Intelligent Digital Twin Simulation for Manufacturing Excellence

Transform your manufacturing operations with Simio’s Intelligent Digital Twin technology—enabling manufacturing leaders to validate process improvements before capital investment while achieving measurable ROI in today’s competitive Industry 4.0 landscape.

Manufacturing Challenges Solved Through Digital Twins

Today’s manufacturers face unprecedented challenges: shrinking production windows, increasing SKU proliferation, labor shortages, and the constant pressure to reduce waste while maximizing asset utilization. The competitive market demands rapid new product introduction while maintaining operational excellence across expanding product portfolios.

Digital twin technology addresses these critical manufacturing challenges by creating an accurate virtual replica of your entire production system. This enables process engineers and operations managers to validate improvement initiatives, optimize machine layouts, and stress-test production plans before implementation—eliminating costly trial-and-error approaches that disrupt production.

By creating a living model of your manufacturing operations, digital twins provide unprecedented visibility into process variability, cycle time fluctuations, WIP accumulation points, and resource utilization patterns that impact on-time delivery performance.

Discrete Event Simulation: Precision Modeling for Manufacturing Systems

Discrete Event Simulation (DES) provides the computational foundation for accurate production system modeling. Unlike static analysis tools, Simio’s Discrete Event Simulation technology captures the dynamic, interconnected nature of manufacturing processes where machine states, material flows, and resource availability constantly change throughout production runs.

Manufacturing leaders choose Simio’s Discrete Event Simulation technology because it accurately models:

Cycle Time Variability

Simio’s digital twin precisely models the natural variation in processing times that occurs in real manufacturing environments. This capability enables production teams to quantify how cycle time fluctuations cascade through the system, affecting throughput, WIP levels, and delivery performance.

Setup and Changeover Sequences

Manufacturing operations with multiple product variants require complex changeovers that impact productivity. Simio accurately models sequence-dependent setup times, allowing planners to optimize production sequences that minimize changeover time while meeting customer delivery requirements.

Buffer Allocation and WIP Management

Strategic placement of work-in-process buffers significantly impacts production flow and throughput. Simio’s simulation capabilities enable optimization of buffer sizes and locations to protect constraints, minimize lead time, and reduce overall inventory investment.

Tool Changes and Fixture Requirements

Tooling and fixture availability often create hidden constraints in manufacturing systems. Simio models the complex relationships between production schedules, tool life, and changeover requirements, ensuring feasible plans that account for these critical resources.

Labor Requirements and Skill Matrices

Workforce availability and skills significantly impact production capacity. Simio’s detailed labor modeling accounts for shift patterns, skill levels, training requirements, and absenteeism to create realistic staffing plans that balance labor costs and availability with production requirements.

Material Handling Systems

Efficient movement of materials between workstations is essential for production flow. Simio simulates complex material handling equipment including conveyors, AGVs/AMRs, cranes, and AS/RS systems, enabling optimization of routing, fleet sizing, and traffic management.

Quality Sampling and Rework Loops

Quality issues create variability that impacts throughput and delivery performance. Simio models inspection points, sampling plans, defect rates, and rework processes to accurately predict how quality issues affect overall production capacity.

Planned and Unplanned Downtime

Equipment availability directly affects manufacturing capacity. Simio incorporates detailed maintenance patterns, including scheduled downtime, random failures, and repair time distributions, ensuring realistic capacity planning that accounts for actual equipment reliability.

By capturing all these critical manufacturing variables in a single integrated model, Simio enables production teams to identify true system constraints, validate improvement strategies, and optimize production schedules with unprecedented accuracy. This comprehensive approach ensures that all decisions account for the complex interactions between resources, materials, and processes synchronized to the production timeline that ultimately determines actual manufacturing performance.

Solving Production Planning Challenges with Advanced Scheduling

Manufacturing organizations struggle with traditional production scheduling approaches that fail to accurately account for detail resource constraints, changeover optimization, labor requirements, tooling requirements and dynamic material availability. This leads to unrealistic schedules, excessive expediting, and missed delivery dates with potential lost sales.

Simio’s Advanced Planning and Scheduling (APS) solution leverages Intelligent Adaptive Process Digital Twin technology to generate feasible production schedules that optimize for multiple objectives:

Production Sequencing Optimization

Minimize changeover time: Reduce non-productive setup time by optimizing product sequencing based on shared attributes and tooling requirements.
Enhance product mix flexibility: Balance production across multiple product families while meeting varied customer demand patterns.
Optimize lot sizing decisions: Calculate ideal production quantities that balance setup costs against inventory carrying costs and due dates.

Resource Utilization Management

Maximize overall equipment effectiveness (OEE): Increase production capacity through improved scheduling of critical equipment and maintenance activities.
Balance labor utilization: Distribute workload across shifts and departments to reduce overtime while maintaining productivity.
Manage skilled operator allocation: Ensure workers with specialized skills are optimally assigned to tasks requiring their expertise.

Material and Inventory Coordination

Synchronize material flows: Coordinate component production and delivery to match assembly requirements across the entire value stream.
Implement pull-based replenishment: Generate signals for upstream processes based on actual consumption and buffer status.
Reduce WIP inventory: Minimize work-in-process through improved flow and synchronizing production across all workstations.

Fully Transparent “Glass Box” Approach

Visualize decision logic: See exactly how scheduling decisions are made rather than relying on hidden algorithms and unexplainable results.
Validate business rules: Test and refine scheduling rules with stakeholders to ensure they align with both business and operational requirements.
Build organizational trust: Create confidence in the scheduling system by providing clear visibility into how priorities and constraints are managed to meet the key KPIs.

Schedule Feasibility and Execution

Generate truly executable schedules: Create plans that account for all material, labor, and equipment constraints simultaneously as well as synchronize it to the execution timeline.
Adapt to disruptions in real-time: Quickly re-optimize schedules when unexpected events occur without destabilizing the entire production process and jeopardize overall supply chain performance.
Prioritize orders strategically: Balance customer delivery commitments by intelligently assigning resources and material to meet service levels.

Unlike conventional scheduling approaches, Simio’s digital twin planning integrates with MES and ERP systems to create executable schedules that account for actual shop floor conditions and timeline requirements, ensuring feasible plans that reduce expediting costs and improve on-time delivery performance.

Inventory Optimization Through DDMRP Simulation

Manufacturers struggle with traditional inventory management approaches that is getting more ineffective in today’s volatile demand and supply environment. Excessive safety stock ties up working capital while simultaneously missing service level targets on critical components can cause lost sales.

Simio’s digital twin technology enhances Demand Driven Material Requirements Planning (DDMRP) implementation by enabling dynamic testing of buffer strategies:

Buffer Strategy Design

Optimize strategic inventory positioning: Identify critical decoupling points across the bill of materials to protect production flow while minimizing total inventory investment.
Validate buffer profiles: Test various buffer profiles and adjustment factors to determine optimal inventory levels for each part or part category.
Implement dynamic adjustments: Simulate seasonal and market-driven buffer adjustment factors to optimize inventory levels with changing demand patterns.

Demand-Supply Synchronization

Test replenishment strategies: Compare various replenishment approaches (Min/Max, Order up-to, DDMRP, etc.) and their impact on service levels, inventory turns, and production stability.
Analyze lead time variability: Quantify how fluctuations in supplier and production lead times affect buffer sizing and service levels.
Optimize order quantities: Determine ideal replenishment order sizes that balance ordering costs with inventory carrying costs across the supply chain.

Supply Chain Resilience Testing

Simulate supply chain disruptions: Test buffer effectiveness during supplier failures, transportation delays, and demand spikes to validate system resilience.
Quantify working capital requirements: Calculate precise investment needed in inventory and resources to achieve the various service level and performance targets.
Evaluate recovery scenarios: Assess how quickly the system recovers or can even maintain performance from disruptions under different buffer management strategies and replenishment rules.

This simulation-driven approach ensures DDMRP implementations deliver the right balance between inventory investment and production performance, optimizing working capital while maintaining or improving customer service levels.

Core Manufacturing Applications

Simio’s digital twin technology provides comprehensive solutions for the most critical challenges facing modern manufacturing operations. By creating accurate virtual replicas of production systems, manufacturers can validate improvements, optimize operations, and drive strategic decision-making across multiple domains:

Constraint Identification and Throughput Optimization

Manufacturing leaders continuously seek to identify and eliminate bottlenecks that constrain throughput and limit production capacity. Simio’s digital twin capabilities enable operations teams to:

Identify primary and secondary constraints: Pinpoint limiting factors in complex production systems by analyzing resource utilization patterns and queue formations across the entire operation.
• Quantify improvement strategies: Evaluate the throughput impact of proposed improvements before implementation, ensuring capital investments target true system constraints.
Validate drum-buffer-rope implementations: Test production control mechanisms that maximize constraint utilization while managing the release of work throughout the system.
Optimize protective capacity: Determine the ideal amount of non-constraint capacity needed to ensure constraining resources remain fully utilized despite normal process variation.
Simulate the impact of setup reduction: Calculate how beter managing changeover times at constraint operations translate to improving overall system throughput.

Plant Layout and Material Flow Design

Inefficient plant layouts and material handling systems create waste, increase lead times, and reduce manufacturing flexibility. Production and Industrial engineers use Simio’s digital twin technology to:

Optimize machine placement: Minimize travel distances between sequential operations while accounting for space requirements and infrastructure constraints.
Test cellular manufacturing configurations: Evaluate the impact of moving from functional to cellular layouts on throughput, WIP, and lead time performance.
Validate material handling system designs: Determine optimal conveyor configuration, AGV numbers and routes, and manual transport methods before physical implementation.
Size buffer locations strategically: Identify critical points in the production flow that require inventory buffers to maintain system throughput and reduce variability in the process.
Simulate multiple layout alternatives: Compare different facility configurations using objective performance metrics to identify the optimal factory design.

Lean Manufacturing Validation

Manufacturing leaders implementing lean initiatives need to validate improvement impacts before full-scale rollout. Simio’s digital twin technology provides quantitative validation of lean manufacturing strategies:

Evaluate value stream improvements: Predict lead time reduction and throughput gains from proposed value stream enhancements before implementation.
Test pull system mechanics: Validate kanban sizing, inventory locations, and replenishment rules to ensure smooth material flow.
Optimize takt time balancing: Analyze line balancing alternatives to achieve consistent workflow matching customer demand patterns.
Simulate standard work implementation: Quantify how standardized work methods affect cycle time variation and overall process stability.
Validate SMED (Single-Minute Exchange of Die) initiatives: Calculate the production impact of reduced changeover times across different product mixes.

New Product Introduction Planning

Manufacturing organizations struggle with accurate capacity and material planning for new product introductions. Simio’s digital twin technology enables production teams to:

Validate capacity requirements: Determine if existing equipment can handle new product variants and identify potential bottlenecks before launch.
• Model labor learning curve impact: Incorporate productivity improvements over time as operators gain experience with new products and equipment.
Analyze changeover impact: Evaluate how new products affect overall changeover patterns and sequence-dependent setup times.
Test line balancing strategies: Optimize workstation assignments when integrating new products into existing production lines.
Predict tooling and fixture requirements: Identify potential constraints in tooling capacity when adding new products to the manufacturing mix.

Quality Management and Process Improvement

Quality issues significantly impact manufacturing performance through rework, scrap, and customer dissatisfaction. Simio’s simulation capabilities help quality teams:

Validate statistical process control strategies: Test sampling plans and control limits to optimize quality monitoring without excessive inspection.
Analyze defect patterns and root causes: Identify how process variation propagates through the system and impacts final product quality.
Quantify improvement initiative ROI: Calculate the operational and financial benefits of Six Sigma and other quality improvement programs.
Optimize inspection point placement: Determine where in the process flow quality checks deliver maximum benefit with minimal disruption.
Model yield rates and fallout: Incorporate realistic quality performance into capacity planning to ensure customer commitments can be met.

Manufacturing System Reliability

Production disruptions from equipment failures and maintenance activities significantly impact manufacturing performance. Simio’s digital twin technology enables maintenance and production teams to:

Optimize preventive maintenance scheduling: Determine ideal timing for planned maintenance activities to minimize impact on production capacity.
Evaluate system robustness: Test production system performance under various failure scenarios to identify vulnerabilities.
Analyze redundancy requirements: Determine where backup equipment or alternate routings provide the greatest protection against disruption.
Balance maintenance resources: Optimize the allocation of maintenance personnel and spare parts inventory across multiple production assets.
Test predictive maintenance strategies: Evaluate how condition-based maintenance approaches affect overall equipment effectiveness and throughput.

Factory-of-the-Future Planning

Manufacturing organizations must continuously evolve their operations to incorporate new technologies. Simio’s digital twin capabilities support technology transition planning:

Simulate human-robot collaboration: Model the integration of collaborative robots into manual workstations to optimize task allocation and safety protocols.
Evaluate automated material handling: Test AGV/AMR deployment strategies to ensure smooth integration with existing operations and infrastructure.
Validate IoT implementation benefits: Quantify the operational decision making improvements from enhanced data collection and real-time monitoring capabilities.
Analyze automation phasing strategies: Determine the optimal sequence for introducing automation to minimize disruption while maximizing benefits.
Test advanced manufacturing technologies: Evaluate how additive manufacturing, advanced robotics, and other Industry 4.0 technologies impact existing operations.

Sustainability and Environmental Impact Analysis

Modern manufacturers must balance operational performance with environmental responsibility. Simio’s digital twin technology helps sustainability teams:

Model energy consumption patterns: Analyze how production schedules and equipment utilization affect overall energy usage and peak demand.
Optimize resource utilization: Identify opportunities to reduce waste through improved process efficiency and material usage.
Evaluate green manufacturing initiatives: Test how sustainable practices impact production KPIs and operational performance.
Analyze carbon footprint reduction strategies: Quantify the environmental impact of alternative production approaches while maintaining productivity goals.
Simulate circular economy implementations: Model closed-loop material flows and recycling processes to reduce environmental impact and material costs.

Real-World Manufacturing Applications

Simio’s digital twin technology has delivered measurable results across diverse manufacturing environments. These case studies demonstrate how organizations have used simulation to solve complex challenges and achieve significant ROI:

$1.5 Million AGV Investment Savings

Company: Global electronics manufacturer.

Challenge: A television manufacturer needed to determine the optimal number of AGVs required for a new assembly line.

Solution: Dijitalis created a digital twin modeling production line operations, AGV paths, and material flow to test multiple fleet size scenarios.

$1.5 Million Savings

Eliminated unnecessary equipment while maintaining production targets.

60% Fleet Reduction

Required only 10 AGVs instead of 25 initially planned.

How Dijitalis Saved $1.5 Million with AGV Optimization Simulation in Electronics Manufacturing

Westinghouse: Nuclear Fuel Production Transformation

Company: Global nuclear fuel and technology provider.

Challenge: Westinghouse needed to optimize complex production scheduling across multiple nuclear fuel manufacturing facilities.

Solution: Their digital twin integrated multiple facilities in a single model with detailed routing logic and real-time constraint management.

30% Cycle Time Reduction

Decreased overall manufacturing lead time.

Improved On-Time Delivery

Enhanced production predictability and customer satisfaction.

Digital Twin Manufacturing: How Westinghouse Transformed Nuclear Fuel Production with Simio

Beverage Giant: Distribution Network Optimization

Company: Leading international beverage producer and distributor.

Challenge: The company needed to evaluate distribution strategies while balancing vehicle capacity and delivery windows.

Solution: The simulation incorporated distribution center operations, vehicle loading sequences, and route planning to compare scenarios.

$12.8 million

in annual operating cost reductions.

22% Transportation Cost Savings

Reduced overall logistics expenses while maintaining service.

Optimizing a Leading Beverage Company’s Last Mile Delivery Network: How a Supply Chain Consultancy Used Simio to Achieve $66 Million in Value

45× Manufacturing Scale-Up

Company: Metal fabrication company.

Challenge: A metal fabrication company needed to scale production from 600 to 26,000 units annually.

Solution: LMAC developed a digital twin modeling current and future processes, resource requirements, and layout alternatives.

4500% Capacity Increase

Successfully scaled production without operational disruption.

Optimized Capital Investment

Precisely identified equipment and labor requirements.

Scaling Manufacturing 45X: How LMAC Group Used Simio to Transform a Metal Fabrication Operation

Snack Producer: Production Line Optimization

Company: Major manufacturer of processed snack foods.

Challenge: The company needed to identify bottlenecks in their continuous processing lines.

Solution: Their digital twin modeled continuous processing operations, packaging configurations, and changeover sequences.

18% Throughput Increase

Enhanced overall production capacity.

Non-Intuitive Bottleneck Identification

Discovered hidden constraints affecting system performance.

Digital Twin Manufacturing: Optimizing Snack Food Production with Simio

Nissan: New Vehicle Assembly Line Design

Company: Global automotive manufacturer and engineering company.

Challenge: Nissan needed to validate production line design for a new vehicle model launch.

Solution: The simulation incorporated assembly operations, material delivery systems, and worker allocations before physical implementation.

Validated Production Capacity

Ensured line design met volume requirements.

Optimized Staffing Levels

Balanced labor resources across all workstations.

Nissan Europe Engineering Selects Simio to Model Production of NV200 Van

Brazilian Producer: Ethanol Production Optimization

Company: Leading ethanol producer from agricultural products.

Challenge: The company needed to balance harvesting capacity with processing capabilities while minimizing waste.

Solution: Their digital twin simulated field operations, transportation logistics, and processing plant capacity.

12% Utilization Improvement

Increased processing plant efficiency.

$500,000 saved

Reduction in planned CAPEX requirements.

Implementation Methodology for Manufacturing Success

Implementing digital twin technology in manufacturing environments follows Simio’s proven methodology:

Production System Analysis

Production Systems Analysis: Comprehensive documentation of all constraints, business rules, decision logic as well as complete material and information flows
Key Performance Metrics: Understanding the current and future business goals and key performance objectives
Current Planning Process: Understand and document the current planning workflow and success criteria including future aspirations
Future Growth Initiatives: Analyze and document future growth and process improvement initiatives including planned capital investment projects

Data Review and Pipeline Development

Relevant Data Sources: Assess all relevant enterprise data sources, including manually maintained Excel and CSV files, needed to generate and drive your Process Digital Twin
Quality of Data: Evaluate data quality, accessibility, and completeness while identifying gaps that could affect model accuracy
Data Integration: Develop automated data flows that connect your digital twin with enterprise systems through direct integration or cloud-based data infrastructure
Data Management Process: Implement validation, transformation, and governance processes to maintain information quality and consistency

Digital Twin Development

Process Logic Modeling: Accurate representation of production routing, decision points, and control logic
Resource Modeling: Detailed modeling of machines, tools, fixtures, and labor resources
Material Flow Simulation: Representation of inventory policies, material handling systems, and WIP management
Schedule Integration: Integrate with existing production planning workflows and reporting/dashboarding requirements

Performance Optimization

Constraint Analysis: Identification of system bottlenecks and throughput limitations
Scenario Testing: Evaluation of improvement alternatives and capital investment options
Schedule Optimization: Refinement of production sequencing, resource allocation and batch sizing strategies including the detailed decision logic at each step
Capacity Analysis: Validation of resource requirements for current and future product mix

Operational Deployment and Integration

Schedule Execution: Integration with MES and shop floor control systems
Performance Monitoring: Comparison of actual versus simulated performance metrics
Model Refinement: Continuous updating of the digital twin to reflect process improvements
Knowledge Transfer: Training of production teams on simulation-based decision making

Frequently Asked Questions

What ROI can manufacturers expect from implementing digital twin technology?

Organizations implementing Simio’s digital twin technology typically see multiple performance improvements, including 15-25% increased throughput through constraint identification and relief, 20-40% reduction in WIP through improved flow, 30-50% decrease in lead time, and 10-15% improvement in on-time delivery performance. Most implementations achieve payback within 3-6 months through a combination of increased production capacity and reduced operating costs.

How long does it take to implement a digital twin solution for manufacturing?

Implementation timelines vary based on project scope and complexity, but most manufacturing organizations can develop their first digital twin models within 4-8 weeks. Full implementation with integration to production systems typically requires 3-6 months. Simio’s templated approach and manufacturing-specific object libraries significantly accelerate model development compared to traditional simulation approaches.

How does digital twin technology compare to traditional manufacturing process improvement methods?

Unlike traditional process improvement approaches that rely on static analysis or limited pilot testing, digital twin technology creates a comprehensive virtual replica of the entire production system. This enables organizations to test improvement hypotheses across multiple scenarios without disrupting current operations, quantify the impact of changes before implementation, and optimize the entire system rather than individual components.

What technical expertise is required to develop and maintain digital twin models?

While Simio’s platform is designed for use by manufacturing engineers and process improvement specialists without extensive programming expertise, most successful implementations include team members with strong analytical skills and manufacturing domain knowledge. Simio provides comprehensive training and implementation support to ensure manufacturing teams can develop and maintain their digital twin models independently.

How does Simio’s digital twin technology integrate with existing manufacturing systems?

Simio provides extensive integration capabilities with manufacturing execution systems (MES), enterprise resource planning (ERP) platforms, industrial IoT devices, and other production data sources. This bidirectional integration enables digital twins to incorporate real-time production data while feeding optimized schedules and recommendations back to operational systems.

Can digital twin technology help with specific manufacturing challenges like high-mix/low-volume production?

Absolutely. Digital twin simulation is particularly valuable for complex manufacturing environments like high-mix/low-volume production, where traditional planning approaches often struggle. By accurately modeling changeover sequences, shared resources, and complex routing logic, Simio’s digital twin technology can optimize production schedules even in the most challenging manufacturing environments.

What level of accuracy can we expect from digital twin simulations?

When properly developed and validated, manufacturing digital twins typically achieve 90-95% accuracy in predicting key performance metrics like throughput, cycle time, and resource utilization. This accuracy improves over time as the model is refined based on actual production data. The level of accuracy is sufficient for confident decision-making while balancing model complexity and development time.

How does digital twin technology support continuous improvement initiatives?

Digital twins provide an ideal platform for testing continuous improvement ideas before implementation. Manufacturing teams can quantify the expected benefits of proposed changes, optimize improvement approaches through what-if analysis, and prioritize initiatives based on their projected impact on key performance indicators. This evidence-based approach significantly increases the success rate of improvement projects.