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Thursday, June 26, 2025

Case Study: High Mix, Low Volume Manufacturing Optimization with Digital Twin Studio

Case Study: High Mix, Low Volume Manufacturing Optimization with Digital Twin Studio®

Problem Statement

A leading high mix, low volume electronics manufacturer faced mounting inefficiencies across its production lines. Challenges included unpredictable cycle times, excess WIP (work-in-process), inconsistent product quality, and limited visibility into scheduling bottlenecks. With growing customer demand for customization and quick turnaround, traditional methods proved inadequate.

Background

Established in 1992, the company operates in a competitive, fast-moving market where adaptability and precision are essential. While they had implemented lean practices and used traditional planning tools, these methods lacked the dynamic, real-time decision-making capabilities needed for modern high mix, low volume manufacturing. Leadership identified digital transformation as a strategic priority to maintain market relevance and improve responsiveness.

Solution

CreateASoft, leveraging its patented Digital Twin Studio®, provided a comprehensive simulation and optimization solution. The platform supports low-code, drag-and-drop modeling, historical and live data integration, and AI-driven insights, offering a highly visual and interactive digital environment to transform decision-making and operations.

Core Capabilities Deployed:
  • Discrete Event, Stochastic, and Smart Agent Simulation
  • Neural Network-Based Optimization
  • Real-Time Process and Resource Scheduling
  • Dynamic Bottleneck Detection
  • What-If Scenario Analysis
  • Predictive Analytics using AI/ML

Phase 1: Discovery

CreateASoft initiated a discovery engagement to map the facility’s overall layout, understand process constraints, and define the unique attributes of high mix, low volume operations. Data from MES, ERP, and manual logs was collected and cleansed. Layout drawings, production rules, and scheduling logic were compiled for simulation readiness.

Phase 2: Model Building and Deployment

Full Digital Twin Build (6–10 weeks):
A detailed digital replica of the facility was created, capturing all operational constraints, routing logic, product variation, and equipment-specific rules. The model accommodated alternate process flows and dynamically handled changeover times and variable batch sizes.

Real-Time Digital Twin Integration (6–10 weeks):
Live connectivity was established with shop floor systems. KPIs were defined and linked to real-time data feeds for continuous monitoring. AI/ML models were embedded to forecast potential delays and alert decision-makers to emerging risks.

Results

Within 30 weeks of deployment and iterative optimization, the results were significant:

  • 12.23% increase in throughput after optimizing material handling and delivery strategies
  • 1.81% additional performance improvement achieved through continuous daily optimization
  • 14% reduction in average cycle time through optimized resource balancing
  • 38% reduction in corrective actions via early detection of production delays
  • 300+ scenarios tested in the simulation environment—without disrupting live production

Manufacturing Operations Benefits with Digital Twin Studio®

Following the implementation, the manufacturer unlocked a range of operational and strategic advantages that extended far beyond initial expectations:

  • Process Optimization: Enabled real-time analysis of material flow, changeovers, and machine utilization. Identified and resolved bottlenecks proactively.
  • Historical Data Validation: Validated model behavior with historical data from MES/ERP systems, ensuring confidence in simulation accuracy and optimization recommendations.
  • Customization and Adaptation: Adapted rapidly to product mix changes, equipment configurations, and shifting production priorities without rebuilding the model.
  • Lean Analysis: Pinpointed waste across scheduling, labor allocation, and material handling. Supported continuous improvement with lean-aligned insights.
  • Scenario-Based Optimization: Enabled evaluation of schedule changes, layout modifications, equipment upgrades, and shift patterns before live implementation.
  • Real-Time Analytics: Delivered continuous visibility into OEE, throughput, downtime, and WIP using real-time data. Facilitated fast, informed decisions.
  • Training and Knowledge Transfer: Served as a training platform for new employees and empowered internal teams to explore and test process enhancements independently.
  • Cost Savings: Reduced labor inefficiencies, downtime, and excessive handling costs. Lowered operating expenses through optimized asset utilization.
  • Risk Management: Simulated worst-case scenarios and demand surges to ensure preparedness for unexpected disruptions.
  • Sustainability: Reduced waste and scrap while optimizing energy and resource consumption—contributing to environmental and cost objectives.

Outcome

The client achieved measurable ROI within the first quarter of full deployment. Product quality improved, scheduling became more consistent, and agility in handling diverse product requirements was significantly enhanced. Most notably, the digital twin became a foundational tool for decision-making, enabling both operational control and strategic foresight.

Conclusion

This case study illustrates how CreateASoft’s Digital Twin Studio® revolutionized operations at a high mix, low volume manufacturer. By merging simulation, AI, and real-time data into one powerful platform, the company transformed its manufacturing processes—achieving sustainable improvements in productivity, quality, and responsiveness. This success demonstrates the value of digital twins not just as a planning tool, but as a core operational intelligence platform for the future of manufacturing.

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