendor selection of a telecommunications system Maggie C.Y. Tama , V.M. Rao Tummalab; ∗ b College
a Alliances & Partners, Hong Kong Telecom, Quarry Bay, Hong Kong of Business, Production=Operations Management, Eastern Michigan University, 412 Owen Building, Ypsilanti, MI 48197, USA
Received 1 June 1998; accepted 21 July 2000
Abstract Vendor selection of a telecommunications system is an important problem to a telecommunications company as the telecommunications system is a long-term investment for the company and the success of telecommunications services is directly a ected by the vendor selection decision. Furthermore, the vendor selection of a telecommunications system is a complex multi-person, multi-criteria decision problem. The group decision-making process can be improved by a systematic and logical approach to assess priorities based on the inputs of several people from di erent functional areas within the company. The analytic hierarchy process (AHP) can be very useful in involving several decision-makers with di erent con icting objectives to arrive at a consensus decision. In this paper, an AHP-based model is formulated and applied to a real case study to examine its feasibility in selecting a vendor for a telecommunications system. The use of the proposed model indicates that it can be applied to improve the group decision making in selecting a vendor that satis es customer speci cations. Also, it is found that the decision process is systematic and that using the proposed AHP model can reduce the time taken to select a vendor. ? 2001 Elsevier Science Ltd. All rights reserved. Keywords: Telecommunications; Systems; Vendors; Selection; AHP
1. Introduction In recent years, the telecommunications (telecom) industry has undergone revolutionary changes. The driving forces for these changes include increasing customer demand, technological advances, and a worldwide trend of deregulation. This is particularly true for the Hong Kong Telecommunications industry as it was deregulated in June 1995. Consequently, three new companies, in addition to Hong Kong Telecom, were licensed to operate in Hong Kong beginning June 1995. Telecom services generally range from providing basic telephone line services to advanced services such as data, videoconferencing and even interactive multi-media services. Business users are growing in sophisticated needs, ∗ Corresponding author. Tel.: +1-734-487-2454; fax: +1-734487-7099. E-mail address:
[email protected] (V.M.R. Tummala).
demanding lower price and higher quality, both at the same time. Along with the deregulation of the telecommunications industry, market competition has become erce in many countries. In order to survive in this competitive environment, telecom companies need to o er new products and services to satisfy the growing needs of telecom customers, which may require the application of appropriate technologies. Quite often these products and services consist of network equipment and systems, and are procured from suppliers of the telecommunications industry. Usually, these systems could last for 5 –10 years or even more and could e ect the strategic positioning of the company. Thus, the selection of vendors is an important problem to a telecom company in meeting the customer needs. In addition, the selection of a telecom system is equally an important problem and could involve many criteria, including the technical requirements of service speci cations and cost, etc. Not only the equipment cost, but also the cost of operating the equipment, and maintenance, upgrade and
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support costs, need to be considered in selecting a particular system. It is important to consider these cost factors carefully to ensure the low cost delivery of service. Similarly, performance-related criteria such as reliability, availability and serviceability must also be assessed to meet the service levels as set in service speci cations and to increase customer satisfaction. Furthermore, technical criteria including system features, upgradability, future development, compliance with technology standards, interfacing with existing systems, and network management capabilities, etc., must be examined carefully. Judging vendor quality is also important and here the criteria might include delivery lead-time, security, accessibility, vendor reputation, and quality of support services, etc. It is important that we examine all these relevant factors in selecting a telecommunications system and a vendor who designs and delivers the system. Even though telecom companies are eager to spend considerable amount of time and money to select appropriate systems and vendors, they may not include all relevant criteria in evaluating telecom systems and vendors. The decision-making process may not be systematic. These factors may result in many changes in selection criteria and costly engineering design changes, which ultimately delay product launches. They may also result in not meeting the nancial objectives with respect to their investment in equipment and systems. Thus, there is a need for developing a systematic vendor selection process of identifying and prioritizing relevant criteria and evaluating the trade-o s between technical, economic and performance criteria. The approach should also reduce time in vendor selection and develop consensus decision making. Narasimhan [1], Nydick and Hill [2], and Partovi et al. [3] suggested the use of the analytic hierarchy process (AHP) approach for vendor selection problems. They suggested AHP mainly because of its inherent capability to handle qualitative and quantitative criteria used in vendor selection problems. Furthermore, it can be easily understood and applied by operating managers [4 – 6]. Also, the AHP can help to improve the decision-making process. The hierarchical structure used in formulating the AHP model can enable all members of the evaluation team to visualize the problem systematically in terms of relevant criteria and subcriteria. The team can also provide input to revise the hierarchical structure, if necessary, with additional criteria. Furthermore, using the AHP, the evaluation team can systematically compare and determine the priorities of the criteria and subcriteria. Based on this information the team can compare several vendor systems e ectively and select the best vendor. This paper investigates the feasibility of applying the AHP in vendor selection of a telecommunications system for a telecom company to improve the group decision making by a more systematic and logical approach. First, in Section 2, we identify the critical success factors for vendor selection of a telecommunications system. The critical success factors will form the basis for identifying important criteria and
subcriteria for vendor selection. These factors will then be used to formulate an AHP model to represent the vendor selection problem as explained in Section 3. The AHP model will then be applied in Section 4 to a case study to demonstrate its application and examine its e ectiveness. The advantages of using the proposed model are also discussed in Section 4. Finally, we conclude the paper with conclusions as described in Section 5. In order to identify the criteria and subcriteria for vendor selection of a telecommunications system, we conducted a survey as explained in Section 2. The purpose of this survey is only to enumerate the critical success factors that will form the basis to identify the speci c criteria and subcriteria to formulate the AHP model. It is not used to determine the priority weights of the criteria and subcriteria, which is the major purpose of AHP. 2. Identifying the criteria and subcriteria Dickson [7] identi ed 23 di erent criteria for vendor selection including quality, delivery, performance history, warranties, price, technical capability and nancial position. The studies of Arbel and Seidmmann [8–10], Beck and Lin [11], Zviran [12], Bard [13] and Liberatore [14] identi ed a number of criteria with respect to nancial, technical and operational aspects that are applicable to selecting a telecommunications system. These factors can be grouped into three major categories of cost, technical and operational success factors. The cost factors include capital investment, unit cost, cost of the billing system, cost of the network management system, operating cost and maintenance cost. The technical factors, on the other hand, consist of technical features=characteristics, system capacity, system performance, system reliability=availability, system redundancy, compliance with international standards, interoperability with other systems, upgradability on hardware and software, and future technology development. Similarly, the operational factors include ease of operations, ease of con guration, performance monitoring capabilities, statistical data reporting capabilities, fault diagnosis capabilities, detailed accounting information, system security features, customer network management features, customized reports generation, and billing exibility, etc. We conducted a survey involving 20 sta members selected randomly from di erent functional areas of the telecom company who are directly involved in the vendor selection process [15]. As explained in Section 1, the purpose of this survey is to assess and identify the above-mentioned cost, technical and operational factors as relevant criteria and subcriteria in formulating the AHP model. A questionnaire consisting of these factors was designed for the survey. Before conducting the survey, a pilot test was conducted with two professional sta members in the Engineering Department of the telecom company. The questionnaire was modi ed, based on the input received
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Fig. 1. Factors a ecting the selection of a telecommunications system.
and some additional criteria were added. The resulting questionnaire was mailed to the selected respondents. In order to identify relevant criteria, the respondents were asked to rate each factor using the three-point scale of “not important”, “somewhat important” and “very important” in selecting a telecommunications system [15]. The results of the survey are summarized in Fig. 1, where the mean value of each factor is determined by multiplying the percentages of respondents with the values of 1, 2 and 3 which are associated with “not important”, “somewhat important” and “very important”, respectively, and adding the resulting products. The criteria are arranged in descending order of their mean values. The cuto value of 2.3 is used and identi ed those factors as relevant criteria for which the mean values are greater than or equal to 2.3. From Fig. 1, we see that the value of 2.3
appears to be the natural cuto point as it is found to be the average of the highest (2.9, see Fig. 1) and the lowest (1.7, see Fig. 2) mean rating values of all factors included in the survey. Also, some of the factors whose mean values are less than 2.3 could be meaningfully grouped into the other factors whose mean values are greater than 2.3. For example, “ease of con guration” factor can be grouped into “ease of operations” criterion. Similarly, “customized report generation” and “detailed accounting information” can be grouped into “billing exibility” (see Fig. 1). In addition, the presence of too many criteria makes the pairwise comparisons in evaluating vendors, as explained in Sections 3.1 and 5, a dicult and time consuming process. It may also lead to evaluators’ assessment bias. To overcome these problems, the cut-o value or some similar method to reduce the number of criteria to a few is desirable.
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Fig. 2. Factors a ecting the selection of a vendor for a telecommunications system.
Thus, we identi ed the criteria with respect to cost, technical and operational factors as shown below. Cost factors • Capital investment • Unit cost • Operating cost • Maintenance cost • Cost of network management system Technical factors • Technical features=characteristics • System reliability=availability • System performance • System capacity • Upgradability on H=W and S=W • System redundancy • Future technology development
• Compliance with international standards • Interoperability with other systems Operational factors • Fault diagnosis capabilities • System security features • Ease of operations • Performance monitoring capabilities • Billing exibility Similarly, the respondents were asked to rate the factors considered in selecting a vendor for a telecommunications system as one of “not important”, “somewhat important” and “very important” [15]. The vendor-speci c factors include the cost and quality of support services, delivery lead time, repair turnaround time, current customers of vendors, vendor’s nancial position and stability, vendor’s reputation, experience in related products, existing supplier of the
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company, quality and technological systems used, capability in design assurance, technical expertise and problem solving capability. The results from the respondents are summarized as shown in Fig. 2, in descending order of mean values. Again, as in the earlier situation, the cuto value of 2.3 for mean ratings is used to identify criteria as shown below. Vendor speciÿc criteria • Quality of support services • Supplier’s problem solving capability • Supplier’s expertise • Cost of support services • Delivery lead time • Vendor’s experience in related products • Vendor’s reputation The above identi ed success factors are now considered as the relevant criteria and subcriteria and are used to formulate an appropriate AHP model for selecting the vendor of a telecommunications system. Theoretically, all the success factors shown in Figs. 1 and 2 can be included in the AHP-based model, as the AHP methodology will enable us to compare and prioritize them. However, it is not practical to include all factors as they increase the number of pairwise comparisons and the related computational e ort. It is also possible that assessment biases may occur in obtaining the pairwise comparison judgments from evaluators. Furthermore, as explained earlier, some of the factors that are not selected can be grouped into other selected criteria. Therefore, we used the cuto value of 2.3 and selected 26 criteria to use them in formulating AHP model. 3. The AHP model The AHP modeling process involves four phases, namely, structuring the decision problem, measurement and data collection, determination of normalized weights and synthesis- nding solution to the problem [16]. Using this four-phase approach, we rst formulate in this section an AHP model for vendor selection that could be applied by the company to any vendor selection of a telecommunication system. 3.1. Structuring the vendor selection problem This phase involves formulating an appropriate hierarchy of the AHP model consisting of the goal, strategic factors, criteria and subcriteria and the alternatives. The goal of our problem is to select the vendor of a telecommunications system that can meet customer requirements, bring pro ts to the rm, and compete strongly in the telecommunications market. This goal is placed on the rst level of the hierarchy as shown in Fig. 3. Two strategic factors, namely cost and quality, are identi ed to achieve this goal, which form the second level of the hierarchy. The cost factor is important because the lower the cost of a service, the higher the productivity and eciency, thus bringing more pro t to the
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company. Quality is equally important as it focuses more on meeting customers’ requirements and becoming competitive in order to stay ahead in the marketplace. The third level of the hierarchy occupies the criteria de ning the two strategic factors of cost and quality of the second level. There are two criteria related to cost, namely capital and operating expenditures. On the other hand, the criteria associated with quality are technical, operational and vendor-speci c. The fourth level consists of the 26 subcriteria, which were identi ed in Section 2 above, and is grouped with respect to the ve criteria occupying the third level, as shown in Fig. 3. The strategic factors, criteria and subcriteria used in these three levels of the AHP hierarchy can be assessed using the basic AHP approach of pairwise comparisons of elements in each level with respect to every parent element located one level above. A set of global priority weights can then be determined for each of the subcriteria by multiplying local weights of the subcriteria with weights of all the parent nodes above it. The fth level of the hierarchy contains the rating scale. This level is di erent from the usual AHP approach in that a rating scale will be assigned to each subcriterion related to every alternative, instead of assessing pairwise comparisons among the alternatives in the usual fashion. The use of a rating scale instead of direct pairwise comparisons among alternatives can be found in Liberatore’s studies [14,17–19]. The major advantage of this method is to overcome the explosion in the number of required comparisons when the number of alternatives is large [17]. For example, if we consider 20 alternatives, the number of pairwise comparisons required for each of the 26 subcriteria would be equal to n(n − 1)=2 = 190, and it becomes computationally dicult and sometimes infeasible. However, this is not the reason for using Liberatore’s rating method in the current case, as the number of alternatives, namely, the vendor systems, is usually below 5. The main reason for adopting this method is that the evaluation of vendors (or vendor proposals) of a particular telecommunications system sometimes involves a large number of technical details consisting of several subcriteria. It may be practically too dicult to make pairwise comparisons among the vendor systems with respect to every subcriteria. Also, it is a time-consuming process. The use of a rating scale can eliminate these diculties as each evaluator can assign a rating to a vendor’s system without making direct comparisons. As suggested by Liberatore, a ve-point rating scale of outstanding (O), good (G), average (A), fair (F) and poor (P) is adopted and the priority weights of these ve scales can be determined using pairwise comparisons as explained below in Section 3.3 [18]. A potential complication might arise when assigning the rating scales by using the ve-point rating system. For example, the relative rating of an “outstanding” vs. a “good” rating may di er for di erent criteria. As stated by Liberatore [18], making such ne discriminations in judgment would be very dicult. Furthermore, we
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Fig. 3. AHP model for vendor selection of a telecommunications system.
want to keep the assessment process as simple as possible. Therefore, we follow Liberatore [18] and obtain one set of ratings and use them to determine the local and global priority weights as explained in Sections 3.3 and 3:5 below. The lowest level of the hierarchy consists of the alternatives, namely the di erent vendor systems to be evaluated in order to select the best vendor system. As shown in Fig. 3, we used three vendor systems to represent arbitrarily three systems that the rm wishes to evaluate. In general, we can include as many vendor systems as the rm wishes to evaluate before selecting the best vendor. The AHP model shown in Fig. 3 is generally applicable to any vendor selection problem of a telecommunications system that a team wishes to evaluate, as it covers the critical success factors and the related criteria and subcriteria for vendor selection of a telecommunications system. Thus, whenever a team needs to select a vendor, then it can assess the vendors by the rating scheme as described above and determine the vendor priority weights to select the best vendor. As explained earlier in Section 1, the model provides the exibility to include any speci c criteria, and goals and objectives that the team may wish to consider in any other situation. 3.2. Measurement and data collection After building the AHP hierarchy, the next phase is the measurement and data collection, which involves forming a
team of evaluators and, as explained above, assigning pairwise comparisons to the strategic factors, criteria and subcriteria used in the AHP hierarchy. The nine-point scale as suggested by Saaty [4,5] is used to assign pairwise comparisons of all elements in each level of the hierarchy. Usually, every member assigns his or her pairwise comparisons, which will be translated into the corresponding pairwise comparison judgment matrices (PCJMs). As suggested by Saaty [4,5], the geometric mean approach, instead of the arithmetic approach, is used to combine the individual PCJMs to obtain the consensus PCJMs for the entire team. Using this approach, an evaluation team of ve members who are frequently involved in vendor selection of telecommunications systems within the organization is formed. Of these ve evaluators, two are senior engineers from the Engineering Department. Each one of them has more than ve years of experience in vendor selection projects for voice, data and transmission networks. Two evaluators are senior product managers from the Marketing Department; one of them has recently completed the vendor selection of a Frame Relay network and the other is presently involved in vendor selection of a Digital Data Network. The last evaluator is a manager from the Operations Department who is responsible for operations of fax and other value-added services. Thus, the evaluators have sucient experience in vendor selection of telecommunications systems and, hence, are quali ed to assign pairwise comparison judgements for the
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proposed AHP model. The opinions expressed by them in their judgements are considered to be representative of the company in evaluating the telecommunications criteria and the vendor selection requirements. A questionnaire consisting of all strategic factors, criteria and subcriteria of the three levels of the AHP model is designed and is used to collect the pairwise comparison judgments from all evaluation team members. This approach is found to be very useful in collecting data. The pairwise comparison judgements are made with respect to attributes of one level of hierarchy given the attribute of the next higher level of hierarchy, starting from the level of strategic factors down to the level of subcriteria. The results collected from the questionnaire are used to form the corresponding pairwise comparison judgment matrices (PCJMs) for determining the normalized weights as explained in the section below. 3.3. Determining normalized weights As explained earlier, the pairwise comparison judgement matrices obtained from ve evaluators in the measurement and data collection phase are combined using the geometric mean approach at each hierarchy level to obtain the corresponding consensus pairwise comparison judgement matrices [4,5]. Each of these matrices is then translated into the corresponding largest eigenvalue problem and is solved to nd the normalized and unique priority weights for each criterion as shown in Table 1. The software system called Expert Choice is used to determine the normalized priority weights [20]. The consistency ratio (CR) of each PCJM is also shown below each matrix. It can be seen that the consistency ratio of each of the PCJM is equal to or less than 0.03, which is well below the rule-of-thumb value of CR equal to 0.1. This clearly implies that the evaluators are consistent in assigning pairwise comparison judgments [4,5]. As explained in Section 3.1, we used Liberatore’s [18] ve-point rating scale of outstanding (O), good (G), average (A), fair (F) and poor (P) and determined the pairwise comparison judgment matrix as shown in Fig. 4. Following Liberatore, we assume the di erence in relative importance between two adjacent scales with respect to a particular scale is constant at 2 times, and obtain the corresponding PCJM for the rating scales (see Fig. 4). This matrix is then translated into the largest eigenvalue problem and, by using Expert Choice, the resulting priority weights of outstanding, good, average, fair and poor are found as 0.513, 0.261, 0.129, 0.063 and 0.034, respectively. 3.4. Synthesis — ÿnding solution to the problem After computing the normalized priority weights for each PCJM of the AHP hierarchy, the next phase is to synthesize the solution for the vendor selection problem. The normalized local priority weights of strategic factors, criteria
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Fig. 4. Pairwise comparison judgement matrix for ve-point rating scale.
and subcriteria obtained from the third phase are combined together with respect to all successive hierarchical levels to obtain the global composite priority weights of all subcriteria used in the fourth level of the AHP model. As explained earlier, the Expert Choice software system is used to determine these global priority weights as shown in Table 2. After calculating the global weights of each subcriterion of level 4, they are rearranged in descending order of priority, as shown in Table 3. It can be seen that the cost factors occupy the top-most rankings in the list, the top rank being the unit cost, followed by operating cost, capital investment, cost of support services, cost of network management system, and maintenance cost. The technical factors that are in the top ten rankings include system reliability=availability and system performance. There are also two operational factors in the top ten rankings, namely the system security features and fault diagnosis features. As explained earlier, the AHP model with all the strategic factors and the de ning criteria and subcriteria, along with their global priority weights can be used in any speci c vendor selection problem. In Section 4 below, we consider two vendor selection problems and show how the model can be applied to select the best vendor. 4. Application of the AHP model to a speciÿc vendor selection problem First we consider a problem of selecting a vendor for a data switching network system for a telecom company and demonstrate how the model can be applied. The data switching network system will be used to o er data services to the public. We consider the strategic factors, and the de ning criteria and subcriteria shown in Fig. 3 as appropriate in evaluating di erent vendor systems and in selecting the best vendor. The company in question is not involved in research or design activities. Any new or improved network equipment and systems must be procured from quali ed vendors in the telecommunications industry. Therefore, three potential vendors were shortlisted for evaluation and one of them should be selected to supply the data switching network system. Although the vendor selection of this case study was already completed using the current vendor selection process, we apply the proposed AHP model in order to demonstrate how it can be used and how the results obtained can
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Table 1 Pairwise comparison judgment matrices of vendor selection problem Goal
Cost
Quality
Priority
Cost Quality
1 0.8
1.3 1
0.565 0.435 CR = 0:0
Cost
CAPEX
OPEX
Priority
Capital expenditure Operating expenditure
1 0.9
1.1 1
0.524 0.476 CR = 0:0
Quality
Technical
Operational
Vendor
Priority
Technical Operational Vendor
1 0.6 0.3
1.7 1 0.5
3.0 1.9 1
0.519 0.313 0.168 CR = 0:0
Capital expenditure
Cl
UC
CNMS
Priority
Capital investment Unit cost Cost of NMS
1 2.3 0.7
0.4 1 0.5
1.5 2.1 1
0.268 0.521 0.211 CR = 0:03
Operating expenditure
OC
MC
CSS
Priority
Operating cost Maintenance cost Cost of support services
1 0.3 0.6
3.0 1 1.3
1.6 0.8 1
0.518 0.195 0.287 CR = 0:01
Technical
TF=C
SC
SR=A
SP
CTS
IWOS
FTD
SR
UHS
Priority
Technical features=characteristics System capacity System reliability=availability System performance Comply to standards Interoperability with other systems Future technology development System redundancy Upgradability on H=W & S=W
1 0.4 1.6 1.0 1.4 1.4 1.4 0.7 2.1
2.3 1 3.8 2.7 1.5 1.7 1.9 1.6 1.5
0.6 0.3 1 0.5 0.4 0.5 0.4 0.5 0.6
1.0 0.4 2.1 1 0.5 0.6 0.5 0.6 0.5
0.7 0.7 2.4 1.9 1 1.3 2.4 1.5 1.7
0.7 0.6 2.1 1.7 0.8 1 1.1 1.3 1.4
0.7 0.5 2.7 1.9 0.4 0.9 1 1.0 0.7
1.5 0.6 2.1 1.6 0.7 0.8 1.0 1 1.3
0.5 0.7 1.6 1.9 0.6 0.7 1.4 0.8 1
0.096 0.054 0.210 0.148 0.077 0.093 0.113 0.094 0.115 CR = 0:03
Operational
EOS
PMC
FDC
BF
SSF
Priority
Ease of operations Performance monitoring capability Fault diagnosis capabilities Billing exibility System security features
1 1.9 2.9 1.8 2.6
0.5 1 1.3 1.7 1.4
0.3 0.8 1 1.0 1.0
0.6 0.6 1.0 1 1.4
0.4 0.7 1.0 0.7 1
0.098 0.214 0.249 0.181 0.258 CR = 0:01
Vendor
DLT
QSS
ERP
PSC
TE
VR
Priority
Delivery lead time Quality of support services Experience in related products Problem solving capabilities Technical expertise Vendor’s reputation
1 2.3 1.6 3.6 1.8 1.2
0.4 1 0.2 0.8 0.8 0.4
0.6 4.4 1 3.3 2.4 0.7
0.3 1.3 0.3 1 0.4 0.3
0.6 1.3 0.4 2.4 1 0.4
0.8 2.6 1.4 3.2 2.4 1
0.084 0.275 0.092 0.29 0.175 0.084 CR = 0:03
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Table 2 Composite priority weights for critical success factors Strategic issues
Local weights
Criteria
Local weights
Success factors (subcriteria)
Local weights
Global weights
Cost
0.565
Capital expenditure
0.524
Capital investment Unit cost Cost of NMS
0.268 0.521 0.211
0.079 0.154 0.062
Operating expenditure
0.476
Operating cost Maintenance cost Cost of support services
0.518 0.195 0.287
0.139 0.052 0.077
Technical
0.519
Technical features=characteristics System capacity System reliability=availability System performance Comply to standards Interoperability with other systems Future technology development System redundancy Upgradability on H=W & S=W
0.096 0.054 0.210 0.148 0.077 0.093 0.113 0.094 0.115
0.022 0.012 0.047 0.033 0.017 0.021 0.026 0.021 0.026
Operational
0.313
Ease of operations Performance monitoring capability Fault diagnosis capabilities Billing exibility System security features
0.098 0.214 0.249 0.181 0.258
0.013 0.029 0.034 0.025 0.035
Vendor speci c
0.169
Delivery lead time Quality of support services Experience in related products Problem solving capabilities Technical expertise Vendor’s reputation
0.084 0.275 0.092 0.29 0.175 0.084
0.006 0.020 0.007 0.021 0.013 0.006
Total:
1.000
Quality
0.435
be compared with the decision reached by the pre-existing selection process. The global priority weights are determined for all 26 subcriteria factors as shown in the last column of Table 2. Similarly, as explained earlier, the priority weights for O, G, A, F, and P of Level 5 are determined as 0.513, 0.261, 0.129, 0.063 and 0.034, respectively (see Fig. 4). If only one evaluator is involved in assigning the rating scales of outstanding, good, average, fair, or poor for each vendor system with respect to each subcriterion, we record his or her rating and transfer them to a spreadsheet as shown in Table 4. On the other hand, if several evaluators are involved in selecting a vendor system, then we can use the Delphi technique to obtain the consensus ratings for all evaluators and transfer them to a spreadsheet as explained above. Once we transfer the global priority weights of all subcriteria and ratings of vendor systems on a spreadsheet, we can nd the global priority weight of each vendor system by multiplying the global priority weight of each subcriterion with the global priority weight of vendor system rating, and adding the resulting values. Or, as suggested by Liberatore [17,18],
we can nd the mean and the median of the global priority weights of vendor systems of team members and use them to select the best vendor. In our case study, one of the authors acted as the evaluator and assigned the ratings to each vendor system with respect to each subcriterion as shown in Table 4. Since the priority weights of each rating is already determined, we use them against each subcriterion on a spreadsheet format and determine the global priority weights of the three vendor systems as shown in Table 4. Notice that these global priority weights need to be normalized as shown in Table 4. Based on the global priority weights of the three vendor systems shown in Table 4, we nd that vendor system C had the highest weight. Therefore, it must be selected as the best system to satisfy the goals and objectives of the telecom company. Interestingly, the evaluation team had selected the same vendor system using the pre-existing vendor selection process. The data services delivered by the data switching network using system C has been in use for six months and customers appear to be satis ed with the services provided by the system. Thus, the actual decision made by the
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Table 3 Ranking of critical success factors Rank
Critical success factors (subcriteria)
Global weights
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
Unit cost Operating cost Capital investment Cost of support services Cost of NMS Maintenance cost System reliability=availability System security features Fault diagnosis capabilities System performance Performance monitoring capability Upgradability on H=W & S=W Future technology development Billing exibility Technical features=characteristics Problem solving capabilities System redundancy Interoperability with other systems Quality of support services Comply to standards Ease of operations Technical expertise System capacity Experience in related products Delivery lead time Vendor’s reputation Total
0.154 0.139 0.079 0.077 0.062 0.052 0.047 0.035 0.034 0.033 0.029 0.026 0.026 0.025 0.022 0.021 0.021 0.021 0.020 0.017 0.013 0.013 0.012 0.007 0.006 0.006 1.000
evaluation team agreed with the best solution determined by using the proposed AHP model. And the vendor selection decision was considered to be successful as the rate of customer gain was highly satisfactory and the company was able to win the bid under intense competition with other network operators. This result also shows that both the pre-existing vendor selection process and the AHP approach can come up with the same successful vendor selection decision. However, by using the pre-existing vendor selection process, the decision took ve months to complete; and this can be signi cantly reduced using the proposed AHP model. Using the AHP approach, the criteria for vendor selection are clearly de ned and the problem is structured systematically. This enables decision-makers to examine the strengths and weaknesses of vendor systems by comparing them with respect to appropriate criteria, and, hence, it is easier for the evaluation team to arrive at a consensus decision. We used the proposed model in another vendor selection problem and arrived at a vendor decision that was considered to be successful [15]. This problem involved selecting a new platform to replace the existing data multiplexing system. Again, we used the general model and rated the vendor systems given each of the 26 subcriteria and determined the corresponding global priority weights. Based on these priority weights, we se-
lected the best vendor. Thus, we can conclude that the use of the proposed AHP model can help facilitating the decision making and signi cantly reducing the time taken to select the vendor. Also, we hope that the success of these two applications would encourage the company in using the proposed model in their future vendor selection problems. All ve evaluators who assigned pairwise comparison judgements appear to be satis ed with the nal selection of the vendor system. Also, the managers of the concerned departments were happy with the application of the proposed AHP model. To overcome the problems of assessing pairwise comparison judgements, the evaluators were rst trained on AHP principles and assessment techniques. The questionnaires were then mailed to obtain the pairwise comparisons from evaluators. Gaining the support and commitment to evaluation team from senior and middle management would also encourage the continued application of the proposed model. 5. Conclusions As explained in Section 1, vendor selection of a telecommunications system is an important problem to a telecom company. We rst identi ed two strategic factors and the de ning criteria and subcriteria, and then formulated an AHP-based model, to select the vendor of a telecommunications system as shown in Fig. 3. The proposed AHP model is generally applicable to any vendor selection problem of a telecommunications system. After nding the global priority weights, they can be transferred easily to a spreadsheet as shown in Table 4 to determine the nal composite priority weights of vendor systems occupying the last level of the hierarchy. The proposed model is applied to two vendor selection problems. In both cases, the decisions reached by using the model agreed with those obtained by using the pre-existing vendor selection process. However, using the AHP model, the criteria for vendor selection are clearly identi ed and the problem is structured systematically. This enables decision-makers to examine the strengths and weaknesses of vendor systems by comparing them with respect to appropriate criteria and subcriteria. Moreover, the use of the proposed AHP model can signi cantly reduce the time and e ort in decision making. In addition, the results can be transferred to a spreadsheet for easy computations. It is easier for the evaluation team to arrive at a consensus decision. From the results of the case studies, it can be concluded that application of the AHP in vendor selection of a telecommunications system to improve the team decision making process is desirable. The AHP model developed in this paper can be used as a basis for implementing vendor selections of telecommunications systems. The suggested ve-point rating system of assessing the vendor systems helps decision-makers in avoiding time consuming pairwise comparison judgments. If new critical success factors,
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Table 4 Application of the AHP model to vendor selection of a data switching network Strategic criteria issues Critical success factors (subcriteria) Cost Capital expenditure Capital investment Unit cost Cost of NMS Operating expenditure Operating cost Maintenance cost Cost of support services Quality Technical Features=characteristics System capacity System reliability=availability System performance Comply to standards Interoperability with other systems Future technology development System redundency Upgradability on H=W & S=W Operational Ease of operations Performance monitoring capability Fault diagnosis capabilities Billing exibility System security features Vendor Delivery lead time Quality of support services Experiences in related products Problem solving capabilities Technical expertise Vendor’s reputation
Global weights
System A Rating
Score
× GW
System B Rating
Score
× GW
Rating
Score
× GW
0.079 0.154 0.062
F F G
0.063 0.063 0.261
0.0050 0.0097 0.0163
A G A
0.129 0.261 0.129
0.0102 0.0403 0.0081
G O O
0.261 0.513 0.513
0.0207 0.0791 0.0320
0.139 0.052 0.077
A F A
0.129 0.063 0.129
0.0180 0.0033 0.0100
A A A
0.129 0.129 0.129
0.0180 0.0068 0.0100
F F A
0.063 0.063 0.129
0.0088 0.0033 0.0100
0.022 0.012 0.047 0.033 0.017 0.021 0.026 0.021 0.026
O G G G G O G G G
0.513 0.261 0.261 0.261 0.261 0.513 0.261 0.261 0.261
0.0111 0.0032 0.0124 0.0087 0.0045 0.0108 0.0067 0.0055 0.0068
A G A G G G A A G
0.129 0.261 0.129 0.261 0.261 0.261 0.129 0.129 0.261
0.0028 0.0032 0.0061 0.0087 0.0045 0.0055 0.0033 0.0027 0.0068
G A G G G G G G A
0.261 0.129 0.261 0.261 0.261 0.261 0.261 0.261 0.129
0.0057 0.0016 0.0124 0.0087 0.0045 0.0055 0.0067 0.0055 0.0033
0.013 0.029 0.034 0.025 0.035
G G G A A
0.261 0.261 0.261 0.129 0.129
0.0035 0.0076 0.0088 0.0032 0.0045
O A G G A
0.513 0.129 0.261 0.261 0.129
0.0068 0.0038 0.0088 0.0064 0.0045
A G G F A
0.129 0.261 0.261 0.063 0.129
0.0017 0.0076 0.0088 0.0016 0.0045
0.006 0.020 0.007 0.021 0.013 0.006
A A O A A G
0.129 0.129 0.513 0.129 0.129 0.261
0.0008 0.0026 0.0035 0.0028 0.0017 0.0016
G A G A A A
0.261 0.129 0.261 0.129 0.129 0.129
0.0016 0.0026 0.0018 0.0028 0.0017 0.0008
A G G A A G
0.129 0.261 0.261 0.129 0.129 0.261
0.0008 0.0053 0.0018 0.0028 0.0017 0.0016
Total scores Renormalized scores
and, hence, new criteria emerge to satisfy changing business needs, then they can be included in the AHP model to select a vendor. Similarly, any new member can be included in the evaluation team to consider his or her input. Also, the vendor selection could be made in a more routine fashion. It should be noted, however, that the data collection and computational problems would increase with the increase in the number of criteria and subcriteria, as well as the number of vendors considered in the selection. This is one of the reasons that we suggested shortlisting the number of vendors rst and then applying the AHP model. Also, as it is shown here, the number of success factors can be grouped to minimize the number of criteria and subcriteria used in formulating the AHP model. The number of evaluators can be increased to collect more data. In fact, we can increase
System C
0.1725 0.2890
0.1785 0.2990
0.2459 0.4120
the number of evaluators and collect data and determine the priority weights to examine whether they are changed. In this fashion, we can conduct sensitivity analysis and determine the optimum number of evaluators to be used to collect data. However, several case studies in the literature using the AHP indicate the use of three to seven evaluators [5]. In this way, the biases of evaluators in assessing pairwise comparisons can be reduced. Acknowledgements The material for this paper was extracted from a M.Sc. dissertation in Engineering Management on “An Application of the Analytic Hierarchy Process in Vendor Selection of a Telecommunication System”, on which Maggie
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