Use case on web-based integrated project-monitoring platform

As of April 2026 , the PAIMANA project-monitoring framework tracks 1,981 ongoing infrastructure projects across 17 Central Ministries/Departments covering 22 infrastructure sectors . These projects have an aggregate original cost of approximately ?37.13 lakh crore, revised cost of approximately ?42.78 lakh crore and cumulative expenditure of approximately ?20.36 lakh crore. The monitored portfolio covers major sectors including Transport & Logistics, Energy, Water & Sanitation, Communication, Social Infrastructure, Coal, Steel and Mining.

Despite the availability of comprehensive project-monitoring data, infrastructure projects frequently encounter challenges such as cost overruns, time overruns, delays in milestone achievement, contractual and implementation bottlenecks, resource constraints and execution risks. These challenges often result in significant escalation of project costs and delays in the creation of public infrastructure assets.

While the existing PAIMANA framework provides robust capabilities for monitoring and reporting project progress, there is a growing need to move beyond descriptive monitoring towards predictive and prescriptive monitoring. The scale, diversity and continuous availability of project data provide an opportunity to strengthen infrastructure project monitoring through data-driven analytical and decision-support systems.

The availability of large-scale historical repository of project data together with continuously updated project information received through integrated digital systems provides a strong foundation for the application of Artificial Intelligence (AI), Machine Learning (ML) and Large Language Models (LLMs). These technologies can be leveraged to develop predictive analytics and early warning decision support systems for identifying cost overruns, schedule delays and implementation risks, thereby enabling proactive interventions and evidence-based decision-making in infrastructure project monitoring.

The solution should assist policymakers, project administrators and monitoring agencies in prioritising interventions, improving project execution outcomes and enhancing the effectiveness of infrastructure project monitoring. The use-case aims to transform project monitoring from a descriptive reporting framework into a predictive and prescriptive decision-support system capable of generating actionable insights for evidence-based decision-making. In this regard, the proposed solution may address the following technical dimensions:

a)Development and evaluation of statistical analysis and predictive models using open-source tools and methodologies for analysing project performance and forecasting cost overruns, time overruns and implementation risks.

b)Assessment of whether Artificial Intelligence (AI) and Machine Learning (ML) techniques provide significant gains over conventional statistical methods in terms of prediction accuracy, early warning capabilities and decision-support for infrastructure project monitoring.

c)Development of prediction and analytical models based on the existing Common Upload Form (CUF) fields available in the project-monitoring framework, along with an assessment of the extent to which predictive performance is attributable to the current CUF fields vis-à-vis additional variables that are not presently captured in the CUF.

It is suggested/ desirable that the proposed solution may leverage any of the following (using Open-Source Tools/ Softwares); (a) Artificial Intelligence (AI), (b) Machine Learning (ML), (c) Big Data Analytics, (d) Forecast Modelling and (e) Large Language Models (LLMs); to predict cost and time overruns, generate project-level risk scores, identify emerging implementation challenges, and provide early warning signals and decision-support mechanisms for timely interventions. However, these suggested techniques are only indicative and non-exhaustive. The students may adopt alternative or additional methodologies, tools, frameworks, or analytical approaches, as deemed appropriate, to achieve the stated objectives.

The above outcomes are indicative and non-exhaustive. Students may propose alternative outputs, features or solution components that effectively address the problem statement. It is desirable that only open-source tools/ software be used to achieve the stated objectives. The selection and application of such methods shall remain at the discretion of the student, subject to demonstrating their suitability, effectiveness, and alignment with the project requirements.