Reinforcement Learning forMulti-Echelon Inventory
Production-grade RL systems that learn optimal reorder policies, reduce waste by up to 61%, and improve service levels across complex supply chains
Multi-Echelon
Manufacturer to retail
Lead Time Risk
Dynamic buffering
Perishability
Shelf life constraints
OR-Gym Based
Production-tested
Traditional Heuristics
Fail Under Uncertainty
EOQ and (s,S) models assume deterministic demand and fixed lead times. Real supply chains face stochastic dynamics, multi-echelon dependencies, and perishability constraints.
The Core Problem: How can we dynamically decide what, when, and how much to order — under uncertainty — while minimizing holding + shortage costs across a multi-tier supply chain? Traditional static rules cannot adapt to demand shifts, supplier disruptions, or inventory interactions.
Four Critical Challenges
Volatile Demand
Seasonality, trends, and random shocks make static policies obsolete
Lead Time Uncertainty
Supplier delays create ripple effects across multi-tier networks
Multi-SKU Complexity
Interactions between 100s of products and storage constraints
Perishability
Expiry windows force trade-offs between service and waste
Our RL-Powered Solution
OR-Gym: Industrial-Grade Simulation
Developed by researchers at Sandia National Labs and Arizona State University, OR-Gym provides benchmark environments for RL in operations research — including inventory control, vehicle routing, and supply chain management.
Modular Environments
OpenAI Gym API compatibility for plug-and-play RL training
Multi-Echelon Design
Manufacturer → Distributor → Retailer → Consumer flows
Stochastic Modeling
Poisson/Normal demand, random lead times, supplier capacity
Cost Functions
Holding, shortage, order, waste, and transportation costs
TensorBlue's Enterprise Extensions
End-to-End System Architecture
From raw ERP data to live order execution — a complete production pipeline for RL-driven supply chain optimization.
Data Pipeline
ERP + POS + Supplier API (sales, lead times, stock, cost, delays)
Demand Modeling
Poisson/ARIMA/LSTM hybrid forecasting with seasonality
RL Environment
Multi-SKU, multi-echelon, perishables, randomized disruptions
RL Agent
PPO/DDPG/A2C/SAC - learn optimal reorder policies
Backtest & Integration
Simulation → ERP → live replenishment workflows
Monitoring Dashboard
Service level, cost, latency, and policy drift tracking
Data Flow Overview
State, Action, Reward Design
Comprehensive MDP formulation for multi-echelon inventory control with stochastic dynamics.
State
- •Inventory levels (per SKU & location)
- •Outstanding orders in transit
- •Demand forecasts (7-30 day horizon)
- •Lead time estimates
- •Shelf life remaining
- •Supplier capacity constraints
Action
- •Order quantity per SKU
- •Transfer between warehouses
- •Supplier selection
- •Expedited shipping decision
Reward Function
Constraints
- •Storage capacity limits
- •Budget caps per period
- •Perishability windows
- •Minimum order quantity (MOQ)
- •Supplier delay distributions
Cost Components Modeled
| Cost Type | Example | Typical Range |
|---|---|---|
| Holding Cost | Warehouse rent, energy | 0.5–5% of SKU cost/month |
| Shortage Cost | Lost margin or penalty | 2–10× SKU margin |
| Order Cost | Fixed + per-unit | ₹200 + ₹0.5/unit |
| Waste Cost | Perishable SKU expiry | 20–30% of unit cost |
| Transportation | Inter-warehouse transfer | ₹2–₹5/km |
Algorithmic Stack
Production-tested RL algorithms for inventory control, from discrete order decisions to continuous replenishment optimization.
PPO / A2C
Ideal for continuous state-action spaces with stable training
Dueling DQN
Discrete order-level decisions with efficient exploration
DDPG / SAC
Continuous control for multi-SKU replenishment optimization
Training Strategy
Curriculum Learning
Start with 1-SKU → extend to multi-SKU → add random disruptions
Reward Shaping
Include service-level KPI (fill rate ≥ 95%) as bonus term
Multi-Agent RL
Each SKU/location acts as autonomous agent with shared reward
Implementation Example
import orgym
from stable_baselines3 import PPO
# Environment: multi-echelon inventory control
env = orgym.make('InventoryManagement-v0',
num_stores=3, num_products=50,
demand_dist='poisson',
holding_cost=0.1, shortage_cost=5,
lead_time=3, perish_rate=0.02)
# RL Agent
model = PPO("MlpPolicy", env, verbose=1,
batch_size=1024, learning_rate=3e-4)
model.learn(total_timesteps=5_000_000)
# Evaluation
rewards = []
obs = env.reset()
for _ in range(365):
action, _ = model.predict(obs)
obs, reward, done, _ = env.step(action)
rewards.append(reward)Case Studies
Production deployments showing measurable improvements in cost, service level, and operational efficiency
Multi-Echelon Pharmaceutical Distribution
Regional distributor manages 400 SKUs across 8 hospitals and 3 suppliers with variable lead times (2-8 days) and shelf lives (7-90 days)
TensorBlue Solution
- •Customized from OR-Gym's InventoryManagement-v0
- •Multi-agent PPO with perishability constraints
- •Storage limits & temperature-controlled warehouses
- •Trained on 12 years of historical demand data
- •Live API connection to ERP for order execution
Results
The RL system learned proactive ordering behavior — pre-empting supplier delays and dynamically increasing buffer stock for critical drugs.
Grocery Chain Inventory with Seasonal Demand
Implementation
- →OR-Gym extended with sinusoidal demand for perishable items
- →DDPG algorithm for continuous control
- →Profit minus wastage penalty reward function
- →3 years training, 1 year test horizon
Outcomes
Production Deployment
From simulation to live ERP integration — a rigorous validation process ensuring safe, measurable deployment.
Three-Stage Validation Process
Offline Backtesting
Compare against reorder point, (s,S), and MILP baselines
Digital Twin Evaluation
Simulate disruptions (supplier delays, demand spikes)
Live A/B Deployment
Route 10% warehouses to RL policy with rollback trigger
Production Architecture
Data Layer
ERP → Kafka → Feature Store (inventory, sales, lead times)
Training
RLlib cluster for distributed multi-agent training
Serving
Policy API integrated into ERP replenishment workflows
Monitoring
Grafana dashboard: service-level KPIs, cost per SKU, policy drift
Key Learnings from Scaled Projects
Lead Time Uncertainty Dominates
RL agents learn to buffer dynamically instead of fixed safety stock
Multi-Agent Cooperation
Decentralized policies with shared reward outperform centralized controllers
Reward Normalization Critical
Stable learning requires normalizing rewards by SKU cost magnitude
Simulation-to-Real Alignment
Digital twin (ERP + forecast simulator) prevents reward hacking
Explainability Tools
SHAP-like analysis for order decisions improves client trust
Optimize Your Inventory with RL
Deploy self-learning reorder policies that adapt to demand uncertainty, minimize waste, and maintain service levels across multi-echelon supply chains.