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Evaluating productive efficiency in telecommunications: evidence from Greece
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Telecommunications Policy 24 (2000) 781}794
Evaluating productive e$ciency in telecommunications: evidence from Greece D. I. Giokas, G. C. Pentzaropoulos* Department of Economics, University of Athens, 8 Pesmazoglou Street, 10559 Athens, Greece
Abstract Many public telecommunications organizations (PTOs) in the OECD domain, and especially in Europe, have central administrations and maintain regional network infrastructures. Using the Hellenic Telecommunications Organization (OTE) as an example PTO, we investigate the productive e$ciency of the OTE's regional network and suggest actions for improving present operational ine$ciencies. Such actions are necessary for enhancing policies and management practices in the "eld of telecommunications in Greece. The evaluation methodology presented in this study, which is based on standardized measurements, could also be applicable to other PTOs with regional infrastructures. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Greece; Hellenic Telecommunication Organization (OTE); Productive e$ciency evaluation; Infrastructure development; Data envelopment analysis
1. Introduction The objective of this study is the application of a methodology for evaluating the productive e$ciency of public telecommunications organizations (PTOs) with regional infrastructures and central administrations. Many PTOs in Europe and elsewhere have adopted the above organizational scheme and are operating accordingly. In trying to make the content of this work as concrete as possible with regard to such aspects as measurements, analysis of data, and advice to management, we have used as an example PTO the Hellenic Telecommunications Organization (OTE). The conduct of the study falls within a period of time characterized by e!orts by many PTOs, including OTE in Greece, to adapt to free competition after a long period of regulation. Therefore,
* Corresponding author. Tel.: #30-1-32-23-758; fax: #30-1-32-28-538. E-mail address: [email protected] (G. C. Pentzaropoulos). 0308-5961/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 8 - 5 9 6 1 ( 0 0 ) 0 0 0 5 3 - 7
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it is important, at least for countries like Greece and others in the same stage of development, to "nd the means that will allow their PTOs to strengthen their current position within the changing international market. One of those means is higher e$ciency in the provision of telecommunications services. In this study, which was conducted independently with regard to OTE's function, we examine the productive e$ciency of telecommunications in Greece using the most recent set of data available o$cially (OTE, 1998). This set is the outcome of standardized measurements performed by OTE's administration: the type and extent of these measurements is common for all European PTOs. For the support of our methodology we have chosen a technique known as data envelopment analysis (DEA). DEA is a mathematical programming technique from the "eld of Operational Research with an extensive range of applications and corresponding bibliography. The reader is referred to Cooper, Thompson and Thrall (1996) and Seiford (1996) for a comprehensive account of new developments in DEA and its applications. The contents of this paper are organized as follows. Section 2 brie#y describes the current developments in the Greek telecommunications sector. This is followed, in Section 3, by the objectives of the present study. The modelling methodology together with a description of the data set used is outlined in Section 4. Numerical results are given in Section 5 with reference to OTE's network of regional centres. In Section 6, we have put together in more abstract terms the steps considered necessary for evaluating and improving the productive e$ciency of telecommunications networks. Finally, in Section 7, we comment on the usefulness and e!ectiveness of the present methodology and outline prospects for further work in this area.
2. Telecommunications developments in Greece Telecommunications and telematics are today recognized as key elements for an advanced information society. The deployment of telecommunication services relies on well-structured, modern networks capable of transmitting both voice and data at very high speeds. To meet this challenge, many countries in the European Union, the US and Japan began the restructuring of their telecommunications networks at an early stage and today have the lead in the provision of advanced services. In Europe, the situation is now di!erent after important steps taken by the European Commission for the establishment of free competition in telecommunications in the EU member states. Today, there is a clear expansion of private companies supplying telecommunication services to the public using the infrastructure of the more traditional PTOs. Many of the EU member states including Greece have experienced signi"cant di$culties in changing from their previous telecommunications status, i.e. state monopolies or duopolies (OECD, 1999). Some governments have used the period until 1998 to allow their national PTOs to adjust to the new challenges. In addition, some European countries have been given an extension beyond 2000 (for Greece until the end of 2000), to fully liberalize their markets. The less favoured regions of the European Union, which include Greece, have in the recent past received signi"cant "nancial aid from EU resources for the modernization of telecommunications infrastructures and services. OTE has participated in many initiatives and has bene"ted signi"cantly. In fact, it has been found that the general impact from the above-mentioned initiatives complemented by the application of a national policy based on the supply-push principle has been
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quite signi"cant for the Greek Information Technology and Telecommunications sector as a whole (Hatziparadissis & Pentzaropoulos, 1994). After three consecutive attempts in recent years towards liberalization, OTE's administration completed in 1999 a further (and "nal) step, which brought private ownership to 49% from 35% which was the previous "gure. It is also worth noting that OTE's shares are currently being traded in both the Athens and New York stock exchanges. This fact places the organization directly in a highly competitive state that requires better administrative policies and more #exible management actions.
3. Objectives of the present study The basis of this study is OTE's main infrastructure of terrestrial lines; therefore, we are not concerned with mobile telephony or data communications. The last two are certainly important elements of the newly forming situation, but we feel that the main infrastructure has priority as this concerns the entire population of Greece. Besides, the data available today for the last two cases are not yet complete or even stable enough to guarantee a thorough study. OTE maintains a wellstructured network of telecommunications centres (TCs) throughout Greece. The main frame of this network consists of eight geographical regions, and each region contains a number of administrative areas called prefectures. Each prefecture hosts a TC which provides its services locally. Further, all of the TCs are interconnected by means of exchanges and other links, thus forming a national infrastructure. OTE's central administration has the duty to periodically collect data in connection with the activities of the above TCs. The gathered data follow International Telecommunications Union (ITU) speci"cations. The data for Greece used for this study are contained in the latest available volume published by OTE (1998). The total number of TCs examined is 36, and this covers the majority of the Greek prefectures. Note that in the Athens metropolitan area (Attica), the Thessaloniki area in Northern Greece, and the Islands, the operating conditions are quite di!erent from those in the rest of the country: the "rst two areas are massively populated with a very dense telephone capacity whereas the Islands present unpredictable seasonal variations. In the context of a DEA application, it makes little sense to try to compare abnormally large or erratic units (here TCs) to ordinary ones since no meaningful comparisons could be achieved in such a manner. Therefore, our decision was to exclude the above three areas from the analysis. The telecommunications conditions in all the other 36 areas allow for comparisons between regions on a more equitable basis. Then, the problem to be solved in the present study can be stated in the following terms. Given the regional structure of OTE's network and the operational characteristics of its centres, (a) "nd those TCs which form the overall ezciency frontier of the network, (b) evaluate the signi"cance of any deviations between the rest of the TCs from the above frontier, and (c) make suggestions on the changes required for bringing the e$ciency levels of the latter TCs closer to the above frontier. The overall objective is the provision of practical advise to OTE's administration, and theoretically to any other PTO with a similar structure, as to the actions considered necessary for achieving optimum productive e$ciency. Such advice, which through best-practice actions can lead to tangible performance gains, should be of immediate value to the administration.
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4. Data envelopment analysis (DEA) For the purposes of the present study, we have formulated a set of six input}output variables. These variables are directly derived from the latest o$cial statistics given by OTE (1998). All measurements concerning telephone usage, labour output, and revenue have been taken into account with some mergers performed in the employee count in order to create three major personnel categories. The steps taken here were aimed at two targets: "rst, to provide an account of the basic factors (people, technology, operation) which contribute most to the observed productive e$ciency; and, second, to integrate the so-formed input}output variables into our approach (see Section 4.2 and the appendix) in order to obtain numerical results on comparative e$ciency. Below, we give the details concerning the above considerations. 4.1. Input}output variables For each of the TCs in OTE's network four inputs and two outputs have been formulated in order to characterize its operation; a description of these follows below. Inputs: E E E E
technical personnel (x ); administrative, operations, accounting and "nance personnel (x ); general duties, special status and temporary personnel (x ); installed network capacity (x ). Outputs:
E tari! units for automatic local, trunk and international telephony (y ); E total number of new connections and transfers of telephone lines (y ). Manpower (x , x , x ) and network infrastructure (x ) provide the means which contribute most to OTE's revenue. Personnel is an integer number counting the number of employees. Installed network capacity represents the number of telephone lines available. Note that the number of lines and the total number of employees have also been used by Sueyoshi (1994) as inputs in comparing
Table 1 Statistical measures of the input}output variables used in DEA Measure (input}output)
x
x
x
x
y (;10)
y
Average Median (M) Std. error Minimum Maximum Lower quartile (Q1) Upper quartile (Q3) Coe!. of variation (%)
147 126 15 30 474 84 197 62.0
55 51 5.6 10 184 33 68 62.0
23 19 2.8 5 75 10 32 73.2
63,197 60,024 5589.5 12,744 156,784 34,138 86,190 53.1
399,096 393,349 39,561.4 43,871 1,245,220 235,645 517,353 59.5
3199 2936 331.0 352 10,353 1841 3931 62.1
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the performance of 24 PTOs within the OECD domain. As regards outputs, tari! units (y ) in OTE's terminology represent the value of telephone calls made: from these, OTE calculates revenue in actual currency. New connections and transfers of lines (y ) indicate labour productivity considered to contribute to OTE's network expansion and/or restructure. Table 1 gives the basic statistical properties of the above input}output variables. The most notable of these properties is the large variation factor in almost all variables which indicates large deviations. The table also reveals non-homogeneous network operational characteristics with respect to its constituent members (i.e. the TCs). 4.2. Modelling approach Our approach, known as data envelopment analysis (DEA), is a mathematical programming technique originally proposed by Charnes et al. (1978), which is based on Farrel's earlier work on e$ciency (Farrell, 1957). The technique measures the relative e$ciency of the so-called decisionmaking units (DMUs) and then examines how a unit operates relative to the other units in a sample. A particular feature of the method is the construction of e$ciency frontiers based on actual measurements. The units on any of the frontiers are considered to be e$cient, while the units positioned inside the frontier are characterized as ine$cient. DEA uses a table containing multiple inputs and outputs which are used for the evaluation of a unit's e$ciency. Using as a reference the so-called peer group members, the technique measures any existing ine$ciencies and suggests the adjustments needed (input reduction and/or output augmentation) that could eventually make an ine$cient unit e$cient. Thus, by analysing any particular set of input}output data, DEA is able to identify (a) the e$ciency frontier which consists of the best-practice units; (b) the most and the least e$cient units which are ranked accordingly. The e$ciency rating of any unit re#ects its distance from the frontier: it is equal to 1 for all e$cient units and is less than 1 for all ine$cient units; (c) an e$ciency reference set, or peer group, for each ine$cient unit. This is a subset of all the e$cient units `closesta to the unit under evaluation; (d) input}output target levels for each ine$cient unit that would, if reached, make that unit relatively e$cient, i.e. increase its rating from less than 1 to exactly 1; and (e) critical inputs and outputs for any ine$cient unit which need to be given priority during the application of an improvement procedure. The #exibility of DEA has been demonstrated successfully in numerous real environments which include schools and universities, hospitals, public enterprises, and the banking sector. DEA is today one of the most successful methods of Operational Research with a wide range of applications and an extensive bibliography. As an indication, Seiford (1996) and Berger et al. (1997) provide a collection of some 1000 references to DEA-related applications which include evaluations of the relative e$ciency of environments such as those mentioned above. The concept of the DMUs also "ts well within the environment of telecommunications and computer networks, as demonstrated by the authors of this paper (Giokas & Pentzaropoulos, 1995, 2000). This particular environment is one of the least studied amongst the range of existing applications of DEA. In the present study, a DMU is assumed to be the model of a TC within OTE's regional network infrastructure.
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The original mathematical model of DEA by Charnes et al. (1978), known as CCR model, has been transformed by many authors in the recent past: the interested reader is referred to Cooper et al. (1996) for a recent survey. The CCR formulation assumes constant returns to scale (r.t.s.), i.e. it is assumed that the underlying production function is linear. However, this assumption cannot be incorporated in the present study, as an increase in the inputs of a unit (here a TC) cannot always be matched by a proportionate increase of the unit's outputs. Thus, variable r.t.s. will have to be used in the present situation. Therefore, the type of DEA model chosen is formulated according to the so-called BCC model, by Banker et al. (1984), which incorporates variable r.t.s. The model is fully described in the appendix. The interested reader can "nd a lively debate amongst members of the DEA community in connection with the criteria and methods of characterizing local economies of scale. In this context, Banker, Charnes and Cooper (1984), Banker and Thrall (1992), and Zhu and Shen (1995) provide alternative methods which, however, are not dissimilar in their "nal outcome.
5. Productive e7ciency of OTE's regional network The preceding analysis can now be applied to the data available from OTE's administration. The same analysis is also applicable in cases of other telecommunications organizations with central management and regional infrastructures in which standardized data sets are available. The present application is intended to provide a coherent approach to better understand how e$ciency (or, conversely, ine$ciency) presents itself in a real situation. The proposed modelling approach (DEA) and the use of the given input}output set can lead to successful evaluation and overall performance improvement. The analyst or manager responsible can give emphasis to input reduction or, alternatively, to output enhancement in order to make any ine$cient TC e$cient. Such an investigation can be performed in conjunction with variable r.t.s. which for any TC can be increasing (I), constant (C) or decreasing (D). 5.1. Ezciency ratings Throughout this study, a particular emphasis has been placed on revenue e$ciency related to services. OTE's revenue can be described as a function of the use of services and the prices charged for these. The overall objective is, therefore, towards revenue maximization which should be the result of improved performance. The "rst results, which are summarized in Tables 2 and 3, show the di!erences in performance between TCs that are e$cient and others which are characterized as ine$cient. All e$cient TCs have ratings equal to 100 (in the DEA model they correspond to the value hH"1; see the appendix). All ine$cient TCs have lower ratings (i.e. less than 100). The I associated r.t.s. for both cases are also shown in the tables. From a total of 36 TCs, 15 have been found to be e$cient and the remaining 21 ine$cient by di!erent ratings which range from 68.9 (TC04) to 99.8 (TC20). DEA also provides for each ine$cient TC its e$ciency reference set. This is the set of relatively e$cient TCs to which each ine$cient TC has been most directly compared in calculating its e$ciency rating when DEA is applied. In this context, each ine$cient TC is compared against its corresponding best-practice set which contains TCs drawn from the list of the e$cient TCs. As an example, we consider the case of the lowest and highest ranking TCs reported above. The "rst (TC04) has the following e$ciency
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Table 2 Performance pro"les of the e$cient TCs No.
E$ciency rating
r.t.s.
Frequency
TC01 TC02 TC03 TC06 TC09 TC12 TC14 TC15 TC19 TC25 TC27 TC29 TC31 TC33 TC36
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
C D I C C D C I D I C C C I I
8 1 1 7 3 6 1 2 4 4 15 8 9 4 11
Note: Frequency indicates the number of times an e$cient TC has been used in the construction of the overall e$ciency frontier. Table 3 Performance pro"les of the ine$cient TCs No.
E$ciency rating
r.t.s.
E$ciency reference set
TC04 TC05 TC07 TC08 TC10 TC11 TC13 TC16 TC17 TC18 TC20 TC21 TC22 TC23 TC24 TC26 TC28 TC30 TC32 TC34 TC35
68.9 71.4 72.4 86.4 82.7 72.4 90.6 93.6 88.1 76.8 99.8 76.3 93.0 98.5 86.4 90.5 88.6 98.6 95.2 99.5 88.7
D I I D D D I D I I D I I I I D I D I I I
(TC12, (TC15, (TC06, (TC19, (TC01, (TC01, (TC01, (TC09, (TC09, (TC01, (TC01, (TC06, (TC27, (TC27, (TC27, (TC19, (TC27, (TC12, (TC01, (TC25, (TC01,
TC29, TC31) TC33, TC36) TC31) TC27, TC29) TC06, TC12, TC27) TC06, TC12) TC27) TC12, TC27) TC27, TC31, TC36) TC06, TC25, TC36) TC19, TC27) TC25, TC29, TC36) TC29, TC36) TC31, TC33, TC36) TC29, TC36) TC27) TC29, TC31, TC36) TC27, TC29) TC27, TC31, TC36) TC31, TC33, TC36) TC06, TC27, TC31)
Note: The e$ciency reference set of an ine$cient TC consists of the e$cient TCs to which the former TC has been most directly compared in calculating its e$ciency rating ((100).
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reference set: (TC12, TC29, TC31). The second (TC20) has the following one: (TC01, TC19, TC27). Later we will show how an ine$cient TC can become e$cient by reducing its input or by augmenting its output in relation to its reference set. The e$cient TCs which are used most frequently in respective reference sets are also of importance because these TCs contribute most to the determination of the e$ciency frontier. These TCs are listed below in descending order: TC27 (freq.15), TC36 (freq.11), TC31 (freq.9), TC01 (freq.8), TC29 (freq.8), TC06 (freq.7) These TCs may be characterized as overall ezcient or model centres in comparative terms. Therefore, it would be interesting for OTE to examine their operational characteristics more closely. In our view, such an examination should be conducted jointly by the central administration and the local authorities concerned, and the "ndings should be made available across the network of the cooperating centres. 5.2. Returns to scale From the analysis it follows that 18 TCs (50%) have increasing r.t.s., 7 TCs (19.4%) constant r.t.s., and 11 TCs (30.6%) decreasing r.t.s. Case (C) is frequent for many of the e$cient TCs: for these, it is not necessary to consider any returns to scale. TCs with increasing r.t.s. should be of considerable interest to management. For the ine$cient TCs operating under case (I), it is suggested that they should increase their level of operation and so become more e$cient. However, it remains to be seen whether their local market could support such an increase. For the TCs operating under (D), our suggestion is towards decreasing their activities. This does not necessarily mean a decrease of their productivity, but it could possibly mean some stepwise decrease in personnel after examination of local market conditions. The decreasing r.t.s. shows a lack of coordination between a TC's capital base and its "nancial achievements relative to produced tari! units and new connections and transfers. Therefore, further analysis and management actions are required in connection with the installed network capacity within the regions of the above TCs. Results also show that most of the larger TCs have decreasing r.t.s., whereas the smaller TCs have increasing r.t.s. These results may also be of interest to other researchers with similar experiences from telecommunications organizations operating under increasing r.t.s. Eldor et al. (1981) and Fuss (1994) debate the presence of economies of scale in telecommunications, concluding that in the post-World-War II period the US and Canadian telecommunications organizations operated under signi"cantly increasing r.t.s. Similar conclusions are reported by Sueyoshi (1996) for NTT in Japan and by Athanassopoulos and Giokas (1998) for OTE in Greece, which is also studied here. Using yearly data covering the period from 1971 to 1993, the above authors have found that in terms of production e$ciency OTE operated on average under increasing r.t.s. 5.3. Synthesis of the results The 36 TCs examined in this study are grouped into eight geographical regions, as stated previously, which are the administrative regions of Greece. Thus, a regional synthesis of the results obtained thus far with respect to e$ciency ratings should give an overall picture of e$ciency in
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Table 4 Mean e$ciency ratings per geographical region of Greece No.
Region
Number of TCs
Mean e$ciency rating
1. 2. 3. 4. 5. 6. 7. 8. *
Central Greece and Euboea Peloponnessos Western Greece Epirus Thessaly Central Macedonia Eastern Macedonia and Thrace Western Macedonia Grand means
5 (TC01}TC05) 5 (TC06}TC10) 3 (TC11}TC13) 4 (TC14}TC17) 4 (TC18}TC21) 6 (TC22}TC27) 5 (TC28}TC32) 4 (TC33}TC36) 36 TCs in total
88.1 88.3 87.7 95.4 88.2 94.7 96.5 97.1 92.2
(3) (2) (1) (2) (1) (2) (2) (2) (15)
Note: The number of e$cient TCs per geographical region is indicated in parentheses in the last column.
telecommunications operations throughout the country. Table 4 summarizes the result of this synthesis. It may be concluded that regions R8 and R7 with ratings above all grand means operate more e$ciently, while regions R4 and R6 come close. Note that the "rst two of the overall e$cient TCs reported above, i.e. TC27 and TC36 belong to the four best-performing regions of Greece. The remaining four regions, R2, R5, R1 and R3, have generally lower e$ciency ratings and, therefore, lag behind the leaders. However, the di!erences in e$ciency are not too large to indicate major operational problems in those regions. For some parts of those regions, i.e. at the level of prefectures, a restructure in the operations of their TCs coupled with a new evaluation in personnel costs would be bene"cial from a management perspective. 5.4. Making inezcient TCS relatively ezcient The predictive capabilities of our modelling approach can be used for the improvement of the network's performance: this can gradually lead to optimum operational ezciency via speci"c management actions. Below, we outline the steps that might be taken in such a course of action using as an example TC21. The same steps also apply to the rest of the ine$cient TCs. From previous analysis, TC21 has been found to be relatively ine$cient with a rating of 76.3% and increasing (I) r.t.s. This suggests that an increase in inputs will result in a proportionately greater increase in outputs. Therefore, if TC21 can be made to increase its level of activity, it will also increase its e$ciency. Its e$ciency reference set consists of the following centres: (TC06, TC25, TC29, TC36). For TC21, this set creates an ideal (or virtual) environment to which the ine$cient TC21 should adapt in order to become e$cient. The DEA model suggests two alternative ways for achieving optimum e$ciency in relation to the above set. The "rst way places emphasis on input reduction for making TC21 e$cient. From model estimates, this TC uses more input in relation to its e$ciency reference set by the following amounts: x : 23.7%, x : 23.7%, x : 35.2% and x : 23.7%. Then, TC21 could be made e$cient by reducing its input wherever possible by the amounts given above. If this procedure is not possible,
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Table 5 Deviations in inputs and outputs for the ine$cient TC21 Input or output
x x x x y y
When input reduction is emphasized
When output enhancement is emphasized
Actual
Target
(%)
Actual
Target
(%)
155 56 23 67,084 371,820 2371
118.2 42.7 14.9 51,159.6 371,819 2541.1
!23.7 !23.7 !35.2 !23.7 0.0 7.2
155 56 23 67,084 371,820 2371
155 56 20 67,084 487,456 3704
0.0 0.0 !13.0 0.0 31.1 56.2
the second way which puts emphasis on output enhancement could be applied. Again, according to the model's estimates, TC21 uses less output in relation to its e$ciency reference set by the following amounts: y : 31.1% and y : 56.2%. Therefore, TC21 could be made more e$cient by increasing its total output by the amounts given above. Table 5 contains in some detail all the requirements for making TC21 e$cient. Apart from the indications given above which relate to speci"c amounts of input reduction or output enhancement, there are some additional opportunities for achieving optimum performance. The contribution of each of the elements of TC21's e$ciency reference set in making this TC e$cient is also important. From the preceding analysis it has been found that TC25 has the most important contribution in almost all of the input}output variables. The relevant range is from 34.5% for x to 51.2% for x . Hence, TC25 plays an important role in the creation of TC21's virtual environment. TC36 has less impact on TC21's input}output variables and hence on the creation of the above environment.
6. Applying the methodology in similar cases The methodology proposed in this study is based on two assumptions. First, that standardized measurements concerning usage of telecommunications networks are available; and, second, that a suitable form of a DEA model can be constructed in which the above measurements can lead to the choice of the required input}output variables. Given the realization of the above assumptions in practice, our approach should be applicable to any regionally structured telecommunications organizations with central administrations, like the OTE examined here. For the bene"t of the researchers who might wish to follow this methodology in similar cases, we summarize below the steps that can be taken there. (i) Aims and objectives: The problem should be stated within the framework of possible limitations concerning the availability of recent data, modelling preferences, and interpretation of foreseen results. (ii) Data collection and organization: This should really be the responsibility of the PTO concerned, but analysts will have the task of choosing the kind of data that best suit their study. In
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this respect, some measurements might be ignored and others merged into speci"c input}output variables. (iii) Model structure and assumptions: Since the problem involves the comparative evaluation of units (TCs) operating in di!erent environments, a proper analytical method will have to be used. The advantages of DEA in this context should be considered seriously bearing in mind the potential of this method and its wide acceptance. The use of alternative DEA models is possible; thus, any preferences here should re#ect the conditions under investigation. (iv) Performance analysis using alternative objectives: The construction of alternative objectives can enhance the appreciation of the complexity of performance problems in telecommunications. In this study, the overall objective was towards revenue maximization. Alternative objectives could be the organization's ability to provide services with less personnel cost or to expand its network via new lines. The examination of di!erent objectives can add more #exibility to the decisionmaking process. (v) Evaluation of the results achieved: This step is a delicate one as the interpretation of certain results is not always clear. There may be, for instance, signi"cant deviations in the e$ciency ratings of the TCs and perhaps di!erent interpratations of the r.t.s. obtained. In such cases, the analysts may decide to favour one interpretation at the expense of some other using their experience in conjunction with the present problem. Attention should also be paid to the best-performing TCs (i.e. the overall e$cient ones) as these can be used as models in a performance improvement procedure. (vi) Suggestions for performance improvement: Following the application of the previous steps, and using their experience, the potential analysts should aim at producing practical suggestions for management. Such suggestions would help management to follow best-practice procedures that should eventually lead to an overall performance improvement. It is understood that some suggestions might not be applicable immediately or even possible (one could think of a drastic reduction in personnel costs or, perhaps, number of employees in a particular region); however, any such suggestions should at least be of concern to the central administration. (vii) Feedback from management and further actions: This "nal step implies an iterative process whose application relies on both the level of responsibility of the administration and the readiness of the analysts to respond quickly to new demands. Ideally, this process should be continuous, but in practice it will probably take the form of a periodic dialogue for solving problems whenever they appear.
7. Concluding remarks The methodology presented in this paper consists of several inter-related components which may be thought of as forming a hierarchy of actions. At the lowest level, are actions related to measurements, the choice of input}output variables, and the statistical characteristics of the above variables. The middle level contains the mathematical analysis of the problem which is realized here by DEA, a successful multicriteria technique from the "eld of Operational Research. At the same level, are results from the analysis of data and relevant interpretations. Finally, the highest level of this hierarchy is occupied by actions and suggestions directed towards the management of a PTO.
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Using the OTE as an example, we have tried in the course of this study to address all aspects of the methodology referred to above. There should be no doubt that the application of a performance evaluation methodology such as the one proposed here is a complex task requiring expertise on the part of the analysts involved and willingness on the part of the administration to confront real-life problems. To this end, synergetic actions including feedback and trial-and-error procedures will be necessary. As for OTE's administration, we hope that the best-practice procedures suggested here will be taken into consideration, especially as the organization is now in a transition state that will soon lead to entirely free competition. The scope and content of the present work could be expanded in several ways. An obvious direction for further research is the examination of the network's performance over successive time periods. Another action includes mobile telephony and data communications, in addition to the main network infrastructure, and the incorporation of the concept of quality of service as an important performance indicator of the network's operation. The realization of the above options could, however, be limited by the absence of reliable data in cases involving PTOs with not very well-organized statistical services. Finally, a somewhat di!erent action line could aim at the comparative evaluation of the e$ciency of di!erent PTOs using recent data. This course of action is being considered at the present time by the authors of this paper for the case of the European telecommunications organizations. Acknowledgements This work has been partially supported by grant no. 70/4/3066/1999 awarded by the Research Committee of the University of Athens, Greece. The authors also wish to thank the reviewers of this paper for their comments on the original manuscript. Appendix A. DEA model with variable returns to scale (r.t.s.) The initial BCC model, by Banker et al. (1984), can be stated as follows:
K Q Minimize h "z !e s # s G P I I GJ P L subject to j x #s "z x (i"1, 2, 3,2, m), H GH G I GI H L j y !s "y (r"1, 2, 3,2, s), H PH P PI H L j "1, H H j , s , s *0 ( j"1, 2, 3,2, n; i"1, 2, 3,2, m; r"1, 2, 3,2, s), H G P z free and 0(e{1. I
(A.1)
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In the above formulation h is the e$ciency rating of the kth TC for which there is a set of m inputs I and s outputs; z is a scalar variable showing the rate of reduction to the input levels of the kth TC; I j is the intensity weight de"ning the convex combination of the best-practice units that are H compared with the kth TC: the non-zero j in the optimal solution indicate the set of TCs with H respect to which the kth TC is considered ine$cient (i.e. the kth TC's reference set); x is the amount GH of the ith input used by the jth TC and y is the amount of the rth output produced by the jth TC; PH e is a small positive number to ensure that inputs and outputs have at least some weighting in the e$ciency measure; and s , s are, respectively, input and output slack variables. G P Note that the original model (CCR) uses constant returns to scale (r.t.s.), as pointed out in the main text. This is avoided here by the introduction of constraint L j "1 above. The e!ect of H H this is to allow more general (i.e. non-linear) forms of the production function. Model (A.1) is repeated n times, for each TC available; i.e. every time the corresponding linear programming (LP) problem is solved to give each TC's e$ciency rating (hH)1). The TCs for I which hH"1 are characterized as ezcient and as such they determine the DEA e$ciency frontier. I The TCs for which hH(1 are characterized as relatively inezcient. In the above notation, hH is the I I optimal value of the present problem, i.e. the optimal value of the e$ciency rating h of the kth TC, I k"1,2,3,2, n. If a TC is relatively ine$cient, the problem is to examine the conditions that could possibly make it e$cient. This type of problem is very important from a managerial perspective, and in the context of the present study we have tried to provide some answers to it. In the general case, the above kth TC could become e$cient by using a target input xH as follows: GI xH "zHx !s I GI G GI
(i"1, 2, 3,2, m; k"1, 2, 3,2, n)
(A.2)
and at the same time by producing a target output yH as follows: PI yH "y #s PI PI P
(r"1, 2, 3,2, s; k"1, 2, 3,2, n).
(A.3)
The dual expression of model (A.1) is also possible, and this can be stated as follows: Q Maximize h " u y !u I P PI I P subject to
K v x "1, G GI G
Q K u y ! v x !u )0, P PH G GH I P G v , u *e (i"1, 2, 3,2, m; r"1, 2, 3,2, s), G P u unconstrained in sign. I
(A.4)
In the above formulation v , u are virtual multipliers (weights) for the ith input and the rth G P output, respectively; and u is an indicator of returns to scale (r.t.s.). I
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Note that the purpose of the dual model is to provide the means for estimating r.t.s. explicitly and this is achieved via the indicator u included in the above formulation. Models (A.1) and (A.4), I formally referred to as duals in the DEA literature, give identical results. For the dual model Banker and Thrall (1992) have shown that r.t.s. can be locally estimated by the sign of uH which is I the value of u corresponding to the optimal solution of the problem. I Then, the following indications can be used:
(0Nincreasing (I) r.t.s.,
uH: "0Nconstant (C) r.t.s., I '0Ndecreasing (D) r.t.s.
(A.5)
References Athanassopoulos, A. D., & Giokas, D. I. (1998). Technical e$ciency and economies of scale in state-owned enterprises The Hellenic Telecommunications Organization. European Journal of Operational Research, 107, 62}75. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale ine$ciencies in data envelopment analysis. Management Science, 30(9), 1078}1092. Banker, R. D., & Thrall, R. M. (1992). Estimation of returns to scale using data envelopment analysis. European Journal of Operational Research, 62, 74}84. Berger, A. N., Brockett, P. L., Cooper, W. W., & Pastor, J. T. (1997). New approaches for analyzing and evaluating the performance of "nancial institutions. European Journal of Operational Research, 98, 169}174. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the e$ciency of decision making units. European Journal of Operational Research, 2, 429}444. Cooper, W. W., Thompson, R. G., & Thrall, R. M. (1996). Extensions and new developments in DEA. Annals of Operations Research, 66, 3}46. Eldor, D., Sudit, E., & Vinod, H. (1981). Economies of scale in telecommunications: A further reply. Applied Economics, 13, 255}256. Farrell, M. J. (1957). The measurement of productive e$ciency. Journal of the Royal Statistical Society Series A, 120(3), 253}281. Fuss, M. (1994). Productivity growth in Canadian telecommunications. Canadian Journal of Economics, 27(2), 371}392. Giokas, D. I., & Pentzaropoulos, G. C. (1995). Evaluating the relative operational e$ciency of large-scale computer networks: An approach via data envelopment analysis. Applied Mathematical Modelling, 19(6), 363}370. Giokas, D.I., & Pentzaropoulos, G.C. (2000). E$cient storage allocation for processing in backlog-controlled queueing networks using multicriteria techniques. European Journal of Operational Research, 124, 539}549. Hatziparadissis, A., & Pentzaropoulos, G. C. (1994). Evaluation of publicly-funded research in Greece: A time-level analysis of the information technology sector. Research Evaluation, 4(2), 89}96. Hellenic Telecommunications Organization (OTE) (1998). Telecommunications statistics in Greece per prefecture for the year 1997 (Greek}English ed.), Athens. Organisation for Economic Co-operation and Development (OECD) (1999). Communications outlook 1999. OECD Publication Service, Paris. Seiford, L. (1996). Data envelopment analysis: The evolution of the state of the art (1978}1985). The Journal of Productivity Analysis, 7, 99}137. Sueyoshi, T. (1994). Stochastic frontier production analysis: Measuring performance of public telecommunications in 24 OECD countries. European Journal of Operational Research, 74, 466}478. Sueyoshi, T. (1996). Divestiture of Nippon telegraph and telephone. Management Science, 42(9), 1326}1351. Zhu, J., Shen, Z. (1995). A discussion of testing DMU's returns to scale. European Journal of Operational Research, 81, 590}596.
Telecommunications Policy 24 (2000) 781}794
Evaluating productive e$ciency in telecommunications: evidence from Greece D. I. Giokas, G. C. Pentzaropoulos* Department of Economics, University of Athens, 8 Pesmazoglou Street, 10559 Athens, Greece
Abstract Many public telecommunications organizations (PTOs) in the OECD domain, and especially in Europe, have central administrations and maintain regional network infrastructures. Using the Hellenic Telecommunications Organization (OTE) as an example PTO, we investigate the productive e$ciency of the OTE's regional network and suggest actions for improving present operational ine$ciencies. Such actions are necessary for enhancing policies and management practices in the "eld of telecommunications in Greece. The evaluation methodology presented in this study, which is based on standardized measurements, could also be applicable to other PTOs with regional infrastructures. 2000 Elsevier Science Ltd. All rights reserved. Keywords: Greece; Hellenic Telecommunication Organization (OTE); Productive e$ciency evaluation; Infrastructure development; Data envelopment analysis
1. Introduction The objective of this study is the application of a methodology for evaluating the productive e$ciency of public telecommunications organizations (PTOs) with regional infrastructures and central administrations. Many PTOs in Europe and elsewhere have adopted the above organizational scheme and are operating accordingly. In trying to make the content of this work as concrete as possible with regard to such aspects as measurements, analysis of data, and advice to management, we have used as an example PTO the Hellenic Telecommunications Organization (OTE). The conduct of the study falls within a period of time characterized by e!orts by many PTOs, including OTE in Greece, to adapt to free competition after a long period of regulation. Therefore,
* Corresponding author. Tel.: #30-1-32-23-758; fax: #30-1-32-28-538. E-mail address: [email protected] (G. C. Pentzaropoulos). 0308-5961/00/$ - see front matter 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 8 - 5 9 6 1 ( 0 0 ) 0 0 0 5 3 - 7
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it is important, at least for countries like Greece and others in the same stage of development, to "nd the means that will allow their PTOs to strengthen their current position within the changing international market. One of those means is higher e$ciency in the provision of telecommunications services. In this study, which was conducted independently with regard to OTE's function, we examine the productive e$ciency of telecommunications in Greece using the most recent set of data available o$cially (OTE, 1998). This set is the outcome of standardized measurements performed by OTE's administration: the type and extent of these measurements is common for all European PTOs. For the support of our methodology we have chosen a technique known as data envelopment analysis (DEA). DEA is a mathematical programming technique from the "eld of Operational Research with an extensive range of applications and corresponding bibliography. The reader is referred to Cooper, Thompson and Thrall (1996) and Seiford (1996) for a comprehensive account of new developments in DEA and its applications. The contents of this paper are organized as follows. Section 2 brie#y describes the current developments in the Greek telecommunications sector. This is followed, in Section 3, by the objectives of the present study. The modelling methodology together with a description of the data set used is outlined in Section 4. Numerical results are given in Section 5 with reference to OTE's network of regional centres. In Section 6, we have put together in more abstract terms the steps considered necessary for evaluating and improving the productive e$ciency of telecommunications networks. Finally, in Section 7, we comment on the usefulness and e!ectiveness of the present methodology and outline prospects for further work in this area.
2. Telecommunications developments in Greece Telecommunications and telematics are today recognized as key elements for an advanced information society. The deployment of telecommunication services relies on well-structured, modern networks capable of transmitting both voice and data at very high speeds. To meet this challenge, many countries in the European Union, the US and Japan began the restructuring of their telecommunications networks at an early stage and today have the lead in the provision of advanced services. In Europe, the situation is now di!erent after important steps taken by the European Commission for the establishment of free competition in telecommunications in the EU member states. Today, there is a clear expansion of private companies supplying telecommunication services to the public using the infrastructure of the more traditional PTOs. Many of the EU member states including Greece have experienced signi"cant di$culties in changing from their previous telecommunications status, i.e. state monopolies or duopolies (OECD, 1999). Some governments have used the period until 1998 to allow their national PTOs to adjust to the new challenges. In addition, some European countries have been given an extension beyond 2000 (for Greece until the end of 2000), to fully liberalize their markets. The less favoured regions of the European Union, which include Greece, have in the recent past received signi"cant "nancial aid from EU resources for the modernization of telecommunications infrastructures and services. OTE has participated in many initiatives and has bene"ted signi"cantly. In fact, it has been found that the general impact from the above-mentioned initiatives complemented by the application of a national policy based on the supply-push principle has been
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quite signi"cant for the Greek Information Technology and Telecommunications sector as a whole (Hatziparadissis & Pentzaropoulos, 1994). After three consecutive attempts in recent years towards liberalization, OTE's administration completed in 1999 a further (and "nal) step, which brought private ownership to 49% from 35% which was the previous "gure. It is also worth noting that OTE's shares are currently being traded in both the Athens and New York stock exchanges. This fact places the organization directly in a highly competitive state that requires better administrative policies and more #exible management actions.
3. Objectives of the present study The basis of this study is OTE's main infrastructure of terrestrial lines; therefore, we are not concerned with mobile telephony or data communications. The last two are certainly important elements of the newly forming situation, but we feel that the main infrastructure has priority as this concerns the entire population of Greece. Besides, the data available today for the last two cases are not yet complete or even stable enough to guarantee a thorough study. OTE maintains a wellstructured network of telecommunications centres (TCs) throughout Greece. The main frame of this network consists of eight geographical regions, and each region contains a number of administrative areas called prefectures. Each prefecture hosts a TC which provides its services locally. Further, all of the TCs are interconnected by means of exchanges and other links, thus forming a national infrastructure. OTE's central administration has the duty to periodically collect data in connection with the activities of the above TCs. The gathered data follow International Telecommunications Union (ITU) speci"cations. The data for Greece used for this study are contained in the latest available volume published by OTE (1998). The total number of TCs examined is 36, and this covers the majority of the Greek prefectures. Note that in the Athens metropolitan area (Attica), the Thessaloniki area in Northern Greece, and the Islands, the operating conditions are quite di!erent from those in the rest of the country: the "rst two areas are massively populated with a very dense telephone capacity whereas the Islands present unpredictable seasonal variations. In the context of a DEA application, it makes little sense to try to compare abnormally large or erratic units (here TCs) to ordinary ones since no meaningful comparisons could be achieved in such a manner. Therefore, our decision was to exclude the above three areas from the analysis. The telecommunications conditions in all the other 36 areas allow for comparisons between regions on a more equitable basis. Then, the problem to be solved in the present study can be stated in the following terms. Given the regional structure of OTE's network and the operational characteristics of its centres, (a) "nd those TCs which form the overall ezciency frontier of the network, (b) evaluate the signi"cance of any deviations between the rest of the TCs from the above frontier, and (c) make suggestions on the changes required for bringing the e$ciency levels of the latter TCs closer to the above frontier. The overall objective is the provision of practical advise to OTE's administration, and theoretically to any other PTO with a similar structure, as to the actions considered necessary for achieving optimum productive e$ciency. Such advice, which through best-practice actions can lead to tangible performance gains, should be of immediate value to the administration.
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4. Data envelopment analysis (DEA) For the purposes of the present study, we have formulated a set of six input}output variables. These variables are directly derived from the latest o$cial statistics given by OTE (1998). All measurements concerning telephone usage, labour output, and revenue have been taken into account with some mergers performed in the employee count in order to create three major personnel categories. The steps taken here were aimed at two targets: "rst, to provide an account of the basic factors (people, technology, operation) which contribute most to the observed productive e$ciency; and, second, to integrate the so-formed input}output variables into our approach (see Section 4.2 and the appendix) in order to obtain numerical results on comparative e$ciency. Below, we give the details concerning the above considerations. 4.1. Input}output variables For each of the TCs in OTE's network four inputs and two outputs have been formulated in order to characterize its operation; a description of these follows below. Inputs: E E E E
technical personnel (x ); administrative, operations, accounting and "nance personnel (x ); general duties, special status and temporary personnel (x ); installed network capacity (x ). Outputs:
E tari! units for automatic local, trunk and international telephony (y ); E total number of new connections and transfers of telephone lines (y ). Manpower (x , x , x ) and network infrastructure (x ) provide the means which contribute most to OTE's revenue. Personnel is an integer number counting the number of employees. Installed network capacity represents the number of telephone lines available. Note that the number of lines and the total number of employees have also been used by Sueyoshi (1994) as inputs in comparing
Table 1 Statistical measures of the input}output variables used in DEA Measure (input}output)
x
x
x
x
y (;10)
y
Average Median (M) Std. error Minimum Maximum Lower quartile (Q1) Upper quartile (Q3) Coe!. of variation (%)
147 126 15 30 474 84 197 62.0
55 51 5.6 10 184 33 68 62.0
23 19 2.8 5 75 10 32 73.2
63,197 60,024 5589.5 12,744 156,784 34,138 86,190 53.1
399,096 393,349 39,561.4 43,871 1,245,220 235,645 517,353 59.5
3199 2936 331.0 352 10,353 1841 3931 62.1
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the performance of 24 PTOs within the OECD domain. As regards outputs, tari! units (y ) in OTE's terminology represent the value of telephone calls made: from these, OTE calculates revenue in actual currency. New connections and transfers of lines (y ) indicate labour productivity considered to contribute to OTE's network expansion and/or restructure. Table 1 gives the basic statistical properties of the above input}output variables. The most notable of these properties is the large variation factor in almost all variables which indicates large deviations. The table also reveals non-homogeneous network operational characteristics with respect to its constituent members (i.e. the TCs). 4.2. Modelling approach Our approach, known as data envelopment analysis (DEA), is a mathematical programming technique originally proposed by Charnes et al. (1978), which is based on Farrel's earlier work on e$ciency (Farrell, 1957). The technique measures the relative e$ciency of the so-called decisionmaking units (DMUs) and then examines how a unit operates relative to the other units in a sample. A particular feature of the method is the construction of e$ciency frontiers based on actual measurements. The units on any of the frontiers are considered to be e$cient, while the units positioned inside the frontier are characterized as ine$cient. DEA uses a table containing multiple inputs and outputs which are used for the evaluation of a unit's e$ciency. Using as a reference the so-called peer group members, the technique measures any existing ine$ciencies and suggests the adjustments needed (input reduction and/or output augmentation) that could eventually make an ine$cient unit e$cient. Thus, by analysing any particular set of input}output data, DEA is able to identify (a) the e$ciency frontier which consists of the best-practice units; (b) the most and the least e$cient units which are ranked accordingly. The e$ciency rating of any unit re#ects its distance from the frontier: it is equal to 1 for all e$cient units and is less than 1 for all ine$cient units; (c) an e$ciency reference set, or peer group, for each ine$cient unit. This is a subset of all the e$cient units `closesta to the unit under evaluation; (d) input}output target levels for each ine$cient unit that would, if reached, make that unit relatively e$cient, i.e. increase its rating from less than 1 to exactly 1; and (e) critical inputs and outputs for any ine$cient unit which need to be given priority during the application of an improvement procedure. The #exibility of DEA has been demonstrated successfully in numerous real environments which include schools and universities, hospitals, public enterprises, and the banking sector. DEA is today one of the most successful methods of Operational Research with a wide range of applications and an extensive bibliography. As an indication, Seiford (1996) and Berger et al. (1997) provide a collection of some 1000 references to DEA-related applications which include evaluations of the relative e$ciency of environments such as those mentioned above. The concept of the DMUs also "ts well within the environment of telecommunications and computer networks, as demonstrated by the authors of this paper (Giokas & Pentzaropoulos, 1995, 2000). This particular environment is one of the least studied amongst the range of existing applications of DEA. In the present study, a DMU is assumed to be the model of a TC within OTE's regional network infrastructure.
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The original mathematical model of DEA by Charnes et al. (1978), known as CCR model, has been transformed by many authors in the recent past: the interested reader is referred to Cooper et al. (1996) for a recent survey. The CCR formulation assumes constant returns to scale (r.t.s.), i.e. it is assumed that the underlying production function is linear. However, this assumption cannot be incorporated in the present study, as an increase in the inputs of a unit (here a TC) cannot always be matched by a proportionate increase of the unit's outputs. Thus, variable r.t.s. will have to be used in the present situation. Therefore, the type of DEA model chosen is formulated according to the so-called BCC model, by Banker et al. (1984), which incorporates variable r.t.s. The model is fully described in the appendix. The interested reader can "nd a lively debate amongst members of the DEA community in connection with the criteria and methods of characterizing local economies of scale. In this context, Banker, Charnes and Cooper (1984), Banker and Thrall (1992), and Zhu and Shen (1995) provide alternative methods which, however, are not dissimilar in their "nal outcome.
5. Productive e7ciency of OTE's regional network The preceding analysis can now be applied to the data available from OTE's administration. The same analysis is also applicable in cases of other telecommunications organizations with central management and regional infrastructures in which standardized data sets are available. The present application is intended to provide a coherent approach to better understand how e$ciency (or, conversely, ine$ciency) presents itself in a real situation. The proposed modelling approach (DEA) and the use of the given input}output set can lead to successful evaluation and overall performance improvement. The analyst or manager responsible can give emphasis to input reduction or, alternatively, to output enhancement in order to make any ine$cient TC e$cient. Such an investigation can be performed in conjunction with variable r.t.s. which for any TC can be increasing (I), constant (C) or decreasing (D). 5.1. Ezciency ratings Throughout this study, a particular emphasis has been placed on revenue e$ciency related to services. OTE's revenue can be described as a function of the use of services and the prices charged for these. The overall objective is, therefore, towards revenue maximization which should be the result of improved performance. The "rst results, which are summarized in Tables 2 and 3, show the di!erences in performance between TCs that are e$cient and others which are characterized as ine$cient. All e$cient TCs have ratings equal to 100 (in the DEA model they correspond to the value hH"1; see the appendix). All ine$cient TCs have lower ratings (i.e. less than 100). The I associated r.t.s. for both cases are also shown in the tables. From a total of 36 TCs, 15 have been found to be e$cient and the remaining 21 ine$cient by di!erent ratings which range from 68.9 (TC04) to 99.8 (TC20). DEA also provides for each ine$cient TC its e$ciency reference set. This is the set of relatively e$cient TCs to which each ine$cient TC has been most directly compared in calculating its e$ciency rating when DEA is applied. In this context, each ine$cient TC is compared against its corresponding best-practice set which contains TCs drawn from the list of the e$cient TCs. As an example, we consider the case of the lowest and highest ranking TCs reported above. The "rst (TC04) has the following e$ciency
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Table 2 Performance pro"les of the e$cient TCs No.
E$ciency rating
r.t.s.
Frequency
TC01 TC02 TC03 TC06 TC09 TC12 TC14 TC15 TC19 TC25 TC27 TC29 TC31 TC33 TC36
100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
C D I C C D C I D I C C C I I
8 1 1 7 3 6 1 2 4 4 15 8 9 4 11
Note: Frequency indicates the number of times an e$cient TC has been used in the construction of the overall e$ciency frontier. Table 3 Performance pro"les of the ine$cient TCs No.
E$ciency rating
r.t.s.
E$ciency reference set
TC04 TC05 TC07 TC08 TC10 TC11 TC13 TC16 TC17 TC18 TC20 TC21 TC22 TC23 TC24 TC26 TC28 TC30 TC32 TC34 TC35
68.9 71.4 72.4 86.4 82.7 72.4 90.6 93.6 88.1 76.8 99.8 76.3 93.0 98.5 86.4 90.5 88.6 98.6 95.2 99.5 88.7
D I I D D D I D I I D I I I I D I D I I I
(TC12, (TC15, (TC06, (TC19, (TC01, (TC01, (TC01, (TC09, (TC09, (TC01, (TC01, (TC06, (TC27, (TC27, (TC27, (TC19, (TC27, (TC12, (TC01, (TC25, (TC01,
TC29, TC31) TC33, TC36) TC31) TC27, TC29) TC06, TC12, TC27) TC06, TC12) TC27) TC12, TC27) TC27, TC31, TC36) TC06, TC25, TC36) TC19, TC27) TC25, TC29, TC36) TC29, TC36) TC31, TC33, TC36) TC29, TC36) TC27) TC29, TC31, TC36) TC27, TC29) TC27, TC31, TC36) TC31, TC33, TC36) TC06, TC27, TC31)
Note: The e$ciency reference set of an ine$cient TC consists of the e$cient TCs to which the former TC has been most directly compared in calculating its e$ciency rating ((100).
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reference set: (TC12, TC29, TC31). The second (TC20) has the following one: (TC01, TC19, TC27). Later we will show how an ine$cient TC can become e$cient by reducing its input or by augmenting its output in relation to its reference set. The e$cient TCs which are used most frequently in respective reference sets are also of importance because these TCs contribute most to the determination of the e$ciency frontier. These TCs are listed below in descending order: TC27 (freq.15), TC36 (freq.11), TC31 (freq.9), TC01 (freq.8), TC29 (freq.8), TC06 (freq.7) These TCs may be characterized as overall ezcient or model centres in comparative terms. Therefore, it would be interesting for OTE to examine their operational characteristics more closely. In our view, such an examination should be conducted jointly by the central administration and the local authorities concerned, and the "ndings should be made available across the network of the cooperating centres. 5.2. Returns to scale From the analysis it follows that 18 TCs (50%) have increasing r.t.s., 7 TCs (19.4%) constant r.t.s., and 11 TCs (30.6%) decreasing r.t.s. Case (C) is frequent for many of the e$cient TCs: for these, it is not necessary to consider any returns to scale. TCs with increasing r.t.s. should be of considerable interest to management. For the ine$cient TCs operating under case (I), it is suggested that they should increase their level of operation and so become more e$cient. However, it remains to be seen whether their local market could support such an increase. For the TCs operating under (D), our suggestion is towards decreasing their activities. This does not necessarily mean a decrease of their productivity, but it could possibly mean some stepwise decrease in personnel after examination of local market conditions. The decreasing r.t.s. shows a lack of coordination between a TC's capital base and its "nancial achievements relative to produced tari! units and new connections and transfers. Therefore, further analysis and management actions are required in connection with the installed network capacity within the regions of the above TCs. Results also show that most of the larger TCs have decreasing r.t.s., whereas the smaller TCs have increasing r.t.s. These results may also be of interest to other researchers with similar experiences from telecommunications organizations operating under increasing r.t.s. Eldor et al. (1981) and Fuss (1994) debate the presence of economies of scale in telecommunications, concluding that in the post-World-War II period the US and Canadian telecommunications organizations operated under signi"cantly increasing r.t.s. Similar conclusions are reported by Sueyoshi (1996) for NTT in Japan and by Athanassopoulos and Giokas (1998) for OTE in Greece, which is also studied here. Using yearly data covering the period from 1971 to 1993, the above authors have found that in terms of production e$ciency OTE operated on average under increasing r.t.s. 5.3. Synthesis of the results The 36 TCs examined in this study are grouped into eight geographical regions, as stated previously, which are the administrative regions of Greece. Thus, a regional synthesis of the results obtained thus far with respect to e$ciency ratings should give an overall picture of e$ciency in
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Table 4 Mean e$ciency ratings per geographical region of Greece No.
Region
Number of TCs
Mean e$ciency rating
1. 2. 3. 4. 5. 6. 7. 8. *
Central Greece and Euboea Peloponnessos Western Greece Epirus Thessaly Central Macedonia Eastern Macedonia and Thrace Western Macedonia Grand means
5 (TC01}TC05) 5 (TC06}TC10) 3 (TC11}TC13) 4 (TC14}TC17) 4 (TC18}TC21) 6 (TC22}TC27) 5 (TC28}TC32) 4 (TC33}TC36) 36 TCs in total
88.1 88.3 87.7 95.4 88.2 94.7 96.5 97.1 92.2
(3) (2) (1) (2) (1) (2) (2) (2) (15)
Note: The number of e$cient TCs per geographical region is indicated in parentheses in the last column.
telecommunications operations throughout the country. Table 4 summarizes the result of this synthesis. It may be concluded that regions R8 and R7 with ratings above all grand means operate more e$ciently, while regions R4 and R6 come close. Note that the "rst two of the overall e$cient TCs reported above, i.e. TC27 and TC36 belong to the four best-performing regions of Greece. The remaining four regions, R2, R5, R1 and R3, have generally lower e$ciency ratings and, therefore, lag behind the leaders. However, the di!erences in e$ciency are not too large to indicate major operational problems in those regions. For some parts of those regions, i.e. at the level of prefectures, a restructure in the operations of their TCs coupled with a new evaluation in personnel costs would be bene"cial from a management perspective. 5.4. Making inezcient TCS relatively ezcient The predictive capabilities of our modelling approach can be used for the improvement of the network's performance: this can gradually lead to optimum operational ezciency via speci"c management actions. Below, we outline the steps that might be taken in such a course of action using as an example TC21. The same steps also apply to the rest of the ine$cient TCs. From previous analysis, TC21 has been found to be relatively ine$cient with a rating of 76.3% and increasing (I) r.t.s. This suggests that an increase in inputs will result in a proportionately greater increase in outputs. Therefore, if TC21 can be made to increase its level of activity, it will also increase its e$ciency. Its e$ciency reference set consists of the following centres: (TC06, TC25, TC29, TC36). For TC21, this set creates an ideal (or virtual) environment to which the ine$cient TC21 should adapt in order to become e$cient. The DEA model suggests two alternative ways for achieving optimum e$ciency in relation to the above set. The "rst way places emphasis on input reduction for making TC21 e$cient. From model estimates, this TC uses more input in relation to its e$ciency reference set by the following amounts: x : 23.7%, x : 23.7%, x : 35.2% and x : 23.7%. Then, TC21 could be made e$cient by reducing its input wherever possible by the amounts given above. If this procedure is not possible,
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Table 5 Deviations in inputs and outputs for the ine$cient TC21 Input or output
x x x x y y
When input reduction is emphasized
When output enhancement is emphasized
Actual
Target
(%)
Actual
Target
(%)
155 56 23 67,084 371,820 2371
118.2 42.7 14.9 51,159.6 371,819 2541.1
!23.7 !23.7 !35.2 !23.7 0.0 7.2
155 56 23 67,084 371,820 2371
155 56 20 67,084 487,456 3704
0.0 0.0 !13.0 0.0 31.1 56.2
the second way which puts emphasis on output enhancement could be applied. Again, according to the model's estimates, TC21 uses less output in relation to its e$ciency reference set by the following amounts: y : 31.1% and y : 56.2%. Therefore, TC21 could be made more e$cient by increasing its total output by the amounts given above. Table 5 contains in some detail all the requirements for making TC21 e$cient. Apart from the indications given above which relate to speci"c amounts of input reduction or output enhancement, there are some additional opportunities for achieving optimum performance. The contribution of each of the elements of TC21's e$ciency reference set in making this TC e$cient is also important. From the preceding analysis it has been found that TC25 has the most important contribution in almost all of the input}output variables. The relevant range is from 34.5% for x to 51.2% for x . Hence, TC25 plays an important role in the creation of TC21's virtual environment. TC36 has less impact on TC21's input}output variables and hence on the creation of the above environment.
6. Applying the methodology in similar cases The methodology proposed in this study is based on two assumptions. First, that standardized measurements concerning usage of telecommunications networks are available; and, second, that a suitable form of a DEA model can be constructed in which the above measurements can lead to the choice of the required input}output variables. Given the realization of the above assumptions in practice, our approach should be applicable to any regionally structured telecommunications organizations with central administrations, like the OTE examined here. For the bene"t of the researchers who might wish to follow this methodology in similar cases, we summarize below the steps that can be taken there. (i) Aims and objectives: The problem should be stated within the framework of possible limitations concerning the availability of recent data, modelling preferences, and interpretation of foreseen results. (ii) Data collection and organization: This should really be the responsibility of the PTO concerned, but analysts will have the task of choosing the kind of data that best suit their study. In
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this respect, some measurements might be ignored and others merged into speci"c input}output variables. (iii) Model structure and assumptions: Since the problem involves the comparative evaluation of units (TCs) operating in di!erent environments, a proper analytical method will have to be used. The advantages of DEA in this context should be considered seriously bearing in mind the potential of this method and its wide acceptance. The use of alternative DEA models is possible; thus, any preferences here should re#ect the conditions under investigation. (iv) Performance analysis using alternative objectives: The construction of alternative objectives can enhance the appreciation of the complexity of performance problems in telecommunications. In this study, the overall objective was towards revenue maximization. Alternative objectives could be the organization's ability to provide services with less personnel cost or to expand its network via new lines. The examination of di!erent objectives can add more #exibility to the decisionmaking process. (v) Evaluation of the results achieved: This step is a delicate one as the interpretation of certain results is not always clear. There may be, for instance, signi"cant deviations in the e$ciency ratings of the TCs and perhaps di!erent interpratations of the r.t.s. obtained. In such cases, the analysts may decide to favour one interpretation at the expense of some other using their experience in conjunction with the present problem. Attention should also be paid to the best-performing TCs (i.e. the overall e$cient ones) as these can be used as models in a performance improvement procedure. (vi) Suggestions for performance improvement: Following the application of the previous steps, and using their experience, the potential analysts should aim at producing practical suggestions for management. Such suggestions would help management to follow best-practice procedures that should eventually lead to an overall performance improvement. It is understood that some suggestions might not be applicable immediately or even possible (one could think of a drastic reduction in personnel costs or, perhaps, number of employees in a particular region); however, any such suggestions should at least be of concern to the central administration. (vii) Feedback from management and further actions: This "nal step implies an iterative process whose application relies on both the level of responsibility of the administration and the readiness of the analysts to respond quickly to new demands. Ideally, this process should be continuous, but in practice it will probably take the form of a periodic dialogue for solving problems whenever they appear.
7. Concluding remarks The methodology presented in this paper consists of several inter-related components which may be thought of as forming a hierarchy of actions. At the lowest level, are actions related to measurements, the choice of input}output variables, and the statistical characteristics of the above variables. The middle level contains the mathematical analysis of the problem which is realized here by DEA, a successful multicriteria technique from the "eld of Operational Research. At the same level, are results from the analysis of data and relevant interpretations. Finally, the highest level of this hierarchy is occupied by actions and suggestions directed towards the management of a PTO.
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Using the OTE as an example, we have tried in the course of this study to address all aspects of the methodology referred to above. There should be no doubt that the application of a performance evaluation methodology such as the one proposed here is a complex task requiring expertise on the part of the analysts involved and willingness on the part of the administration to confront real-life problems. To this end, synergetic actions including feedback and trial-and-error procedures will be necessary. As for OTE's administration, we hope that the best-practice procedures suggested here will be taken into consideration, especially as the organization is now in a transition state that will soon lead to entirely free competition. The scope and content of the present work could be expanded in several ways. An obvious direction for further research is the examination of the network's performance over successive time periods. Another action includes mobile telephony and data communications, in addition to the main network infrastructure, and the incorporation of the concept of quality of service as an important performance indicator of the network's operation. The realization of the above options could, however, be limited by the absence of reliable data in cases involving PTOs with not very well-organized statistical services. Finally, a somewhat di!erent action line could aim at the comparative evaluation of the e$ciency of di!erent PTOs using recent data. This course of action is being considered at the present time by the authors of this paper for the case of the European telecommunications organizations. Acknowledgements This work has been partially supported by grant no. 70/4/3066/1999 awarded by the Research Committee of the University of Athens, Greece. The authors also wish to thank the reviewers of this paper for their comments on the original manuscript. Appendix A. DEA model with variable returns to scale (r.t.s.) The initial BCC model, by Banker et al. (1984), can be stated as follows:
K Q Minimize h "z !e s # s G P I I GJ P L subject to j x #s "z x (i"1, 2, 3,2, m), H GH G I GI H L j y !s "y (r"1, 2, 3,2, s), H PH P PI H L j "1, H H j , s , s *0 ( j"1, 2, 3,2, n; i"1, 2, 3,2, m; r"1, 2, 3,2, s), H G P z free and 0(e{1. I
(A.1)
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In the above formulation h is the e$ciency rating of the kth TC for which there is a set of m inputs I and s outputs; z is a scalar variable showing the rate of reduction to the input levels of the kth TC; I j is the intensity weight de"ning the convex combination of the best-practice units that are H compared with the kth TC: the non-zero j in the optimal solution indicate the set of TCs with H respect to which the kth TC is considered ine$cient (i.e. the kth TC's reference set); x is the amount GH of the ith input used by the jth TC and y is the amount of the rth output produced by the jth TC; PH e is a small positive number to ensure that inputs and outputs have at least some weighting in the e$ciency measure; and s , s are, respectively, input and output slack variables. G P Note that the original model (CCR) uses constant returns to scale (r.t.s.), as pointed out in the main text. This is avoided here by the introduction of constraint L j "1 above. The e!ect of H H this is to allow more general (i.e. non-linear) forms of the production function. Model (A.1) is repeated n times, for each TC available; i.e. every time the corresponding linear programming (LP) problem is solved to give each TC's e$ciency rating (hH)1). The TCs for I which hH"1 are characterized as ezcient and as such they determine the DEA e$ciency frontier. I The TCs for which hH(1 are characterized as relatively inezcient. In the above notation, hH is the I I optimal value of the present problem, i.e. the optimal value of the e$ciency rating h of the kth TC, I k"1,2,3,2, n. If a TC is relatively ine$cient, the problem is to examine the conditions that could possibly make it e$cient. This type of problem is very important from a managerial perspective, and in the context of the present study we have tried to provide some answers to it. In the general case, the above kth TC could become e$cient by using a target input xH as follows: GI xH "zHx !s I GI G GI
(i"1, 2, 3,2, m; k"1, 2, 3,2, n)
(A.2)
and at the same time by producing a target output yH as follows: PI yH "y #s PI PI P
(r"1, 2, 3,2, s; k"1, 2, 3,2, n).
(A.3)
The dual expression of model (A.1) is also possible, and this can be stated as follows: Q Maximize h " u y !u I P PI I P subject to
K v x "1, G GI G
Q K u y ! v x !u )0, P PH G GH I P G v , u *e (i"1, 2, 3,2, m; r"1, 2, 3,2, s), G P u unconstrained in sign. I
(A.4)
In the above formulation v , u are virtual multipliers (weights) for the ith input and the rth G P output, respectively; and u is an indicator of returns to scale (r.t.s.). I
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Note that the purpose of the dual model is to provide the means for estimating r.t.s. explicitly and this is achieved via the indicator u included in the above formulation. Models (A.1) and (A.4), I formally referred to as duals in the DEA literature, give identical results. For the dual model Banker and Thrall (1992) have shown that r.t.s. can be locally estimated by the sign of uH which is I the value of u corresponding to the optimal solution of the problem. I Then, the following indications can be used:
(0Nincreasing (I) r.t.s.,
uH: "0Nconstant (C) r.t.s., I '0Ndecreasing (D) r.t.s.
(A.5)
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