**6. Additional value for companies**

The problem of current metric benchmarking has been to distinguish between a good and a bad company. So far a water supplier with low costs per m³ is supposed to be a good company. Such a company might however face very favourable conditions which would actually be the reason for this low relative costs compared to other operators. Applying the concept "Learning from the Best" is thus misleading. As mentioned earlier the same holds true for the classical OLS. If a company is, however, good according to both the DEA as well as the SFA (in Figure 5: the company marked by the bottom arrow) this operator would quite surely be an interesting candidate for a discussion with those laggards which should have rather high efficiency potentials (in Figure 5 the company marked by the upper arrow).

Besides, in better identifying a good and a bad company, this approach also helps to quantify an efficiency potential. This potential can of course be displayed in various ways. It is possible to list the DEA-, the SFA- or for example the average of DEA- and SFA-result. The table below could thus be read as follows: Whereas no. 79 is the most efficient company and thus encounters only minor options to decrease operational distribution costs, no. 136 seems to face major inefficiencies. Approximately 2.3 million € per year could be saved according to these first calculations.

<sup>15</sup> The rank correlation results for the medium companies are 75% and for the small ones 41%. To put these values in context with rank correlations of other sectors and other countries see Sumiscid AB (2007, p. 34). As seen there, first rank correlations from German energy models are between 70 and 75%. This value is said to be very high compared to other models used internationally, like for example in Sweden with rank correlations of 40%.

ln (Distribution pipes) 2,019\*\*\* 0,188

(excl. re-distribution)) 0,509\*\*\* 0,186

Table 4. SFA-Model Small Companies (< 0.5 mill. m³ per year) for operational distribution

The DEA and SFA results for the largest group of companies are plotted in the figure below to verify consistency of the efficiency analyses. Because the efficiency measures are not always comparable due to the different methods used, the rank correlation of the results are determined (according to Spearman) and plotted. A value of, for example, 0,78, means that

The problem of current metric benchmarking has been to distinguish between a good and a bad company. So far a water supplier with low costs per m³ is supposed to be a good company. Such a company might however face very favourable conditions which would actually be the reason for this low relative costs compared to other operators. Applying the concept "Learning from the Best" is thus misleading. As mentioned earlier the same holds true for the classical OLS. If a company is, however, good according to both the DEA as well as the SFA (in Figure 5: the company marked by the bottom arrow) this operator would quite surely be an interesting candidate for a discussion with those laggards which should have rather high efficiency potentials (in Figure 5 the company marked by the upper arrow). Besides, in better identifying a good and a bad company, this approach also helps to quantify an efficiency potential. This potential can of course be displayed in various ways. It is possible to list the DEA-, the SFA- or for example the average of DEA- and SFA-result. The table below could thus be read as follows: Whereas no. 79 is the most efficient company and thus encounters only minor options to decrease operational distribution costs, no. 136 seems to face major inefficiencies. Approximately 2.3 million € per year could be saved

<sup>15</sup> The rank correlation results for the medium companies are 75% and for the small ones 41%. To put these values in context with rank correlations of other sectors and other countries see Sumiscid AB (2007, p. 34). As seen there, first rank correlations from German energy models are between 70 and 75%. This value is said to be very high compared to other models used internationally, like for example in

the ranks of a firm resulting from DEA and SFA analyses correlate with 78%.15

Table 3. SFA-Model Medium Companies (0.5-2.5 mill. m³ per year) for operational

ln (Number of household connections) 1,390\*\*\* 0,048

**Variable Coefficient Standard Deviation** 

**Variable Coefficient Standard Deviation** 

0,6709\*\* 0,283

The best models for the two other groups of companies are as follows:

ln (Distribution pipes per household connection)

ln (Distribution pipes to accounted water

**6. Additional value for companies** 

according to these first calculations.

Sweden with rank correlations of 40%.

distribution costs

costs

Fig. 5. Rank comparison DEA and SFA, Cluster "Large companies" (Bottom [Upper] arrow: Relatively efficient [inefficient] companies according to both DEA and SFA]


Table 5. Efficiency analysis techniques and implications for individual efficiency potential

We would certainly always suggest to not only analyze the results of the "operational distribution costs". It is worth doing the same calculations for "total costs" and the remaining sub-categories "operational production costs", "capital costs" and "administration costs". In such a way potential trade-offs between, for example, operational and capital costs can be observed and interpreted. In addition, an analysis would also need to take into account different quality provision between companies which would need to be backed by willingness-to-pay-studies.
