Economic order quantity model

This basic formula has been used to develop an economic order quantity (EOQ) model (Carroll, 1998 Huffman, 1996 Silbiger, 1999 Tootelian and Gaedeke, 1993). While the EOQ model may be difficult to derive and calculate, it is often incorporated into computer software used by many pharmacies to manage their inventory and make purchasing decisions. The EOQ model describes the level of inventory and reorder quantity at which the combined costs of purchasing and carrying inventory are at a minimum. The formula is... 

Economic order quantity (EOQ) model, 545, 1670, 2022, 2023 Economic service life, 2332 Economic tax life, 2332 Economic want, 2299 The Economist, 36 Economy ... 

What does this model surest First, if S = 1, then it is optimal to purchase the economic order quantity. In other words, increased purchases... 

Fixed reorder quantity inventory model—A form of independent demand item management model in which an order for a fixed quantity, Q, is placed whenever stock on hand plus on order reaches a predetermined reorder level, R. The fixed order quantity Q may be determined by the economic order quantity, by a fixed order quantity (such as a carton or a truckload), or by another model yielding a fixed result. The reorder point R, may be deterministic or stochastic, and in either instance is large enough to cover the maximum expected demand during the replenishment lead time. Fixed reorder quantity models assume the existence of some form of a perpetual inventory record or some form of physical tracking, e.g., a two-bin system, that is able to determine when the reorder point is reached. These reorder systems are sometimes called fixed order quantity systems, lot-size systems, or order point-order quantity systems. 

The second question to be answered by the inventory model is how much to order. This question is answered by the order quantity rule in use. The economic order quantity rule is explained in the next section. But note, that in many situations, it is suitable to order just what has been used. When this is the case, then the rule that is in use is the Lot-for-Lot (LFL or L4L) order rule. 

Economic Order Quantity (EOQ) A fixed order quantity model that determines the amount of an item to be purchased or manufactured at one time. The model minimizes the combined costs of acquiring and carrying inventory. When production rates are closer to consumption rates, as in a synchronized supply chain, the production quantity approaches infinity, or continuous operation. 

The first distinction we make about demand is whether it is deterministic, with a known function according to parameters like price. An example of a problem with deterministic demand is the Economic Order Quantity (EOQ) model, which a number of researchers have considered with the addition of price as a decision. Demand may also be assumed to be stochastic or random. Generally in this case, it is assumed that there is some known portion that is based on price (e.g., linear demand curve), with an additional stochastic element. 

Many research problems that address pricing and production decisions with fixed production set-up cost fall within the area of the Economic Order Quantity (EOQ) model). The general EOQ model inventory model has been studied frequently in inventory literature (see [164] for a review). The problem consists of multiple periods in a fixed time horizon, with a stationary deterministic function in each period ordering or production costs have a fixed and variable component. Since demand is deterministic, the optimal policy will leave zero inventory at the end of each time cycle, so each period may be considered... 

One of the first to consider pricing and inventory decisions for products experiencing deterioration in demand is Cohen [39]. The author considers a modified Economic Order Quantity (EOQ) model with production set-up costs and zero inventory at the beginning of each cycle, where the objective is to determine a price and order quantity for each cycle. Cohen assumes demand is a deterministic linear function of price with exponential decay that is proportional to the on-hand inventory, and he derives the profit maximizing solution. Sensitivity analysis indicates that for a fixed price, the optimal cycle length decreases as the decay rate increases. Further, the optimal order rate increases with an increase in the decay rate and decreases with increasing price if price is an external parameter. 

Cost curves for economic order quantity (EOQ) model. 

The classic economic order quantity (EOQ) model has tended to channel our thinking towards the idea that there is some optimum amount to order (and hence to hold in stock). The EOQ model arrives at this optimum by balancing the holding cost of inventory against the cost of issuing replenishment orders and/or the costs of production set-ups (see Figure 5.10). 

Often the inventory model chosen depends on the way inventory is tracked. Is inventory tracked perpetually (all the time), much like retailers and grocery stores do as the products universal product code is scanned at checkout Or is inventory counted at a fixed time interval (periodically), much like a small bar owner who counts the bottles of liquor in the storeroom each Thursday. A simple Economic Order Quantity (EOQ) model or a variation of this model, can be used if ... 

Batch crystallizers are often used in situations in which production quantities are small or special handling of the chemicals is required. In the manufacture of speciality chemicals, for example, it is economically beneficial to perform the crystallization stage in some optimal manner. In order to design an optimal control strategy to maximize crystallizer performance, a dynamic model that can accurately simulate crystallizer behavior is required. Unfortunately, the precise details of crystallization growth and nucleation rates are unknown. This lack of fundamental knowledge suggests that a reliable method of model identification is needed. 

The SCD model can be applied qualitatively or quantitatively. A qualitative application generally uses the model to pattern a set of reactivity data, from experiment or computations, by finding the organizing quantities of the data based on the SCD of the target reaction. Usually one uses independently known reactivity factors (e.g., promotion energies) and seeks the most economical combination which allows one to predict the trends in a satisfactory manner. In comparison, a quantitative application seeks to extract the theoretical quantities of the SCD for a set of reactivity data in order to learn about the magnitudes and trends of the reactivity factors. The two application modes provide complementary insight into chemical reactivity. 

---