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

Introduction

When a New Zealand metal fabrication company needed to scale production of their proprietary product from 600 to 26,000 units—a 45-fold increase—they faced a critical strategic decision. Could they achieve this dramatic scaling while keeping manufacturing onshore and reducing unit costs? Rather than making capital investment decisions based on assumptions, they partnered with LMAC Group to develop a data-driven approach using Simio simulation software.

This case study examines how LMAC Group utilized simulation to model the entire production process, identify constraints, test optimization scenarios, and design a future state factory capable of meeting ambitious production targets. The project demonstrates how simulation technology can transform manufacturing decision-making by providing concrete data before physical implementation, ultimately reducing risk and optimizing capital investments.

Customer Background

LMAC Group is a New Zealand-owned and operated productivity consulting firm founded in 2005. With consultants based across New Zealand, Australia, the Asia-Pacific region, and Europe, LMAC specializes in helping organizations achieve high performance through strategic operational improvements. Their approach integrates lean methodologies, process optimization, and technology implementation to drive transformation at both organizational and industry levels.

“We pride ourselves on being independent,” explains Adam, the LMAC representative who led this project. “Our job is to help organizations understand their strategy, the transformation they need to go through to achieve that strategy, and then provide independent advice based on what software, what automation, or what markets they should be entering.”

The client in this case study is a New Zealand-based metal fabrication company specializing in engineering, fabrication, and production of metal products. They had developed a proprietary product that had successfully completed a pilot production run of 600 units using their existing facility, equipment, and workforce. The product had proven successful in the market, creating an urgent need to scale production dramatically to meet demand.

Challenge Statement

The metal fabrication company faced a complex scaling challenge with multiple constraints and objectives:

• Production Volume: Scale production from 600 units to approximately 26,000 units in the same timeframe—a 45-fold increase in output.
• Cost Reduction: Simultaneously reduce the per-unit cost of production to maintain competitiveness.
• Onshore Manufacturing: Keep production in New Zealand rather than outsourcing to offshore facilities, supporting local employment and maintaining quality control.
• Product Flexibility: Accommodate variations in product size and shape while maintaining the same basic production process.
• Capital Investment Optimization: Make data-driven decisions about facility modifications, new equipment purchases, or complete redesign to achieve the required scale.

The manufacturing process involved multiple steps: laser cutting of metal sheets into components, folding, assembly, and finishing. The existing setup had been sufficient for the pilot run but would clearly require modification to achieve the dramatic scaling target.

“The challenge for them now is how do we get to scale of that production,” Adam explained. “The pilot itself was very successful. But in order to reach the scale that they need, they’re actually going to have to look at a new facility or at least optimization of their current facility in some way.”

• Before committing to significant capital expenditure, the company needed to understand:
• The maximum potential production capacity of their current factory configuration
• The specific constraints limiting production in the current setup
• Potential optimization strategies that could be implemented without major capital investment
• The optimal future state design if new equipment or facilities were required

Simulation Approach

LMAC Group partnered with Simio to develop a comprehensive simulation approach that would answer these critical questions. The project followed a structured methodology:

1. Process Mapping and Data Collection

The team began by mapping the entire production process using design software to document each step in the manufacturing flow. They collected detailed data on:

• Machine processing times
• Setup times for each operation
• Material handling requirements
• Worker movement patterns
• Storage capacity and locations

This data provided the foundation for building an accurate simulation model.

2. Current State Model Development

Working with Simio, LMAC developed a detailed simulation model representing the complete production process from raw material to finished product. The model included:

Material Flow Modeling: The simulation tracked metal sheets from initial storage through cutting operations (where larger sheets were transformed into multiple smaller components), to component storage, through various processing stations (folding, shaping, etc.), and finally to assembly and finished product storage.

Worker Behavior Modeling: One of the most complex aspects of the model was accurately representing worker behavior, including:

• Transportation of materials between stations
• Operation of machines requiring manual intervention
• Prioritization of tasks based on production requirements
• Batch movement of materials where appropriate

“The biggest hurdle for this specific model, when it came down to modeling the workers themselves, is understanding how do we prioritize correctly the seizing at a machine and the picking from a rack,” explained Chiara, the Simio solutions engineer who worked on the project.

Storage Rack Modeling: The team developed sophisticated process logic to model the storage and retrieval of components from racks throughout the facility:

• Interrupting holding of specific products when needed downstream
• Releasing products based on production requirements
• Tracking inventory levels at each storage location

Machine Processing: The model incorporated accurate representations of each machine’s capabilities, including:

• Processing times for different product variations
• Setup requirements between product changes
• Capacity constraints

3. Scenario Development and Experimentation

With the baseline model established, the team designed experiments to test various optimization scenarios:

Worker Allocation Strategies: Testing different approaches to worker assignment and task prioritization to optimize resource utilization.

Shift Pattern Modifications: Evaluating the impact of implementing multiple shifts to increase production without additional equipment.

Equipment Modifications: Assessing the potential impact of adding capacity to specific machines identified as constraints.

Storage Capacity Adjustments: Testing whether increasing storage capacity at key points would improve overall flow.

Each scenario was evaluated based on throughput (completed units), machine utilization, worker utilization, and identification of production bottlenecks.

Implementation Details

The implementation of the simulation project involved several key phases and technical challenges:

Model Development Process

The team took an iterative, layered approach to building the simulation model:

• Foundation Layer: Initially establishing the basic process flow without workers to validate the production sequence.
• Storage Logic Development: Creating sophisticated process logic to model the storage racks and component flow between operations.

“When we first started modeling, the most important part when starting a model is really to break down the pieces into manageable sized sub-projects,” Chiara explained. “We had to start with what the flow overall looked like. Not really focusing too much at the beginning on the worker allocations, but starting with maybe just getting the flow and getting the processes and getting the storage areas correctly modeled.”

• Worker Behavior Implementation: Adding workers with complex decision-making logic to represent realistic behavior.

“We needed to use process logic to add some sort of decision making to be able to modify their behavior and say, okay, based on your ride capacity, which is going to be the maximum quantity, when do I want you to go pick the product? Do I want you to pick it when there’s 25 pieces available? Do I want you to wait when all 50 are available? Or do I just want you to go pick one at a time?” Chiara described.

• Data Integration: Connecting the model to data tables that allowed easy modification of parameters like processing times, capacities, and worker schedules.
• Experiment Setup: Creating a structured approach to testing different scenarios and collecting results.

Technical Challenges and Solutions

The team encountered and overcame several technical challenges during implementation:

Storage Rack Modeling: Initially, the team used servers to model holding areas, but this approach proved limiting. They transitioned to using racks with custom process logic to interrupt holding and release products as needed.

“We then transitioned to using racks. But how can we interrupt the holding of only specific products at certain times? And so to do that we really used the support of some of our SIM bits to understand what is the basic process logic that we would want to use to interrupt that holding,” Chiara explained.

Worker Prioritization: Developing logic to ensure workers prioritized the most critical tasks rather than getting stuck in inefficient patterns.

“We needed to take all of that into consideration. And the biggest hurdle for this specific model, when it came down to modeling the workers themselves, is understanding how do we prioritize correctly the seizing at a machine and the picking from a rack?” Chiara noted.

Batch Movement Modeling: Creating flexible logic to represent workers moving materials in batches of varying sizes based on product characteristics.

Validation and Verification

The team validated the model by comparing its output to the known production results from the pilot run of 600 units. This ensured the simulation accurately represented the current production system before using it to predict future scenarios.

Results and Business Impact

The simulation project delivered several valuable insights and business impacts:

Current State Capacity Analysis

The baseline simulation confirmed that the current factory configuration could not achieve the target production volume of 26,000 units within the specified timeframe. This validated the need for either significant process optimization or capital investment in new equipment.

Constraint Identification

The model identified specific constraints in the production process:

Worker Allocation Inefficiencies: The simulation revealed that workers were sometimes underutilized in some areas while creating bottlenecks in others. By visualizing worker movement and utilization, the team identified opportunities for improved task allocation.

Machine Capacity Constraints: The model highlighted specific machines that were limiting overall throughput, providing clear targets for potential capacity increases.

“The dashboards that show things like machine utilization are really powerful in this because we’re able to show we can produce this much in a day or in a week. But look at where the machines are starved of material, where they’ve got downtime,” Adam explained.

Storage Capacity Limitations: The simulation identified points in the process where insufficient storage capacity was creating bottlenecks.

Optimization Opportunities

Scenario testing revealed several optimization opportunities:

Shift Pattern Impact: Implementing dual-shift operations significantly increased production output without requiring capital investment.

Worker Allocation Improvements: Modifying worker allocation rules improved flow and reduced waiting time, increasing overall efficiency.

Batch Size Optimization: Adjusting batch sizes for material transport optimized worker movement throughout the facility.

Data-Driven Investment Planning

Perhaps most importantly, the simulation provided a data-driven foundation for capital investment decisions:

ROI Calculation: By modeling the impact of new equipment or facility modifications before implementation, the company could calculate expected ROI with greater accuracy.

Risk Reduction: The ability to test multiple scenarios virtually reduced the risk associated with major capital investments.

Phased Implementation Planning: The simulation helped identify which improvements would deliver the greatest impact, allowing for prioritized, phased implementation.

Conclusion and Future Applications

The partnership between LMAC Group and Simio successfully delivered a comprehensive simulation solution that answered the metal fabrication company’s critical questions about scaling production. The approach demonstrated how simulation technology can bridge the gap between current capabilities and future requirements, reducing risk and optimizing investment decisions.

“A lot of the logic that we’ve been able to develop in here in terms of workers, in terms of racks, in terms of that first server as well, because that first server takes one piece in and produces multiple pieces out… it’s really given us a foundation to be able to take this to other clients as well, because a lot of these challenges that we’re talking about here are very common in manufacturing,” Adam concluded.

The next phase of the project will involve developing a detailed 3D model of the proposed future state factory, incorporating material flow, labor movement, and equipment layout. This model will enable precise ROI calculations for capital investments and support implementation planning.

The approach demonstrated in this case study has broad applicability to manufacturing organizations facing similar scaling challenges, particularly those seeking to maintain domestic production while competing globally. Key transferable elements include:

• End-to-End Process Modeling: The ability to simulate complete production systems rather than isolated components.
• Worker Behavior Simulation: Sophisticated modeling of human resources to optimize allocation and identify training needs.
• Scenario Testing: Virtual experimentation with different configurations before physical implementation.
• Data-Driven Decision Making: Replacing assumptions with concrete data for capital investment planning.

As manufacturing organizations worldwide face increasing pressure to optimize operations, reduce costs, and respond quickly to market demands, simulation approaches like the one demonstrated by LMAC Group and Simio will become increasingly valuable tools for strategic decision-making and operational excellence.