Mohamed, E; Jafari, P and Hammad, A (2022) Mixed qualitative–quantitative approach for bidding decisions in construction. Engineering, Construction and Architectural Management, 29(6), pp. 2328-2357. ISSN 0969-9988
Abstract
Purpose: The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision. Design/methodology/approach: This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision. Findings: Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision. Originality/value: This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | bid/no-bid decision; construction management; expert systems; fuzzy logic; procurement management; tendering |
| Index terms: | construction project, bias, methodology, expert system, construction contractor, body of knowledge, bidding, bid/no-bid, functionality, construction company, qualitative factor, modelling, fuzzy logic, procurement management, decision-making process, Canada |
| Subjects: | research methods, production management, Geography, probability and distributions, data management, decision analysis, procurement strategy, organization, practitioner, design features, data science, bidding, knowledge management, data analysis and analytics, analytical methods |
| Topics: | Design Practice, Digital Applications, Project Management, Geographical Context, Research Practice, Engineering Principles, Information Management, Business Strategy, Procurement, Roles and Professions, Risk Management |
| Descriptive scope: | 4 PCTA |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here