In the high-stakes world of industrial logistics, the margin between a fluid operation and an inbound logistics bottleneck is often dictated by “invisible” variables. To navigate this complexity, Globant’s Digital Twins Studio has introduced the Yard Lab: a dynamic, stochastic simulation designed to move beyond static modeling. As the inaugural phase of a comprehensive four-phase Supply Chain roadmap, the Yard Lab provides a digital proving ground for inbound logistics, allowing operators to stress-test facility throughput and resource management in a risk-free virtual environment before a single asset is deployed.
This innovation directly targets a multibillion-dollar friction point that has become a primary driver of global supply chain inflation. Inbound congestion and facility bottlenecks currently impose an annual $108.8 billion burden on the industry, a financial drain equivalent to over 435,000 truck drivers sitting idle for an entire year. By addressing the root causes of this “idle engine”, which costs individual fleet owners an average of $7,588 per truck and wastes 6.4 billion gallons of fuel annually, the Yard Lab transforms logistics from a reactive cost center into a proactively optimized strategic instrument.
Quantifying Resource Risk in a Volatile Environment
Traditional logistics models often rely on averages, a method that consistently fails to account for the “waves” of congestion caused by real-world variability. In practice, inbound logistics are frequently impacted by compounding bottlenecks, with administrative delays at the gate exacerbated by physical handling and storage issues. The Yard Lab moves beyond these static assumptions by using Discrete-Event Simulation (DES) to create a digital twin logistics and quantify real-time risk of resource contention.
By isolating the variables that trigger facility friction, the simulation tracks the lifecycle of a unit through several critical modular events:
- Stochastic Arrival Modeling: Instead of predictable schedules, the model simulates the “bursty”, irregular arrival of containers, mirroring the unpredictability of global supply chains.
- Orchestration Queuing: When gates or yard spots reach capacity, the system accurately models the resulting entry queues and calculates the exact operational cost of every minute of delay.
- Service Variability: By accounting for the natural fluctuations in paperwork processing and mechanical unloading times, the simulation reflects the reality of human and mechanical inconsistency.
- Dynamic Storage Assignment: Units are managed through indexed slots or global counters until a “Warehouse Call” is triggered, enabling precise evaluation of yard density and throughput.
Armed with this visibility, decision-makers can adjust their infrastructure to handle the specific pressures of a volatile supply chain.
Driving Value Through Supply Chain Predictive Analytics
By establishing a high-fidelity Digital Twin of the yard, logistics leaders can pivot from crisis management to strategic oversight. The Yard Lab serves as a strategic sandbox where operational assumptions are tested against data-driven realities:
- Bottleneck Anticipation: Quantify risk by calculating average and maximum queuing times before they manifest in the physical yard.
- Risk-Free Policy Testing: Evaluate the impact of new gate protocols or administrative shifts without disrupting live operations.
- Optimal Capacity Sizing: Determine the precise number of gates and yard spots required to maintain target service levels during peak volatility.
- Saturation Forecasting: Leverage live data feeds to predict yard saturation within a critical 4-to-8-hour window, enabling tactical pivots.
To ground these benefits, the Yard Lab provides actionable insights across various industrial scenarios:
- Gate Capacity Optimization: Simulating lane expansion to determine if a fourth lane justifies the investment during peak hours.
- Yard Saturation Management: Evaluating how fluctuating arrival intervals impact the density and efficiency of a 108-spot facility.
- Digital Twin Integration: Integrating real-time gate data into 3D environments for immediate, “at-a-glance” operational adjustments.
The Engine Behind: A High-Performance Data Pipeline
While the interface is intuitive, the underlying architecture is a sophisticated, multi-tier pipeline designed for speed and clarity. The process begins with C++, which handles the heavy lifting of the discrete-event simulation with maximum performance. This raw data is refined via Python, transforming complex variables into structured, readable insights, while Unity serves as the visualization layer, converting data into a 3D digital representation of the yard. Finally, the system uses automated shell scripts to facilitate parametric sensitivity analysis, enabling the team to run and compare multiple scenarios rapidly to identify the optimal configuration.
Expanding the Roadmap into Inventory Management
The completion of the Yard Lab marks the successful conclusion of Phase 01 of the Warehouse LAB initiative, establishing a functionally complete model for the stochastic timing and resource contention inherent in inbound logistics. With the inbound flow now accurately simulated, the project is poised to transition into Phase 02: Inventory Management. In this next evolution, the digital assets managed within the yard will serve as the direct input stream for downstream warehousing tasks, bringing the initiative one step closer to a fully integrated, end-to-end supply chain simulation that views facility orchestration as a synchronized, data-driven ecosystem.
Find more about our work at the Globant Digital Twin Studio here.
