digital-twin-modeling

What it does

The skill provides a framework to build virtual replicas of supply chain systems for testing, scenario analysis, optimization, and predictive insights. It is oriented toward digital twins of warehouses, networks, or production, and includes guidance on enabling what-if analysis, scenario testing, and operational testing.

How it works

• It presents a Digital Twin Framework with multiple maturity levels (Descriptive to Autonomous) and a layered architecture (Digital Twin Layer, Analytics Layer, Data Integration Layer, Physical Assets Layer).
• It explains Simulation Modeling Approaches, focusing on Discrete Event Simulation (DES) as the core method for modeling orders, resources, queues, and processes. It provides a Python example using SimPy to model a warehouse digital twin with docks, workers, and processes (receive, pick, pack, ship), plus performance metrics (orders completed, average cycle time, queue times, throughput).
• It includes a Scenario Analysis workflow: define a base configuration, specify scenarios (e.g., Add_Pickers, Add_Packing), run multiple replications, collect metrics, and compare results with a DataFrame and summary statistics. It also shows how to visualize results (boxplots) for cycle time and throughput.
• It covers Advanced Digital Twin Components (Agent-Based Modeling) with sample ABM components such as SupplierAgent and ManufacturerAgent scaffolding for distributed decision-making and emergent behavior, including step methods and order handling, though the supplied snippet is partial.

When to use it

• Use when designing a digital twin for testing supply chain operations, testing policies, or conducting what-if analyses prior to real deployments.
• Use when you need to simulate DES-based processes (order fulfillment, manufacturing, warehousing, transportation) and compare scenario impacts on metrics like cycle time and throughput.

What it can touch

• Tools: claude-code (declared tool)
• Code structure examples provided include Python with SimPy, and ABM code scaffolds (Mesa) for agent-based modeling.

Caveats

• The skill relies on Python and library-specific constructs (SimPy, potentially NumPy, Pandas, Matplotlib/Seaborn for visualization). Ensure environment compatibility and licensing (MIT).
• The material includes example code snippets that illustrate modeling approaches but may require adaptation to real data and systems. The AR/AR details and implementation specifics may vary by use case.