Feasibility study of the application of AI to project management, operations and labour sourcing in the residential construction industry

人工智能应用于住宅建筑行业项目管理、运营和劳动力采购的可行性研究

• 批准号: 10079095
• 金额: $ 6.36万
• 依托单位: HARDY WILCOX LTD
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=10079095
• 关键词: Feasibility; study; application; AI; project

**Project Overview**Hardy Wilcox Ltd, under the name of TradeWork, is an early-stage startup aiming to improve processes, cost, and time associated with managing property-related projects. Our services are relevant to landlords, property developers, trade contractors, professionals, and labourers. This Project aims to investigate the feasibility of leveraging AI within TradeWork's core platform for the benefit of its customers and its own business case.**Project Objectives**The objectives of this project are:* Research the feasibility of the application of AI/ML to improve the processes and outcomes in relation to sourcing and managing quality trades for residential construction projects* Research the business opportunities for TradeWork to leverage these findings for commercial benefit within its business* Prove that the current UK housing stock deficit situation could be improved by utilising AI from small to large developers as well as councils across the UK**Project Outcomes**The expected outcomes of the TradeWork project are:* Prove hypothesis that if AI is used in the construction industry then better outcomes (speed/value/time/risk etc) can be achieved.* Market opportunity assessment for TradeWork leveraging AI* Proof of concept integration of AI/ML into TradeWorks core platform* Validate TradeWork's business case and demonstrate the potential of AI to improve the UK residential construction industry**Project Approach**The TradeWork project will be implemented in three phases:_Phase 1: Research_* Relevant business processes and data* AI/ML technology, applications, and the art of the possible_Phase 2:_* Build AI/ML capability based on the research into the core TradeWork platform_Phase 3:_* Market research and business case verification and feasibility assessment**Project Team**The TradeWork project team is composed of experienced professionals with expertise in the construction industry, AI, and business development. The team is led by Piers Wilcox, who has over 20 years of experience in computer science and consultancy, supported by experts in residential construction and trade.**Project Timeline**The TradeWork project is scheduled to be completed in six months.**Project Budget**The total cost of the TradeWork project is **£57,929.20**. The grant that you are requesting is £50,000.00\. TradeWork will finance their contributions (~88%/£44k) via a business loan and additional Director Loan contributions. OneFourFOur will fund via cash reserves (~£6k).**Project Benefits**TradeWork has the potential to significantly improve the efficiency and accuracy of the construction process. This could lead to a number of benefits, including:* Reduced costs* Improved quality* Shorter project timelines* Increased productivity* Reduced Waste

* * 项目概述 ** 哈代威尔科克斯有限公司，在TradeWork的名义下，是一个早期阶段的启动，旨在改善流程，成本和时间与管理物业相关的项目。我们的服务与业主、房地产开发商、贸易承包商、专业人士和劳工有关。该项目旨在研究在TradeWork的核心平台中利用人工智能的可行性，以使其客户和自己的商业案例受益。项目目标 ** 该项目的目标是：* 研究应用AI/ML的可行性，以改善与住宅建设项目的采购和管理质量交易相关的流程和结果 * 研究TradeWork的商业机会，以利用这些发现在其业务范围内实现商业利益 * 证明当前英国住房库存短缺的情况可以通过利用从小到大的开发商以及英国各地的议会来改善 ** 项目成果 ** TradeWork项目的预期成果是：* 证明假设，如果人工智能用于建筑行业，那么可以实现更好的结果（速度/价值/时间/风险等）。TradeWork利用AI的市场机会评估 * 将AI/ML集成到TradeWorks核心平台的概念验证 * 验证TradeWork的商业案例，并展示AI改善英国住宅建筑行业的潜力 ** 项目方法 ** TradeWork项目将分三个阶段实施：_第1阶段：研究_* 相关业务流程和数据 * AI/ML技术、应用和艺术的可能性_阶段2：_* 在研究的基础上构建AI/ML能力融入核心TradeWork平台_阶段3：_* 市场调研和商业案例验证及可行性评估 ** 项目团队 ** TradeWork项目团队由在建筑行业、人工智能和业务开发方面具有丰富经验的专业人士组成。该团队由Piers Wilcox领导，他在计算机科学和咨询方面拥有20多年的经验，并得到住宅建筑和贸易专家的支持。* * 贸易工作项目计划在六个月内完成。项目预算 ** TradeWork项目的总成本为 ** 57，929.20英镑 **。你申请的补助金是5万英镑。TradeWork将通过商业贷款和额外的董事贷款捐款为他们的捐款（约88%/4.4万英镑）提供资金。OneFourFour将通过现金储备（约6000英镑）提供资金。项目效益 ** TradeWork有潜力显著提高施工过程的效率和准确性。这可能带来许多好处，包括：* 降低成本 * 提高质量 * 缩短项目时间表 * 提高生产力 * 减少浪费