**4. Discussion and conclusions**

In this way it was determined that the efficiency frontier was on average most frequently determined by Forest offices of Forest administrations A and C (CCR and SE model) i.e. Forest

ABCDEF G H

ABCDEF G H

Forest administration

Forest administration

I II III IV

The average relative efficiency of forest management in different geographical regions is also calculated as the weighted (by areas) mean efficiency of Forest offices situated in individual regions. The highest average efficiency was achieved in the area (I) lowland flood-prone forests – 0.907, somewhat lower in the area (II) hilly forests of the central part and area (III) moun‐ tainous forest – 0.862 and 0.890, and the lowest in the area (IV) Karst/Mediterranean area – 0.773, according to the CCR model. According to the BCC model, the average efficiency of lowland, hilly and mountainous forest offices is 0.924, 0.874 and 0.899, respectively, while the

 The average relative efficiency of forest management in different geographical regions is also calculated as the weighted (by areas) mean efficiency of Forest offices situated in individual regions. The highest average efficiency was achieved in the area (I) lowland flood-prone forests – 0.907, somewhat lower in the area (II) hilly forests of the central part and area (III) moun‐ tainous forest – 0.862 and 0.890, and the lowest in the area (IV) Karst/Mediterranean area – 0.773, according to the CCR model. According to the BCC model, the average efficiency of lowland, hilly and mountainous forest offices is 0.924, 0.874 and 0.899, respectively, while the

 The average relative efficiency of forest management in different geographical regions is also calculated as the weighted (by areas) mean efficiency of Forest offices situated in individual regions. The highest average efficiency was achieved in the area (I) lowland flood-prone forests – 0.907, somewhat lower in the area (II) hilly forests of the central part and area (III) moun‐ tainous forest – 0.862 and 0.890, and the lowest in the area (IV) Karst/Mediterranean area – 0.773, according to the CCR model. According to the BCC model, the average efficiency of lowland, hilly and mountainous forest offices is 0.924, 0.874 and 0.899, respectively, while the

Geografic region

Geografic region

I II III IV

CCR BCC SE

CCR BCC SE

CCR BCC SE

CCR BCC SE

1 In this way it was determined that the efficiency frontier was on average most frequently 2 determined by Forest offices of Forest administrations A and C (CCR and SE model) i.e. Forest

1 In this way it was determined that the efficiency frontier was on average most frequently 2 determined by Forest offices of Forest administrations A and C (CCR and SE model) i.e. Forest

administrations G and H according to the BCC model (table 3).

3 administrations G and H according to the BCC model (table 3).

3 administrations G and H according to the BCC model (table 3).

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

32 **Figure 7.** Average relative efficiency of Forest administrations

**Figure 7.** Average relative efficiency of Forest administrations

32 **Figure 7.** Average relative efficiency of Forest administrations

Relative efficiency

Relative efficiency

20 Computational and Numerical Simulations

470 Computational and Numerical Simulations

20 Computational and Numerical Simulations

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

**Figure 8.** Average relative efficiency by geographic regions

5 **Figure 8.** Average relative efficiency by geographic regions

5 **Figure 8.** Average relative efficiency by geographic regions

Relative efficiency

Relative efficiency

4

4

31

31

In this very dynamic period of management of natural resources, when forest experts face the challenges of professional and responsible management of forests and forest land, having to observe at the same time the protection requirements of their ecological, social and economic functions, as well as challenges of profitable management of forestry companies, managers need different models for converting the accounting and financial data into useful information. In this paper the models of Data Envelopment Analysis were applied for the assessment and comparison of organisational units in croatian forestry. In applying these models, a number of variables can be taken into consideration, so as to obtain a more comprehensive indicator for evaluating business activities of organisational units in forestry.

Organizational units in forestry, besides final 'products' (volume of the harvested wood, length of the constructed forest roads, renewed forest areas etc.), provide through forest management a range of services and beneficial functions that forests offer to users. Because of that the efficiency of forestry units is more difficult to assess than the efficiency of the ordinary production units which are dealing with simple commodity production. Specifically, it is difficult to quantify the amount of resources (inputs) that are needed to 'produce' a certain amount of such services and the general goods. It is also difficult to quantify the amount of these outputs. Thus, a common feature of the organizational units in forestry is that a part of their output consists of services and general benefits, most of which are difficult to express materially. The business analysis in forestry requires that such 'intangible' outputs are in the best way possible replaced by other more easily accessible and measurable substitute variables. Comprehensive business analysis also imposes the need to use multiple methodologies and models which together can give more integral description of production and business results and provide better performance indicators.

In this paper, Data envelopment analysis is presented and used for the evaluation and comparison of forestry organizational units' performance i.e. efficiency of Forest offices. DEA represents methodology which at the same time considers multiple variables, so that it can provide a more comprehensive measure of business conduct in forestry. As a technique for measuring productivity and efficiency DEA experienced wide usage in many areas. However, in the field of natural resource management it is still not represented enough. In the forestry literature there is only a limited number of papers based on the determination of the efficiency by nonparametric techniques such as DEA. This as well as other non-traditional methods should yet to be introduced and accepted in forestry as a management tool on both strategic and operational level of planning and decision-making.

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

On the average, global technical efficiency obtained by CCR model amounts to 0.829. Local pure technical efficiency, obtained by BCC model is 0.904, and scale efficiency is 0.919. A higher level of efficiency is averagely achieved by forest offices with an area from 10 to

efficiency is achieved by units in continental regions. The analysis of amounts and causes of inefficiencies shows that inefficiency is more significantly affected by outputs O2 and

DEA solutions and the results of relative efficiency like the ones in the presented re‐ search can be interesting to forestry experts, managers and researchers due to three

**•** Improvements proposed by the model to inefficient units are based on achieved results of

**•** Considering the problems with DEA is an alternative and indirect approach to specifying abstract statistical models and decision making based on residual analysis or analysis with

In this way, DEA with its characteristics can become a new management tool in forestry which can be used for the analysis of business efficiency that enables a new approach to organization and data analysis, cost-benefit analysis, estimation of the frontier and the

Undoubtly, additional research is required to generalise the evidence provided in this study, in particular regarding the explanation of the underlying differences in the use of particu‐ lar inputs and the production of certain outputs that could improve efficiency of forest management units. Nevertheless, some interesting insights regarding the performance of the forest management units in Croatia may have been provided. It is also considered that by the development and application of Data envelopment analysis and other models of multi-criteria decision making, it is possible to enrich the forestry science and practice by an approach that should provide easier analysing, planning and predicting in forest

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

**•** Characterisation of each organisational unit by a single result of relative efficiency,

/ha. A relatively higher

http://dx.doi.org/10.5772/57042

473

Nonparametric Model for Business Performance Evaluation in Forestry

15,000 hectares and with the growing stock from 200 to 300 m3

O3 (allowable cut and investments).

units that manage their business efficiently,

theory of learning from the most successful ones.

and Matija Landekić

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

properties of this method:

coefficients – parameters.

management.

**Author details**

Mario Šporčić\*

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 "Proportionbased Weights" [19].

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 inefficiency.

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.

On the average, global technical efficiency obtained by CCR model amounts to 0.829. Local pure technical efficiency, obtained by BCC model is 0.904, and scale efficiency is 0.919. A higher level of efficiency is averagely achieved by forest offices with an area from 10 to 15,000 hectares and with the growing stock from 200 to 300 m3 /ha. A relatively higher efficiency is achieved by units in continental regions. The analysis of amounts and causes of inefficiencies shows that inefficiency is more significantly affected by outputs O2 and O3 (allowable cut and investments).

DEA solutions and the results of relative efficiency like the ones in the presented re‐ search can be interesting to forestry experts, managers and researchers due to three properties of this method:


In this way, DEA with its characteristics can become a new management tool in forestry which can be used for the analysis of business efficiency that enables a new approach to organization and data analysis, cost-benefit analysis, estimation of the frontier and the theory of learning from the most successful ones.

Undoubtly, additional research is required to generalise the evidence provided in this study, in particular regarding the explanation of the underlying differences in the use of particu‐ lar inputs and the production of certain outputs that could improve efficiency of forest management units. Nevertheless, some interesting insights regarding the performance of the forest management units in Croatia may have been provided. It is also considered that by the development and application of Data envelopment analysis and other models of multi-criteria decision making, it is possible to enrich the forestry science and practice by an approach that should provide easier analysing, planning and predicting in forest management.
