In many companies, decisions made in other departments significantly influence logistics costs. Expanding the product range or increasing the number of orders while maintaining the same total volume leads to increased logistical efforts and, consequently, costs. Detailed cost calculations can uncover such relationships and should be considered in decision-making. This enables logistics to proactively contribute to the positive development of the results rather than just reacting to consequences.
While the costs of transport logistics can often be evaluated through external invoices, warehouse costs are frequently allocated to products as a flat percentage. But is the value of a product a good measure of logistical effort? Attributes such as size, weight, “pickability,” demand-quantity structure, packaging units, and stability are often more indicative of the required effort in the warehouse.
With a deep understanding of these cost drivers, logistical processes can be optimized. For example, in order-related pick runs, single-line orders cause above-average effort. If the volume is sufficiently high, switching to batch picking might be worthwhile. In an average-based cost analysis, this relationship is often overlooked. A detailed analysis, however, offers much greater potential for efficiency improvements and cost savings.
Discussions on changing logistics costs often miss the point. Changes in conditions lead to changed efforts and thus to changed costs. These conditions are consequences from decisions in other areas of the company. Product range planning, pricing, and even product design, such as packaging design, directly influence logistical efforts. Ideally, if these dependencies can be highlighted in advance, they will influence holistic decision-making and lead to better company results.
Interestingly, in most cases, sufficient data is available from warehouse operations to calculate warehousing costs in detail. In the flumiq 3P model, efforts for each process step executed are collected. Specific analysis models consolidate these efforts into costs depending on the given properties. This allows not only for the evaluation of already executed orders but also for estimates of future orders. Calculations are performed at the most granular level of detail and are then condensed from the bottom up, so to speak.
Not all properties need to be explicitly present as data; hidden properties implicitly influence the required effort per process step. For example, if the packaging type of an item is not known, the effort and thus cost implications are considered as an influence of the item.
Traditional calculation methods in logistics often rely on averages and assume stable conditions (“top-down”). This often unconscious assumption can lead to inaccurate results. The flumiq approach makes such assumptions explicit and allows for their adjustment, recognizing that logistics is inherently dynamic.
Using this approach, logistics costs can be evaluated with high precision. Thanks to the detailed level of analysis, cost drivers can be precisely identified. Questions like “What are the fixed costs per order?”, “What impact do the number of items and the quantity have?”, or “Do different items have an influence?” (see hidden properties) can be specifically investigated. Based on these insights, very specific goals can be set, consciously considering their implications. For instance, while logistics might need to reduce fixed costs per order, sales and marketing could take measures to increase the number of items per order, thereby reducing relative costs. Thus, logistics costs affect more than just the logistics department.
Logistics costs are only linear within a certain range. As a warehouse operation approaches capacity limits, “jams” and “blockages” can occur, leading to increased effort and inefficiencies. If capacity limits are exceeded, costs can suddenly rise due to measures like overflow warehouses, additional shifts, or further investments. Even here, the Flumiq 3P model offers valuable calculation possibilities. There is enormous potential in the existing data when this model is applied.