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CASE STUDY

Discrete Event Simulation for Manufacturing Optimization

Allowed manufacturing stakeholders to test operational changes in a virtual environment, eliminating costly real-world trial disruptions.

Situation

A manufacturing client needed to evaluate operational changes such as automation adoption, shift schedule adjustments, and workflow modifications. Physical testing was expensive, disruptive to production, and limited in scalability.

Solution

Developed a digital twin of factory operations using discrete event simulation to enable scenario testing across production configurations and operational strategies.

OUTCOMES

31% fewer
bottleneck-driven delays
$3.8M saved
disruption testing costs
6x faster
scenario planning cycles
Modeled shifts
for workforce schedules
22% higher
line throughput capacity

Challenges

Testing

  • Expensive physical testing
  • Production disruption risks
  • Limited scalability testing

Operations

  • Automation introduction complexity
  • Shift schedule adjustments
  • Workflow reconfiguration impact

Solutions

01

Factory Digital Twin

Modeled production lines, machines, and human operators as discrete simulation entities.

  • Represented machines as discrete actors
  • Modeled operator workflow interactions
  • Simulated end-to-end production flow
02

Throughput Simulation

Simulated throughput, bottlenecks, and queue dynamics across production systems.

  • Identified production bottlenecks early
  • Modeled queue accumulation patterns
  • Quantified throughput variability drivers
03

Variable Input Modeling

Incorporated configurable operational inputs across production scenarios.

  • Modeled shift pattern variations
  • Simulated equipment performance changes
04

Resource Reconfiguration

Simulated adding or removing resources across workflows and production stages.

  • Tested staffing level adjustments
  • Evaluated machine allocation strategies
  • Measured utilization improvements
05

Workflow Optimization

Evaluated alternative workflow structures under simulated production conditions.

  • Modeled alternative process layouts
  • Compared routing sequence options
  • Identified efficiency improvements
06

Peak Load Testing

Stress-tested production systems under peak operating conditions.

  • Simulated demand surge scenarios
  • Evaluated resilience thresholds
  • Identified scaling constraints early