Identifying and evaluating factors affecting the risk of tender failure, case study: a contracting company
Subject Areas : Project ManagementMohammad Hossein Karimi Gavarashki 1 , Karim Atashgar 2 , Fateme Malekaee Ashtiyani 3
1 - Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.
2 - Associate Professor, Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran.
3 - . Ph.D. candidate ,Department of Industrial Engineering, Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Tehran, Iran
Keywords: contractor", , ", design of experiment", , ", fuzzy inference system", , ", regression", , ", tender failure risk,
Abstract :
The goal of contractors for participating in a tender is to make profit; so, they should identify and evaluate the effective factors that contribute to the success of the tender. Hence, the purpose of this study is to identify the key factors affecting the risk of failure in design and construction tenders and to estimate the risk of failure of the tender for the contracting company. The most important factors affecting the risk of tender failure were identified by using the design of experiment method at the 95% confidence level. The risk assessment was conducted based on the records of 64 tenders in which a contracting company in Tehran participated, supplemented by the data collected through a questionnaire. The validity of the questionnaire was confirmed by experts in terms of content and form, and its reliability measured by Cronbach's alpha was 0.7463. The risk of failure was estimated by Using regression analysis method and fuzzy inference system, in Mini Tab and MATLAB software. The RMSE value of the regression model was 0.57 which is equal to 0.75 in the fuzzy inference model. By comparing this criterion across the models, the superiority of the regression model over the fuzzy inference model for estimating the risk of tender failure was observed. Among the factors affecting the risk of tender failure, the contractor's financial ability, contract adjustment, the employer's financial situation and the financing method variables have a high impact, prepayment has a moderate impact, and the consortium members' and the state of machinery and equipment have a low impact on the risk of failure.
Key Words: contractor, design of experiment, fuzzy inference system, regression, tender failure risk
- Introduction
When an organization seeks to delegate executive operations to external companies, it initiates a tendering process, inviting several contractors to enter the competition. The company that offers the best quality at the lowest cost is chosen as the winner of the tender. The purpose of contractors in participating in tenders is to compete for earning a profit. These companies strive to offer the most competitive quality and price. In order to make profit, contractor companies must identify and evaluate the key factors that influence their success in the tender process. Therefore, contractors assess the risk of failure and the profitability of a tender by analyzing the provided documents. If a tender has an acceptable risk of failure and sufficient profitability, it is justified to participate in it. A powerful technique in investigating such processes is the design of experiments, which enables identifying the variables affecting the desired quality characteristic and determine the optimal levels of these variables along with the intensity of their effect. Since uncertainty is an inseparable part of institutions, the risk of tender failure was calculated using the method of fuzzy inference system in the uncertainty space and the estimated results were compared with the actual value of the risk of tender failure in the studied company. Therefore, this research sought to answer the following question: what are the variables affecting the risk of tender failure, to what extent is the effect of these factors and how these variables relate to risk?
- Literature Review
A comprehensive literature review establishes the foundational framework for this study. Existing research highlights the significance of identifying and evaluating factors affecting the risk of tender failure. The review of studies shows that while various methods have been used to identify and rank the factors affecting bidding success, none has developed a model to explain how these variables are interrelated or to examine the combined effect of multiple variables on the risk of tender failure. Hence, in this research, a 95% confidence level was established using the design of experiments method to identify the factors affecting the specified risk and to develop a regression model for predicting the risk of tender failure.
- Methodology
Typically, risk analysis is conducted using fault tree analysis; however, in this study, the risk is estimated using the model obtained from the design of experiments combined with a fuzzy inference system.
A- Estimating the risk of tender failure from the test design method: The design of experiments method includes the following steps:
1- Selecting the factors and determining the levels of the factors.
2- Selecting the response variable.
3- Selecting the test plan.
4- Collecting data and conducting experiments.
5- Statistical analysis.
B-Estimating the risk of failure in the tender using the method of fuzzy inference system: In the phase of fuzzification, using the experts’ opinions, attribute functions are defined for input and output variables. The rules are obtained from the combination of the states of the variables and are converted into membership degree functions using fuzzy inference rules. The rules are de-fuzzified after execution. After interviewing the experts, based on the degree of belonging of an element to a set in the form of the lowest (l), most probable (m) and highest (u) value, the triangular fuzzy number in the form of l, m, u for the variables is considered.
- Results
The real risk and estimated risk were recorded from the implementation of design of experiment and fuzzy inference system models in the following table.
The actual and adjusted values of the risk
|
Run |
Y |
|
run |
Y |
|
||
|
1 |
9 |
9 |
8 |
33 |
5 |
4 |
5 |
|
2 |
7 |
7 |
8 |
34 |
9 |
8 |
9 |
|
3 |
9 |
9 |
7 |
35 |
8 |
8 |
8 |
|
4 |
6 |
6 |
7 |
36 |
9 |
9 |
9 |
|
5 |
6 |
5 |
5 |
37 |
9 |
9 |
9 |
|
6 |
9 |
9 |
7 |
38 |
8 |
9 |
8 |
|
7 |
5 |
6 |
7 |
39 |
8 |
8 |
7 |
|
8 |
8 |
8 |
7 |
40 |
7 |
7 |
8 |
|
9 |
9 |
9 |
8 |
41 |
8 |
8 |
8 |
|
10 |
7 |
7 |
7 |
42 |
5 |
5 |
5 |
|
11 |
6 |
7 |
6 |
43 |
6 |
6 |
6 |
|
12 |
5 |
5 |
6 |
44 |
4 |
5 |
4 |
|
13 |
5 |
6 |
5 |
45 |
9 |
9 |
8 |
|
14 |
7 |
8 |
8 |
46 |
9 |
9 |
8 |
|
15 |
5 |
5 |
6 |
47 |
4 |
5 |
4 |
|
16 |
9 |
9 |
8 |
48 |
9 |
8 |
8 |
|
17 |
8 |
7 |
7 |
49 |
8 |
8 |
8 |
|
18 |
8 |
8 |
8 |
50 |
9 |
9 |
9 |
|
19 |
9 |
8 |
8 |
51 |
6 |
6 |
6 |
|
20 |
7 |
7 |
7 |
52 |
9 |
8 |
8 |
|
21 |
6 |
6 |
6 |
53 |
5 |
4 |
5 |
|
22 |
9 |
9 |
9 |
54 |
2 |
3 |
2 |
|
23 |
7 |
7 |
7 |
55 |
9 |
8 |
8 |
|
24 |
7 |
8 |
7 |
56 |
8 |
8 |
8 |
|
25 |
6 |
6 |
6 |
57 |
7 |
8 |
7 |
|
26 |
9 |
9 |
9 |
58 |
7 |
7 |
6 |
|
27 |
9 |
9 |
9 |
59 |
9 |
9 |
9 |
|
28 |
7 |
6 |
7 |
60 |
6 |
6 |
6 |
|
29 |
8 |
8 |
7 |
61 |
6 |
6 |
6 |
|
30 |
9 |
9 |
9 |
62 |
3 |
2 |
2 |
|
31 |
2 |
2 |
2 |
63 |
3 |
5 |
4 |
|
32 |
9 |
9 |
8 |
64 |
1 |
1 |
1 |
The validity of the models has been measured using the criterion RMSE according to the following equation:
Since the RMSE value of the regression model was 0.57, the fuzzy inference was 0.75, and the value of the adjusted coefficient of determination of the regression model was 91.05%, it can be concluded that there is less difference between the estimated output and the actual output and the performance of the regression method compared to inference Fuzzy system is better.
- Discussion
By examining the research background, it is evident that previous studies have reported one or a combination of factors that are identified in this study as influencing risk. While earlier research primarily ranked these risk factors, this study, in addition to ranking, measured the effect of these risk factors. Moreover, the severity of the effect of each factor’s effect was categorized into low, medium and high levels. Also, this study establishes a regression model to analyze the relationships between the main factors and their mutual interactions. Also, in the previous studies, methods such as fault tree analysis were used for risk analysis, but in this study, risk analysis was performed using regression model and fuzzy inference system. By Using the RMSE criterion, the validity of these models was investigated. The RMSE value of the regression model was 0.57 and for the fuzzy inference model was 0.75. The RMSE criterion for the regression model is lower than that of the fuzzy inference system, indicating that the regression model performs better. Among the factors analyzed, the contractor's financial ability, contract adjustment, employer's financial situation and financing method exhibit a high effect on risk. The advance payment variable shows an average effect intensity while the consortium member variable and the condition of machinery and equipment have a low effect on risk.
Conflict of interest: none
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