Benchmarking Pitfalls

Some of the major pitfalls identified with benchmarking include the costs associated with its implementation, the lack of predictive power, the potential difficulty in obtaining information for which to benchmark against, and a significant lag time between implementation and results. 

Compared with other types of analysis techniques, benchmarking does not have any predictive power to anticipate future performance, results, or benefits (Holloway, Lewis and Mallory 1995, 148). Although great care may have been taken in the analysis of other companies and the activities they developed to reach certain performance levels, there is no guarantee that those same activities will yield the same results in another organization. The differences may be due to a number of reasons, including a perceived cause and effect between performance and the activities that really does not exist, differences in the organizations safety climates, and differences in the exposure levels to various hazards in the workplace. 

Costs have also been identified as a potential problem with benchmarking. When done properly, the benchmarking process requires a substantial amount of time and resources analyzing other companies data and activities, developing and implementing new activities in the workplace, and the ongoing continual improvement process of data collection, analysis, and modifications. 

A third area that can become a problem for benchmarking is the availability of valid data from other organizations. In order to establish effective benchmarks, one must know the performance levels of other organizations. It may be difficult to obtain the necessary data from other companies, and if it is obtained, comparisons may be difficult due to differences in measurements used across industries. 

Fourth, the benchmarking process requires a considerable amount of time from when the decision is made to benchmark to when measurable results are realized. As with any safety program activity designed to improve performance, the results of the performance may not be evident for some time. In the case of measuring activities designed to increase the workers use of personal protective equipment, results may be evident in a relatively short period of time following the implementation of the program. Supervisors can go out to the production areas and observe worker performance. However, in the case where the activity is supposed to reduce the number of recordable injuries, the lag time may be a year before the decrease in cases can be substantiated. 

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While there are many services in the market that have evolved to share benchmark data, there are many pitfalls. Companies need to... 

After considering the available options in the market, and judging them against these pitfalls, most companies will find that benchmark data available in the industry is expensive and usually not of a great value. 

This research uses minimum-variance instead of the mean-variance approach to create a benchmark. There are several reasons for implementing the minimum-variance instead of originally proposed mean-variance approach. One of the disadvantages of mean-variance is the requirement of choosing the expected return, which is hard to estimate. Errors in estimation of that parameter lead to inefficient portfolios. As a consequence, weights become highly unstable. The other pitfall of the mean-variance approach is the sensitivity to small changes in the mean returns of portfolio s assets. Michaud [6] concludes that mean-variance method is the error-maximization method. In order to avoid the problems connected to mean-variance optimization, we concentrate on the minimum-variance portfolio. 

Using these techniques, it has generally been found that both QCs and CMAs are stable against reconstruction or surface segregation they are usually found to be bulk-terminated, except for interplanar relaxation. The terminating plane, or set of closely spaced planes, is usually rich in the element with the lowest elemental surface energy (A1 in the case of Al-rich QCs and CMAs). These conclusions draw heavily on comparison with bulk structural models. However, using bulk models as benchmarks to interpret surface structure for a QC usually involves subtleties and pitfalls that are not present for a more typical crystalline material. Chemical decorations of local motifs is one of the main such features. 

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