Cassava foliage harvesting machine selection decision making factors: The case study in Thailand

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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.

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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 http://www.iaeme.com/IJMET/index.asp 39 editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in Thailand 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. http://www.iaeme.com/IJMET/index.asp 40 editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri Kaewtrakulpong Table 1 Expected Cassava Foliage Harvesting Factors and Definition Expected Cassava Foliage Harvesting Factors and Definition 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 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 1 2 3 4 5 6 7 8 9 10 11 12 Supplier Slection Development of a Manufacturing Factors Jewel Factory Rice Farmers’ Machine Selection Design And DESIGN AND Multi-Crop Olive harvesting Harvesting and Reference Literatures in Automobile Mechanical local machine suit Influencing Machine Selection Decision to by AHP and Calculation Of FABRICATION OF Harvesting Machine Machine Postharvest industry Harvesting for harvesting Decision Making to Purchase TOPSIS Methods Solar Power HARVESTING Management Machine for High- sugar beet Middle Size Tractor Agricultural Operated MACHINE-Reaper density Citrus Machine for Land Sugarcane M/C Soybean Groves Preparation Harvesting Machine Surakrit Redmond Ramin A. F. Abed Atthasat et al Rattarut Kritsada Rubayet Karim, et Prashant Amar et al Ravindra Lahane Ashkan Carl J. Bern Shamshiri Rabou,et al al Inkane,et al et al AHP Purposive Machine Design Machine Design AHP Descriptive AHP and TOPSIS Machine Design Machine Design Machine Design AHP Machine Design Criteria-Subcriteria and Scores Interview+LR statistics 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 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 http://www.iaeme.com/IJMET/index.asp 41 editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in Thailand 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. Framework of Cassava Foliage Harvesting Machine Decision Making Selection Factors Source Prior Empirical Literature Review AHP Study Survey 1. In-depth Interview 1. Review Literature Process 1 Structuring the Framework for factors concerned empharsized on Agriculture Machine 2. Questionnaire Design 2. Machine Selection Selection Factors Factors in 3. Sampling & Questionnaire Survey Qualitative 2. Factors Concerned Interview Grouping 4. Weigh the priorities 5. Results and Conclusion 3. Conclusion 3. Review Factors on Interview and Literature 7 Samples Samples 260 Samples 12 Papers on 2-Cassava Farmers : Head of the Association/ Cooperation Description Cassava Farmers Agriculture 2-Machine Designer & Maker Machine Design 3-Agriculture Expert (Cassava Foliage) 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 Factors concerned from Literature Review 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 http://www.iaeme.com/IJMET/index.asp 42 editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri Kaewtrakulpong 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) http://www.iaeme.com/IJMET/index.asp 43 editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in Thailand Table 4 AHP Questionnaire Sample Comparison Score Sub- Criteria : Machine Sub- Criteria : Machine More than Equal Less than 1-1 Easy to Use 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-2 Productivity 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-3 Quality of Output 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-4 Suitability to Scale of production 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-5 Safety 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-6 Durable 9 7 5 3 1 3 5 7 9 1-1 Easy to Use 1-7 Techonology 9 7 5 3 1 3 5 7 9 1-1 Easy to Use Pairwise comparison is set from a scale of numbers that can evaluate the level of each criteria on another one [2] (Table 5). Table 5 Scale of Evaluation Scale of Evaluation (Satty,1980,2008) Intensity of Explanation Definition Importance 1 Equal Importance Two criterias contribute equally to the objective. 3 Moderate Importance Experience and judgement slightly favor one criteria over another 5 Strong Importacne Experience and judgement strongly favor one criteria over another 7 Very Strong Importance An criteria is favored very strongly over another. 9 Extreme Importance The criteria favoring one activity over another os of the highest possible order of affirmation. 3.3.3. Sampling and Questionnaire Survey The samples using for AHP analysis are 7 people selected from three groups which are cassava farmers, agriculture machine expert and agriculture expert. The description and details are shown in Table 6. Table 6 Respondent Description Agriculture/Machi No. Description Gender Age Work Experience ne Experience 1 Cassava Farmer-National Outstanding Farmer Male 51-60 Years > 30 Years 21-30 Years 2 Agriculture Machine Expert Male 41-50 Years 21-30 Years 21-30 Years 3 Agriculture Farming Expert Male 51-60 Years 21-30 Years 11-20 Years 4 Cassava Farmer-manager of the Cooperative Female 51-60 Years 21-30 Years < 5 Years 5 Agriculture Farming Expert Female 41-50 Years 11-20 Years < 5 Years 6 Agriculture Farming Expert Male 31-40 Years < 5 Years < 5 Years 7 Agriculture Machine Expert Male 41-50 Years 11-20 Years 11-20 Years http://www.iaeme.com/IJMET/index.asp 44 editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri Kaewtrakulpong 3.3.4. Weigh the Priorities The criteria and sub-criteria listed are one by one compared to evaluate which one is more importance. Pairwise Comparison Matrix are decided to match with the answers from the respondent. The eligible factors selection under the balance of the different opinion together with the ranking under the weighted score.The matrix are developed into each level: criteria and sub-criteria. The answers from the samples will be fulfilled to the degree of importance for the cassava foliage harvesting machine selection decision making factors. (Table 7) Table 7 Pairwise Matrix Comparison Sample: Machine Factors 1-1 1-2 1-3 1-4 1-5 1-6 1-7 Easy to Use Productivity Quality Suitability to Safety Durable Techonology Scale of production 1-1 Easy to Use 1.00 1-2 Productivity 1.00 1-3 Quality of Output 1.00 1-4 Suitability to Scale 1.00 of production 1-5 Safety 1.00 1-6 Durable 1.00 1-7 Techonology 1.00 We use Super Decisions software to analyze the answers weighed from the respondent. The analysis is shown in the next section. 4. RESULT AND DISCUSSION 4.1. Result For the main criteria, the score for cost factor is 0.3333 and the score for machine is 0.6667 with no inconsistency. (Table.8) Table 8 Main Criteria Result from AHP Main Criteria Factors Normalized Cost 0.3333 Machine 0.6667 Inconsistency - The inconsistency for sub-criteria less than 0.10 which shows the accepted consistence of the answers [2]. The inconsistency value of machine factors is 0.0629 and the value of cost factors is 0.0562. (Table 9) http://www.iaeme.com/IJMET/index.asp 45 editor@iaeme.com
Cassava Foliage Harvesting Machine Selection Decision Making Factors: the Case Study in Thailand Table 9 Sub-Criteria Result from AHP Sub-criteria : Machine Factor Sub-criteria : Cost Factor Factors Normalized Factors Normalized 1-1 Easy to Use 0.0640 2-1 Economical Investment 0.0776 1-2 Productivity 0.1099 2-2 Reduce Labor Cost 0.0798 1-3 Quality of Output 0.1734 2-3 Energy Saving 0.2292 1-4 Suitability to Scale of production 0.0921 2-4 Maintenance Cost 0.2314 1-5 Safety 0.1809 2-5 Low Cost of Harvesting 0.3821 1-6 Durable 0.1988 1-7 Techonology 0.1809 Inconsistency 0.0629 Inconsistency 0.0562 From the priorities for both set of sub-criteria, we can set the rank of priorities from pair- wise comparison. In sub-criteria: machine factor, durable is the first rank at 0.1988. The second rank is safety and technology at the same 0.1809 score. The third rank is quality of product at 0.1734. For cost factor, low cost of harvesting is the first rank with 0.3821.The second important cost sub-criteria is maintenance cost with 0.2314 score and the third rank is energy saving with 0.2292 score. All the criteria are re-prioritized by weigh with the main criteria score so we have the new priority started from durable as the first priority at 0.1326 score (Table 10). Low cost of harvesting is the second priority with 0.1274 score. The third rank is technology and safety at 0.1206 score. From the model, we can set the priority of Cassava Foliage Harvesting Machine Selection Decision Making Factors Selection by using AHP which can reduce the confusion of the mathematics score by paring the factors. Table 10 Factors Conclusion, Score and Ranking Factors Conclusion, Score and Ranking Main Criteria A Sub-Criteria B Scores (A*B) RANK 1 Machine Factors0.6667 1-1 Easy to Use 0.0640 0.0427 9 1-2 Productivity 0.1099 0.0733 7 1-3 Quality of Output 0.1734 0.1156 4 1-4 Suitability to Scale of production 0.0921 0.0614 8 1-5 Safety 0.1809 0.1206 3 1-6 Durable 0.1988 0.1326 1 1-7 Techonology 0.1809 0.1206 3 2 Cost Factors 0.3333 2-1 Economical Investment 0.0776 0.0259 11 2-2 Reduce Labor Cost 0.0798 0.0266 10 2-3 Energy Saving 0.2292 0.0764 6 2-4 Maintenance Cost 0.2314 0.0771 5 2-5 Low Cost of Harvesting 0.3821 0.1274 2 1.0000 http://www.iaeme.com/IJMET/index.asp 46 editor@iaeme.com
Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasindhu and Kriengkri Kaewtrakulpong 5. CONCLUSION This research aimed to evaluate cassava harvesting machine selection decision making factors which has never be created before. The case study was developed under the circumstance of Thai cassava plantation. The data collection began from the author’s prior empirical survey, machine design and decision factors literature review. The most important criteria and sub- criteria for the objective were identified to prepare the hierarchical structure. It can be used not only cassava foliage harvesting machine selection case but also the other agriculture field requirement. Then, the paired comparison of the criteria and sub-criteria from the samples was filled in and calculated by SuperDecisions software together with the consistency of the criteria and sub- criteria verified. Since we have two levels in criteria and sub-criteria, priorities analysis was used to synchronize the relative priorities by calculating the score weight of criteria to the score weight of the sub-criteria (Table 11) Table 11 Factors Prioritized by Weighted Score Criteria and Sub-criteria Factors Prioritized by Weighted Score Criteria and Sub-criteria 1 Machine Factors 1-6 Durable 0.1326 1 2 Cost Factors 2-5 Low Cost of Harvesting 0.1274 2 1 Machine Factors 1-7 Techonology 0.1206 3 1 Machine Factors 1-5 Safety 0.1206 3 1 Machine Factors 1-3 Quality of Output 0.1156 4 2 Cost Factors 2-4 Maintenance Cost 0.0771 5 2 Cost Factors 2-3 Energy Saving 0.0764 6 1 Machine Factors 1-2 Productivity 0.0733 7 1 Machine Factors 1-4 Suitability to Scale of production 0.0614 8 1 Machine Factors 1-1 Easy to Use 0.0427 9 2 Cost Factors 2-2 Reduce Labor Cost 0.0266 10 2 Cost Factors 2-1 Economical Investment 0.0259 11 The further study is to design the model in cassava foliage harvesting selection factors basing on the result factors which can meet the stakeholder’ requirement, not only the cassava farmers but also the engineer and the end-user of the cassava foliage. The limitation of this study is that it doesn’t link the actual cost with the machine specification required and the limited number of sample in this study. The further contribution is the AHP analysis by using the weighted criteria and sub-criteria to evaluate the suitable cassava foliage harvesting together with cost concerned. Feasibility study of the machine selected is one of the important tools to study both output and outcome of the machine. REFERENCES [1] Supattra Buasaengchan, Somchai Pengprecha, Pakpachong Vadhanasin, Kriengkri Kaewtrakulpong. The reason why we can’t use cassava leaf for commercial purpose in Thailand, International Conference on Sustainable Agriculture (Icsa-19), Bangkok, 2019, pp. 49-56 [2] Andre Andrade Longaray, Joao de Deus Rodrigues Gois, Paulo Roberto da Silva Munhoz. Proposal for using AHP method to evaluate the quality of services provided by outsourced http://www.iaeme.com/IJMET/index.asp 47 editor@iaeme.com
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