In manufacturing and merchandising companies, inventory management is often overemphasized, as the inventory covers the major part of the asset pie. EOQ, being one of the proven, adopted and efficient models for inventory management, helps to calculate the optimum purchase quantity which would be sufficient to meet the needs and avoid money blocked in the inventory.
The Economic Order Quantity is not a new concept for the manufacturing industry. For many products that are bought, there is a volume and time where administration, delivery and storage costs are optimized. This leads to savings for both parties, buyers and sellers, who can benefit from optimized decision-making.
The theory behind EOQ
The traditional EOQ formula is the following: The optimized order quantity (Q*), is impacted by the yearly consumption of the material (D) as well the administration and the transportation cost (S). The warehousing cost, which is for many consumers low or close to zero, is reflected in (h), which also includes the financing cost. Material cost (C) is important for the financing calculation.
By calculating these different cost factors for various order sizes, the comparison shows a Q* that results in a minimization of costs and a maximization of efficiency. The difficulty is to gather the right data which is only possible when processes are digital and connected.
But how does this principle work in practice?
As with every optimization process, the EOQ is not that easy to calculate without having the right data. The process of collecting the information is time-consuming and often impossible for one single company. A third party can help to collect the data of different industry participants and create patterns in order to optimize the processes for all stakeholders.
In our case, the platform Metalshub knows the cost of transportation, the cost of finance as well as the administrative cost of the transaction. The price of the material itself is not impacted by the EOQ. Important is, that all market participants can get an advantage out of this optimization. For buyers and sellers, the transportation, financing and administrative costs can be reduced significantly.
But there are other positive side effects that this optimization can achieve. A better usage of the space available in trucks gives an advantage to logistics companies, who can reduce the cost per ton transported.
In our example, we were asked to analyze the ordering behaviour of a German metal consumer. Let’s assume the production facility is somewhere in the Rhineland-Palatia and the yearly consumption of FeSi is 240mt. The material has been ordered eight times a year, with an order size of 30mt and six deliveries per order, making a total of 48 deliveries.
After a recalculation with data from different parties involved, the order size was increased to 3 orders with three deliveries of 24mt and one order with one delivery of 24mt, making up to a total of 10 deliveries. Although financing cost increased and warehouse capacity was needed (and available, at a certain cost), the reduction in transportation and administrative costs was so significant, that 10,848€ (4.2%) were saved – independent from the development of the material price.
An optimization potential exists for most metal customers– as a lack of data is making it hard to calculate the EOQ for consumers of metals and ferroalloys themselves.
Join Metalshub now in order to benefit from digital processes and contribute to a more efficient market for ferroalloys and metals!