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We have available a forecast of product demand d t over a relevant time horizon t=1,2,...,N (for example we might know how many widgets will be needed each week for the next 52 weeks). There is a setup cost s t incurred for each order and there is an inventory holding cost i t per item per period ( s t and i t can also vary with time if desired).
The average inventory is the average of inventory levels at the beginning and end of an accounting period, and COGS/day is calculated by dividing the total cost of goods sold per year by the number of days in the accounting period, generally 365 days. [3] This is equivalent to the 'average days to sell the inventory' which is calculated as: [4]
By using many variables as inputs the MPS will generate a set of outputs used for decision making.Inputs may include forecast demand, production costs, inventory money, customer needs, inventory progress, supply, lot size, production lead time, and capacity.
Demand for items from inventory is continuous and at a constant rate; Production runs to replenish inventory are made at regular intervals; During a production run, the production of items is continuous and at a constant rate; Production set-up/ordering cost is fixed (independent of quantity produced) The lead time is fixed
Without inventory optimization, companies commonly set inventory targets using rules of thumb or single stage calculations. Rules of thumb normally involve setting a number of days of supply as a coverage target. Single stage calculations look at a single item in a single location and calculate the amount of inventory required to meet demand. [11]
The time period of shipping activity should be compared against the forecast that was set for the time period a specific number of days/months prior which is call Lag. Lag is based on the leadtime from order placement to order delivery. For example, if the lead time of an order is three months, then the forecast snapshot should be Lag 3 months.
In a base-stock system inventory position is given by on-hand inventory-backorders+orders and since inventory never goes negative, inventory position=r+1. Once an order is placed the base stock level is r+1 and if X≤r+1 there won't be a backorder. The probability that an order does not result in back-order is therefore:
Time: The time lags present in the supply chain, from supplier to user at every stage, requires that you maintain certain amounts of inventory to use in this lead time. However, in practice, inventory is to be maintained for consumption during 'variations in lead time'. Lead time itself can be addressed by ordering that many days in advance. [5]