Final results

Figure 6.5 presents the final global scores of alternatives obtained by using the

SuperDecisions ‘Synthesis’ command. The results are displayed in three different ways: The Raw column shows the limiting priorities obtained from the limiting supermatrix; the most usual form used to report priorities is the Normals column, in which the results are normalized by the cluster weight, and finally the Ideals column shows results obtained by dividing the values in either of the two columns by the largest value in that column.

Graphic Scale Alternatives Ideals Normals Raw Ranking

G1 Fibre optic cable 0.3174 0.1438 0.0322 3

G2 Power line communication 0.2555 0.1158 0.0259 4

G3 Microwave link 1.0000 0.4532 0.1013 1

G4 Satellite communication 0.6337 0.2872 0.0642 2

Figure 6.5 Final results as obtained from SuperDecisions synthesis command The obtained results indicate that the Microwave link technology is the most preferred alternative with a normalised priority of 45.32%, Satellite communication is the second

one, with a score of 28.72%, followed by the Fibre optic cable and the Power line communication with 14.38% and 11.58%, respectively. According to the ‘Ideals’ priorities; Microwave has a priority of 1.0, i.e. 100%, so the scores of Satellite, Fibre optic and Power line are 63.37%, 31.74% and 25.55%, respectively. SuperDecisions has also been used to produce the priorities shown in Table 6.14. It contains the relative importance of all criteria considered in the model. For example, under the limiting (raw) priorities’ column, one can observe that the most important factors among all are the Return on investment criterion with a priority of 17.15% followed by the Funding criterion with 15.94%.

Table 6.14 The relative importance of the criteria

Priorities (%)

Cluster Criteria

Normalised Limiting

(A1) Reliability 18.73 2.42

(A2) Ease of maintenance 21.70 2.81

(A3) Remote network management 17.05 2.21

(A4) Compatibility 2.62 0.33

(A5) Ease of installation 2.80 0.36

(A6) Scalability 11.98 1.55 (A7) Bandwidth 13.42 1.74 (A8) Flexibility 3.50 0.45 (A) Technical (A9) Latency 8.21 1.06 (B1) Coverage range 58.32 7.96

(B2) Security of physical infrastructure 5.04 0.69

(B3) Proposed usage 12.07 1.65

(B4) Availability of skilled technicians 3.24 0.44

(B5) Access to existing ~ infrastructure 4.74 0.65

(B6) Remoteness of area 3.22 0.44 (B7) Rollout time 5.84 0.80 (B) Infrastructure (B8) Parallel infrastructure 7.53 1.03 (C1) Operating cost 16.39 7.53 (C2) Funding sources 34.70 15.94 (C3) Capital cost 7.84 3.60 (C4) Return on investment 37.32 17.15 (C) Economic

(C5) Economic development of area 3.75 1.72

(D1) Demand 64.96 1.74

(D2) Affordability 20.49 0.55

(D3) Population density 9.91 0.27

(D) Social

(D4) Community of interest 4.65 0.12

(E1) Spectrum availability 56.99 0.67

(E2) Licensing constraints 27.45 0.32

(E) Regulatory

(E3) Rights of way 15.56 0.18

(F1) Terrain topography 56.81 0.71

(F) Environmental

(F2) Climatic conditions 43.19 0.54

According to the Normalised priorities column, the most important criterion is ‘Demand’ with a priority of 64.96%, followed by ‘Coverage’ with 58.32%. Among the technical criteria ‘Ease of maintenance’, ‘Reliability’ and ‘Remote network management’ criteria have the highest priorities of 21.71%, 18.73% and 17.05%, respectively. The ‘Spectrum’

and ‘Terrain topography’ factors are regarded as the most important within the regulatory and environmental clusters, with priorities of 56.99% and 56.81%, respectively. The relative importance of all criteria considered in the model can be read from table 6.14.

6.4 Conclusion

This chapter reports on the application of the analytic hierarchy/network processes to enhance the selection process of an essential rural infrastructure technology. Initially, a general AHP model for rural telecommunication infrastructure selection was created using a phase-by-phase approach, from structuring the selection problem, measurement and data collection, determining the normalised weights, to synthesis – finding solution to the problem. A four level hierarchy is structured to represent the problem at hand. This hierarchy adopts a basic AHP approach that includes pairwise comparisons of all elements. Pairwise comparisons are made with respect to elements of one level of hierarchy given the element of the next higher level of hierarchy, from the level of criteria down to the level of alternatives. To build up such a general model, telecommunication experts from all over the world were invited to make pairwise comparison judgements on the matrices derived from the AHP hierarchy. The collected data were then combined using the geometric mean and the normalised priority weights were determined for the combined matrices using Excel. Finally, at the synthesis phase, the model was established by combining the normalised priority weights in each level of the hierarchy.

The final results showed that Satellite technology is the most preferred alternative as it achieved the highest score in this AHP model with a normalised priority of 29.21%. Microwave came next with a priority of 28.59% and then Fibre and Power line technologies with 22.07% and 20.08% respectively. In addition, regarding criteria comparison with respect to the goal, it was found that the technical criterion had the highest priority of 29.31%, expressing a certain advantage among others, which indicates more importance of the technical aspects in comparison to other economic, infrastructure, etc. factors. The lowest priorities were for social, environmental and regulatory aspects with 6.90%, 5.84% and 5.63%, respectively. The most important subcriterion among all was the ‘operating cost’ with a priority of 11.47% followed by ‘coverage’ with 9.20% and ‘funding’ with 6.37%. The top ten factors comprised a variety of subcriteria that belong to all criteria except the regulatory criterion. The least important subcriteria among others considered in the model with priorities of less then 1% were ‘climatic conditions’, ‘latency’, ‘proposed usage’, ‘spectrum’ and ‘demand’ with 0.93, 0.70, 0.62, 0.40 and 0.28 respectively.

The AHP was capable of structuring the problem and providing a systematic approach to decision making allowing for diverse qualitative factors to be examined in a mathematical model, which can help to reduce the time needed to evaluate the alternatives. However, the priority scores of the four technologies were actually quite close to each other and although satellite technology achieved the highest score, it was only above Microwave’s score by less than 1% and fibre optic’s score was just less than 2% higher than that of power lines. Hence, it becomes questionable for the decision makers to arrive at a consensus decision to select any of the alternatives. This outcome implies that the simplicity of the hierarchical structure and linear unidirectional hierarchical relationship among criteria and subcriteria in the AHP method hid important issues, such as interdependence among qualitative factors and interaction among decision making levels and so oversimplified the problem. The ANP method can overcome the shortcomings of the strict hierarchical structure inherent in AHP. Subsequently, a generic ANP model for rural telecoms infrastructure selection developed in this chapter considered all kinds of dependencies systematically. This task was fulfilled through a new survey questionnaire, which included the dependency half-matrix that was distributed to experts. The response rate reached 70%. The majority rule was used to aggregate the responses into a single matrix in accordance to a predefined scale. A majority condition of 57% experts’ consensus was considered as a minimum requirement for any entry that indicates the existence of a direct relationship between any pair of elements.

In this chapter, it was shown numerically and graphically that the problem has inner dependencies, outer dependencies and feedback links developed among the elements in the network structure, which excludes the hierarchy form and calls for the network form to model such a rural technology selection problem. Once all possible dependencies among elements were identified, it was found that 92 judgement matrices, which include 674 pairwise comparison questions, were required in this network. SuperDecisions was used to construct the model accordingly and the experts were consulted again to assess the strength of these dependencies through pairwise comparison questions questionnaires. A particular focus was given to the web-based surveys because they brought greater value to group decision making by allowing users to give critical input from anywhere in the world. Since the clusters in this network are not equally important, their influences on each other were also pairwise compared and their weights were tabulated in the cluster matrix.

The computations of the relative importance of the elements by estimating the relative importance between any two elements in each matrix and calculating the relevant

eigenvectors were done by SuperDecisions. The software was also used to deal with the issue of improving matrices’ consistency, by measuring the inconsistency of each matrix and identifying the most inconsistent judgements and eventually constructing the supermatrix using the eigenvectors of the individual matrices. The final values of priorities of all the elements were obtained by normalising each block, so that the columns of the limit supermatrix became identical.

The obtained results indicate that Microwave technology is the most preferred alternative with a normalised priority of 45.32%, Satellite communication is the second one, with a score of 28.72%, followed by the Fibre optic and the Power lines with 14.38% and 11.58%, respectively. The relative importance of all criteria considered in the model was also obtained. For example, the most important criterion among all is the ‘demand’ with a priority of 64.96% followed by the ‘coverage’ with 58.32%. Among the technical criteria ‘ease of maintenance’, ‘reliability’ and ‘remote network management’ criteria have the highest priorities of 21.71%, 18.73% and 17.05%, respectively. The ‘spectrum’ and ‘terrain topography’ factors are regarded as the most important within the regulatory and environmental clusters, with priorities of 56.99% and 56.81%, respectively.

The ANP model developed in this chapter provides value to telecoms planners by raising their awareness to the availability of such a method and to researchers by demonstrating a new and novel application of ANP, which is meant to be a generic model applicable across different rural areas. It is recognised that the decision making process involved in any particular implementation would be different depending on the rural environment involved. In fact, this is one of the strengths of the ANP representing its ability to adapt a basic framework to a particular situation. Each application area can have defined for it a set of criteria deemed important for that area. A decision criterion that a telecoms company considers to be crucial can easily be added to this generic model.

One of the limitations of the ANP is the dependency on the decision makers because the weightings obtained are based on the expert’s subjective opinion. Hence, the obtained results reflect the preferences of experts who made the judgements and therefore, should not be viewed as an objective assessment of the relative suitability of the four technologies as backbone infrastructure in rural areas. However, they should be thought of as an input to the decision-making process rather than its end. This process could be refined with experience, optimising the accuracy and time taken to reach proper decisions in this regard.