Determinant Based Selection of Benchmarking Partners and Performance Indicators (diss.)

Researchers:

A. Sennheiser

Financed by:

The Professorship’s own resources

Motivation:

With a change in the definition of success, measurement of performance and achievements must also change. Research in the last decade has come up with a multitude of new approaches for assess-ing the long-term success of a company using performance metrics. Non-financial and qualitative performance indicators have certainly sharpened the view on corporate performance in a more holis-tic way, while, at the same time, increasing the effort required in order to gather, compare, and evalu-ate the metrics chosen. Hence, benchmarking initiatives are often criticized for promising only li-mited improvement potential, as soon as the choice of possible benchmarking partners is restricted to one industry. In addition, the influence of performance indicators on higher corporate targets is, in many cases, not intuitively comprehensible. The present work, however, focuses clearly on simplifica-tion and explanation of the influencing factors. It will thus lower the entry barrier for medium-sized enterprises to set up a corporate performance management system and to ease participation in benchmarking initiatives.

Objectives

The aim of this project is, first, to develop a concept for the efficient selection of benchmarking part-ners. Second, the aim is to elaborate an approach to identify suitable performance indicators that takes the specifics of the company’s logistics into account.
The main result of this thesis is a concept that allows companies to characterize their company 
logistics with the aid of characteristic features. It further helps the user to identify potential bench-marking partners via the assignment of a company type. With the performance indicators assigned to each company type, a basis for comparison is suggested.

Activities Completed:

A target system called the SCDD depicts influencing factors of the logistics on business success, posi-tions performance indicators in a cause and effect context. This is achieved by assigning feasible
performance indicators to target areas measuring the respective outcome measures. 
To characterize the logistics of a company, 26 characteristic determinants in the areas product, 
market, and production were defined and gathered in more than 50 companies. An aggregation of these determinants in clusters results in four company types. These types are abstract role models, grouping existing companies according to their comparability of logistical challenges. It was shown that real companies match the clusters to a high degree, and, that companies assigned to the same cluster are valid benchmarking partners.

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A heuristic that analyzes the relevance of each performance indicator, given the characteristic fea-tures of a company, was developed. This heuristic was applied to each company type, resulting in a tailored set of prioritized performance indicators for each type.
In the case studies, establishing the characteristic determinants of a company’s logistics has shown to be very intuitive for the user. The determinant values were judged to be well suited to characterize the principles of the company’s logistics and the evolving characteristic challenges. Finally, the per¬for¬mance indicators suggested for the companies were measured and rated as being highly relevant and, in many cases, critical to business success.
A software prototype helps the user to navigate through the methodology. First, the characterization of the company is supported, the assignment of the company to either one of the type is automated, and finally, performance indicators are suggested. Further, the user can perform benchmarking by comparing the company’s own performance indicator values against those stored in the benchmark-ing database.

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