**Author details**

Through comparisons by DEA methods it is possible to determine the greatest achievements which are objectively feasible for the most important natural and financial business segments and the total business results, but also to identify the resources whose use, taking into account the objective circumstances, isn't efficient enough. In addition, this approach allows detection of possible improvements in the business, but also the sources of the failure in business management. Based on the presented research of business performance evaluation in the paper it is considered that the application of DEA in forestry could be, as well as in many other

As for the disadvantages and limitations of DEA, one of the major drawbacks of DEA method is low discrimination of in/efficient units in the upper range of efficiency. Specifically, the number of single-efficient units increases with the number of input and output variables. The number of decision making units considerably larger than the number of variables (n >> m + t) is not always sufficient enough for a 'harsher' i.e. more severe distinction of efficiency. The reason for that partly lies in the flexibility of the method and the described way of determining the weights of inputs and outputs. In order to overcome this problem, several different models have been developed like "Cone-Ratio Method", "Assurance Region Method" and "Proportion-

Another limitation is the overall complexity of the method. Since the standard formulation of DEA model calculates separate linear program for each compared unit, extensive comparisons can be computationally intensive. Therefore, the model can seem quite complex and less attractive. Furthermore, DEA method is good in estimating "relative" efficiency, but it stretches very slowly the absolute efficiency. In other words, the analysis shows how efficient a particular decision making unit is in comparison to other units, but not how successful the DMU is compared to the "theoretical maximum". One of the main disadvantages of DEA method is its sensibility to extreme observations and random errors. The basic assumption is that there are no random errors and that all deviations from efficiency frontier represent

The advantage of DEA methodology over traditional techniques (i.e. multiple regression, stochastic frontier) is in the comparison of units with multiple inputs and outputs, whereby they can be expressed in different units of measure. Furthermore, the selected inputs and outputs are assumed to have a correlation, however it is not necessary to know the explicit form of this correlation. DEA enables a direct comparison of the DMU with other units or a combination of units with similar work/production technologies and similar tasks. Using the best units as the reference values (benchmarks), DEA indicates to inefficient units what changes in their resources are needed in order to improve their business performance.

In this paper the relative efficiency of organisational units of 'Croatian Forests' ltd is calculated based on CCR and BCC output-oriented DEA models. Shares have been determined of projected values of inputs and outputs in empirical values, as well as sources and amounts of inefficiency. Scale efficiency of Forest offices has also been determined. The effect of structural characteristics on relative efficiency of forest offices is determined, and so is the average

efficiency of Forest administrations and geographic regions.

business systems, a very strong support to planning and decision-making.

based Weights" [19].

472 Computational and Numerical Simulations

inefficiency.

Mario Šporčić\* and Matija Landekić

\*Address all correspondence to: sporcic@sumfak.hr

University of Zagreb, Faculty of Forestry, Department of Forest Engineering, Zagreb, Croatia
