Employment of CHAID and CRT decision tree algorithms to develop bid/no-bid decision-making models for contractors (original) (raw)
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Bid decisions either leads to procuring good opportunities or incurring large loss due to selection of inappropriate projects. The decisions to bid or not, made on experience and instincts have lower success rate compared to decisions made on real time facts governing the entire process. Smart contractors tend to be more factual rather being more heuristic while bidding for a project. To improve the bidding process and the competitiveness over a global market, the contractors need to identify and analyze the key factors influencing the bid process, which in turn boosts the economy of the country. This paper reports the factors influencing the bid decisions obtained through the response from the survey questioning various contractors from different construction projects in India. This study also ranks the factors obtained based on their importance weightages and the top ranked factors being studied using a statistical tool.
Revista de la Construcción. Journal of Construction, 2019
Construction in developing countries is often encountered with multifarious challenges including contractor's performance due to lack of qualification and resources. The lowest bid criterion is binding in public procurements. However, contractors exploit the loopholes in the bid process management system. This paper scrutinizes the prevalent rules for the bid evaluation and investigates the criterion used by both clients and consultants in selecting the contractors during the bids evaluation phase of construction projects in Pakistan. The current research uses the relative importance index and severity index approach to analyze the data. It was discovered that proper planning, credit worthiness, transition plans, plant and equipment holding, financial stability, past performance, and quality, are the most imperative factors, influencing the contractor's selection procedures used by clients and consultants. Likewise, a high probability of success is presaged if the contractors are selected using the multi-criteria method. The study contributes to the body of knowledge by revealing the significant factors impacting the contractor's selection and bid evaluation process, especially in a developing country. Its results and methodology can also be generalized with caution in other developing countries having similar work environment.
Critical factors influencing the bid/no bid decision in Pakistan construction industry
CITC-11 : Proceedings of the 11th International Conference on Construction in the 21st Century, 2019
In construction industry, adequate and effective decision-making can mean the difference between success and failure. Bidding is the most important element of construction business since it is a mean by which contractors obtain work. This is probably the only option for any contractor firm to sustain in the market and achieve its objective of earning the profits by winning tenders. The capability to select most appropriate ventures not only defines the success and wellbeing of a contractor firm, but even its survival and sustainability in the industry. This research has been opted considering the local construction industry of Pakistan in order to examine the critical success factors from contractors' perspective while making bidding decisions, listing and evaluating critical factors in order of their importance. Literature review and questionnaire are used for identification and quantification of factors affecting bid/no bid decision-making. Statistical methods of ranking analysis were used for analysis. It is found that profitability, need for work and financial health of client are the most decisive factors in bid/no bid decision-making while project size, project type, fulfilling the tender conditions imposed by the client and relationship, identity & reputation of client are least impact factors in bid/no bid decision-making.
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PurposeThe purpose of this paper is to examine the use of bid information, including both price and non‐price factors in predicting the bidder's performance.Design/methodology/approachThe practice of the industry was first reviewed. Data on bid evaluation and performance records of the successful bids were then obtained from the Hong Kong Housing Department, the largest housing provider in Hong Kong. This was followed by the development of a radial basis function (RBF) neural network based performance prediction model.FindingsIt is found that public clients are more conscientious and include non‐price factors in their bid evaluation equations. With the input variables used the information is available at the time of the bid and the output variable is the project performance score recorded during work in progress achieved by the successful bidder. It was found that past project performance score is the most sensitive input variable in predicting future performance.Research limita...
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Several contracts are left at incompletion stage to become white elephant projects. This may be attributed to awarding contract to an incompetent contractor. Several tools have been used to evaluate the competencies of contractors based on certain criteria before selection of the contractor from a list of bidders. Among such tools are the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). The two techniques are based on multi criteria pairwise comparison. ANP goes further to incorporate a feedback mechanism and interactions among dimensions or cost factors as well as the hierarchical relationship of alternatives. Datasets of more than 200 contractors bidding for 20 contracts at different times were collected along with those that were awarded the contracts. With these datasets, the AHP and ANP techniques were employed independent of the other in evaluating the contractors. The execution of the design framework was implemented in super decision software in windows 8...
Enhancement of Bid Decision-Making in Construction Projects: A Reliability Analysis Approach
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Various risks significantly influence pricing of bids and a wide range of factors impact bid pricing risks. Of these, client’s reputation and the record of projects owned by a client have vital contribution on the issue. Current practices however fail to capture the impacts of client-related factors. There is a need for developing a practical quantitative approach, which enables estimators to process bid risk allocation easily. Through reliability analysis, the developed method proposed in this study enables practitioners to make informed bid/no-bid decisions based on estimating the probabilities of schedule and cost overruns. Estimating the probability of project failure enables estimators to quantify the risk element of bid price. In addition, schedule and cost overrun cumulative probability distributions can be used to estimate the expected value of these variables. The practicability of this proposed method is tested by empirical data obtained from 40 university construction pro...
Lowest bid has often been favoured for award of construction contracts in most competitive bidding due to its perceived advantage to the client. However, thispractice has also been found to eclipse with anomalies. For example, some bid figures are unrealistically low because desperate contractors cut down the figure to enhance the chances of winning contracts with the hope to recover the losses during project implementation. As a result, researchers recommends lowest bid that is responsive in place of just any lowest bid for the award. Bid evaluation has been used to identify the most responsive lowest bid where clients go through all bid documents in a process called bid evaluation. This process of bid evaluation could be very tedious if bidders are many. This research develops a model that can identify the lowest responsive bid very early among competitors without the need to go through tedious bid analysis.It is a further research consideration after Carr (2005)"s model. A set of 36engineering projects of diverse magnitude that went through competitive bidding process in Nigeria were obtained and reports on bid analyses collated. Extracted from the reports are the Consultant's Estimate, The Bid Prices, Error Analysis and the Number of Bidders. Literature documents that these fourfactors influence significantly the lowest responsive bid in competitive bidding. Using the four factors as independent variables and the lowest responsive bid as dependent variable,four simple and three multiple regression models were generated and compared along Carr (2005)'s model. Findingsshow that the number of bidders and consultant's estimateare best variables to predict the lowest bid if combined in a regression model. Apart from eliminating unrealistically low bids, the model abstracts the need for tedious bid analysis andreduce the time taken in bidding process.Furthermore, error was found not dependent on the magnitude of a project. Bidders should stick to ethics of estimating to reduce error in bids. Researchers should consider combining three and the fourvariables in future models to determine comparatively, the one that offers the best predictive power.