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Process Optimization Solutions

The Industrial Internet of Things (IIoT) and Artificial Intelligence/Machine Learning (AI/ML) are revolutionizing manufacturing by enabling intelligent process optimization. Here’s a breakdown of how these technologies work together:

IIoT: Capturing Real-Time Data from the Shop Floor

  • IIoT refers to the network of sensors, actuators, and machines that collect and transmit data across a manufacturing facility. Sensors attached to equipment monitor various parameters like vibration, temperature, energy consumption, and production output.

  • This real-time data provides a granular view of what’s happening on the shop floor, enabling a deeper understanding of process performance.

IIoT and AI/ML are powerful tools that, when combined, can transform manufacturing processes. By leveraging real-time data and AI-powered insights, companies can achieve significant improvements in efficiency, quality, cost savings, and overall business performance.

Digital Edge offers Vadict Process Optimization Solutions backed by AI / ML. Vadict helps companies to improve, optimize, and stabilize manufacturing processes. Vadict expertise helps companies analyse, adjust, and control processes to drive improvements in quality and overall process efficiency resulting in improvement in Overall Equipment Efficiency (OEE).
Vadict Process Optimization solutions encompasses OT & IT integration, It is backed by IIoT to connect and collect data from Operational Technologies (OT), at the same time, they can collect data from ERP. Vadict data visualization tools can quickly analyse large data sets of historical parameters set for processes and result achieved. Using these data points, Vadict process optimization solutions can identify opportunities for Process Optimization


Key features of Vadict Customized Process Optimization Solutions -
IIoT backed with IT integration capabilities
AI/ ML capabilities to identify causes of low performance
Visual simulation of process improvements with trends
Visualisation of historical data for better understanding of issues
Identification of data points/ aspects to be modified or modelled
Capability of processing and analysing large historical production data sets

process optimization using AI/ML backed analytic-solutions

Important Data Points

Plant automation systems (PLC, SCADA, DCS, Smart Meters)
Plant execution systems e. g. Online Logbooks, Safety and Air Quality monitoring systems
Enterprise Systems (SAP, MRP),
Proprietary systems/applications/Excel sheets

Why Digital Edge?

  • Strong domain expertise.
  • Vast experience of the founder and Vadict team
  • Focussed approach and timely completion
  • Continuous handholding post project deployment

“If I had one hour to save the world, I would spend fifty-five minutes defining the problem and only five minutes finding the solution. “

Albert Einstein

Scientist

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    FAQs

    AI/ML algorithms analyze the vast amount of data collected by IIoT sensors. These algorithms can identify patterns, trends, and anomalies that would be difficult or impossible for humans to detect manually.

    • By analyzing this data, AI/ML models can:
      • Predict equipment failures

      • Optimize production processes

      • Reduce waste and defectsAutomate repetitive tasks

    • Increased productivity
    • Reduced downtime

    • Improved quality

    • Lower costsEnhanced decision-making

    • Improved safety

    • Predictive maintenance 
    • Optimized Production Scheduling
    • Efficient Resource Allocation
    • Real-time inventory management
    • Reduced Energy Consumption
    • Automated quality control
    • Dynamic pricing and production adjustments
    • Supply Chain Optimization
    • Improved Sustainability
    • Continuous Improvement

    While process optimization offers significant benefits, it’s not without its challenges. Here are some of the key hurdles companies face:

    • Data Silos and Integration Issues
    • Lack of Skilled Workforce
    • Resistance to Change
    • Security Concerns
    • Cost of Implementation
    • Lack of Clear Goals and Objectives
    • Integration with Legacy Systems
    • Data Quality and Standardization