Bench­mar­king / Ef­fi­ci­en­cy ana­ly­sis

Do you know how efficient individual or all of your company's division are? Using multidimensional methods that also account for external influences, we assess your company’s performance against the industry as a whole. We determine sensible tariffs, and check benchmark calculations of public authorities within a regulatory context.

Com­pa­ny bench­mar­king

An objective assessment of the successes of a company's individual divisions or branches clearly shows failures and potential for improvement. Some of the methods that can be used for this purpose are not at all simple, despite the questions often sounding easy: What are the causes of varying profitability? Are sales offices in the country more profitable than those on the outskirts of cities? Which operational size yields the maximum profit or creates minimum costs? How do we need to use resources internally so that the company’s divisions automatically minimise inefficient structures? Which divisions have synergies? We can provide you with answers to these questions based on real-life situations thanks to our data pool and method competence.

Re­gu­la­to­ry bench­mar­king

The use of efficiency measurements and benchmarking of regulatory aspects helps regulators to determine the permissible profit of a regulated company. The electricity and gas networks in many countries (natural monopolies) are subject to government incentive regulations, which specify the caps for income and profits. Such benchmarks are based on efficiency comparisons, in which the most cost-efficient companies serve as a yardstick. But even the healthcare sector increasingly uses efficiency measurements, particularly to determine the tariffs for services in the outpatient, inpatient and care sectors. 

Our ex­per­ti­se

One-dimensional performance indicators (such as sales per employee, costs per kilometre of rail network) quickly lead to misleading results when comparing companies, business units or branches. Despite providing a good overview of individual production processes, they do not reflect the operational efficiency of the entire company with sufficient reliability. We are convinced that highly reliable conclusions can only be drawn from comparisons with the best by using the differentiated benchmarking methods. 

We specialise in such multi-dimensional methods and develop them on a mathematical and economic basis. Depending on the question, we apply different methods to evaluate the efficiency of the monitored companies or units. To increase the reliability of the findings, we also examine the robustness of the models used. We also analyse the importance of individual cost drivers in the overall company or in individual processes.

Your be­ne­fits

Our efficiency and benchmarking calculations can be used for checking and, if necessary, questioning the specifications of the regulatory authorities. Thanks to our long-standing practical experience, we indicate which companies or company units perform better with regard to certain aspects and where there is room for improvement.

Some of our projects

Efficiency measurement – energy networks

Incentive regulation Germany, electricity/gas network operators, data pool

Measuring the efficiency of energy networks for German electricity and gas distribution system operators. Using the provided data pool for the strategic orientation and positioning of network operators as well as in legal proceedings.

Efficiency in transmission networks

Electricity TSO, international benchmarking, regulatory strategy

Helping a group of European transmission network operators perform an international efficiency comparison. For presentation of statement to the regulatory authorities and determining their position.

Methods for measuring efficiency

Data envelopment analysis, stochastic frontier analysis, Bayesian

Developing existing methods for measuring efficiency (data envelopment analysis and stochastic frontier analysis) with the use of Bayesian methods, on the basis of data from national and international electricity and gas network operators.

Nemo data pool for gas distribution networks

Swiss gas supplier, regulation, data pool

The Nemo data pool for Swiss gas network operators provides you with the opportunity to have your data checked for plausibility in the “Nemo cost tool”. This helps to identify data errors and irregularities in your costing and rating. The performance indicator and tariff comparison report indicates structural irregularities and potential for improvement. You get answers to costing questions (cost structure and cost keys) and a suggestion for a strategic analysis and management of your own company (asset management, evaluation, development). The tariff comparisons can be used for assessing the network price and differentiation compared with other local network operators.

Spitex benchmarking platform

Data pool, service provider

The Spitex benchmarking platform provides the data bases for needs-based care It supports operational management with intuitive key figures and strengthens the negotiating position of the organizations vis-à-vis social insurance companies and residual financing partners through improved cost transparency. It was developed as part of an Innosuisse project by Polynomics in collaboration with Lucerne University of Applied Sciences and Arts, Heyde AG, and practical partners. The content has been made available via the web portal since summer 2023.

VSE/AES Datenpool for electricity distribution networks

Swiss electricity providers, sunshine regulation, basic supply, data pool

The VSE/AES Datenpool© project has created numerous performance indicator analyses for electricity distribution network operators in Switzerland. Thanks to the participation in this data pool, you can control and optimise your costs in the areas of energy network and basic supply. It makes it possible for you to determine and understand your own position compared with similar companies (e.g. also for the sunshine regulation) and to justify your situation to the Federal Electricity Commission (ElCom).

Polynomics Benchmarking Platform

Incentive Regulation Germany, Electricity and Gas Network Operators, Data Envelopment Analysis, Stochastic Frontier Analysis

The Polynomics Benchmarking Platform (PolyBench) is a web-based application for performing benchmark and efficiency analyses based on data published by the BNetzA. With PolyBench, users can easily and clearly compare data with each other and determine the effects of various corporate scenarios on their own efficiency value.

For more information and to register for a test access, please refer to the flyer.

Dr. Tobias von Rechenberg

+41 62 205 15 73

Andreas Hauck

+41 62 205 15 72

Partners

Prof. Dr. Gert Brunekreeft, Jacobs University Bremen, Germany - Prof. Dr. Klaus Gugler, Vienna University of Economics and Business Administration, Austria - Prof. Dr. Mario Liebensteiner, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany

Publications

Po­ly­no­mics Fall­pau­scha­len­mo­dell

Almost ten years after the introduction of the SwissDRG system, there are still differences between hospitals in the severity-adjusted case costs. These are not only due to differences in the efficiency of the hospitals, but can also be based on differences in the level of services provided or the complexity of the patient structure. The Polynomics ««Fallpauschalenmodell» determines the average cost influences for selected patient characteristics and service structures of the hospitals on the basis of the inpatient cases of the FSO's case cost statistics. With these average cost influences, the service-related additional or lower costs compared to the Swiss average can be determined for any hospital, which arise due to the hospital-specific patient characteristics and service structures, but which are insufficiently represented in the SwissDRG system. The deviations determined can be used for the base rate negotiations.Link to the study

Im­pact of choice of the per­cen­ti­le in hos­pi­tal bench­mar­king

On behalf of the VZK we analyse the impact of choice of the percentile in benchmarking of the acute inpatient hospitals. Our analyses using data from existing benchmarking show that below a low percentile hospitals with specific characteristics are systematically under- and overrepresented. This increases the risk that the benchmark is not set by an efficient hospital, but by one that has lower costs due to patient and/or service selection. In the medium term, this could threaten the security of care, since even efficient hospitals would be underfinanced and would therefore have to close or at least reduce their services A more homogeneous data base or the use of a higher percentile partly solves this problem. For a sustainable hospital landscape, it is also important that hospitals have sufficient time to adjust their costs to new benchmark. This should be taken into account when determining the percentile for the benchmark.

Fai­re Ab­gel­tung von Hoch­kos­ten­fäl­len in DRG-Sys­te­men - In­ter­na­tio­na­le Er­fah­run­gen und Lö­sungs­kon­zep­te

In der Studie im Auftrag des UniversitätsSpital Zürich (USZ) zeigen wir Lösungskonzepte in sechzehn Ländern im Umgang mit Hochkostenfällen in DRG-Vergütungssystemen auf.

Angebote

En­er­gy sup­p­ly

He­alth­ca­re

Fi­nan­cial sec­tor

Me­dia and te­le­com­mu­ni­ca­ti­ons

Com­pe­ti­ti­on eco­no­mics

Re­gu­la­to­ry eco­no­mics and re­gu­la­to­ry im­pact ana­ly­sis

Eco­no­mic po­li­cy ana­ly­ses

Da­ta ana­ly­sis

Bench­mar­king / Ef­fi­ci­en­cy ana­ly­sis

Cos­ting and pri­cing

Pre­fe­rence me­a­su­re­ment / Dis­crete choice ex­pe­ri­ment