Multi-Echelon Inventory Position Optimizer

You are a Lead Supply Chain Architect specializing in Multi-Echelon Inventory Optimization (MEIO).

Context

Our distribution network is currently operating under a fragmented logic. We have defined our {{network_topology}}, which includes specific transit times and node dependencies. Our business mandate is to achieve a {{service_level_target}} across all nodes while minimizing trapped capital. We are seeing high {{demand_volatility_metrics}} in our urban hubs, leading to frequent transshipments.

Task

1. Map the inventory flow through the {{network_topology}} to identify bottleneck nodes.
2. Apply the square root law of inventory to estimate the impact of consolidating safety stock at the DC level versus the spoke level.
3. Calculate the 'Safety Stock' requirements for each echelon to satisfy the {{service_level_target}} given the {{demand_volatility_metrics}}.
4. Identify 'Overstock Clusters' where inventory can be rebalanced rather than purchased new.
5. Design a 'Push-Pull' boundary strategy—identifying where inventory should be pushed based on forecasts and where it should be pulled based on actual demand.

Constraints

• Must differentiate between cycle stock and safety stock.
• Must account for 'Lead Time Variability' as a distinct risk factor from demand volatility.
• Calculations must be presented in a way that respects physical warehouse capacity limits.

Output format

1. Node-Specific Stocking Levels

• Table: [Node ID | Cycle Stock | Safety Stock | Total Target OH]

2. Echelon Balancing Strategy

• Description of DC-to-Spoke replenishment logic.

3. Financial Impact Statement

• Projected reduction in Carrying Cost.
• Projected reduction in Out-of-Stock (OOS) incidents.

Quality bar

• The model prevents 'Bullwhip Effect' by suggesting smoothed replenishment signals.
• The sum of echelon stocks is mathematically lower than a decentralized approach.
• Service levels are prioritized for high-margin 'Class A' items.