Inventory Optimization Techniques
Inventory optimization is a crucial aspect of operations management, as it directly impacts both service levels and costs. You must strike a careful balance between having enough stock to meet customer demand and avoiding the excessive costs associated with overstocking. Stockouts can lead to lost sales and dissatisfied customers, while holding too much inventory increases storage and capital costs. Effective inventory optimization enables you to minimize these risks, ensuring that your organization maintains just the right amount of inventory at all times.
1234567891011121314151617181920212223def calculate_eoq(demand, ordering_cost, holding_cost): """ Calculate the Economic Order Quantity (EOQ). Parameters: demand (float): Annual demand for the product (units per year) ordering_cost (float): Cost per order ($) holding_cost (float): Holding cost per unit per year ($) Returns: float: EOQ (units) """ from math import sqrt eoq = sqrt((2 * demand * ordering_cost) / holding_cost) return eoq # Example usage: annual_demand = 5000 # units per year ordering_cost = 100 # dollars per order holding_cost = 2 # dollars per unit per year eoq = calculate_eoq(annual_demand, ordering_cost, holding_cost) print(f"Economic Order Quantity: {eoq:.2f} units")
The Economic Order Quantity (EOQ) is a classic formula used to determine the optimal order size that minimizes the total cost of ordering and holding inventory. The EOQ formula is derived from balancing two types of costs: the cost to place an order and the cost to hold inventory. By using EOQ, you can decide how much to order each time to keep overall costs as low as possible. In practice, this helps you avoid both frequent ordering (which increases ordering costs) and excessive inventory (which increases holding costs). Applying the EOQ model is especially helpful in environments where demand, ordering costs, and holding costs are relatively stable and predictable.
123456789101112131415161718192021222324import matplotlib.pyplot as plt def simulate_inventory(demand, ordering_cost, holding_cost, periods=12): eoq = calculate_eoq(demand, ordering_cost, holding_cost) inventory = [] current_inventory = eoq monthly_demand = demand / periods for month in range(periods): if current_inventory < monthly_demand: current_inventory += eoq # Place a new order current_inventory -= monthly_demand inventory.append(current_inventory) plt.plot(range(1, periods + 1), inventory, marker='o') plt.title('Inventory Levels Over Time Using EOQ') plt.xlabel('Month') plt.ylabel('Inventory Level (units)') plt.grid(True) plt.show() # Simulate inventory levels for 12 months simulate_inventory(5000, 100, 2, periods=12)
1. What is Economic Order Quantity (EOQ)?
2. How does optimizing inventory benefit operations?
3. Which Python data structure is best for tracking inventory levels over time?
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Inventory optimization is a crucial aspect of operations management, as it directly impacts both service levels and costs. You must strike a careful balance between having enough stock to meet customer demand and avoiding the excessive costs associated with overstocking. Stockouts can lead to lost sales and dissatisfied customers, while holding too much inventory increases storage and capital costs. Effective inventory optimization enables you to minimize these risks, ensuring that your organization maintains just the right amount of inventory at all times.
1234567891011121314151617181920212223def calculate_eoq(demand, ordering_cost, holding_cost): """ Calculate the Economic Order Quantity (EOQ). Parameters: demand (float): Annual demand for the product (units per year) ordering_cost (float): Cost per order ($) holding_cost (float): Holding cost per unit per year ($) Returns: float: EOQ (units) """ from math import sqrt eoq = sqrt((2 * demand * ordering_cost) / holding_cost) return eoq # Example usage: annual_demand = 5000 # units per year ordering_cost = 100 # dollars per order holding_cost = 2 # dollars per unit per year eoq = calculate_eoq(annual_demand, ordering_cost, holding_cost) print(f"Economic Order Quantity: {eoq:.2f} units")
The Economic Order Quantity (EOQ) is a classic formula used to determine the optimal order size that minimizes the total cost of ordering and holding inventory. The EOQ formula is derived from balancing two types of costs: the cost to place an order and the cost to hold inventory. By using EOQ, you can decide how much to order each time to keep overall costs as low as possible. In practice, this helps you avoid both frequent ordering (which increases ordering costs) and excessive inventory (which increases holding costs). Applying the EOQ model is especially helpful in environments where demand, ordering costs, and holding costs are relatively stable and predictable.
123456789101112131415161718192021222324import matplotlib.pyplot as plt def simulate_inventory(demand, ordering_cost, holding_cost, periods=12): eoq = calculate_eoq(demand, ordering_cost, holding_cost) inventory = [] current_inventory = eoq monthly_demand = demand / periods for month in range(periods): if current_inventory < monthly_demand: current_inventory += eoq # Place a new order current_inventory -= monthly_demand inventory.append(current_inventory) plt.plot(range(1, periods + 1), inventory, marker='o') plt.title('Inventory Levels Over Time Using EOQ') plt.xlabel('Month') plt.ylabel('Inventory Level (units)') plt.grid(True) plt.show() # Simulate inventory levels for 12 months simulate_inventory(5000, 100, 2, periods=12)
1. What is Economic Order Quantity (EOQ)?
2. How does optimizing inventory benefit operations?
3. Which Python data structure is best for tracking inventory levels over time?
Obrigado pelo seu feedback!
