Economic Order Quantity (eoq) - Just in Time (jit) Model Paper

There are several models that have been developed to deal with the trade-off between ordering and carrying costs of inventory. The two that will be discussed is the Economic Order Quantity (EOQ) model and the Just-in-time (JIT) model. First, the history and definition of the theories will be discussed. Secondly, there will be a comparison of these two models presented. Thirdly, organizations that employ the EOQ and JIT model will be discussed and an explanation will be given on how each organization benefited in their operations from using these particular models.

The EOQ model is a mathematical model that minimizes the total of short-term ordering costs plus short-term carrying cost for the period. In addition, it specifies the size of order to place every time inventory is ordered (Ainsworth & Deines, 2011). The EOQ model was developed by F. W. Harris in 1913, but R. H. Wilson, a consultant who applied it broadly, is given credit for his early thorough analysis of it (Hax, 1984).

The JIT inventory model is a long-run model based on the principle that inventory should arrive just as needed for production in the quantities needed (Ainsworth & Deines, 2011). JIT is a Japanese management philosophy which has been applied in practice since the early 1970s in many Japanese manufacturing organizations. It was first developed and perfected within the Toyota manufacturing plants by Taiichi Ohno as a means of meeting consumer demands with minimum delays. Taiichi Ohno is frequently referred to as the father of JIT (Monden, 1993).

There are many differences between the EOQ and JIT model. The EOQ model reflects only short-term carrying and order costs. The EOQ model assumes that inventory ordering and inventory usage transpire in uniform cycles throughout the period. However, the JIT is a long-term model based on the principle that inventory should arrive just as needed for production in the quantities needed (Ainsworth & Deines, 2011). Furthermore, EOQ is a mathematical model that regulates the optimal order size. JIT, on the other hand, is a visual or electronic model that uses a kanban system to determine the need for inventory. A kanban system is a pull system that uses cards to visually signal the need for inventory (Ainsworth & Deines, 2011). In this model, production is determined by customer demand and the need for raw materials is determined by production. The EOQ system considers batch-related ordering costs and unit-related carrying costs. JIT deliberates all levels of cost and focuses on reducing or eliminating nonvalue-added costs (Ainsworth & Deines, 2011).

Effective inventory management is essential in the operation of any business. The Economic Order Quantity (EOQ) model can be used by a restaurant to minimize the total ordering costs plus short-term carrying costs. The EOQ model is based on certain assumptions: (Ainsworth & Deines, 2011)

* Demand is essentially uniform throughout the year

* Lead time is constant

* The entire order is received at the same time

* No quantity discounts - assumes inventory costs are the same regardless of the size of order

* Inventory size is not limited - orders of any size are possible

* Storage costs are irrelevant

The above graph illustrates the variation of the inventory level over time for the classic EOQ model. The downward sloping curve shows inventory level is being reduced at a constant rate over consumption time. Inventory level is shown as Q when a new order is received. The inventory is gradually depleted until it reaches zero just as the new order is received. The average inventory (Q/2) is equal to 1/2 within the same period.

The object of effective inventory control is to purchase materials in the amount that will prevent an interruption in supplies at the least costs. Once the size of order is determined, next you must determine the reorder point or the inventory level that indicates it is time for additional inventory. In order to do this, you must determine the daily demand and lead time. Daily lead time is calculated by dividing