Performance evaluation of forest management plans (Case study: Iranian Caspian forests)

Document Type: Research Paper

Authors

1 University of Guilan

2 Islamic Azad University - Rasht branch

Abstract

The aim of this research was to measure the relative efficiency of forest management plans in north of Iran. In order to fulfill the research, data of 12 forest management plans were collected from the financial balance sheets of Shafaroud Forest Company during a ten years period. First of all, basic Data Envelopment Analysis (DEA) models (BCC and CCR) were used to determine the efficiency. Then, due to the structure of the forest management plan, cost efficiency and revenue efficiency models based on DEA were used in order to measure the efficiency. Results indicated that 8 forest management plans were efficient based on BCC and CCR models. Furthermore, the results indicated that only one forest management plans were efficient based on cost efficiency and revenue efficiency models. These results could be due to the input oriented properties of the models, rational management and optimal use of resources.

Keywords


[Research]

Performance evaluation of forest management plans (Case study: Iranian Caspian forests)

 

J. Malaei Boosari1, S. Mohammadi Limaei*1, A. Amirteimoori2

 

1- Dept. of Forestry, Faculty of Natural Resources, University of Guilan, SowmehSara, Iran.

2- Dept. of Mathematics, Faculty of Basic Sciences, Islamic Azad University - Rasht branch, Rasht, Iran.

* Corresponding author’s E-mail: limaei@guilan.ac.ir

(Received: Jan. 23.2015, Accepted: June. 20.2015)

ABSTRACT

The aim of this research was to measure the relative efficiency of forest management plans in north of Iran. In order to fulfill the research, data of 12 forest management plans were collected from the financial balance sheets of Shafaroud Forest Company during a ten years period. First of all, basic Data Envelopment Analysis (DEA) models (BCC and CCR) were used to determine the efficiency. Then, due to the structure of the forest management plan, cost efficiency and revenue efficiency models based on DEA were used in order to measure the efficiency. Results indicated that 8 forest management plans were efficient based on BCC and CCR models. Furthermore, the results indicated that only one forest management plans were efficient based on cost efficiency and revenue efficiency models. These results could be due to the input oriented properties of the models, rational management and optimal use of resources.

 

Key words: Data envelopment analysis, cost efficiency models, revenue efficiency models, Iranian Caspian Forests.


INTRODUCTION

A forest management plan is a plan in which the amount, the location and the time of harvest along with performance duration and utilization method as well as practical and civil work is written carefully.

There are many forest management companies are involved in management of forest and wood supply in north of Iran. One of the biggest of them is Shafaroud Forest Company. This company is located in Guilan province, north of Iran. About 135000 ha of forests are managed under supervision of Shafaroud Forest Company which is located in north of Iran.  This forest is divided to 73 districts and each district is managed based on a forest management plan regulation (Shafaroud Forest Company, 2013).

One of the optimal ways of improvement and progress in forest activities is to perform the procedures and activities in these plans in order to increase efficiency. In this regard, there are different methods for measurement of efficiency of different plans in which one of the most appropriate and suitable method of measurement is data envelopment analysis (DEA). This method is a non-parametric approach that used to measure the efficiency of decision making units. In operations research and economics, DEA is used for the estimation of production frontiers. It is used to empirically measure productive efficiency of decision making units. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management, where a set of measures is selected to benchmark the performance of manufacturing and service operations. Efficiency is an important prerequisite for profitability, but maximum profits and efficiency do produce as close to the point of maximum profits as possible to ensure future productivity gains and participation. Operations working at the point of maximum profitability can offer several benefits to the wood supply system including, flexibility during periods of high and low demand, improved safety performance, improved environmental performance, and increased professionalism (Shannon, 1998).

One way to promote forestry activities in the implementation of projects and increase productivity and efficiency, is doing things right (Jason et al, 1991).

Building on the ideas of Farrell (1957), the seminal work "Measuring the efficiency of decision making units" by Charnes et al., (1978) applies linear programming to estimate an empirical production technology frontier for the first time. Therefore, DEA was introduced by Charnes et al., (1978) as an efficient model to evaluate the performance of organizations in the field of operations research. 

Many papers have been presented about DEA and various models are introduced for evaluating the performance of homogenous decision making units. DEA is used to measure the efficiency of forest industries in the various countries. For example, the use of DEA in the analysis of forestry area, forest management, operation, sawmills factories, paper mills and etc. Non-parametric models can easily combine several inputs and outputs regardless of their market values. (Kao & Yang, 1991, 1992) were in fact among the first people who used DEA to measure the efficiency of forest industries. Their research began to establish new branches in performance studies in forestry development to future, for example Joro and Viitala (1999), and Bogetoft et al (2003) on forest management, Lebel & Stuart (1998), and Hailu & Veeman (2003) on forest exploitation, Fotiou (2000), Nyrud & Baardsen (2008), Salehirad & Sowlati (2005) in sawmill factories.

Mohammadi Limaei (2013) investigated the efficiency of 14 Iranian forest companies and forest management units in north of Iran. Efficiency of the companies was estimated using a traditional DEA model and a two-stage DEA model.

Zadmirzaei & Mohammadi Limaei (2013) investigated the efficiency of Mazandaran Wood and Paper Company using DEA in Iran. Network DEA models with parallel structure were used to evaluate and measure its performance. Results showed that the company in all studied years had good performances according to the parallel models in constant and variable returns to scale (CCR, BCC models) and the scale efficiency (SE). There is not any research related using DEA in a different forest management plans at a company so far in Iran. Therefore, the aim of this research is to investigate the performance evaluation and determine the relative efficiency of Shafaroud Forest Company using DEA models to disseminate the necessary information for manager of forest plans until they will be able to adjust the units’ operating scale and become more efficient through growth.

 

MATERIAL AND METHODS

Study area

Data collection

 In this study the required data were collected from financial balance sheets and the profit-loss sheets of Shafaroud Forest Company. All forest management plans managed for 10 years periods (2001 to 2011). Nominal data of 12 forest management plan including 4 inputs (fixed costs, variable costs, area of forest management plan and stock) and 3 outputs (total revenue, profit and amount of wood production) were collected (Table 1). The nominal data were deflated using Consumer Price Index (CPI) of Iran on the base year of 2004 (Table 2).

 

Methods

CCR Model

Performance evaluation is an important task for a decision making unit (DMU) to find its weaknesses so that subsequent improvements can be made. DEA model introduced by Charnes, Cooper and Rhodes in 1978 is called CCR model. Since the pioneering work of Charnes et al. (1978), DEA has demonstrated to

be an effective technique for measuring the relative efficiency of a set of DMUs which utilize the same inputs to produce the same outputs. Suppose there are n DMUs. The kth DMU utilizes m inputs , m to produce s outputs , r = 1,. . . , s. Its efficiency  is calculated via the following CCR model (Charnes et al., 1978):

 

      

= amount of input i used by unit j,

= amount of output r produced by unit j, = the weight given to input i.Where  and  are the most favorable multipliers to be applied to the rth output and ith input for DMUk in

calculating its efficiency  and e is a small non- Archimedean quantity (Charnes et al., 1978; Charnes and Cooper, 1984) which prohibits any input/output factor to be ignored. CCR model is a constant return to scale model.

 

 

                                                                      (1)

Table 1. Forest management plans in Shafaroud Forest Company, north of Iran.

 

 

Number of plan

Name of plan

1

Avardim-9

2

Siyahbil-8 Loomir

3

Dasht Daman -8

4

Nave Asalam

5

Raze Darposht

6

Janbe Sara

7

Nave Asalm, district 1

8

Kheje Dare, district 2

9

District 16, region 9

10

District 5,region Shanderman

11

District 3,region Chafrood

12

District 2,region Chafrood

 

Table 2. The real (deflated) data.

 

 

Input

Output

Plans

 

Fixed cost (Iranian million Rials)

Variable costs(Iranian million Rials)

Area of forest management plan (ha)

Stock (m3/ha)

Total revenue(Iranian million Rials)

Profit(Iranian million Rials)

Amount of wood production(Iranian million Rials)

1

 

1705

1247

2203

45101

2219

9460

9980

2

 

8936

1280

1772

18350

1638

43000

1638

3

 

8227

1612

1353

21496

1169

1336

6327

4

 

1805

1227

3222

61750

4569

1376

9593

5

 

1134

2379

2041

22450

1781

3253

5395

6

 

3771

4039

1849

13840

1142

3563

1142

7

 

5679

4158

2252

34408

3048

1894

2474

8

 

6525

5242

1742

19650

12044

3041

4549

9

 

1474

2590

1324

17900

8680

2891

1363

10

 

6305

5357

2378

28800

2086

5475

4472

11

 

8964

3183

172

21700

2139

3353

2161

12

 

8706

4118

2240

36000

2713

7886

4917

 

BCC model

BCC introduced by Banker, Chames and Cooper (1984). The input-oriented BCC model evaluates the efficiency of  {o=1…n) by solving the following linear program form (Mehrgan, 2004):

                                                                              (2)

BCC model is a variable return to scale model. The difference between this model and CCR model is a free variable (W). In BCC model for each unit we can determine the W variable returns to scale.

If w<0, we have decreasing return to scale.

If w=0, we have constant return to scale.

If w>0, we have increasing return to scale.

 

 

Data weighting

In order to determine the appropriate weight of the data in DEA model, the questionnaires were used.

Questionnaires were prepared in order to weight the input and output data  ​​due to differences in their importance based on the interviewers views at different forest manage-ment plans. Therefore, 35 questionnaires were distributed and completed by the profess-ionals. The score of weight was from 0 to 100% in questionnaires.

The average weight gained from questionnaires is given in Table 3.

 

 

Table 3. Input and output weight of different plans.

 

input(percent)

Output (percent)

Plans number

Fixed cost

Variable costs

Area of forest management plan

Stock

Total revenue

Profit

Amount of wood production

1

62.1

63.25

31.75

59.6

60.55

74.65

64.25

2

67.65

62.2

28.75

55.7

52

71.6

56.05

3

77.3

68.85

23.45

63.1

55.95

68.55

57.8

4

75.3

76.45

31.2

59.5

62.85

74.25

59.4

5

72.8

66.5

31.7

62.25

62.35

73.3

71.3

6

70.45

68.65

28.15

55.5

61.2

70.65

65.8

7

78.55

73.55

27.05

63.35

58.8

70.6

68.45

8

71.3

66.4

31.05

60.85

58.25

68.45

67.3

9

75.45

71.65

30.5

61.1

59.3

71.3

63.8

10

73.1

69.55

27.1

68.15

54.35

59.55

56.7

11

73.4

71.55

25.85

65.6

65.85

79.4

62.95

12

70.8

62.1

24.45

55.7

57.35

70.95

61.1

 


Cost efficiency model

The cost efficiency model is presented such as:

(3)

Revenue efficiency model

The revenue efficiency model is given below:

(4)

Where  and  are the unit price of the input  and unit price of the output  of , respectively. These price data may vary from one  to another. The cost efficiency and revenue efficiency of  is defined as:

 

And

 

The cost and revenue efficiency scores are within the range of 0 and 1 (Zhu, 2008).

 

Data analysis

In order to do the analysis, first of all the basic DEA models (BCC and CCR) were analyzed using DEA Solver software (Cooper et al, 2007). Then, for advanced models (cost efficiency and revenue efficiency) GAMS software was used (Rosenthal, 1988.).

RESULTS

Results of BCC and CCR models are shown in Tables 4 and 5.

Results indicated that the forest management plans numbers (2, 4, 7, 9, 10 and 12) are efficient and their score efficiency is 1 and the rest are deficient.

As it shown in Table 5, plans numbers (2, 3, 4, 6, 7, 9, 10 and 12) are effective and the rest are deficient. Table 6, shows the input-oriented CCR model. Input and output must be modified for each forest management plan in order to reach the efficiency frontier. It means that each plan should decrease its input and increase its output. 

For example plan number 1 is not efficient and in order to reach the efficiency we should change all of inputs. For instance, the first entry is the fixed costs and the amount is 1705 Iranian million Rials (Table 2) and the company should decrease 82.05% of this input in order to be efficient.

The second input which includes the variable cost and its value is 1247 Iranian million Rials (Table 2) and the company should decrease 83.58%of this input in order to be efficient and so on. As it shown in Table 5, plans (2, 4, 7, 9, 10 and 12) are efficient.

Table 7 shows how much ineffective plans in term of percentage have to change to achieve the efficiency frontier model (BCC).

 

 

Table 4. Results of CCR model.

Plans

 

Efficiency (CCR)

 

Rank

 

Rank

 


Efficiency, Rating

1

0.522

 

7

 

0.522

2

 

1

 

6

 

1.187

3

 

0.154

 

11

 

0.154

4

 

1

 

1

 

29.094

5

 

0.409

 

9

 

0.409

6

 

0.515

 

8

 

0.515

7

 

1

 

2

 

2.940

8

 

0.096

 

12

 

0.096

9

 

1

 

3

 

1.559

10

 

1

 

5

 

1.214

11

 

0.382

 

10

 

0.382

12

 

1

 

4

 

1.396

               

Table 5. Results of BCC model.

Plans

 

Efficiency (BCC)

 

Rank

 

Rank


Efficiency, Rating

1

 

0.779

 

12

0.779

2

 

1

 

3

1.313

3

 

1

 

5

1.112

4

 

1

 

6

1

5

 

0.807

 

11

0.807

6

 

1

 

1

606.004

7

 

1

 

6

1

8

 

0.874

 

9

0.874

9

 

1

 

2

1.560

10

 

1

 

4

1.237

11

 

0.871

 

10

0.871

12

 

1

 

6

1

 

Table 6. The percentage changes of deficient plans in order to achieve the efficiency frontier model (CCR).

 

Input

 

                                            Output

Plans

Fixed cost (Iranian million Rials)

Variable costs(Iranian million Rials)

Area of forest management plan (cubic meters)

Stock (ha)

Total revenue

(Iranian million Rials)

Profit(Iranian million Rials)

Amount of wood production (Iranian million Rials)

1

-82.05%

-83.58%

-47.78%

-60.58%

0.00%

0.00%

0.00%

2

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

3

-90.96%

-84.5%1

-85.36%

-84.51%

%92.21

0.00%

0.00%

4

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

5

-82.37%

-59.02%

-71.48%

-59.02%

0.00%

0.00%

0.00%

6

-48.45%

-48.45%

-99.94%

-99.89%

0.00%

0.00%

0.00%

7

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

8

-91.85%

-98.3%5

-93.82%

-90.37%

0.00%

99.90%

0.00%

9

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

10

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

11

-80.76%

-72.5%6

-68.72%

-61.73%

0.00%

0.00%

0.00%

12

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

 

Table7. Percentage change ineffective Plans to achieve the efficiency frontier model (BCC).

 

input

output

Plans

Fixed cost (Iranian million Rials)

Variable costs(Iranian million Rials

Area of forest management plan (cubic meters)

Stock (ha)

Total revenue

(Iranian million Rials)

Profit(Iranian million Rials)

Amount of wood production (Iranian million Rials)

1

-80.41%

-74.30%

-22.05%

-44.56%

0.00%

%0.00

%0.00

2

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

3

-0.00%

-0.00%

-0.00%

-0.00%

0.00%

0.00%

0.00%

4

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

5

-55.96%

-35.42%

-19.24%

-19.24%

0.00%

0.00%

13.91%

6

-0.00%

-0.00%

-0.00%

-0.00%

0.00%

0.00%

0.00%

7

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

8

-81.28%

-67.14%

-12.60%

-12.60%

0.00%

99.90%

99.90%

9

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

10

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

11

-60.31%

-33.25%

-12.83%

-12.83%

0.00%

0.00%

51.58%

12

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

0.00%

                 

 

 

The results of cost efficiency and revenue efficiency models are shown in Table 8. Plans number 6, 7 and 12 have efficiency score of 1 or efficient in cost efficiency model. Furthermore, Plans number 3, 4, 6 and 9 are efficient in revenue efficiency model. Results also show that just plan number 6 is efficient in both cost efficiency and revenue efficiency model.

 

 

Table 8. Results of cost efficiency and revenue efficiency models.

Plans

cost efficiency

revenue efficiency

1

0.211

0.14

2

0.536

0.83

3

0.098

1.000

4

0.997

1.000

5

0.217

0.33

6

1.000

1.000

7

1.000

0.34

8

0.056

0.23

9

0.909

1.000

10

0.951

0.43

11

0.215

0.41

12

1.000

0.38

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Discussion

Evaluation of forest management plan after its implementation is an important task for forest decision makers in economics and environmental aspects. In this research the relative efficiency of Shafaroud Forest Company in Iran was determined. The advanced models of DEA (cost efficiency and revenue efficiency models) were used, according to the structure of the company and for more accurate performance assessment. The input oriented of DEA models is used for efficiency evaluation because the manufacturing units can optimally use theirs inputs by reduction of inputs as much as possible with keeping constant output level. This procedure can increase the efficiency and profitability of the company.

In fact by using the advanced models of DEA we can compare the forest management plans together and check that how the efficient forest management plans use their resources and their inputs for example forest management plan number 4 and number 9 are efficient in all of DEA basic models and they also are efficient in revenue efficiency model with 100% efficiency score.

 

Furthermore, these forest management plans have rather good efficiency score in cost efficiency model. Therefore, we can say that they use the resources and inputs in good ways and can be used as a sample for the other forest management plans. Forest management plan number 7 and number 12 are efficient in all of DEA basic models and in cost efficiency model with 100% efficiency score. These forest management plans have rather good efficiency score in revenue efficiency model. Therefore, we can say that they use the resources and inputs in good manner and can be used as a sample for the other forest management plans. (Hu et al, 2009) used DEA approach to measure cost, allocative and overall technical efficiencies of international tourist hotels (ITHs) in Taiwan during 1997–2006.The cost efficiency of these hotels is from overall technical inefficiency International tourist hotels in Taiwan have an average efficiency of 57%. Chain systems, non-metropolitan areas and occupancy rate have significantly positive impacts on all efficiency scores. The distance from the nearest international airport significantly worsens their efficiency scores. Fundamental DEA model was used to measure the productive efficiency of forest enterprises in Mediterranean Region of Turkey (Korkmaz, 2011). Results indicated that the lowest efficiency scores were evaluated to be on the employee productivity with an average value of 0.698. The inefficiency of the total productivity was thought to be due to the amount of workers and thus the amount of fees paid in big amounts. Efficiency of the Iranian forest companies was estimated using traditional DEA model and two-stage (harvesting and marketing sub-processes) DEA model (Mohammadi Limaei, 2012). Wilcoxon’s signed-rank test was used to identify the main reason of weakness between efficiency average of harvesting sub-process and marketing sub-process. Results showed that weakness performance of the companies in harvesting sub-process is the cause of their low efficiency in 2010. Färe and Grosskopf (2000) proposed a network model for measuring the efficiency of the system. However, the operation of each component of the system is treated independently, without considering the relationship among the components. Also, there is one study using parallel structure of DEA model in forestry (Kao 2009). Traditional DEA models deal with measurements of relative efficiency of DMUs regarding multiple-inputs versus multiple outputs (Tone and Tsutsui 2007). These models neglect linking activities. Traditional DEA does not make any assumptions concerning the internal operations of a DMU. Kao and Yang (1991) were, in fact, among the first to use DEA for performance measurement of forest industries. Their research started a new branch of performance studies in forestry that has since expanded. A two-stage DEA model was used for efficiency evaluation of banks (Luo 2003). Eight forest area of Taiwan performance was evaluated by the parallel model of the DEA. The results indicated that all the were inefficient, while the basic models as the only two areas had been considered inefficient, because of its higher resolution and differentiated network models with parallel structure in the inefficient units as efficient units. At this research, the input was costs, forest management area and stock. The output was total revenue, profit and amount of harvested wood. However, the results can vary if we include more input and output at the DEA models such as number of employee, spices type etc. as the input as well as the number of regeneration, growth etc. as the output. The forest companies may use the results of this research in order to increase the efficiencies of the forest management units by reducing their fixed and variables costs or increasing the profit and income.

Banker, RD, Charnes, A, & Cooper, WW, 1984, Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science, 30: 1078-1092.

Bogatoft, P, Thorsen, BJ & Strange, N, 2003, Efficiency and merger gains in the Danish forestry extension service. Forest Science, 49(4):585-595.

Charnes, A, Cooper, W, & Rhodes, W, 1978, Measuring the efficiency of decision- making units. European Journal of Operational Research, 2: 429–444.

Cooper, W, seiford, L, & Tone, K, 2007, Data Envelopment Analysis, 2nd Ed, Springer publication. 490 pp.

Charnes, A, & Cooper, W, 1984, the non-Archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and Färe. European Journal of Operational Research, 15: 333-334.

Fotiou, SI, 2000, Efficiency measurement and logistics-an application of DEA in Greek sawmills. In: Proc. Logistics in the forest sector, Timber Logistics Club, Helsinki, Finland. pp. 189-204.

Hailu, A, & Veeman, TS, 2003, Comparative analysis of efficiency and productivity growth in Canadian regional boreal logging industries. Canadian Journal of Forest Research, 33(9):1653-1660.

Hu, J, Shieh, H, Huang, CH & Chiu, N, 2009, Cost Efficiency of International Tourist Hotels in Taiwan: A Data Envelopment Analysis Application. Asia Pacific Journal of Tourism Research, 14(4): 371-384.

Johnson, KN, Franklin, JF, Thomas, JW, & Gordon, J, 1991, Alternatives for management of late-successional forests of the Pacific Northwest. A report to the Agriculture Committee and the Merchant Marine and Fisheries Committee of the US, House of Representatives, 59 pp.

Joro, T, & Viitala, E, 1999, the efficiency of public forestry organizations: A comparison of different weight restriction approaches, Interim report No, IR-99-059, Inter, Inst. for Applied Systems Analysis, Laxenburg, Austria. 19pp.

Kao, C, & Yang, Y, 1991, measuring the efficiency of forest management, Forest Science, 37(5):1239-1252.

Kao, C, & Yang, Y, 1992, Reorganization of forest districts via efficiency measurement. European Journal of Operational Research, 58(3): 356-362.

Kao, C, & Hwang, S, 2008, Efficiency decomposition in two- stage data envelopment analysis: An application to non-life insurance companies in Taiwan. European Journal of Operational Research, 185(1): 418−429.

Korkmaz, M, 2011, measuring the productive efficiency of forest enterprises in Mediterranean Region of Turkey using data envelopment analysis. African Journal of Agricultural Research, 6(19): 4522-4532.

Label, LG, & Stuart, WB, 1998, Tech, efficiency evaluation of logging contractors using a nonparametric model. Journal of Forest Engineering, 9(2):15-24.

Luo X, 2003, evaluating the profitability and marketability efficiency of large banks: An application of data envelopment analysis. Journal of Business Research, 56(8): 627−635.

Mehregan, M, & R, 2nd Ed 2004, Quantitative models for performance evaluation organization - DEA, Tehran, Tehran University Management Faculty Publishing, 173 pp.

Mohammadi Limaei, S, 2013, measuring the Efficiency of Iranian Forest Industry, Using DEA models. Journal of Forestry Research, 24 (4): 759-765.

Nyrud , AQ, & Baardsen, S 2003, Production efficiency and productivity growth in Norwegian sawmilling Scand. Journal Forest Research, 49(1):89-97.

Rosenthal, RE, 1988, Chapter 2: A GAMS tutorial, GAMS: A User's Guide, The Scientific Press, Redwood City, California.

Salehirad, N, & Sowlati, T, 2005, Performance analysis of primary wood producers in British Columbia using data envelopment analysis. Canadian Journal Forest Research, 35(2):285-294.

Shannon, J, 1998, Productivity, Cost and Technical Efficiency Evaluation of Southeastern U.S. Logging Contractors, MSc thesis, Faculty of the Virginia Polytechnic Institute and State University, Virginia, USA, 165 pp.

Zadmirzaei, M, Mohammadi Limaei, S, & Amirteimoori A, 2013, Determining of relative efficiency based on Data envelopment analysis (Case study: Mazandaran Wood and Paper Company in Iran), International Advances in Engineering and Technology, 15:200-211.

Zhu, J, 2002, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Excel Solver”, Norwell, MA: Kluwer Academic Publishers Group, International Series in Operations Research & Management Science, Vol. 126.