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UNIVERSITI PUTRA MALAYSIA

DECISION SUPPORT SYSTEM MODEL FOR SPECIES SELECTION IN FOREST PLANTATIONS IN MALAYSIA

SHAFINAH BINTI KAMARUDIN

FSKTM 2008 17

DECISION SUPPORT SYSTEM MODEL FOR SPECIES SELECTION IN

FOREST PLANTATIONS IN MALAYSIA

By

SHAFINAH BINTI KAMARUDIN

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia,

in Partial Fulfilment of the Requirement for the Degree of Master of Science

August 2008

Abstract of thesis presented to the Senate of Universiti Putra Malaysia in partial fulfilment of the requirement for the degree of Master of Science

DECISION SUPPORT SYSTEM MODEL FOR SPECIES SELECTION IN

FOREST PLANTATIONS IN MALAYSIA

By

SHAFINAH BINTI KAMARUDIN

August 2008

Chairman : Associate Professor Mohd Hasan Selamat

Faculty : Computer Science and Information Technology

Decision Support System (DSS) is a computer technology that is used to support

decision making and problem solving processes. It was first implemented in forestry

in early 1980s mainly for temperate forests but few for Malaysian forests. A DSS

model for forest plantation in Malaysia is therefore needed because the forest

plantation programmes is rapidly taking place. The objectives of this study are to

propose a specific DSS model for supporting decision making process in selecting

appropriate suitable species based on site conditions and economic parameters, and

to develop a prototype based on the initial designed DSS model.

In this study, four tree species were used as data samples to develop the model and

these are Azadirachta excelsa, Acacia mangium, Hevea brasiliensis and Tectona

grandis. An economic module was also incorporated in the model giving users the

ability to make predictions on potential cost and benefit margins of the four timber

species to be planted. The prototype was developed using Windows platform with

Oracle JDeveloper. Java programming language was used to develop this prototype.

ii

The development of the prototype follows the initial designed of the DSS model. A

survey was also conducted using questionnaire as well as interviews to evaluate the

usability of this model. Eight usability factors used in the survey include efficiency,

understandability, operability, attractiveness, error prevention, learnability, accuracy

and effectiveness.

Results from the survey indicated that the user satisfaction index for all usability

factors of the DSS model was 0.43. This implies that the respondents were quite

satisfied with the model. Furthermore, this study also shows that the factors of

understandability, attractiveness and operability of the model have the highest user

satisfaction index compared to other usability factors. Results from the interview

session reflect some weaknesses of this model and suggestions were given to

improve the model.

In conclusion, the objectives of this study which are to develop a DSS model and a

prototype for Malaysian forest plantation management were achieved. The DSS

model is usable to support the decision making process for commercial tree planting

in Malaysia. The economic parameters gave new insight on costing and the

environmental parameters also complement the DSS model. This study revealed that

respondents were quite satisfied with the proposed DSS model. However, additional

environmental parameters particularly slope elevation and drainage need to be

included. Other tree species and the use of weighing method should also be included

to improve the DSS model.

iii

Abstrak tesis yang dikemukakan kepada Senat of Universiti Putra Malaysia sebagai memenuhi sebahagian keperluan untuk Ijazah Master Sains

MODEL SISTEM SOKONGAN KEPUTUSAN UNTUK PEMILIHAN

SPESIES BAGI HUTAN LADANG DI MALAYSIA

Oleh

SHAFINAH BINTI KAMARUDIN

Ogos 2008

Pengerusi : Professor Madya Mohd Hasan Selamat

Fakulti : Sains Komputer dan Teknologi Maklumat

Sistem Sokongan Keputusan (SSK) adalah teknologi komputer yang digunakan

untuk menyokong pembuatan keputusan dan penyelesaian masalah. Pertama kali ia

dilaksanakan dalam bidang perhutanan pada awal tahun 1980an terutamanya untuk

hutan beriklim sederhana tetapi hanya sedikit untuk hutan Malaysia. Model SSK bagi

hutan ladang di Malaysia diperlukan kerana program hutan ladang sedang

berkembang dengan pesat. Objektif kajian ini adalah untuk mencadangkan model

SSK yang spesifik bagi menyokong proses pembuatan keputusan dalam pemilihan

spesies yang bersesuaian berdasarkan keadaan tempat dan parameter ekonomi, dan

untuk membangunkan prototaip yang berasaskan rekabentuk asal model SSK.

Dalam kajian ini, empat spesies pokok digunakan sebagai persampelan data dalam

proses pembangunan model iaitu Azadirachta excelsa, Acacia mangium, Hevea

brasiliensis dan Tectona grandis. Modul ekonomi juga digabungkan dalam model

ini membolehkan pengguna membuat anggaran bagi potensi margin kos dan

keuntungan bagi keempat-empat spesies untuk ditanam. Prototaip yang dibangunkan

iv

menggunakan platform Windows dengan Oracle JDeveloper. Bahasa pengaturcaraan

Java digunakan untuk membangunkan prototaip ini. Prototaip ini dibangunkan

berasaskan model SSK. Kaji selidik dijalankan dengan pengagihan borang soal

selidik dan juga temubual dilaksanakan untuk menilai terhadap tahap kebergunaan

model SSK. Lapan faktor kebergunaan digunakan dalam kaji selidik ini iaitu

keberkesanan, pemahaman, operasi, tarikan, penghalang kesilapan, kebolehan

belajar, ketepatan dan kecekapan.

Keputusan dari kajian ini menunjukkan bahawa indeks kepuasan pengguna bagi

keseluruhan faktor kebergunaan model SSK adalah 0.43. Ini menunjukkan responden

agak berpuas hati dengan model yang dicadangkan. Seterusnya, keputusan ini juga

menunjukkan faktor pemahaman, tarikan dan operasi bagi model yang dicadangkan

mempunyai indeks kepuasan pengguna yang tertinggi berbanding faktor

kebergunaan yang lain. Keputusan dari sesi temubual memberi refleksi terhadap

kelemahan model dan cadangan untuk memperbaiki model SSK diterima.

Kesimpulannya, objektif kajian untuk membangunkan model SSK dan prototaip bagi

hutan ladang Malaysia telah dicapai. Model SSK membantu dalam proses pembuatan

keputusan bagi penanaman pokok komersial di Malaysia. Penambahan parameter

ekonomi memberikan pendekatan baru dalam penilaian kos disebalik faktor-faktor

parameter alam sekitar yang juga melengkapi model ini. Hasil kajian menunjukkan

responden agak berpuashati dengan model SSK. Namun begitu, penambahan

parameter alam sekitar khususnya darjah kecerunan dan saliran adalah diperlukan.

Spesies pokok lain dan penggunaan pemberat juga perlu untuk ditambah bagi

memperbaiki model SSK.

v

ACKNOWLEDGEMENTS

Alhamdullillah, I have finally completed this study. I would like to extend my

deepest gratitude and appreciation to my supervisors, Assoc. Prof. Mohd. Hasan

Selamat, Prof. Dato’ Dr. Nik Muhamad Nik Ab. Majid, Assoc. Prof. Dr. Awang

Noor Abd. Ghani and Dr. Rusli Abdullah for their guidance, advice and support.

I would like to thank my colleagues and friends for their help, ideas and knowledge

shared when we were having our valuable discussions during the course of this

study.

Many thanks go to Faculty of Computer Science and Information Technology

members, Faculty of Forestry members, foresters from Department of Forestry of

several states and Forest Research Institute Malaysia (FRIM) for their cooperation.

Finally, I would like to share this moment of happiness with my parents, my family

and my beloved husband, Mohd Ikram Mohammad for their encouragement and

support all these years.

vi

I certify that an Examination Committee has met on 28 August 2008 to conduct the final examination of Shafinah Binti Kamarudin on her Master of Science thesis entitle “Decision Support System Model for Specific Species Selection in Malaysian Forest Plantations” in accordance with Universiti Pertanian Malaysia (Higher Degree) Act 1980 and Universiti Pertanian Malaysia (Higher Degree) Regulations 1981. The Committee recommends that the student awarded the degree of Master Science. Members of the Examinations Committee were as follows:

Hamidah Ibrahim, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Chairman) Md. Nasir Sulaiman, PhD Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia (Internal Examiner) Ahmad Ainuddin Nuruddin, PhD Associate Professor Faculty of Forestry Universiti Putra Malaysia (Internal Examiner) Mohammed Yusoff, PhD Professor Faculty of Information Technology and Multimedia Communication, Open University Malaysia (External Examiner)

________________________________ HASANAH MOHD. GHAZALI, PhD

Professor/ Deputy Dean School of Graduate Studies Universiti Putra Malaysia

Date:

vii

This thesis was submitted to the Senate of Universiti Putra Malaysia and has been accepted as fulfilment of the requirement for the degree of Master of Science. The members of the Supervisory Committee were as follows: Mohd Hasan Selamat, MSc Associate Professor Faculty of Computer Science and Information Technology Universiti Putra Malaysia Nik Muhamad Nik Ab. Majid, PhD Professor Faculty of Forestry Universiti Putra Malaysia Awang Noor Abd. Ghani, PhD Associate Professor Faculty of Forestry Universiti Putra Malaysia Rusli Abdullah, PhD Faculty of Computer Science and Information Technology Universiti Putra Malaysia

________________________________

HASANAH MOHD. GHAZALI, PhD Professor and Dean School of Graduate Studies Universiti Putra Malaysia Date: 15 January 2009

viii

DECLARATION

I declare that the thesis is my original work except for quotations and citations which have been duly acknowledged. I also declare that it has not been previously, and is not concurrently, submitted for any other degree at UPM or at any other institutions.

______________________________

SHAFINAH BINTI KAMARUDIN

Date: 28 July 2008

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TABLE OF CONTENTS

Page

ABSTRACT ii ABSTRAK iv ACKNOWLEDGEMENTS vi APPROVAL vii DECLARATION ix LIST OF TABLES xiii LIST OF FIGURES xiv LIST OF ABBREVIATIONS xvi CHAPTER 1 INTRODUCTION

1.1 Background 1 1.2 Problem Statements 2 1.3 Research Objectives 4 1.4 Research Scope 4 1.5 Organization of the Thesis 6

2 LITERATURE REVIEW 2.1 Decision Support System (DSS) 7 2.2 Definition of DSS 7 2.3 Characteristics and Capabilities of DSS 8 2.4 DSS Application 9 2.5 DSS Application in Forestry 12

2.5.1 Decision Support System Dobrova (DSD) 12 2.5.2 Queensland Tree Selector (QTS) 15 2.5.3 Texas Tree Planting Guide (TTPG) 18 2.5.4 Forest Time Machine (FTM) 20 2.5.5 NorthEast Decision (NED) 21 2.5.6 Forest Vegetation Simulator (FVS) 23 2.5.7 Forest Stand Software Support System (4S Tool) 25

2.6 Summary 29

3 METHODOLOGY 3.1. Introduction 31 3.2. The Development of DSS Model 31

3.2.1. Problem Diagnosis 32 3.2.2. DSS Objectives and Resources 32 3.2.3. System Analysis 33 3.2.4. System Design 33 3.2.4.1 Logic Modeling 34

3.3. The Development of Prototype 39 3.3.1. System Construction 39 3.3.2. System Implementation 40

3.4. System Evaluation 40 3.4.1. Data Collection 41

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3.4.2. Sample 43 3.4.3. Questionnaire Design 44 3.4.4. Interview Guideline Design 49 3.4.5. Data Analysis 49

3.5. Summary 52 4 SYSTEM DEVELOPMENT

4.1. Introduction 54 4.2. Decision Support System Model 54 4.3. Species Matching Module 57

4.3.1. System Analysis 57 4.3.1.1.Functional Requirements 57 4.3.1.2.Interface Requirements 58 4.3.1.3.Coordination Requirements 60

4.3.2. System Design 61 4.3.2.1.System Overview 61 4.3.2.2.Logic Modeling 63

4.4. Economic Module 71 4.4.1. Financial Value Form Design 73 4.4.2. Yield of Latex Form Design 76 4.4.3. Cash Flow (Benefit) Form Design 77 4.4.4. Cash Flow (Cost) Form Design 79 4.4.5. A Summary of Cash Flow Form Design 80 4.4.6. Criteria For Financial Analysis 83

4.5. System Construction 85 4.5.1. Species Matching Algorithm 85

4.6. System Implementation 88 4.6.1. Species Matching Module 88 4.6.2. Description of Definitions 90 4.6.3. Tree Information 91 4.6.4. Economic Module 92

4.6.4.1. Financial Value Form 93 4.6.4.2. Yield Latex Form 94 4.6.4.3. Cash Flow (Benefit) Form 95 4.6.4.4. Cash Flow (Cost) Form 95 4.6.4.5. A Summary of Cash Flow Form 96 4.6.4.6. Criteria for Financial Analysis 97

4.6.5. Error Messages 98 4.7. Summary 99

5 SYSTEM EVALUATION

5.1. Introduction 101 5.2. System Evaluation Result 101

5.2.1. Eight-Usability Factor Result 102 5.2.2. Efficiency Factor 103 5.2.3. Understandability Factor 104 5.2.4. Operability Factor 105 5.2.5. Attractiveness Factor 106 5.2.6. Error Prevention Factor 107 5.2.7. Learnability Factor 109

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5.2.8. Accuracy Factor 111 5.2.9. Effectiveness Factor 113 5.2.10. Overall Evaluation 114

5.3. Summary 116 6 CONCLUSIONS AND RECOMMENDATIONS

6.1. Conclusion 118 6.2. Recommendations 121

REFERENCES 122 APPENDICES 128 BIODATA OF STUDENT 197

xii

LIST OF TABLES

No

Page

2.1 Existing DSS models 28 3.1 The DSS Model Development Phases 31 3.2 Activities for Requirements 33 3.3 Activity for System Design Phase 34 3.4 Logic Model Recommendation and Conditions 39 3.5 Usability Evaluation Methods 42 3.6 Sample Sizes for Research 43 3.7 Examples of Questionnaire 44 3.8 The HAS Evaluation Questionnaire 45 3.9 The PASS Questionnaire 46 3.10 The LUMM Questionnaire 46 3.11 Comparison of HAS, PASS and LUMM towards Usability Factor 47 3.12 Overview of Usability Factor 47 3.13 Definitions of Usability Factors 48 3.14 Satisfaction Scale Used for Questionnaire Result 52 4.1 List of Parameters and Related Characteristics 58 4.2 Description of Suitability Percentage Definition 60 4.3 Function for Coordination Requirements 61 4.4 Suitability of Azadirachta excelsa with User Input 64 4.5 Calculation Equation 75 4.6 Example of the User Inputs 88 5.1 Profile of Respondents 102 5.2 Efficiency Factor Tabulation 104 5.3 Understandability Factor Response 104 5.4 Operability Factor Response 105 5.5 Operability Factor Tabulation 106 5.6 Attractiveness Factor Tabulation 107 5.7 Error Prevention Factor Tabulation 108 5.8 Accuracy Factor Tabulation 112

xiii

LIST OF FIGURES

No

Page

2.1 User Interface of Cow Culling Decision Support System 10 2.2 Schematic representation of the main processes of the DSD v 1.1

master model 13

2.3 Screenshot of the graphical user interface of DSD v 1.1 14 2.4 User Interface of the Queensland Tree Selector 15 2.5 Example of QTS Result Form 16 2.6 User Interface of the Texas Tree Planting Guide 19 2.7 Schematic Structure of the Forest Time Machine 21 2.8 The NED-2 Architecture 23 2.9 The FVS Program Execution 24 2.10 The Outline of Data Flow in 4S Tool 26 3.1 Structured English with Nesting Block 35 3.2 The standard format used for presenting a decision table 36 3.3 Example of Decision Table 37 3.4 Example of Decision Tree 38 3.5 Decisional Guidance Research Model 48 3.6 Data Interpretation Process 50 3.7 Data Interpretation Process for Negative Statement 50 4.1 DSS Model for Specific Selection in Malaysian Forest Plantations 56 4.2 Form Design 59 4.3 Result Form Design 60 4.4 System Overview 62 4.5 Decision Table Representation 67 4.6 Example of Comparison and Rules Argument 71 4.7 Flow Chart of Economic Module 72 4.8 User Interface of Financial Value Form 74 4.9 Yield of Latex User Interface 77 4.10 Cash Flow (Benefit) Form Design for Hevea brasiliensis 78 4.11 User Interface of Cash Flow (Cost) Form 80 4.12 User Interface for a Summary of Cash Flow Form 83 4.13 Algorithms for Soil Type 86 4.14 Average of the Suitability Total Value 86 4.15 Algorithms for Tree Species Percentage 87 4.16 Definition of Result 87 4.17 Algorithms for Suggested One Tree Species 88 4.18 Species Matching Form with User Input 89 4.19 Example of Result 90 4.20 Description of Definitions Snapshot 91 4.21 Information for Azadiractha excelsa 92 4.22 Financial Value Form 93 4.23 Yield Latex Form 94 4.24 Cash Flow (Benefit) Form 95 4.25 Cash Flow (Cost) Form 96 4.26 A Summary of Cash Flow Form 87 4.27 Criteria for Financial Analysis 98 4.28 Example of Error Massage 98

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4.29 Error Massage for Economic Module 99 5.1 The User Satisfaction Index Score for the Eight-Usability Factor 103 5.2 User Satisfaction Index Score for Learnability Factor Questions 110 5.3 User Satisfaction Index Score for DSS Model Modules 110 5.4 User Satisfaction Index Score for the Effectiveness Factor

Questions 114

5.5 The User Satisfaction Index Score for Overall Evaluation 115

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xvi

LIST OF ABBREVIATIONS

4S Tool Forest Stand Software Support System BC ratio Benefit-Cost Ratio CTP Commercial tree planting DSS Decision Support System FTM Forest Time Machine FVS Forest Vegetation Simulator GIS Geographical Information System HAS Hotel Advisory System IRR Internal Rate of Return LUMM Land Use Model Metronamica MIS Management Information System NED NorthEast Decision PASS Project Analysis and Selection System TTPG Texas Tree Planting Guidance QTS Queensland Tree Selector

CHAPTER 1

INTRODUCTION

1.1 Background

Good decision making means that the person is informed with relevant, accurate and

appropriate information on which to base the choice. Information includes facts,

numbers, historical data, graphics, pictures, and sounds (Sauter, 1997). A decision-

making process is a method that guides an individual or group through a series of

tasks from problem identification and analysis to design of alternatives and selection

of an alternative (Mintzberg et al., 1976; Reynolds, 2005). Decision making

sometimes can be a very difficult and challenging task.

Forest management is a system of practice for stewardship and use of forest

resources aimed at fulfilling relevant ecological (including biological biodiversity),

economic and social functions of the forest in a sustainable manner. In forest

management, forest managers are obliged with responsibilities to make decisions that

might affect forest sustainable. Most of the forest managers use models to tackle

problems of forest management. Models are defined as abstract representations of the

real world that are useful for purposes of thinking, forecasting and decision making.

System model are tools that managers can use to predict the consequences of their

actions. Although system models are mathematically formulated, both mathematics

and algorithms are crucial for the system models to be functional. Algorithms have

been immensely improved by computer technology (Buongiorno and Gilles, 2003).

Decision Support System (DSS) is an example of a system model that uses

mathematics and algorithms. It is a computer technology solution that can be used to

support complex decision making process and problem solving (Shim et al., 2002).

Forest plantation management is an important component for forestry activities.

Malaysia currently has 250,000 ha of forest plantations located mainly in Sabah,

Sarawak, Johore and Pahang. To strengthen its commitment to the world timber

market without compromising biodiversity and environmental values, Malaysia aims

to increase the forest plantation area to 500,000 hectares. Thus, Malaysia timbers will

be felled from a sustainably managed forest. Decision to select appropriate species to

be planted in forest plantation is a difficult task faced by foresters both in the

government and private sectors. In order to make a good decision, there should be

proper technique to be implemented and DSS is one approach in solving this

problem.

1.2 Problem Statement

Decision Support System (DSS) has played a prominent role in the implementation

of sustainable forest management since the early 1980s (Reynolds, 2005). Booth

(1995) mentioned that decision makers and researchers in developing countries have

limited access to DSS technologies. In addition, most of the DSS technologies and

applications were purposely developed or designed for temperate forests.

2

A developing country with huge natural forest resources such as Malaysia has

limited access to DSS technologies and applications. Currently, only Geographical

Information System (GIS) such as ArcView and ArcInfo are considered as DSS tools

widely used by foresters in Malaysia to support their decision making. In the

Department of Forestry, the decision support tools that have been used widely are

Geographical Information System (GIS), GIS Interactive for Analysis and Mapping

(GIAM) and Forest Recreation’s Operational and Management System (FROMS). In

a GIS, every record or digital object has an identified geographical location. This

property is GIS’s most distinguishing characteristic (Turban, 1998). Even though

GIS has the ability to support decision making process it was developed only for

spatial purposes. Example of usage of GIS is to support decision making for forest

harvesting (Cabral, 2000).

Difficulty in selecting suitable tree species is one of the problems in forest plantation

programmes in Malaysia (Lim et. al, 2002). DSS application is one of the solutions

to help the decision makers to select the most appropriate tree species but there were

no reported or documented DSS applications used for this in Malaysia. The existing

DSS models such as Queensland Tree Selector (QTS) and Texas Tree Planting Guide

(TTPG) have the similarity in solving the species matching problems but both

models were for temperate countries and therefore not suitable for Malaysian

environment.

The Queensland Tree Selector (QTS) was proposed to suit the Queensland

environment in Australia. However, the design of the QTS’s result page lacks the

explicit explanation on the results rating. The Texas Tree Planting Guide (TTPG)

3

faced the difficulty in suggesting the appropriate tree species when the user considers

more than two environmental parameters. Both models only consider environmental

factors and are not embedded in the model with other components particularly

economic parameters which are crucial in the decision making process.

1.3 Research Objectives

The objectives of this study are:

a. to propose a specific DSS model for supporting decision making process in

selecting suitable tree species based on site conditions and economic

parameters.

b. to develop a prototype based on the initial designed DSS model.

1.4 Research Scope

Decision Support System (DSS) Model for species selection is a model developed to

reduce the problems in forest tree plantation establishment in Malaysia. Difficulty to

select appropriate species is the main problem that motivates this research to be

conducted. Therefore, this model was designed with the ability to help users to

choose suitable tree species according to site conditions and economic parameters.

Reconstruction from previous model (Anderson et al., 2005; Lexer et al., 2005) and

combination of information from a species matching form was applied in the

development process of the model. Information on tree species was provided as user

4

references. Even though this study was developed for selecting tree species,

economic module was also provided to help facilitate user decision making.

This study is limited to the forest plantation and only four tree species were selected

as the data sample. The species are Azadirachta excelsa, Acacia mangium, Hevea

brasiliensis and Tectona grandis. These species were selected because they are fast-

growing industrial species and according to Ab. Rasip et al., (1996) these species are

among the potential species for forest plantation programmes in Malaysia.

Usability inquiry was conducted to verify that this DSS model will meet user needs.

The key factors used in this usability inquiry are efficiency, learnability,

understandability, operability, attractiveness, error prevention, accuracy,

effectiveness and overall evaluation. The target users for this usability testing are:

a. Forest managers

b. Companies that are involved in the timber industry

c. Researchers and institutions oh higher learning

The data to verify the usability of the DSS model were collected from the

Department of Forestry (Peninsular Malaysia and Sarawak), Universiti Putra

Malaysia and Forest Research Institute Malaysia through questionnaire and interview

sessions. Data gathered from the questionnaire were analysed by incorporating

Bailey and Pearson (1983) instrument measurement as indication towards the

usability level of the proposed model. Meanwhile, the interview questions were

constructed to identify any usability weakness associated with the model.

5

6

1.5 Organization of the Thesis

CHAPTER 1: INTRODUCTION, a brief description of the research was

discussed, followed by a discussion of the problem statement and objective of the

research. The outline of the rest of the thesis is as follows.

CHAPTER 2: LITERATURE REVIEW cover the related studied on previous

DSS model. This chapter introduced the DSS terminologies, characteristics and

capabilities of DSS and reviews the existing DSS application for forestry.

CHAPTER 3: METHODOLOGY, the work or process flow of this research as

well as methods, techniques and approach of developing DSS model were discussed.

The approaches to conduct a survey for system evaluation were discussed.

CHAPTER 4: SYSTEM DEVELOPMENT, the further detail discussion on

system development process which focused ob system analysis phase, system design

phase, system construction and system implementation.

CHAPTER 5: SYSTEM EVALUATION, the results regarding the proposed model

which obtained from questionnaires and interviews were discussed.

Overall of this research was summarized in CHAPTER 6: CONCLUSION.

Limitation of this DSS model was listed and suggestion of future works was stated.

CHAPTER 2

LITERATURE REVIEW

2.1 Decision Support System (DSS)

In the 1960s, the major information system is the transaction processing and it

became apparent that the information provided by the management information

system (MIS) was adequate in supporting many decision making needs. Tian et al.,

(2007) stated that the concept of DSS was introduced in late 1960s. The DSS are

computer-based systems that bring together information from a variety of sources to

assist in the organization and analysis information. The DSS technology and

applications are built to support the decision making process. Below are the

terminologies of DSS, its characteristics and compatibilities, and a review of the

existing system in forestry.

2.2 Definition of DSS

Decision Support Systems (DSS) is one of major types of Information Systems (IS).

There are many terminologies that have been used by researchers. Turban (1998)

stated that the earliest concept involved in DSS was articulated in the early 1970s by

Scott-Morton under the term “management decision systems” and defined DSS as an

interactive computer-based system which helped decision makers utilize data and

models to solve unstructured problems.

7

Reynolds (2005) defined DSS as a computer-based system comprising of a language

system, presentation system knowledge-system, and problem-processing system

which collectively can be used in decision making. A decision-making process is a

method that guides an individual or group through a series of tasks of problem

identification and analysis to design alternatives and selection of an alternative

(Mintzberg et al., 1976).

Morley and Parker (2007) defined DSS as a type of information system typically

used by the management. It provides the tools and capabilities to organize and

analyze decision making information. DSS was usually tailored for the needs of an

individual or group and was designed to help specific type of decisions.

DSS can help managers make decisions by providing information, models, and tools

for analyzing information. These systems give users good judgment in using the

system and making decisions and they may provide methods and formats for portions

of a decision process such as the common framework for analyzing and explaining a

decision.

2.3 Characteristics and Capabilities of DSS

The following are eight characteristics and capabilities reported by Cabral (2000) in

his study related to the approach of designing and developing a tactical timber

harvesting planning using GIS application.

8