SUcomp TechSolutions

Menu

Digital Twin and Analytics

Enhancing Operational Efficiency in an Automobile Parts Manufacturing Plant

Client Spotlight

Our client is a leading US car automaker with a significant global presence, operating numerous business and plant facilities worldwide. Renowned for its cutting-edge automotive technology and manufacturing excellence, the client sought to enhance their plant operations with advanced Digital Twin and analytics solutions. This case study highlights how our collaboration aimed to address complex operational challenges and drive innovation in their production processes.

  • Industry

    Automobile

  • Location

    India

  • Duration

    6 months

Challenges

Visualization Challenges

Plant operators struggled to visualize and simulate issues occurring during plant operations, making it difficult to diagnose and address problems effectively.

Data Granularity

The lack of detailed data at the individual cycle and signal levels hindered accurate fault identification and in-depth analysis, limiting the ability to troubleshoot and optimize processes.

Comparative Analytics

Without robust analytics to compare cycle times across different operations, efforts to enhance overall plant performance and efficiency were impeded.

Uncertain Solution Requirements

The client faced uncertainty regarding the final solution as no similar solution existed in their current ecosystem. This made defining the precise requirements challenging and positioned the project as a pioneering endeavor.

Our Solution

  • Development of Digital Twin

    Our team, in collaboration with a Core Engineering partner company in Germany specializing in Simulation, Robotics, and Digital Twin Technologies, developed a comprehensive Digital Twin of the live production cell.

  • Data Extraction

    We utilized out-of-the-box Data Recorder software, which employed the OPC UA protocol to extract real-time data from the PLC and physical robot. This software facilitated seamless data capture from the operational devices.

  • Central Data Repository

    A central data repository was established to aggregate real-time data feeds from the Data Recorder software, with data being transferred at intervals of 50 milliseconds. This repository served as a foundational element for data management.

  • Data Transformation and Enrichment

    The data collected in the central repository was transformed, organized, and enriched to support advanced AI and Digital Twin solutions. This process ensured the data was ready for complex analysis and simulations.

  • Automated Workflow Integration

    We developed an automated workflow to continuously feed the Digital Twin model with real-time data from the central repository. This integration allowed for dynamic "What-If" simulations using actual data, enhancing the model’s accuracy and relevance.

  • Advanced Analytical Reporting

    Analytical reports were generated to compare different cycles and identify fault and delay signals. These reports provided actionable insights for improving plant operations and troubleshooting issues.

Journey To Success

Our journey with the client was marked by a meticulous approach to address their unique challenges using a Waterfall model of the software development lifecycle. The project, spanning six months, involved several critical phases:

Initial Planning and Requirement Definition

The project began with an in-depth planning phase where we worked closely with the client to define use cases and project objectives. Given the uncertainty surrounding the requirements, this phase was crucial in setting a clear direction for development.

Development Phases

Due to the pioneering nature of the project, the development team had to be adaptable, incorporating new changes and refining requirements as they emerged. This flexibility was essential for effectively addressing the novel challenges of creating a Digital Twin and analytics solution.

Core Engineering Collaboration

The project required close collaboration with the Core Manufacturing and Electrical Engineering teams. Their expertise was vital in developing the Digital Twin technology, ensuring that the solution accurately mirrored the client’s production environment and provided meaningful insights.

Systematic Implementation

Following the Waterfall model, each phase of the project was executed sequentially, from requirements gathering to design, implementation, testing, and deployment. This structured approach helped in maintaining clarity and focus throughout the development cycle.

Adaptation and Flexibility

Throughout the project, the team demonstrated flexibility to accommodate new insights and changes, which was essential given the project’s innovative nature. This adaptability ensured that the final solution met the client’s evolving needs and expectations.

Successful Delivery

The project culminated in the successful delivery of a cutting-edge Digital Twin and analytics solution. The structured approach, combined with collaborative efforts and flexibility, resulted in a valuable tool that significantly enhanced the client’s operational capabilities.

Technology in Action

  • .NET icon .Net
  • C#
  • Python
  • PostgreSQL

Impact & Results

Groundbreaking Solution

As the first of its kind, the project successfully demonstrated the feasibility and benefits of combining Digital Twin technology with advanced analytics. By extracting data from a live production cell, the project showcased how real-time data integration can enhance operational insights and simulations.

Foundation for Centralized Strategy

The project’s results were instrumental in shaping the central Digital Twin solution and architecture strategy at the organizational level. The insights gained from this project provided a solid foundation for developing a cohesive strategy to implement Digital Twin solutions across the client’s global facilities.

Enhanced Operational Insights

The implementation allowed for in-depth analysis of plant operations, including fault identification and performance optimization. The ability to run real-time simulations with actual data enabled more accurate and actionable insights, leading to improved decision-making and process efficiency.

Strategic Impact

The success of this pioneering project positioned the client as a leader in adopting advanced digital technologies in manufacturing. It demonstrated the potential of integrating Digital Twin and analytics solutions to drive innovation and operational excellence.

Improved Data Utilization

By developing a central data repository and advanced analytical tools, the project enhanced the client’s ability to utilize data effectively. This improvement in data management supports more informed decisions and continuous performance enhancement.

Lessons Learned

Early Cross-Functional Collaboration

Forming a cross-functional team early in the project is crucial for success. Collaborating with Mechanical, Electrical, and Software Engineers ensured that all necessary expertise was integrated from the start, allowing for a more cohesive and comprehensive solution.

Flexibility in Development Approach

A strict Waterfall model was challenging to apply due to the project's evolving nature. It was essential to incorporate flexibility in the requirements phase, allowing for iterative changes and development to proceed in parallel. This adaptability helped accommodate new insights and adjustments as the project progressed.

Iterative Requirement Refinement

For pioneering projects with uncertain requirements, iterative refinement is vital. Regular reviews and adjustments of the project scope and requirements facilitated alignment with the evolving understanding of the project’s needs.

Importance of Prototyping and Testing

Developing prototypes and conducting iterative testing were crucial in validating the solution’s effectiveness and identifying potential issues early. This approach helped in refining the Digital Twin model and analytics tools to meet the client’s needs more accurately.

Stakeholder Engagement

Continuous engagement with stakeholders throughout the project ensured that their expectations and feedback were consistently addressed. This proactive approach helped in managing requirements and aligning the solution with the client’s objectives.

Let's start Digital Transformation!

Talk to Us
Scroll to Top