Application of Data Envelopment Analysis for Performance Efficiency Evaluation of Oil Palm Empty Bunch Fruit Composites in The Aerospace Industry
DOI:
https://doi.org/10.24002/ijieem.v5i2.7741Keywords:
decision making, performance measurement, inputs, outputs, linear programmingAbstract
In this study, we propose the data envelopment analysis method as a scheme to determine the technical efficiency of a
set of parametric inputs of the water absorption process when developing the oil palm particulate composite treated with
an alkali solution. Although alkali-treated oil palm bunch composites have been analyzed previously for water
absorption, a single parameter such as water absorption rate prevails in analyses. Unfortunately, multiple inputs and
multiple outputs have been ignored and the efficiency evaluation of such composites has been missing in the literature.
To address this gap, the present study exploits the linear programming theory and formulated models for each decisionmaking unit and solves that formulation for optimum value determination for inputs of the composites. This study
investigates the technical efficiency of the water absorption in the oil palm empty fruit bunch composite development
process. Overall, judging the performance of the parameters regarding the frequency of attaining 100% efficiency,
analysis was performed on the average performance of all parameters in all sixteen scenarios. In this regard, the
efficiency of particulate loading was 36.1%, for composite weight plus mold, it was 96.3% and for initial weight, the
average efficiency score was 67.8%. It is suggestive that composite weight plus mold with an average efficiency of
96.3% is the best parameter while particulate loading with 36.1% is the worst parameter. Thus result is consistent with
the result based on each scenario. From the perspective of DMUs, DMU11 with a score of 78.4% is the best ranking
unit while DMU14 is the work ranking unit with an efficiency score of 60.9%. Besides, the average efficiency score for
all the DMUs is 66.7%. The work is important to composite development engineers and for policy decision-making.
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