Why Is This Not Possible For This Company To Use The Calculated Eoq As Their Order Size?

The Economic Guild 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 tin benefit from optimized controlling.

The theory behind EOQ

The traditional EOQ formula is the following: The optimized order quantity (Q*), is impacted by the yearly consumption of the textile (D) as well the administration and the transportation price (S). The warehousing cost, which is for many consumers low or close to zero, is reflected in (h), which as well includes the financing cost. Textile cost (C) is important for the financing calculation.

formula

By calculating these unlike 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 information which is simply possible when processes are digital and connected.

But how does this principle work in practice?

Every bit with every optimization process, the EOQ is non that easy to summate without having the correct data. The process of collecting the information is time-consuming and often impossible for i single company. A third party tin aid to collect the data of unlike industry participants and create patterns in order to optimize the processes for all stakeholders.

In our instance, the platform Metalshub knows the price of transportation, the cost of finance also equally the administrative cost of the transaction. The price of the material itself is not impacted by the EOQ. Important is, that all marketplace participants can get an reward out of this optimization. For buyers and sellers, the transportation, financing and administrative costs can exist reduced significantly.

But at that place are other positive side furnishings 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, nosotros were asked to analyze the ordering behaviour of a German metal consumer. Let'due south presume the product 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 half-dozen deliveries per social club, making a full of 48 deliveries.

After a recalculation with data from different parties involved, the social club size was increased to iii orders with three deliveries of 24mt and one order with one delivery of 24mt, making up to a total of 10 deliveries. Although financing price increased and warehouse capacity was needed (and available, at a certain price), the reduction in transportation and authoritative costs was so significant, that 10,848€ (4.2%) were saved – independent from the evolution of the textile price.

An optimization potential exists for near metallic customers– as a lack of information is making information technology hard to calculate the EOQ for consumers of metals and ferroalloys themselves.

Bring together Metalshub now in lodge to benefit from digital processes and contribute to a more efficient market for ferroalloys and metals!