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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 10, Issue 04, April 2019, pp. 39-48. Article ID: IJMET_10_04_006
Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=10&IType=4
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication Scopus Indexed
CASSAVA FOLIAGE HARVESTING MACHINE
SELECTION DECISION MAKING FACTORS:
THE CASE STUDY IN THAILAND
Supattra Buasaengchan
Technopreneurship and Innovation Management, Graduate School, Chulalongkorn
University, Bangkok, Thailand.
Somchai Pengprecha
Faculty of Science, Chulalongkorn University, Bangkok, Thailand.
Pakpachong Vadhanasindhu
Faculty of Commerce and Accountancy, Chulalongkorn University, Bangkok,Thailand.
Kriengkri Kaewtrakulpong
Faculty of Agriculture, Kasetsart University, Bangkok, Thailand.
ABSTRACT
Machine and tooling selection are very important for agriculture economy which
base on labor intensive that increase time usage and cost. Cassava foliage harvesting
selection is very challenging in choosing the machine since it will be the key importance
to change the cassava supply chain that cannot bring cassava foliage to use in the
commercial way. The framework of this study start from the cassava farmers’ aspect,
link with factors concerned from literature review and then grouping the suitable
criteria and sub-criteria. The specific questionnaire was conducted with the
representative of the cassava farmer, agriculture machine maker and the expert user in
cassava foliage. The Analytical Hierarchy Process (AHP) is used to set the hierarchy
structure of the criteria, rating and prioritization. The results of the study illustrate the
machine factors and cost for cassava foliage harvesting machine selection decision
making. The prioritized factors are durability, low cost of harvesting, safety, technology
and quality of output respectively. It can be used not only cassava foliage harvesting
machine selection case but also the other agriculture machine or equipment.
Keywords: cassava foliage harvesting machine, AHP, agriculture machine selection,
multi criteria decision making
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
http://www.iaeme.com/IJMET/index.asp 40 editor@iaeme.com
Cite this Article Supattra Buasaengchan, Somchai Pengprecha, Pakpachong
Vadhanasindhu and Kriengkri Kaewtrakulpong, Cassava Foliage Harvesting Machine
Selection Decision Making Factors: The Case Study in Thailand, International Journal
of Mechanical Engineering and Technology, 10(4), 2019, pp. 39-48.
http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=10&IType=4
1. INTRODUCTION
Cassava foliage, cassava leaf or cassava hay in Thailand is accepted in the high crude protein
nutrition for animal feeds comparing to the other sources such as fish meal or soy bean. From
the prior empirical study of the author “The reason why we can’t use cassava leaf for
commercial purpose in Thailand” [1] shows the importance of machine as unmet need. 75% of
the samples are interested in harvesting tools due to the lack of labor, wastes in process, time
which bring to the high cost of harvesting and unprofitability.
The objective of the study is to identify the suitable factors for cassava foliage harvesting
machine selection decision making that can generate revenue and profit from the cassava
foliage with productivity, fit to Thai farming characteristics, easy to use, and reduce labor cost.
The suitable model for machine selection factors and process are essential in order to maximize
the harvesting outcome.
This article is divided into five sections. The introduction shows the importance for this
study, literature review with the theoretical base and relevant researches, and the methodology
of the study. The result of the study from both the survey and the Analytical Hierarchy Process
(AHP). The last section is conclusion, discussion of the result, and the recommendation for
further study.
2. LITERATURE REVIEW
Analytic Hierarchy Process (AHP) method is one of the well-known decision-making
consideration with multiple criteria developed by Thomas Saaty [2]. AHP can be used in both
qualitative and quantitative criteria for the judgment in decision-making. The steps in AHP
comprise of structuring the framework, questionnaire design, sampling & questionnaire survey,
weight the priorities, and then summarize the results and conclusions.
In the process of comparison, the numbers are identified accordingly to the importance scale
of each comparison in line with the definition [3]. The absolute numbers are assigned for each
pair of factors to represent the importance of factor to be selected by the respondent and then
calculated to be used for the systematic decision making.
From the literature review, the criteria, machine and cost, and sub-criteria are defined as in
Table1 in order to group the various criteria and definition from the twelve literatures together
with the result from the empirical study. The factors are 2 major criteria: the Machine factor
and the Cost factor. The machine factors consist of 7 sub-criteria: easy to use, productivity,
quality, suitability to scale of production, safety, durable and technology.
For the Cost Factor, the 5 sub-criteria are economical investment, reduce labor, energy
saving, maintenance cost and low cost of harvesting.
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
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Table 1 Expected Cassava Foliage Harvesting Factors and Definition
Agriculture machine selection is one of the importance topics for agriculture development
purpose in many countries. Twelve papers published during the year of 2008 to 2019 was
reviewed as shown in Table 1. The tools reference in each paper are various, 50% were in
machine design to meet customers’ expectation [4-9]. Thirty three percent use AHP Model [10-
13], the others uses descriptive statistics [14] and purposive interview [15].
The twelve literature review of the criteria and sub-criteria are scored as shown in Table2.
Low cost of harvesting has the highest score at 10 among all criteria. The second one is
productivity with 9 scores, the third one is easy to use with 8 scores. These criteria will be used
to map with the factors in the questionnaire as shown in the framework of the study (Figure.1).
Table 2 Literature Review on Agriculture Machine Design and Selection
3. METHODOLOGY
The framework of cassava foliage harvesting machine selection decision making factors (Figure
1.) for this study was set in 3 steps. The first step is the result summary of the cassava foliage
harvesting perception from the author’s prior empirical study [15]. The second step is the
questionnaire design covering the factors concluded from literature review and the survey
Main Criteria Sub-Critetia Definition
1 Machine Factors 1-1 Easy to Use Easy to Use/Control/Ergonomics
1-2 Productivity Effectiveness/Reduce Harvesting Time/Productivity
1-3 Quality of Output Quality/Low Foreign material
1-4 Suitability to Scale of production Suitability to Scale of production/Shape of Tree
1-5 Safety Safety
1-6 Durable Durable
1-7 Techonology Techonology/ Automation
2 Cost Factors 2-1 Economical Investment Economical Cost of M/C
2-2 Reduce Labor Cost Reduce Labor Cost
2-3 Energy Saving Energy Saving
2-4 Maintenance Cost Maintenance Cost
2-5 Low Cost of Harvesting Low Cost of Harvesting
Expected Cassava Foliage Harvesting Factors and Definition
1 2 3 4 5 6 7 8 9 10 11 12
Supplier Slection
in Automobile
industry
Development of a
Mechanical
Harvesting
Machine for High-
density Citrus
Groves
Manufacturing
local machine suit
for harvesting
sugar beet
Factors
Influencing
Decision Making to
Middle Size Tractor
Jewel Factory
Machine Selection
Rice Farmers’
Decision to
Purchase
Agricultural
Machine for Land
Preparation
Machine Selection
by AHP and
TOPSIS Methods
Design And
Calculation Of
Solar Power
Operated
Sugarcane
Harvesting Machine
DESIGN AND
FABRICATION OF
HARVESTING
MACHINE-Reaper
M/C Soybean
Multi-Crop
Harvesting Machine
Olive harvesting
Machine
Harvesting and
Postharvest
Management
Surakrit
Redmond Ramin
Shamshiri
A. F. Abed
Rabou,et al
Atthasat et al Rattarut Kritsada
Rubayet Karim, et
al
Prashant
Inkane,et al
Amar et al
Ravindra Lahane
et al
Ashkan Carl J. Bern
AHP
Purposive
Interview+LR
Machine Design Machine Design AHP
Descriptive
statistics
AHP and TOPSIS Machine Design Machine Design Machine Design AHP Machine Design
Main Criterias Sub Criterias 2008 2009 2011 2013 2014 2014 2016 2017 2018 2018 2018 2019 Scores
1. Machine Factors
1-1 Easy to Use ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 8
1-2 Productivity ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 9
1-3 Quality of Output ✓ ✓ ✓ ✓ 4
1-4 Suitability to Scale of production ✓ ✓ ✓ ✓ ✓ ✓ 6
1-5 Safety ✓ ✓ ✓ 3
1-6 Durable ✓ ✓ ✓ ✓ 4
1-7 Techonology ✓ ✓ 2
2. Cost Factors 2-1 Economical Investment ✓ ✓ ✓ ✓ ✓ ✓ ✓ 7
2-2 Reduce Labor Cost ✓ ✓ ✓ ✓ 4
2-3 Energy Saving ✓ ✓ ✓ ✓ ✓ 5
2-4 Maintenance Cost ✓ ✓ ✓ ✓ 4
2-5 Low Cost of Harvesting ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ ✓ 10
Criteria-Subcriteria and Scores
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in
Thailand
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mentioned in step 1. The last importance step is the AHP analysis of the data gathered from the
result of the study in step 2.
Figure 1. Framework of Cassava Foliage
Harvesting Machine Decision Making Selection Factors
3.1. Prior Empirical Survey
The author’s empirical survey for the cassava foliage harvesting machine selection to know the
factors concerned revealed 2 factors comprised of machine factors and cost factors.
3.2. Factors Review from Literature
The further step is to review factors from the interview and literature that can be grouped into
2 main criteria which are machine factors and cost factors. The sub-criteria of each factor are
summarized as shown in Table 3.
Table 3 Factors concerned from Literature Review
Framework of Cassava Foliage Harvesting Machine Decision Making Selection Factors
Prior Empirical
Study
Literature Review AHP
Survey
1 Structuring the Framework
2. Questionnaire Design
5. Results and Conclusion
1. Review Literature
empharsized on
Agriculture Machine
Selection Factors
260 Samples
Cassava Farmers
Samples
Description
12 Papers on
Agriculture
Machine Design
7 Samples
2-Cassava Farmers : Head of the Association/ Cooperation
2-Machine Designer & Maker
3-Agriculture Expert (Cassava Foliage)
2. Factors Concerned
Grouping
1. In-depth Interview
for factors concerned
2. Machine Selection
Factors in
Qualitative
Interview
3. Conclusion
3. Review Factors on
Interview and
Literature
Process
Source
4. Weigh the priorities
3. Sampling & Questionnaire Survey
Main Criteria Sub-Criteria Scores
1 Machine Factors
1-1 Easy to Use 8
1-2 Productivity 9
1-3 Quality of Output 4
1-4 Suitability to Scale of production 6
1-5 Safety 3
1-6 Durable 4
1-7 Techonology 2
2 Cost Factors 2-1 Economical Investment 7
2-2 Reduce Labor Cost 4
2-3 Energy Saving 5
2-4 Maintenance Cost 4
2-5 Low Cost of Harvesting 10
Factors concerned from Literature Review
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri
Kaewtrakulpong
http://www.iaeme.com/IJMET/index.asp 43 editor@iaeme.com
3.3. AHP Survey
The step of AHP survey are as followed. There are five phases which are structuring the
framework, questionnaire design, sampling and questionnaire survey, weigh the priorities and
results and conclusion.
3.3.1. Structuring the framework
From the factors identified, the hierarchical structure of the criteria is conducted as in Figure 2.
Starting from the top of the hierarchical structure, Level 1, the objective of the model is to
evaluate the cassava foliage harvesting machine selection decision making factors. In Level 2,
the main criteria in both machine function and cost function are directly related to Level 1.
Level 3, the sub-criteria directly linked to criteria in Level 2 are set to evaluate the multiple
alternative in decision-making process.
Figure 2. Hierarchical Structure of the Criteria
3.3.2. Questionnaire Design
We design questionnaire to interview the samples using the pairwise comparison for each
factor. The sample of the questionnaire are shown as below: (Table 4)