Dr. Bernhard Hartmann, A.T. Kearney, Stuttgart, Germany, Thomas Schellenberg, A.T. Kearney, Z├╝rich, Switzerland, Dr. Florian Haslauer, A.T. Kearney, Vienna, Austria, Dr. Matthias Cord, A.T. Kearney, Berlin, Germany, Arthur Ramos, A.T. Kearney, Sao Paulo, Brazil

Benchmarking is a tried and tested methodology used to define competitive position and to pinpoint improvement potential. It is increasingly used in the utilities industry, and when applied to hydropower plants, presents a number of opportunities.

In Europe and in the USA, electricity markets have been liberalized over the past few years to reduce prices for end-customers. The development started in the USA 25 years ago and 12 years later the grid and generation value-added chains were also separated in the UK. The subsequently evolving price cuts of up to 30 per cent now place high demands on utilities’ competitiveness.

Successful companies responded in the short-term with measures such as process efficiency enhancements, adaptation of the organizational structure, modification of the maintenance strategy, and the forging of strategic partnerships. International benchmarking studies have been established in the utilities industry as an essential tool to define competitive position and to systematically pinpoint efficiency enhancement potential.

Figure 1. A complexity index ensures a high degree of comparability of individual power plants
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Spurred by the initiative of the utilities industry and backed by A.T. Kearney, a wide array of comprehensive global benchmarking studies have been conducted over the past few years on all relevant business segments and in all components of the value chain. Special focus was on generation, particularly European and global best practices. Findings from these studies reveal that such best practice companies produce 40 per cent more efficiently than the average and at up to 70 per cent lower cost than worst practice companies.

Benchmarking is understood as a comparison of the individual participants’ competitiveness in comparison to an international group with the following objectives:

  • Ensure transparency of own cost position across all business processes
  • Identify and quantify strengths and weaknesses
  • Understand the relevant cost and quality drivers
  • Identify and quantify relevant approaches for improvement from best practice benchmarking
  • Develop a concrete action plan for each participant.

An international group of participants is essential to assess the participants’ competitive position against the backdrop of the increasingly tough regional market and competitive situation. Companies like AES, EDF, Electrabel, EnBW, ENEL, E.ON, Fortum, Iberdrola, Innogy, NTPC and TXU have all taken part in A.T. Kearney’s benchmarking studies.

Hydropower challenge

One special challenge is to apply A.T. Kearney’s benchmarking methodology to hydropower plants. Today around 20 per cent of the electricity generated worldwide is from hydropower plants. A broad spectrum of technologies and power plant types are used in various load sectors with very different capacities. Differentiation is made between run-of-river, storage, pump-storage, tidal, wave and mini hydropower plants.

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In addition to this technologically-driven complexity, the relevant age structure and the variable natural environment (morphology, geology, hydrology) must be taken into account. This complex situation makes it difficult to ensure data comparability. In A.T. Kearney’s methodology, that is why a number of factors (shown in Table 1) are considered.

Capturing data at any price is not recommended because plants or plant groups with less than 50 MW installed capacity and in turn low process costs (maintenance, operations, overhead) do not meet the prerequisite for comprehensive benchmarking. And exotics like tidal and glacier power stations should not be the focus of a study: due to the lack of comparative parameters, data cannot be prepared in a meaningful way.

Success factors

In 2001 A.T. Kearney conducted an extensive international benchmarking study on hydropower. This study covered 20 reservoir power stations in the Alps with a combined installed output of around 8000 MW. Typically a project of this scope is completed within 3-6 months. The duration of the project depends on the number of participants and the quality of data. The success of the study is based on the consideration of four key factors: data capture, clustering and normalization of data, presentation of results, and formulation of recommended action plans.

Data capture

As basic data, the various cost components are gathered first. In principle, operationally controllable and non-operationally controllable product costs must be distinguished. Costs that can be controlled directly encompass the areas of power plant operations, maintenance and overhead costs. Power plant operators can control these cost components directly by means of suitable business processes and the right organization as positioning factors. That is why they are of particular interest for power plant operators.

From a purely operational perspective less interesting, however, but necessary for the calculation of total costs is the inclusion of non-controllable costs. This includes water management, plant construction, as well as overlapping items such as insurance premiums. Operationally non-controllable costs often make up the largest percentage of total production costs in hydropower plants which is why they must be recorded systematically.

Alongside the cost components described, a wide array of additional data is required. This includes technical information (e.g. number of water collections, line grids, dams, buildings), employee data (e.g. target working time, days of vacation), as well as geographic data.

Personal care of benchmarking participants is fundamental for data capturing. Cases of doubt and questions should be clarified by means of personal contact and via a hotline that answers questions with professional competence. Intensive care of benchmarking participants forms the framework for high quality data, enabling errors in data capture to be excluded.

Figure 2. Exemplary approaches to improve a typical hydropower plant, that will be detailed further during the course of implementation
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Data clustering and normalization

Two steps are pivotal for evaluating data: the establishment of comparison groups (clustering) and the filtering out of country-specific handicaps (normalization).

Each hydropower plant has its own design and distinguishes itself from other hydropower plants by its geographic and hydrological situation and technical equipment. To achieve comparability and validity of data despite these differences, the power plants should be grouped in line with a complexity index.

The complexity index bundles power plants into different ‘clusters’ where comparisons are drawn. This ensures that the individual benchmarking participants are measured against the right comparison group. In doing so, the various technologies of the power plant must be taken into account. For example, core focus on storage and pump storage power plants is on hydrological systems, and in contrast on the region of operations for run-of-river power stations.

The second central theme of benchmarking is the filtering out of country-specific handicaps. In a first step, chronological peak values as well as discontinuities are determined and equalized by applying statistical methodologies. In a second step, national handicaps such as differences in working times or in wages are calculated. These factors are typically country-specific. The filtering out of these specifications from the benchmarking is what first makes it possible to compare power plant data with each other.

Figure 3. The ‘best practice’ plant reveals considerably lower costs in off-site operations as opposed to power plant A
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Presentation of results

After gathering and evaluating the data, results of the benchmarking are mapped in ‘best practice comparisons’. A company’s cost items are compared with those of the best in the group. A.T. Kearney’s methodology enables the comparison of cost categories in operations, maintenance and overhead as well as of individual cost components like costs for main administration of on-site operations and off-site operations. The high degree of detail enables improvement potential to be pinpointed and target improvement measures to be defined.

One of the key results benchmarking participants receive is a standardized and comparable analysis of their cost structure. Based on this, direct implications of the organization’s efficiency and processes can be determined. This analysis is typically developed for operations as well as for maintenance costs and overheads. Another result analysed for benchmark participants is the positioning of their own power plant as opposed to other power plants in the same cluster.

Recommended action plans

What good would pure comparative analyses be without recommended action plans? None at all! That is why tremendous importance must be placed on working out the right measures for improvement without delaying benchmarking projects. These recommendations need to be formulated so that they can be directly implemented in practice.

Success factors

As a pioneer of the benchmarking methodology in the utilities industry, A.T. Kearney can draw on extensive experience in practically all sectors of the industry. An experienced external consultant can contribute methodological know-how and expertise to the project, while also ensuring strictly confidential handling of business data.

Due to the positive experiences acquired in the international benchmarking study on storage power plants in the Alps, plans are now ongoing to extend the study to run-of-river plants.