Construction Industry

Finance, Operational, and Risk Analytics.
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Data Engineering for Construction

We engineer your data from various systems and maximize operational efficiency.

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Analytics in
Construction

We develop analytical solutions to monitor the KPIs of the project at the program and portfolio level.

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AI / ML in
Structural Design

We solve extremely non linear structural problems using
AI/ML techniques.

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Software Development for Construction

We develop and customize software and mobile applications to maximize operational efficiency.

Our Value-Added Solutions
To Manufacturing Industries

Digital Construction

Leverage cutting-edge digital technologies to construct more effectively with superior quality.

Finance Analytics

Maximize profits and prevent project cost overruns using predictive data analytics.

Project Management

Plan and manage your operations from design to construction through a single digital platform.

Material Cost Indexing

Forecast the price fluctuation of construction materials and accurately plan future projects.

Advanced Tendering

Implement an electronic tendering system to accelerate the supplier selection process.

Incident Handling

Get insights on precautionary measures and strive for zero-incident construction sites

Vendor Management

Nurture and maintain long-term business relationships with all your vendors seamlessly.

AI-Powered Dashboards

Improve real-time visibility to stakeholders using customized dashboards powered by AI/ML.

Sustainability Solutions

Implement sustainable practices through digital research for cleaner and greener construction.

CCI Prediction

Challenge

It is always challenging to price construction projects due to the fluctuating costs that trend toward increasing over time. Using Machine Learning algorithms, we provide stakeholders with an accurate way to predict upcoming project costs​.

Solution

  •  Historical data about different material prices were collected for the desired study period​.
  •  Formula was derived to compute Construction Cost Index (CCI) and a base year was selected​.
  • The CCI was forecasted for each year using ML algorithms such as Neural Networks, Linear Regression,
    and Time Series ​.
  •  The models were evaluated for high accuracy to choose the best forecasting method​​.

Spend Analytics

Challenge

The complex and dynamic nature of construction projects has led to a series of cost overrun issues over the years. This cost failure is because of the presence of complexity and uncertainty in a project system.

Solution

  • A comprehensive analytics framework is required to track and control the construction costs throughout the project life cycle​.
  • Both internal and external data were collated across all the functions/departments in real-time​.
  • Most important financial KPIs were identified in coordination with the domain experts and project managers​​.
  • Developed interactive dashboards that generate insights, alerts, and recommendations customized for each stakeholder​.

Risk Assessment

Challenge

Project management can be challenging when it comes to large capital construction projects. They’re often late, over budget, and poorly executed. It is found that large-scale construction projects take 20% longer to finish than scheduled and are up to 80% over budget.​

Solution

  • To meet the unique requirements of each client, a customized ERP system has been designed and developed​.
  • A single ERP platform was used to manage all transactions and communications between the Owners, Main Contractors, and Subcontractors​.
  • A 360° view of the construction operations was available to project managers and stakeholders throughout the entire project​​.
  • Using our project management software, construction companies were able to effectively met budget and project timeline targets and prevented potential risks.

Smart Management

Challenge

An automotive manufacturer wanted to improve the efficiency of the painting process as a part of continuous improvement.

Solution

  • Proposed Mechanistic Data Science approach to forecast and optimize the process parameters.
  • Collected historic and live data of every quality incident and correlated them with the predefined KPIs.
  • Integrated the mathematical model for every process stage with our analytics platform and recommended improved process parameters.

Connected Construction

Challenge

A wheel manufacturing company faced a higher rejection and rework rate in the MIG welding process than in any other area of production.

Solution

  • Conducted a detailed research study on the possible causes of the most repeating welding defects Correlated the weld parameters, production reports, and testing results with welding defects to figure out the exact root cause.
  • Recommended the corrective action and developed intelligent dashboards to predict the process deviation.

Sustainable Sites

Challenge

An automotive parts supplier approached us to improve operational efficiency while reducing manufacturing costs.

Solution

  • Connected all the machines across the production line and gathered operational data in real-time to perform advanced analytics.
  • Developed interactive dashboards to monitor and optimize machine utilization, production loss, and process capability Increased visibility to the leaders to effectively plan resource and inventory management.