Improving Efficiency with Field Data & Construction Project Performance Metrics | Construction Technology

    Data from the field has always been vital for contractors to understand the progress and health of their projects. Advancing construction technology is making field data easier to collect, analyze and share with project teams. This SmartMarket Report explores what types of data and which tools and processes contractors work with most frequently to gather construction project performance metrics. While there are many types of data that could potentially be valuable for analysis, this research focuses on five key types that are widely available:

    • Project progress
    • Man hours
    • Productivity
    • Safety
    • Equipment management

    Survey respondents were asked how they currently gather, manage, secure, analyze and report on each of these types of construction project performance metrics, with a specific focus on their current level of satisfaction and plans for making future changes. Key trends that emerged among those findings are explored more fully in the report, including:

    • A rapid shift away from paper-based forms and spreadsheets to digital tools and platforms, some leveraging cloud construction technology
    • Increasingly frequent use of apps on mobile phones and digital cameras in the field
    • The desire for more accurate field data that enables trend analysis across projects
    • Keen focus on data security with frequent use of anti-malware software and enterprise-grade firewalls

    Contractors have benefited greatly from better data, construction technology and project performance metrics. One of these benefits is improved budget and schedule compliance. Contractors also report having greater productivity and profitability, all while improving construction site safety. Lastly, the study explores contractors’ current understanding of and engagement with several emerging types of construction technology that are poised to dramatically impact the industry, including predictive analytics, machine learning and artificial intelligence.