Question 1 - Contrast the significance of the term lead time in the traditional EOQ context and in an MRP system. In the traditional context, lead time is fixed—either as a discrete time or as a probability distribution. Such lead time constancy or variation is outside of the inventory model. Lead time in an MRP system is assumed to be a variable. While specific lead times are stated for planning purposes, these times may be speeded up or delayed as conditions warrant. Indeed, it is this ability to detect needed changes in lead times, either by expediting or de-expediting, that many users cite as one of the most valuable features of MRP. MRP has had some degree of success in environments where requirements are relatively certain and demand and lead time variability are not excessive. A remanufacturing operation, in contrast, is typified by a great deal of variability and uncertainty due to the very nature of repair. The experimental methodology involved the development of computer simulation models of EOQ and MRP systems. Demand uncertainty, demand variability, and lead time variability were then varied at three levels each to develop a full factorial experimental design. The results were used to test EOQ and MRP using two different performance measures: average number of awaiting parts (AWP) days per repair and total annual inventory cost. The results lend support for the use of MRP in a remanufacturing environment. The number of AWP days was significantly reduced from that of the EOQ system, albeit at an increased inventory cost. When the two measures are combined, however, MRP appears to outperform EOQ in aggregate. Economic order quantity is the stage of inventory that minimizes the total inventory investment costs and ordering costs. It is one of the oldest traditional invention scheduling models. The structure used to decide this order size is also known as Wilson EOQ Model or Wilson Formula. The model was developed by F. W. Harris in 1913, but R. H. Wilson, a specialist who applied it widely, is given praise for his early in-depth investigation it. Assume that the order for a product is stable over the year and that each new order is delivered in complete when the supply reaches zero. There is a predetermined cost charged for each order sited, not considering of the number of units ordered. There is also a holding or storeroom cost for each unit held in luggage compartment (sometimes articulated as a percentage of the pay for cost of the item). We want to verify the optimal number of units of the invention to order so that we reduce the total cost related with the delivery, purchase, and storage of the manufactured goods.
The necessary parameters to the resolution are the total order for the year, the procure cost for each item, the fixed cost to situate the order and the storage charge for each piece per year. Note that the quantity of times an order is located will also have an effect on the total cost, however, this number can be determined from the other parameters. Underlying assumptions
* The lead time is fixed
* The purchase price of the item is constant i.e. no discount is available * The replenishment is made instantaneously; the whole batch is delivered at once. * The ordering cost is constant.
* The rate of demand is constant
EOQ is the measure to order, so that ordering cost and carrying cost finds its lowest. Lead times in MRP systems represent the planned amount of time allowed for orders to flow through the manufacturing system. Setting lead times is a major issue in the operation of MRP systems. There exists, however, very little documentation on just how lead times should be set. A major finding is that changes to the level of planned lead times have both transient and steady state effects that may not necessarily operate in the same direction.
Question 2 – What are the sources of demand in an MRP system? Are these dependent or independent and how are they used as...
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