How Inventory Management AI-Agent Helped a Global Tier-1 Auto Supplier

How did ThinkDigits Implement an AI Agent for Optimal Inventory Replenishment?

An AI agent for optimal inventory replenishment should balance demand variability, supplier lead times, and inventory holding costs while minimizing stockouts and overstocking. The AI agent will learn an optimal replenishment policy by predicting demand, adjusting reorder points, and continuously improving based on feedback.

Step-by-Step Implementation

Step 1: Define the Problem as an MDP (Markov Decision Process)

1. State (s):

Current levels of:

• • • Raw material inventory
    • WIP inventory
    • Finished goods inventory
  • Demand for the current time step.
  • Remaining lead time for ongoing orders.

2. Action (a):

• • The quantity of raw material to order (discrete or continuous).

3. Reward (r):

• • A reward function that balances:
    • Holding cost (negative reward for high inventory levels).
    • Stockout penalty (negative reward for stockouts).
    • Ordering cost (penalty for placing frequent small orders).

4. Transition Function (P(s'|s, a)):

• • Determined by the stochastic nature of demand and lead time.

Step 2: Collect and Preprocess Data

• Historical data needed:
  • Daily demand for finished goods.
  • Supplier lead times and variability.
  • Production capacity and consumption rates of raw materials.
  • Inventory levels (raw materials, WIP, finished goods).
• Preprocess data by:
  • Handling missing values.
  • Creating time-series features (e.g., moving averages).
  • Normalizing data for ML models.

Step 3: Train a Demand Forecasting Model

1. Model Choice:
   • Use a time-series forecasting model (e.g., ARIMA, LSTM, Prophet) to predict daily demand.
2. Training:
   • Train the model using historical demand data.
3. Output:
   • Forecasted demand for the next time steps, which will guide the AI agent’s decisions.

Step 4: Reinforcement Learning for Replenishment

1. Reinforcement Learning Algorithm:
   • Use Deep Q-Networks (DQN) or Proximal Policy Optimization (PPO).
2. Simulation Environment:
   • Create a digital twin of the manufacturing plant to simulate production flow, demand, and supply.
   • Train the RL agent in this simulated environment by allowing it to:
     • Place replenishment orders.
     • Observe inventory levels, demand, and rewards.
3. Reward Function:
   • The reward function should be designed to:
     • Penalize stockouts heavily.
     • Penalize high inventory holding costs.
     • Encourage ordering in optimal quantities (e.g., bulk discounts).

Example reward function:

R=−(cholding⋅Inventory Level)−(cstockout⋅Stockout Days)−(corder⋅Order Frequency)R = – (c_{holding} \cdot \text{Inventory Level}) – (c_{stockout} \cdot \text{Stockout Days}) – (c_{order} \cdot \text{Order Frequency})

R=−(cholding⋅Inventory Level)−(cstockout⋅Stockout Days)−(corder⋅Order Frequency)

Where:

• choldingc_{holding}

   

  cholding: Cost per unit of holding inventory.

• cstockoutc_{stockout}

   

  cstockout: Cost per unit of stockout.

• corderc_{order}

   

  corder: Cost per order placed.

Step 5: Testing and Validation

1. Offline Testing:

   • Test the trained RL agent in the digital twin to evaluate performance in different scenarios (e.g., normal demand, demand spikes, supplier delays).

2. Performance Metrics:

   • Stockout rate.
   • Average inventory levels.
   • Inventory holding cost.
   • Order frequency.

3. Online Testing:

   • Deploy the agent in a controlled production environment with real-time data.
   • Monitor performance and compare with baseline metrics.