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Lot size optimization: definition & important aspects for buyers
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Batch size optimization in a nutshell:
Lot size optimization is the systematic determination of the economically optimal order quantity, taking into account opposing costs such as ordering costs and storage costs. For purchasing, this enables a balance between minimum total costs and maximum security of supply, while at the same time avoiding excess stock.
Example: An automotive supplier optimizes its order quantity for standard screws from 1,000 pieces per week to 4,000 pieces per month, thereby reducing its total annual costs by EUR 2,400 through savings in order processing and transport costs while maintaining the same level of supply security.
Contents
Optimal batch sizes in purchasing
What is batch size optimization?
Batch size optimization refers to the determination of the optimal order or production quantity in order to minimize the total costs in the procurement and production process. This involves balancing warehousing and ordering costs. The aim is to increase efficiency in materials management and to make optimum use of resources and costs by adjusting batch sizes.
Core elements of batch size optimization
Significance for purchasing
In purchasing, lot size optimization is essential to reduce costs and increase supply chain efficiency. By optimally determining lot sizes, companies can reduce stock levels and free up capital without jeopardizing their ability to deliver. This leads to a leaner inventory, lower warehousing costs and improved flexibility in procurement.
Application of lot size optimization
Lot size optimization uses the Andler formula to calculate the optimal order quantity, which minimizes the sum of inventory and ordering costs. This enables companies to reduce stock levels and save costs.
Calculation example
Given:
- Annual requirement (B): 12,000 units
- Order costs per order (Kf): 60 €
- Storage cost rate (Ks): 15% of the unit price
- Unit price (P): 8 €
Calculation of the optimum order quantity (Q):
Q = √((2 × B × Kf) / (P × Ks))
Q = √((2 × 12.000 × 60 €) / (8 € × 0,15))
Q = √((1.440.000 €) / (1,20 €))
Q = √1.200.000
Q ≈ 1,095 units
Interpretation: It is optimal to place orders for around 1,095 units. This reduces the total costs of stock-keeping and ordering to a minimum.
Evaluation and strategic findings
✓ Critical success factors
→ Precise database: exact recording of all relevant costs (ordering, storage, transportation) for reliable calculations
→ Dynamic adjustment: regular reassessment of batch sizes in the event of changes to requirements or cost structures
→ System support: integration of calculations into existing ERP systems for automated optimization
⚠ Challenges and limits
→ Fluctuating requirements: Classic Andler formula assumes constant requirements - reality often requires adjustments
→ Supplier conditions: Minimum order quantities or graduated prices can limit optimum batch size
→ Storage capacities: Physical limitations can make theoretically optimal quantities impossible
Future trends and implications:
"The future of batch size optimization lies in AI-supported real-time adjustment taking multiple variables into account."
→ Machine learning for more precise demand forecasts
→ Integration of sustainability factors in calculations
→ Automatic adaptation to market changes
→ Networking with supplier systems for optimized supply chains
Conclusion on batch size optimization
Batch size optimization is an indispensable tool for efficient supply chain management. By systematically calculating optimal order and production quantities, it enables significant cost savings while ensuring delivery capability. While the classic Andler formula serves as a basis, modern challenges such as fluctuating demand and complex supplier relationships increasingly require dynamic, AI-supported solutions. The success of batch size optimization depends largely on precise data, regular adjustments and good system integration.
