The Feasibility of Implementing AI in Bid/No-Bid decision-making
No Thumbnail Available
Date
2024-09-02
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Bath
Abstract
This report explores the feasibility of implementing an Artificial Intelligence (AI)-based Benchmarking Tool to enhance the bid/no-bid decision-making process at Cundall, a multi-disciplinary consultancy firm. The study addresses critical inefficiencies in the current decision-making system, including data inconsistencies, subjective judgments, and underutilization of historical data, which hinder alignment with Cundall's Blue Ocean Strategy. Through a combination of qualitative and quantitative research methods, the report identifies key decision factors, evaluates data readiness, and proposes an AI solution designed to provide data-driven insights, improve decision-making consistency, and align bid decisions with strategic objectives.
The proposed AI Benchmarking Tool integrates historical data analysis, machine learning algorithms, and an interactive dashboard to deliver explainable, user-friendly recommendations. The report includes a proof-of-concept using PowerBI and Random Forest machine learning models, demonstrating the tool's potential to improve bid success rates and operational efficiency. Ethical considerations, stakeholder engagement, and implementation challenges, such as data quality and system integration, are thoroughly addressed.
The findings highlight the transformative potential of AI in construction decision-making, positioning Cundall as an industry leader in innovation and strategic differentiation. Recommendations emphasize a phased implementation approach, robust governance, and continuous refinement to ensure alignment with Cundall's sustainability and innovation goals. This study contributes to the growing discourse on AI adoption in the construction industry, providing actionable insights for leveraging technology to create competitive advantages.
Description
This report investigates the feasibility of integrating an AI-based Benchmarking Tool into Cundall’s bid/no-bid decision-making process. It aims to address inefficiencies caused by subjective judgments, inconsistent data, and limited use of historical insights. By leveraging machine learning and data analytics, the proposed solution aligns decision-making with Cundall's strategic objectives, particularly its Blue Ocean Strategy, which focuses on innovation and market differentiation.
The study includes a comprehensive analysis of Cundall's current processes, a SWOT analysis, stakeholder engagement insights, and a proof-of-concept developed using PowerBI and Random Forest models. The report emphasizes ethical considerations, stakeholder collaboration, and a phased implementation approach to ensure successful adoption. It also provides a roadmap for aligning AI integration with Cundall's sustainability and innovation goals while addressing challenges like data quality, system integration, and user trust.
The findings highlight the potential of AI to transform decision-making processes, enhance project selection, and support strategic growth in the construction industry. This work serves as a valuable reference for organizations seeking to adopt AI for operational and strategic advantages.
Keywords
Artificial Intelligence (AI), AI Benchmarking Tool, Bid/No-Bid Decision-Making, Business Process Optimization, Data-Driven Decision-Making