Economic Order Quantity (EOQ)

A holistic methodology that extends EOQ's single-node logic to simultaneously optimize stock levels across the entire supply chain network. Unlike EOQ, which isolates a single warehouse, MEIO models the interdependencies between raw material suppliers, manufacturing plants, and distribution centers to minimize total system-wide costs.

• Key Distinction: EOQ asks 'how much to order for this location?'; MEIO asks 'where should we hold inventory across the network?'
• Mechanism: Uses stochastic demand and lead time data to calculate optimal safety stock placements at each echelon.
• Outcome: Reduces total inventory investment by 15-30% compared to single-stage EOQ policies.

The predetermined minimum inventory level that triggers a new replenishment order. While EOQ calculates the optimal order quantity, the Reorder Point determines the optimal timing of that order.

• Formula: ROP = (Average Demand × Lead Time) + Safety Stock
• Integration with EOQ: A continuous review system pairs a fixed EOQ quantity with a dynamic ROP to minimize total cost while preventing stockouts.
• Limitation: Assumes constant demand and lead time, making it vulnerable to the Bullwhip Effect in volatile environments.

The algorithmic process of calculating the precise buffer inventory required to absorb demand and supply variability. EOQ assumes deterministic, constant demand, which is a critical limitation in real-world operations.

• Purpose: Protects against stockouts caused by forecast errors and lead time variability.
• Calculation: Safety Stock = Z-score × √(Lead Time × σ²_demand + Avg Demand² × σ²_lead time)
• Trade-off: Excess safety stock increases Inventory Carrying Cost, directly undermining the cost-minimization goal of the EOQ model.

A supply chain phenomenon where small fluctuations in retail demand cause progressively larger oscillations in upstream orders. EOQ's batch ordering logic is a primary driver of this distortion.

• EOQ's Role: The fixed, lumpy order quantities dictated by EOQ amplify demand signals as they move up the chain, misleading suppliers about true market demand.
• Mitigation: Strategies like Vendor-Managed Inventory (VMI) and information sharing replace batch ordering with continuous replenishment based on actual consumption data.
• Impact: Can increase upstream inventory costs by 20-50% due to unnecessary safety stock accumulation.

A single-period stochastic inventory model that addresses EOQ's core weakness: uncertain demand. It determines the optimal order quantity for products with a limited selling season, balancing the cost of overstocking against the cost of understocking.

• Critical Fractile: Optimal order quantity is found where the probability of demand being less than or equal to the order quantity equals the critical ratio: Cost of Underage / (Cost of Overage + Cost of Underage).
• Contrast with EOQ: EOQ minimizes ordering and holding costs for continuous replenishment; the Newsvendor model maximizes expected profit for a single, perishable opportunity.
• Applications: Fashion retail, fresh food, and holiday merchandise.

A coordinated ordering strategy that groups multiple items from the same supplier into a single purchase order. This directly challenges EOQ's assumption of independent, single-item ordering.

• Cost Structure: A major ordering cost is shared across all items in the group, while minor item-specific costs and holding costs are optimized individually.
• Efficiency Gain: Reduces total ordering cost by consolidating shipments, but requires solving a more complex optimization problem to find the optimal group replenishment interval.
• Trade-off: May increase holding costs for some items to achieve a lower total system cost.