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.
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