Evans Project Controls Ltd

How the AI Modelling Works

Data Collection

The process begins with comprehensive data collection from both completed and ongoing projects. This data encompasses all the project registers. By gathering detailed information, we ensure that the analysis is based on robust and accurate data, providing a solid foundation for actionable insights. When orders are processed, templates for the registers are emailed out for completion. Downloads from online contract management systems can we updated to match the templates for easier data transfer.

Data Formatting

one received the data is formatting into 60+ input variables which could be significant indicators based on the latest academic research findings. This is then transferred into the AI data model.

Initial Analysis

Using advanced AI-driven methodologies, we conduct an initial analysis of the collected data for completed projects. The AI software, provided by Microsoft, enables us to identify key factors influencing project costs and durations. This initial analysis helps us pinpoint areas that require further investigation and forms the basis for the detailed reporting.

Detailed Reporting

The detailed reporting phase involves a thorough examination of the identified key factors. Our analysis reveals the impact of various elements on project performance. Based on the findings, we provide tailored recommendations to improve project management practices.

Further Analysis

The live information from ongoing projects is fed into the model and targets are identified where performance of the ongoing projects needs rectification.

Consultation and Implementation

To ensure the successful implementation of our recommendations, we offer an initial consultation meeting. During this meeting, we discuss the findings in detail and develop a tailored implementation plan. For clients who opt for our subscription service, we provide ongoing support through regular monthly updates and consultations. This continuous engagement allows us to monitor project performance and make necessary adjustments to strategies in real-time.

Delivery

The initial report is custom-written and delivered within two weeks. It is emailed in both PDF and Word formats, allowing for easy editing and integration into the client's project management processes. For subscription service clients, we provide monthly updates to track performance and offer continuous insights, ensuring that clients stay on track and optimise project outcomes.

Project Updates

For live projects, ongoing reporting is crucial to ensure continuous performance monitoring and timely interventions. Our service includes monthly updates that provide real-time insights into project progress against targets. These updates enable project managers to stay informed about current performance metrics and make data-driven decisions. Regular consultations accompany these reports, offering expert guidance.

Model Updates

Incorporating additional completed projects into the model through monthly updates enhances the accuracy and relevance of our analysis. Each new dataset refines the AI algorithms, allowing for more precise identification of trends and key performance indicators (KPIs). This dynamic approach ensures that the KPIs evolve with the project's progression, providing up-to-date insights and enabling continuous improvement. By regularly integrating new project data, we offer a robust and adaptive tool that helps project managers stay ahead of potential issues and optimise project outcomes.

MODELLING CONTRACTUAL COMMUNICATIONS RESEARCH

ABSTRACT By Dr George M Evans

This research set out to identify if an early warning system for project failure based on the communication patterns between the contracting parties could be developed to help improve the communication and project management of construction project using the new engineering contract. The research used a combination of correlation and regression analysis to identify statistically significant variables between elements of project contractual communications and project outcomes of completed projects. The model was then applied to ongoing projects to predict project outcomes prior to completion to allow for mitigation measures to be applied.

Within the current dataset the research was able to identify that project cost increases had a positive relationship with the number of client risks identified on the risk register, the amount of cost fluctuation and inversely the total number of documents. It also identified that time overruns had a positive relationship with the cost fluctuation and the average response time for risks on the risk register. A further detailed data analysis identified the themes within the meta-data which would allow for a more structured efficient mitigation strategy. The research highlights that communication patterns can have an impact on overall project success. Applied in a commercial setting this model would offer cost savings and efficiencies for companies looking to improve their project management communication. By applying different datasets to the model, the different indicators and impacts can be identified depending on the contractual communication and project management maturity.

This research identifies a novel way in which a construction company can use their contract data to improve the outcomes of projects. The originality and value, comes from the new data source which has previously not been examined in the academic field. The findings are novel as they give the specific areas of variables which impact project success as well as their magnitude.

Key Words: Construction, Project, Management, Contract, Communication.

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