An advanced supply chain and logistics optimization tool that handles demand forecasting, inventory reduction, route planning, and simulation framework modeling.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install supply-chain-optimizer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install supply-chain-optimizer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Supply Chain Optimizer?
The Supply Chain Optimizer is a comprehensive Openclaw Skills extension built to modernize enterprise operations. By bridging multi-model predictive engineering with practical business realities, this tool streamlines multi-echelon supply chain networks. It enables businesses to move away from spreadsheet-bound logistics and scale toward systematic, data-driven decisions that cut down carbon emissions, minimize holding costs, and secure material workflows.
At its core, this agent skill provides a unified workflow for demand forecasting, inventory tuning, multi-constraint vehicle routing problem (VRP) solving, and robust vendor benchmarking. Developers and operation leads can integrate this system to continuously recalibrate operational pipelines and discover hidden return on investment (ROI) within intricate supply structures.
Supply Chain Optimizer Use Cases
- Manufacturing enterprise inventory turnover optimization to lower warehouse footprints.
- Logistics and delivery fleet routing optimization using time-window and weight constraints.
- Procurement division workflow handling automated vendor evaluation, filtering, and contract negotiation preparation.
- Risk management simulations preparing alternative operational blueprints against global supply chain disruptions.
How Supply Chain Optimizer Works
- Data Ingestion: The agent prompts users to upload historical sales figures, current stock ledgers, and logistics transport network data maps.
- Network Modeling: It constructs a programmatic multi-level supply chain structure, binding real-world physical and financial constraints to the data model.
- Analysis & Optimization: Algorithmic components execute mathematical optimization models to output peak safety stock layouts, replenishment timelines, and delivery sequences.
- Simulation Validation: The plugin feeds the strategic outputs through Monte Carlo simulations to pressure test systemic resilience against chaotic demand spikes or fulfillment gaps.
- Actionable Delivery: A definitive, machine-readable report is compiled, mapping out deep analytical performance matrices, key performance indicators (KPIs), and estimated ROI projections.
Supply Chain Optimizer Setup
To deploy this skill within your local Openclaw environment, you must initialize the required data analysis and simulation dependencies. Run the following installation commands in your terminal environment:
# Install the Openclaw plugin directly via the manager
openclaw install skill supply-chain-optimizer
# Enable system dependencies for core forecasting frameworks
pip install pmdarima prophet torch
# Verify your environment setup satisfies Openclaw Skills prerequisites
openclaw verify --skill supply-chain-optimizer
Supply Chain Optimizer Data Schema & Taxonomy
The skill establishes a strict repository taxonomy under your project's data directory. It produces standard, structured CSV tables alongside interactive dashboard matrix metrics:
| Data File Component | Output Content Schema | Analytical Metrics Extracted |
|---|---|---|
demand_forecast.csv |
Multi-model projections (ARIMA, Prophet, LSTM) | Confidence intervals, seasonal patterns, promotion impacts |
inventory_plan.csv |
Multi-echelon stock levels, Economic Order Quantity (EOQ) | Safety stock thresholds, ABC inventory classification tags |
route_schedule.json |
VRP route sequences, warehouse source nodes | Target time windows, capacity utilization rates, carbon offsets |
vendor_matrix.csv |
Analytic Hierarchy Process (AHP) scorecard evaluations | Price matrix, quality metrics, lead time data, ESG ratings |
simulation_report.md |
Risk distribution graphs from Monte Carlo passes | Mitigation strategies, scenario variances, ROI estimates |
Supply Chain Optimizer Advanced Features
- Multi-model demand estimation running ARIMA, Prophet, and Long Short-Term Memory (LSTM) networks simultaneously to choose the cleanest data vector.
- Multi-constraint VRP algorithms tracking multi-depot configurations, vehicle payload ceilings, tight delivery windows, and carbon footprints.
- Multi-criteria Analytic Hierarchy Process (AHP) scorecards weighting pricing, fulfillment speeds, QA history, and corporate ESG scores.
- Interactive What-If simulation modules mimicking acute supply lines disruption, pricing updates, or extreme regional demand shifts.
SKILL.md
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