Digital Twin Manufacturing: Optimizing Snack Food Production with Simio

Executive Summary

This case study examines how Argon Consulting implemented a Simio-based digital twin solution for a major Australian snack food manufacturer. The client faced significant scheduling challenges across multiple production sites, including a complex transition to a new state-of-the-art facility. By replacing manual Excel-based scheduling with a dynamic Simio model, Argon delivered a solution that optimized production scheduling, improved resource utilization, and supported strategic decision-making. The digital twin manufacturing approach enabled the client to visualize complex production constraints, test scenarios virtually, and seamlessly transition production between facilities while maintaining high customer service levels.

Client Background

The client is a major producer of snack food products in the Australian market, manufacturing multiple product types including potato chips, corn chips, and extruded or blown pellet snacks. Their products are sold in various formats, from large share bags to smaller lunchbox-sized multipacks. The company operated two manufacturing sites in Sydney but recently completed construction of a new state-of-the-art factory intended to replace the original facilities.

Argon Consulting had established a long-term relationship with the client, delivering various operational excellence projects including:

• Scenario modeling and design for the greenfield site
• Project management for commissioning and vertical startup
• Detailed capacity modeling of their automated warehouse
• Labor planning and process improvements

The manufacturing environment presented significant complexity, with multiple shared assets such as fryers and seasoning drums, intricate product routing, and strict operational constraints that made scheduling particularly challenging.

Challenge: Complex Scheduling in a Multi-Constraint Environment

The client’s scheduling process faced numerous challenges that limited operational efficiency:

Excel-Based Scheduling Limitations

Prior to the Simio implementation, all scheduling was performed using Excel spreadsheets. This manual approach couldn’t effectively handle the complex interactions between shared assets across the factory. The manufacturing simulation software needed to address several critical constraints:

• Complex Asset Sharing: Multiple product lines shared critical resources like fryers and seasoning drums, creating intricate dependencies that Excel couldn’t model effectively.
• Capacity Understanding Gaps: The true production capacity with varying product mixes wasn’t well understood, particularly the delicate balance required between small and large bag production.
• Rapid Innovation Cycles: The snack industry’s constant introduction of new products and flavors created additional complexity that was difficult to calculate using static tools.
• Siloed Planning Processes: Operations, maintenance, and planning teams struggled to align their activities, making it difficult to minimize disruption from engineering shutdowns while maintaining customer service levels.
• Manual Process Inefficiencies: Schedule changes required slow, manual updates to spreadsheets, limiting responsiveness to production issues.

The food production scheduling challenges were further complicated by specific operational requirements:

• Fryers needed to maintain constant output rates
• Flavor changeovers required specific sequencing from light to dark flavors
• Small bag production required simultaneous large bag production to balance fryer output

Solution: Implementing Digital Twin Manufacturing with Simio

Argon developed a comprehensive digital twin manufacturing solution using Simio to address these challenges. The implementation followed a phased approach:

Phase 1: Initial Model Development

• Created a scheduling model in Simio for the two existing sites
• Integrated the model with the client’s ERP system to automate data inputs
• Validated the model against actual production data
• Implemented the model into the regular planning process

Phase 2: Expansion to New Site

• Extended the model to include the new greenfield site
• Used the model for scenario testing to inform asset selection and site layout
• Supported the volume transition between old and new sites

Phase 3: Training and Handover

Trained the planning team to use Simio effectively

Established processes for schedule manipulation and optimization

Set up output tables to feed into operational dashboards

Manufacturing Simulation Software Architecture

The Simio model was designed to accurately represent the complex production environment:

• Process Modeling: The model focused on the critical production stages from frying to case packing, with assumptions that raw material inputs and warehouse operations were not constraints.
• Data Integration: The solution integrated with the client’s ERP system to import SKU master data, demand information, and bill of materials. Additional configuration data was maintained in Excel with Power Query.
• Resource Modeling: All production assets were modeled, including:
  • Fryers with specific output capacities
  • Seasoning drums with flavor constraints
  • Baggers for different package sizes
  • Case packers and inner highways
  • Shared resources and their interconnections
• Scheduling Logic: The model implemented complex scheduling rules:
  • Fixed sequence of product types (French fries → thin cut → crinkle)
  • Flavor sequencing from light (salt) to heavy (barbecue, chili)
  • Balancing of small and large bag production to maintain fryer output
  • Resource allocation based on availability and constraints
• Flow Modeling: The solution used Simio’s flow capabilities to continuously calculate bagger draw on fryers, ensuring proper balance and utilization.

Technical Implementation Details

The simulation modeling for manufacturing implementation leveraged several key Simio capabilities:

Model Structure

• Sources linked to demand tables where orders were released for each product stream
• Servers representing baggers that seized required resources
• Flow assets modeling the continuous production from fryers to baggers
• Resources representing shared assets like case packers and seasoning drums

Scheduling Intelligence

The model’s intelligence was primarily implemented through selection expressions and conditions that determined which orders were assigned to which baggers. Key constraints included:

• Fryer Cut Matching: Ensuring products were only assigned to fryers making the appropriate cut type (thin, crinkle, etc.)
• Flavor Drum Compatibility: Verifying that products were assigned to baggers with compatible flavor drums or dedicated seasoning
• Overdraw Management: Maintaining sufficient bagger capacity to handle fryer output while preventing excessive overdraw that would result in low utilization

User Interface and Interaction

The model provided multiple ways for planners to manipulate schedules:

• Drag-and-Drop Scheduling: Using Simio’s Operations view to move orders between lines
• Forced Routing: Assigning specific products to particular baggers through data tables
• Route Exclusion: Preventing certain products from running on specific lines
• Downtime Modeling: Adding planned maintenance or downtime to evaluate impact

Output Integration

The model generated detailed output tables that fed into:

• Power BI production dashboards
• Factory floor displays and tablets
• Daily, weekly, and monthly operational reviews

Results: Factory Scheduling Optimization Benefits

The implementation of the Simio-based digital twin delivered significant benefits across multiple areas:

Operational Improvements

• Enhanced Schedule Quality: More efficient production sequences with fewer changeovers
• Better Resource Utilization: Improved fryer utilization and balanced production
• Reduced Manual Effort: Planners spent less time creating schedules and more time on strategic decisions
• Increased Visibility: Schedule information available throughout the factory on displays and tablets

Strategic Capabilities

• Scenario Testing: Ability to evaluate different production scenarios before implementation
• Asset Investment Analysis: Data-driven decisions about when to invest in new equipment
• Volume Transition Support: Seamless management of production across three sites during transition

Organizational Alignment

• Cross-Functional Coordination: Better alignment between operations, maintenance, and planning
• Data-Driven Decisions: Shared understanding of constraints and capabilities
• Improved Service Levels: Maintained high customer service during complex site transition

Long-Term Value

The manufacturing capacity planning capabilities provided by the model continue to deliver value as the client:

• Evaluates future capacity needs
• Plans for new product introductions
• Optimizes production across their new facility

Future Applications and Ongoing Development

The success of the initial implementation has led to several ongoing and planned extensions:

Capacity Planning

The model is being used to evaluate long-term capacity requirements, helping the client determine:

• When additional equipment will be needed based on growth projections
• How to optimize the product mix to maximize existing capacity
• Which constraints should be addressed first for maximum benefit

New Product Introduction

The digital twin is now an integral part of the new product introduction process:

• Testing production of new flavors and products before launch
• Evaluating the impact on overall capacity and scheduling
• Optimizing packaging formats based on production constraints

Continuous Improvement

The model continues to evolve with:

• Additional data integration points
• Enhanced visualization capabilities
• More sophisticated scheduling algorithms

Conclusion: The Value of Digital Twin Manufacturing

The implementation of Simio’s manufacturing simulation software for this snack food manufacturer demonstrates the significant value of digital twin technology in complex production environments. By creating a dynamic, accurate model of the production process, Argon enabled the client to:

• Replace static, manual scheduling with dynamic, constraint-aware planning
• Gain unprecedented visibility into complex production interactions
• Make data-driven decisions about scheduling, capacity, and asset investments
• Successfully navigate a complex transition between manufacturing sites

The food production scheduling solution continues to deliver value as the client optimizes their new facility and plans for future growth. The digital twin approach has transformed scheduling from a tactical necessity to a strategic advantage, enabling more efficient operations and better decision-making throughout the organization.

This case study illustrates how Simio’s simulation capabilities can address even the most complex manufacturing challenges, delivering both immediate operational benefits and long-term strategic value.