Dijitalis Consulting, a leading simulation and optimization firm, was tasked with optimizing Automated Guided Vehicle (AGV) investments for a global electronics manufacturer. The client was planning significant facility upgrades, including replacing their outdated AGV fleet of 132 vehicles that frequently caused production delays. Using Simio’s powerful simulation capabilities, Dijitalis created a comprehensive digital twin manufacturing model of the 72,000m² facility, including 15 assembly lines, 6 AGV parking areas, and 169 delivery points.
The simulation revealed that only 95 AGVs were required—37 fewer than initially proposed—resulting in capital expenditure savings exceeding $1.5 million. Beyond cost reduction, the Simio model became an invaluable continuous improvement tool, allowing the client to test layout modifications, process changes, and production schedules before implementation. This case study demonstrates how simulation-based decision making can deliver substantial ROI while ensuring operational excellence.
Dijitalis Consulting was established in 2006 in Istanbul and has successfully delivered over 250 projects for more than 400 clients across 15 countries. The company specializes in building mathematical models to analyze material flow, identify inefficiencies, and test facility improvements for clients in automotive, manufacturing, logistics, and textile sectors.
The client, a global electronics manufacturer, operates a 34,000m² production facility with 15 assembly lines. Their existing AGV fleet of 132 vehicles was outdated and frequently caused production delays by getting stuck in the path network and failing to deliver materials on time. The company was initiating major investments, including automated warehouses, a new paint shop, increased production capacity, and a new AGV fleet.
The client faced a complex decision regarding their AGV investment. While the primary question was how many AGVs to purchase, the challenge extended far beyond simple fleet sizing:
AGV manufacturers typically aim to sell as many vehicles as possible without conducting detailed analysis to determine the optimal number. They rarely develop complex models to prove their recommendations, leaving clients to make decisions based on rough estimates or past experience.
The facility’s material handling system was highly complex:
The client needed answers to numerous interconnected questions:
Traditional spreadsheet-based calculations couldn’t account for the dynamic interactions between these factors, making simulation the only viable approach for comprehensive optimization.
Dijitalis employed a structured approach using Simio simulation software to create a comprehensive digital twin of the facility’s material handling operations:
The team collected and validated extensive data including:
After careful analysis, the team designed a push-based supply system rather than a pull system. This decision was based on several factors:
Implementation Practicality: A pull system would require calculating optimal reorder points and quantities for thousands of different SKUs, making it impractical to implement and manage.
Operational Simplicity: The push system required only one parameter—how many minutes before changeover to begin supplying materials for the next product.
Storage Availability: The assembly lines had sufficient storage space to accommodate the push system’s approach.
The process used production plan data, pallet information, and bill of materials to calculate remaining production time and trigger material supply for the next product at the appropriate moment before changeover.
The Simio simulation software’s object-oriented structure and data-driven capabilities were instrumental in creating an accurate, flexible model:
The model utilized Simio’s data table functionality to import and manage:
Simio’s capabilities enabled:
The model provided powerful visualization capabilities:
The simulation revealed several design issues that would have caused operational problems:
The initial design included a bidirectional single lane in a high-traffic area. The simulation demonstrated that this would cause deadlocks as AGVs from opposite directions would block each other. The team recommended changing to single-directional paths in opposite directions, eliminating the deadlock risk.
Traffic Optimization
Heat maps generated from the simulation identified:
These insights led to layout modifications that improved traffic flow and prevented bottlenecks.
Using Simio’s experiment module, the team conducted 35 scenarios testing different combinations of:
The primary KPI was production delay, with a target of zero minutes. Secondary KPIs included AGV utilization rates and the number of AGVs available in parking areas as a buffer for maintenance or breakdowns.
The simulation-based optimization delivered substantial benefits:
The optimal scenario required only 95 AGVs instead of the initially proposed 132, representing a 28% reduction in fleet size. With an average AGV cost of $50,000, this translated to capital expenditure savings exceeding $1.5 million.
The simulation determined:
The optimized scenario achieved average AGV utilization rates of 65-66%, representing an efficient balance between resource availability and operational requirements. The simulation revealed utilization patterns throughout the day, with peaks during morning changeovers and afternoon batch productions.
Most importantly, the optimized configuration ensured zero production delays due to material delivery issues, maintaining production efficiency while minimizing capital investment.
The value of the Simio model extended far beyond the initial AGV fleet sizing:
The simulation model became a continuous improvement tool for:
The single simulation project provided multiple tools:
As the client’s product portfolio and production requirements evolve, the Simio model continues to provide value by:
This case study demonstrates the transformative impact of simulation-based decision making on capital investment planning and operational optimization. By replacing rough estimates and vendor recommendations with data-driven analysis, Dijitalis helped their client:
The project showcases how Simio’s powerful simulation capabilities can deliver substantial ROI while providing insights that would be impossible to obtain through traditional analysis methods. For manufacturing and logistics operations facing complex material handling challenges, simulation offers a proven approach to optimize investments and enhance operational performance.