In other words, the inventory is extremely liquid.Īlong the same line, more liquid inventory means the company’s cash flows will be better. Shorter days inventory outstanding means the company can convert its inventory into cash sooner. This calculation also shows the liquidity of inventory. In other words, it shows how fresh the inventory is. The days sales in inventory shows how fast the company is moving its inventory. Older, more obsolete inventory is always worth less than current, fresh inventory. Both investors and creditors want to know how valuable a company’s inventory is. It measures value, liquidity, and cash flows. This is an important to creditors and investors for three main reasons. In other words, the days sales in inventory ratio shows how many days a company’s current stock of inventory will last. A stock cover greater than the expiration lifetime of the product represents an absurd situation where, assuming a FIFO (First In, First Out) inventory, no product would get out of the storage location without hitting first its expiration date.The days sales in inventory calculation, also called days inventory outstanding or simply days in inventory, measures the number of days it will take a company to sell all of its inventory. Indeed, $H$ represents the friction associated with having inventory in the first place. Perishable products present another twist: if the stock cover, that is to say the stock expressed in days rather than in unit of stock, gets close to the product expiration lifetime, then $H$, the carrying cost increases toward an infinite value. Stock-outs cause incremental lost sales to reach the point where one extra stock-out causes the loss of the whole recurring client.Warehouse is full, and there is a point where 1 extra unit of stock actually involve the massive overhead of getting an extra warehousing location.However, in practice, brutal non-linearities can be found such as: The service level formula given here above is indeed based on a simplistic assumption where costs, both storage and stock-outs, are stricly linear. H(p), before going for the minimization of the total cost C(p). Thus I am wondering how to better express such potential over-stocks risks.īack to your formula, its further development could be to try to find a relationship between H and p or to make H a function of p, i.e. In case of big overforecasting, which usually causes expiry issue, forecast errors are not normally distributed. the risk of potential stock-out – two risks that work in opposite directions while they have the same origin by nature – i.e. This tradeoff can be described as the risk of potential over-stocks vs. This happens when your sales are well below the forecast. The most interesting and tricky component here is H – the carrying cost and the question of its proper value in practice.įor example, for short-life dairy products one of the important part of H should be not only pure financial cost of cash frozen in inventory and operational logistics storage cost but also the cost of potential losses due to write-off of expired products or sales with discounts when we are trying to sell-out more just before expiration. I am working on Forecasting and Supply Planning for short-life dairy products where the optimal service level is a very important subject. Perishable foodQuestion raised by Vyacheslav Grinkevych, supply chain expert, : Hence, instead of considering the more usual annual carrying cost $H_y$, we are considering $H = \frac 1.5\approx 0.0055$.īased on those values and on the formula for optimal service level obtained here above, we obtain $p\approx 98.5\%$ which is a typical value for must-have fresh products stored in warehouses feeding grocery store networks. (1) The time scope considered here is the lead-time. $M$ be the marginal unit cost of stock-out (2).ĭownload Excel sheet: service-level-formula.xlsx (illustrated calculation).$H$ be the carrying cost per unit for the duration of the lead time (1).Below, we propose to compute an optimal service level by modeling the respective cost of inventory and stock-outs. Model and formulaThe classical supply chain literature is somewhat fuzzy concerning the numerical values that should be adopted for service level. However, a few years later, we now realize that there are much better options available from the quantitative supply chain perspective which entirely removes the need to optimize the service levels when the technology is powered by probabilistic forecasts. The article has been written from a classic forecasting viewpoint back in 2011.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |