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Economic order quantity. Economic order quantity ( EOQ ), also known as financial purchase quantity or economic buying quantity, [citation needed] is the order quantity that minimizes the total holding costs and ordering costs in inventory management. It is one of the oldest classical production scheduling models.
The economic production quantity model (also known as the EPQ model) determines the quantity a company or retailer should order to minimize the total inventory costs by balancing the inventory holding cost and average fixed ordering cost. The EPQ model was developed and published by E. W. Taft, a statistical engineer working at Winchester ...
The figure graphs the holding cost and ordering cost per year equations. The third line is the addition of these two equations, which generates the total inventory cost per year. The lowest (minimum) part of the total cost curve will give the economic batch quantity as illustrated in the next section.
Dynamic lot-size model. The dynamic lot-size model in inventory theory, is a generalization of the economic order quantity model that takes into account that demand for the product varies over time. The model was introduced by Harvey M. Wagner and Thomson M. Whitin in 1958. [ 1][ 2]
Material requirements planning. Material requirements planning ( MRP) is a production planning, scheduling, and inventory control system used to manage manufacturing processes. Most MRP systems are software -based, but it is possible to conduct MRP by hand as well. An MRP system is intended to simultaneously meet three objectives:
A good's price elasticity of demand ( , PED) is a measure of how sensitive the quantity demanded is to its price. When the price rises, quantity demanded falls for almost any good ( law of demand ), but it falls more for some than for others. The price elasticity gives the percentage change in quantity demanded when there is a one percent ...
Mathematical optimization (alternatively spelled optimisation) or mathematical programming is the selection of a best element, with regard to some criteria, from some set of available alternatives. [ 1][ 2] It is generally divided into two subfields: discrete optimization and continuous optimization.
Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship ("model") to calculate that derived quantity. The model used to convert the measurements into the derived quantity is usually based on ...
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