A Decision Tree-Based Model for Tender Evaluation

Abstract

AbstractSubjective tender evaluation and contract award in public procurement is prevalent in various contexts. This has contributed to low quality of goods, services and projects. Successful implementation of building projects is heavily impacted by taking the right decision during tendering processes. Manning tender procedures can be complex and uncertain, involving coordination of numerous tasks and persons with different priorities and objectives. Bias and inconsistent decisions are inevitable if the decision-making process is wholly dependent on intuition, subjective judgement, or emotions. In making transparent decision and beneficial competition tendering, there is need for a flexible tool that could facilitate fair decision making. The purpose of this research was to present a model of an IT solution integrating the concepts of supervised machine learning techniques in the context of tender evaluation in public procurement. Independent variables used as inputs included “Experience”, “Equipment capacity”, “Professionalism”, and “Number of Personnel”. A set criteria was used to determine the values of the variables based on the documents submitted by applicants. The model combines the values of these attributes and determines the category of the entity as either “PASS” or “FAIL”. J48 decision tree classifier was used for this classification problem. This algorithm was preferred due to its relatively simple model among other benefits stated herein. The dataset was divided into test data and training data for the model. The performance appraisal of the model was based on the accuracy of the classification, the precision, recall ratio, ROC curve and the F-Measure. The model was proven to be impressively accurate with an accuracy of 91.1765% while the precision obtained was 0.857. The recall ratio was 1 and an F-measure of 0.923.KeywordsDecision treeTender evaluationProcurementSupervised learningTendering process