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Bulletin of the Belgian Mathematical Society, Simon Stevin
A note on two inventory modelsIn inventory models the major objective consists of minimizing the total inventory cost and to balance the economics of large orders or large production runs against the cost of holding inventory and the cost of going short. In the present paper we analyse the uctuations in the stock and starting from some basic assumptions we obtain bounds between which the stock varies. The main purpose and use of our results is that we are able to determine the exact upper and lower stockbounds. In the paper we formulate a deterministic and a stochastic version of our model.
The fundamental reason for carrying inventories is that it is physically impossible and economically impractical for each stock item to arrive exactly where it is needed exactly when it is needed. The goal of inventory management is to ensure the consistent delivery of the right product in the right quantity to the right place at the right time. Most of the researchers in inventory system were directed towards non-deteriorating products. However, there are certain substances, whose utility do not remain same with the passage of time. Deterioration of these items plays an important role and items cannot be stored for a long time. Deterioration of an item may be defined as decay, evaporation, obsolescence, loss of utility or marginal value of an item that results in the decreasing usefulness of an inventory from the original condition. When the items of the commodity are kept in stock as an inventory for fulfilling the future demand, there may be the deterioration of items in the inventory system, which may occur due to one or many factors i.e. storage conditions, weather conditions or due to humidity. INTRODUCTION Most of the inventory models were formulated in a static environment where the demand is assumed to be constant and steady. In fact, the constant demand assumption is only valid dur ing the maturity phase of time. In realistic business situations many items of inventory such as electronic products, fashionable clothes, tasty food products and domestic goods generate increasing sales after gaining consumer " s acceptance. Therefore it is more realistic if we consider demand rate as time dependent. Many businesses are not as successful as they could be simply because they lack the know-how or the will to implement sound inventory management and control practices. Successful inventory is a compromise between low inventory levels and meeting targeted fill rates. Investing in the right inventory and reducing excess will improve customer fill rates, inventory turnover and cash flow and profits. The purpose of the study is to develop and analyze some inventory models for decaying items with variable demand rates for different realistic business situations.
A company is planning to purchase 90 800 units of a particular item in the year ahead. The item is purchased in boxes, each containing 10 units of the item, at a price of £200 per box. A safety stock of 250 boxes is kept. The cost of holding an item in stock for a year (including insurance, interest and space costs) is 15% of the purchase area. The cost of placing and receiving orders is to be estimated from cost data collected relating to similar orders, where costs of £5910 were incurred on 30 orders. It should be assumed that ordering costs change in proportion to the number of orders placed. 2% should be added to the above ordering costs to allow for inflation. Required: Calculate the order quantity that would minimize the cost of the above item, and determine the required frequency of placing orders, assuming that usage of the item will be even over the year. (8 marks) ACCA Foundation Stage Paper 3 Most textbooks consider that the optimal reorder quantity for materials occurs when 'the cost of storage is equated with the cost of ordering'. If one assumes that this statement is acceptable and also, in attempting to construct a simple formula for an optimal reorder quantity, that a number of basic assumptions must be made, then a recognised formula can be produced using the following symbols: C o = cost of placing an order C h = cost of storage per annum, expressed as a percentage of stock value D = demand in units for a material, per annum Q = reorder quantity, in units Q/2 = average stock level, in units p = price per unit
In the present paper a volume flexible manufacturing system is considered for a decaying item with an inventory-level-dependent demand rate. In reality, the demand rate remains stock-dependent for some time and then becomes a constant after the stock falls down to a certain level. Many factors like limited number of potential customers and their goodwill, price and quality of the goods, locality of shop, etc. can be accounted for the change in the demand pattern. INTRODUCTION Inventory is a part of every fact of business life. Without inventory any business can not be performed, whether it being service organization. Under increased competition, inventory based business are forced to better coordinate their procurement and marketing decisions to avoid carrying excessive stock when sales are low or shortages when demand are high. An effective means of such coordination is to conduct the inventory control and manufacturing decision jointly. The main task is to determine the optimal rate of production and inventory policy for a given time varying demand. In the Classical Economic Production Lot Size(EPLS) model, the production rate of a machine is regarded to be pre-determinded and inflexible1.Alder and Nanda (1974), Sule (1981), Axsater and Elmaghraby (1981), Muth and Spearmann (1983) extended the EPLS model to situations where learning effects would induce an increase in the production rate. Proteus (1986), Rosenblat and Lee (1986) and Cheng (1991) considered the EPLS model in an imperfect production process in which the demand would exceed the supply. Schweitzer and Seidmann (1991) adopted, for the first time, the concept of flexibility in the machine production rate and discussed optimization of processing rates for a FMS (flexible manufacturing system). Obviously, the machine production rate is a decision variable in the case of a FMS and then the unit production cost becomes a function of the production rate. Khouja and Mehrez (1994) and Khouja (1995) extended the EPLS model to an imperfect production process with a flexible production rate. Silver (1990), Moon, Gallego and Simchi-Levi (1991) discussed the effects of slowing down production in the context of a manufacturing equipment of a family of items, assuming a common cycle for all the items. Gallego (1993) extended this model by removing the stipulation of a common cycle for all the items. But the above studies did not consider the demand rate to be variable. It is a common belief that large piles of goods displayed in a supermarket lead the customers to buy more. Silver and Peterson (1985) and Silver (1979) have also noted that sales at the retail level tend to be proportional to the inventory displayed. Baker and Urban (1988) and Urban (1992) considered an inventory system in which the demand rate of the product is a function of the on-hand inventory. Goh (1994) discussed the model of Baker and Urban18 relaxing the assumption of a constant holding cost. Mandal and Phaujder (1989) then extended this model to the case of deteriorating items with a constant production rate. Datta and Pal (1990) presented an inventory model in which the demand rate of an item is dependent on the on-hand inventory level until a given inventory level is achieved, after which the demand rate becomes constant. Giri , Pal , Goswami and Chaudhuri (1995) extended the model of Urban (1992) to the case of items deteriorating overtime. Ray and Chaudhuri (1997) discussed an EOQ (economic order quantity) model with stock-dependent demand, shortage, inflation and time discounting of different costs and prices associated with the system. Ray, Goswami and Chaudhuri (26 studied the inventory problem with a stock-dependent demand rate and two levels of storage, rented warehouse (RW) and own warehouse (OW). Giri and Chaudhuri (1998) extended the model of Goh (1994)
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