There are several types of warehouse-related operational costs that are affected by production lot sizes. As I demonstrated in my recent simulation study report for a enameled copper wire production process, lot size and batch size optimization is an important aspect of operational cost optimization. This is especially true for process industry with continuous processes and high setup and changeover costs. In this article I will point out the overall group of warehousing costs. Specifically, I will focus on eight sub-categories of lot-size dependent warehousing costs.
Storage costs increase with lot size
The amount of space required to store goods in a warehouse is directly related to lot sizes. Larger lot sizes require more storage space, which can increase rent or lease costs.
Larger lot sizes increase inventory holding costs
Holding costs include expenses like insurance, taxes, and interest on the value of the inventory. Larger lot sizes typically result in higher inventory holding costs because more goods are tied up in storage.
Warehouse handling costs should generally decrease with lot size
The cost of moving, sorting, and organizing inventory within the warehouse can be influenced by lot sizes. Exemplary handling costs are picking costs, packing costs, relocation costs, put-away and retrieval costs, and shipping costs. Referring to my recent simulation study report, e.g. referring to a simulation-based production planning application in copper wire industry, larger lot sizes may contribute to cost reductions for various reasons:
- If e.g. boxed products are stored onto pallets, larger lot sizes make it easier to fill entire pallets with just one product. If this entire pallets can be shipped, this reduces picking and shipping costs.
- From above logic it also follows that putaways and retrievals from the storage rack are reduces in frequency when lot sizes are large, and it also easier to fill and prepare pallets for putaway in the warehouse.
Order fulfillment accuracy costs increase or decrease with lot size
Smaller lot sizes can lead to greater order accuracy because it’s easier to identify and pick the correct items when they are stored in smaller, more manageable quantities. This is true if products are e.g. shipped as parcels, and small batches of the product range are stored in handy storage units in proximity to the packaging and shipping stations.
However, the larger lot sizes may also reduce order fulfillment accuracy costs. I have observed such examples when products are shipped pallet-wise, as pure or mixed pallets, and when products are heavy, bulky, and/or have large dimensions.
Quality control costs decrease with lot size
Quality control checks may need to be conducted on each lot of incoming goods. Smaller lot sizes may result in more frequent quality inspections, which can increase quality control costs.
Transportation costs may increase or decrease with lot size
Lot sizes can impact transportation costs. Larger lots may require larger vehicles or multiple shipments, potentially increasing transportation expenses.
Product obsolescence costs increase with lot size
Holding large lot sizes for extended periods can increase the risk of product obsolescence, leading to potential losses if products become outdated or expire.
Opportunity costs fom supplier negotiations increase with small lots
Lot sizes can affect negotiations with suppliers. Ordering larger lot sizes may lead to volume discounts but also require more storage space and capital.
Concluding remarks and related content
Warehousing costs are an important cost group that is controlled by production lot size. Depending on the production process at hand, larger lot sizes may increase or decrease warehousing costs. For example handling costs might decrease with large lot sizes, while inventory holding costs will generally increase with larger lot sizes.
If you want to learn more about cost efficiency in production processes and intralogistics systems, here are some articles for you to consider:
Data scientist focusing on simulation, optimization and modeling in R, SQL, VBA and Python
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