**2. Current benchmarking in the German water supply sector**

Benchmarking can be defined as "the process whereby a company compares and improves its performance by learning from the best in a selected group" (BDEW, 2010, p. 4). 36 of such, so called *process benchmarking* projects are carried out in the German water and wastewater sector (ATT et al., 2011, p. 94ff.). Parts of the value chain are analyzed in detail mainly between a limited number of companies. Up to 20 companies are participating in the various projects (ATT et al., 2011, p. 94ff.). The concept is displayed in the following figure.

Fig. 2. Concept of Benchmarking (BDEW, 2010, p.4)

The process starts out with a comparison of key indicators. For each single company the deviation between its actual value and the benchmark is determined. The different factors which may explain the difference are then intensely discussed between the specialists of the companies for the particular process. Quantifiable measures which are then implemented shall diminish the gap between own value and benchmark. The relative efficiency of the company within this particular process increases. Process benchmarking is, therefore, characterized by a continuous process to learn from the best.

The 36 water and wastewater programmes have approximately 12 participants, on average. Very often the same companies take part in several projects covering different processes. For

Analysis of the Current German Benchmarking

participating in a benchmarking project.

should thus be collected.

reluctant to do so.

Approach and Its Extension with Efficiency Analysis Techniques 275

out of the 16 *Bundesländer* (ATT et al., 2011, p. 92f.). Based on drinking water quantities 30 % (Baden-Württemberg and Bavaria) up to 100 % (city states of Berlin, Hamburg and Bremen)

However, these kinds of metric benchmarking projects are also activating a number of additional water service providers which are not participating in the very intense performance benchmarking projects. Its main intention is to give the companies a first insight of how good they actually seem to perform. Similar to the performance benchmarking projects, discussions between the relative good and bad companies are intended to take place. The problem, however, is to distinguish a good and a bad company. The current approach shows that, where ones costs in a certain part of the value chain are solely compared with those of others without actually taking into account differences in basic conditions, benchmarking is not as efficient as it could be. Due to very different conditions for companies to deliver water services, costs can be very different between companies for good reason. A company with rather unfavorable conditions and higher costs might be more efficient than another one with more favorable conditions and lower costs. As a result, current metric benchmarking projects seem not to fullfill the high expectations. In nearly all of the metric benchmarking projects the number of participating companies

Current metric benchmarking approaches should, therefore, employ techniques which are able to assess costs, taking into account the relevant environmental conditions in which the company actually operates. The Data Envelopment Analysis (DEA) and the Stochastic Frontier Analysis (SFA) are the scientifically established tools, which are giving a good indication about the relative efficiency of a company. Water suppliers which are performing badly according to both DEA and SFA – given their particular, not influenceable environmental conditions – ought to have potentials to improve efficiency. Such an enhanced benchmarking can thus improve the information a company may receive from

It is worth noting that such an enhanced metric benchmarking project is better displaying the relative performance of a company. It is, however, not giving advice on how a company might increase its efficiency. In order to determine the correct measures a company might participate in a process benchmarking project, install certain working groups within its company or employ consulting companies. A metric benchmarking project is thus very often a necessity for a company to deal with its own performance relative to others. After detecting certain inefficiencies, the company should encounter incentives to install programs which help in improving their performance. Time series data of a company's performance

All European countries which are employing metric benchmarking systems will, therefore, sooner or later face the necessity to decide which kind of information they want to display publically and whether companies should be obliged to participate. The Netherlands, for example, made it compulsory to take part in such programs whereas Germany is very

There are also, however, other means to give incentives to companies to participate in enhanced metric benchmarking systems. Those German water suppliers, which are setting prices, are currently under the supervision of cartel offices. Currently, these regulatory

are covered by the projects. Based on the number of companies it is far less.

remains either constant, over time, or diminishes (ATT et al., 2011, p. 90ff.).

the 16 water supply projects 100 different companies might participate. Compared to more than 6,000 German operators, this number is quite negligible.

The question, therefore, arose of how to activate more companies to participate. Particularly, since the Federal Government and the Bundestag have submitted its so called "modernization strategy", *metric benchmarking* projects increased in number.6 The "modernization strategy" – approved by the German Parliament on the 22nd of March 2002 - acknowledged the benchmarking concept and asked the German water associations to continue implementing them in the various *Bundesländer*. Benchmarking projects in the water supply sector are now performed in each of them (see Figure 3). Public reports are available for 12

Fig. 3. Metric benchmarking in German Bundesländern (BDEW, 2010, p.9)

<sup>6</sup> A metric benchmarking system is not going into such detail as a process benchmarking system does. It merely compares companies by employing key performance indicators. The link to the German "modernization strategy", however, does not imply that metric benchmarking, as such, is a new invention. The *Betriebsvergleich kommunaler Versorgungsunternehmen* (Benchmarking of public water supply utilities), run by the German Water Association VKU, was first installed about 50 years ago.

the 16 water supply projects 100 different companies might participate. Compared to more

The question, therefore, arose of how to activate more companies to participate. Particularly, since the Federal Government and the Bundestag have submitted its so called "modernization strategy", *metric benchmarking* projects increased in number.6 The "modernization strategy" – approved by the German Parliament on the 22nd of March 2002 - acknowledged the benchmarking concept and asked the German water associations to continue implementing them in the various *Bundesländer*. Benchmarking projects in the water supply sector are now performed in each of them (see Figure 3). Public reports are available for 12

than 6,000 German operators, this number is quite negligible.

Fig. 3. Metric benchmarking in German Bundesländern (BDEW, 2010, p.9)

6 A metric benchmarking system is not going into such detail as a process benchmarking system does. It merely compares companies by employing key performance indicators. The link to the German "modernization strategy", however, does not imply that metric benchmarking, as such, is a new invention. The *Betriebsvergleich kommunaler Versorgungsunternehmen* (Benchmarking of public water supply utilities), run by the German Water Association VKU, was first installed about 50 years ago.

out of the 16 *Bundesländer* (ATT et al., 2011, p. 92f.). Based on drinking water quantities 30 % (Baden-Württemberg and Bavaria) up to 100 % (city states of Berlin, Hamburg and Bremen) are covered by the projects. Based on the number of companies it is far less.

However, these kinds of metric benchmarking projects are also activating a number of additional water service providers which are not participating in the very intense performance benchmarking projects. Its main intention is to give the companies a first insight of how good they actually seem to perform. Similar to the performance benchmarking projects, discussions between the relative good and bad companies are intended to take place. The problem, however, is to distinguish a good and a bad company. The current approach shows that, where ones costs in a certain part of the value chain are solely compared with those of others without actually taking into account differences in basic conditions, benchmarking is not as efficient as it could be. Due to very different conditions for companies to deliver water services, costs can be very different between companies for good reason. A company with rather unfavorable conditions and higher costs might be more efficient than another one with more favorable conditions and lower costs. As a result, current metric benchmarking projects seem not to fullfill the high expectations. In nearly all of the metric benchmarking projects the number of participating companies remains either constant, over time, or diminishes (ATT et al., 2011, p. 90ff.).

Current metric benchmarking approaches should, therefore, employ techniques which are able to assess costs, taking into account the relevant environmental conditions in which the company actually operates. The Data Envelopment Analysis (DEA) and the Stochastic Frontier Analysis (SFA) are the scientifically established tools, which are giving a good indication about the relative efficiency of a company. Water suppliers which are performing badly according to both DEA and SFA – given their particular, not influenceable environmental conditions – ought to have potentials to improve efficiency. Such an enhanced benchmarking can thus improve the information a company may receive from participating in a benchmarking project.

It is worth noting that such an enhanced metric benchmarking project is better displaying the relative performance of a company. It is, however, not giving advice on how a company might increase its efficiency. In order to determine the correct measures a company might participate in a process benchmarking project, install certain working groups within its company or employ consulting companies. A metric benchmarking project is thus very often a necessity for a company to deal with its own performance relative to others. After detecting certain inefficiencies, the company should encounter incentives to install programs which help in improving their performance. Time series data of a company's performance should thus be collected.

All European countries which are employing metric benchmarking systems will, therefore, sooner or later face the necessity to decide which kind of information they want to display publically and whether companies should be obliged to participate. The Netherlands, for example, made it compulsory to take part in such programs whereas Germany is very reluctant to do so.

There are also, however, other means to give incentives to companies to participate in enhanced metric benchmarking systems. Those German water suppliers, which are setting prices, are currently under the supervision of cartel offices. Currently, these regulatory

Analysis of the Current German Benchmarking

be avoided.

**4. Data set** 

over time.

Approach and Its Extension with Efficiency Analysis Techniques 277

Here, in contrast to the linear regression model, the deviation from the optimum need not be resulting purely from inefficiencies, but also from so called "White Noise". Hence, interpreting these deviations purely as efficiency potentials may be misleading and should

The aim of the Data Envelopment Analysis (DEA) is also to measure the efficiencies of respective firms relative to a threshold firm. The productivity of single entities is compared to an efficiency frontier, which is derived from a linear connection between efficient firms (so called "peers"). The DEA is a non-parametric method so that the efficiency frontier is not

In other grid-reliant sectors (like electricity, gas, telecommunications and even water supply in other countries) the DEA and SFA methods are well established, while the linear

We use the dataset of Rödl & Partner, the biggest consultancy which conducts metric benchmarking for German water supply utilities. The original data set comprised 612 observations from the years 2000 to 2007. Each of these observations contained 179 firm specific units of information. First, all observations from different years of the same company were eliminated, keeping the most current one.9 Second, all observations from before 2006 were deleted in order to minimize the problems of inflating cost data from older years to the base year of 2007. Third, all companies without any distribution network, or with mainly bulk water supply, were removed from the dataset. Fourth, all observations where crosschecks revealed inconsistencies were deleted.10 196 observations remained.

2007 served as the base year. Using the producer-price index "Water and Water Services" from the German Federal Statistical Office, the data were made comparable by restating 2006 data in terms of 2007 prices. To reach a maximum of comparability we then deducted

The sample is as close in line with the overall structure of the German water supply sector as possible. However, Figure 4 shows that the distribution, according to the size of the companies between our sample and the overall situation, differs. 30.2 % of approximately 6,400 water supply utilities (ATT et al., 2008, p. 12) in the German water sector supply more than 500,000 m³ annually. In our sample this percentage of companies, which supply more than 500,000 m³ annually, is nearly 80 %. In the whole German water supply sector 92.6 % of water output is supplied by companies with an annual water delivery of more than 500,000 m³. The figure for our sample is nearly 99 %. This implies that our sample contains relatively

9 Panel data might be interesting in the future to follow the efficiency development of a single company

10 Rödl & Partner have been very cautious to crosscheck, in particular, all cost data. No inconsistencies were found. Over time however, the set of data slightly changed. Particular older observations with

11 For our calculations in the production/treatment segment we deduct the water abstraction charges. DEA and SFA for total costs imply that concession levy, water abstraction charges and compensatory

estimated empirically but calculated by a linear optimization program.

regression model does not provide robust and consistent results.

the concession levy from the operational distribution costs.11

bigger companies than the overall German average.

payments for agriculture have to be subtracted.

lacking structural variables were, therefore, removed from the data set.

institutions are investigating those companies which have high prices per m³. This is particularly ridiculous because, due to very different conditions, a company with high water prices might be much more efficient than a company with low ones. An incentive for companies to participate in metric benchmarking projects could, therefore, be to either start investigations in companies which are not participating in metric benchmarking projects at all or which seem to be relatively inefficient at first sight. For other European countries it might be worth considering attaching the granting for subsidies to a successful participation in benchmarking projects.
