This paper is concerned with the single vendor single buyer integrated production inventory problem. To make this problem more practical, space restriction and lead time proportional to lot size are considered. Since the space for the inventory is limited in most practical inventory system, the space restriction for the inventory of a vendor and a buyer is considered. As product’s quantity to be manufactured by the vendor is increased, the lead time for the order is usually increased. Therefore, lead time for the product is proportional to the order quantity by the buyer. Demand is assumed to be stochastic and the continuous review inventory policy is used by the buyer. If the buyer places an order, then the vendor will start to manufacture products and the products will be transferred to the buyer with equal shipments many times. The mathematical formulation with space restriction for the inventory of a vendor and a buyer is suggested in this paper. This problem is constrained nonlinear integer programming problem. Order quantity, reorder points for the buyer, and the number of shipments are required to be determined. A Lagrangian relaxation approach, a popular solution method for constrained problem, is developed to find lower bound of this problem. Since a Lagrangian relaxation approach cannot guarantee the feasible solution, the solution method based on the Lagrangian relaxation approach is proposed to provide with a good feasible solution. Total costs by the proposed method are pretty close to those by the Lagrangian relaxation approach. Sensitivity analysis for space restriction for the vendor and the buyer is done to figure out the relationships between parameters.

The single vendor single buyer integrated production inventory problem with lead time proportional to lot size and space restriction is studied. Demand is assumed to be stochastic and the continuous review inventory policy is used for the buyer. If the buyer places an order with lots of products, then the vendor will produce lots of products and the products will be transferred to the buyer with equal shipments many times. Mathematical model for this problem is defined and a Lagrangian relaxation approach is developed.

As order quantity is increased, the ordering cost per item will be cheaper due to saving of transportation and material handling costs. In this paper, two realistic assumptions such as quantity discount and budget limit are considered. Quantity discount means that all units in the order will be discounted according to the predetermined order levels. Budget limit represents that the costs for inventory investments are bounded. This paper develops a Lagrangian relaxation approach for a continuous review inventory model with a budget constraint and quantity discounts. Computational results indicate that the proposed approach provides a good solution. Sensitivity analysis is done to get some insights on budget limit and quantity discount. As budget limit or the amount of discount according to order quantity is increased, order quantity is increased, whereas reorder point is not always increased.

The modular assembly system can make it possible for the variety of products to be assembled in a short lead time. In this system, necessary components are assembled to optional components tailor to customers’ orders. Budget for inventory investments composed of inventory and purchasing costs are practically limited and the purchasing cost is often paid when an order is arrived. Service cost is assumed to be proportional to service level and it is included in budget constraint. We develop a heuristic procedure to find a good solution for a continuous review inventory system of the modular assembly system with a budget constraint. A regression analysis using a quadratic function based on the exponential function is applied to the cumulative density function of a normal distribution. With the regression result, an efficient heuristics is proposed by using an approximation for some complex functions that are composed of exponential functions only. A simple problem is introduced to illustrate the proposed heuristics.

In many inventory situations, items for sales are generally stocked in a multiple of variations called stockkeeping units, such as size, color, style, and so on. For better management performance on sales items, proper and effective management is necessary for the stockkeeping units. In dealing with many items and those stockkeeping units, individual inventory analysis for each stockkeeping unit needs large amount of time or cost. Also the individual approach in inventory planning increases the demand variation of an item as the result by combining of demand variations of all stockkeeping units, accordingly the inventory turnover ratio and profitability are dropped down. This research suggests an effective method of systematic control of total stockkeeping units by generating from the total item basis, and shows how to reduce the safety stock and the average inventory with attaining a planned customer fill rate of the item and each stockkeeping units.

Optimum lot size calculation for real world manufacturing environment has been focused since last few decades. Several extensions have been made to the basic economic order and production order quantity models to realize the possible practical situations in industry. However, focus on work-in-process inventory has been ignored relatively. This paper provides a comprehensive review of the models developed for group technology based manufacturing environment focusing on work-in-process inventory. Models have been extended from a perfect manufacturing conditions to an imperfect manufacturing situation considering rework, rejection and inspection. Optimum lot size has been evaluated using a simple algebraic optimization approach. Significant parameters are highlighted using sensitivity analysis for the developed models. Numerical example is used to illustrate the utilization of such models in day-to-day production setups and the impact of significant factors’ variation on total cost and optimum lot size.

We develop an optimization algorithm for a periodic review inventory system under a stochastic budget constraint. While most conventional studies on the periodic review inventory system consider a simple budget limit in terms of the inventory investment being less than a fixed budget, this study adopts more realistic assumption in that purchasing costs are paid at the time an order is arrived. Therefore, probability is employed to express the budget constraint. That is, the probability of total inventory investment to be less than budget must be greater than a certain value assuming that purchasing costs are paid at the time an order is arrived. We express the budget constraint in terms of the Lagrange multiplier and suggest a numerical method to obtain optional values of the cycle time and the safety factor to the system. We also perform the sensitivity analysis in order to investigate the dependence of important quantities on the budget constraint. We find that, as the amount of budget increases, the cycle time and the average inventory level increase, whereas the Lagrange multiplier decreases. In addition, as budget increases, the safety factor increases and reaches to a certain level. In particular, we derive the condition for the maximum safety factor.

This paper deals with the economic value analysis of meteorological forecasts for a hypothetical inventory decision-making situation in the pharmaceutical industry. The value of Asian dust (AD) forecasts is assessed in terms of the expected value of profits by using a decision tree, which is transformed from the specific payoff structure. The forecast user is assumed to determine the inventory level by considering base profit, inventory cost, and lost sales cost. We estimate the information value of AD forecasts by comparing the two cases of decision-making with or without the AD forecast. The proposed method is verified for the real data of AD forecasts and events in Seoul during the period 2004~2008. The results indicate that AD forecasts can provide the forecast users with benefits, which have various ranges of values according to the relative rate of inventory and lost sales cost.

In this paper, we develop an efficient approach to solve a continuous review inventory system with a budget constraint when the semi-finished product and optional components are required to be assembled. We are, in particular, interested in a budget constraint that includes a service level. The service cost, such as labor and facility costs, tends to increase as the service level increase, and it makes the problem difficult to solve. Assuming that the reorder point for a semi-finished product is given, we show that the order quantity for the semi-finished product and the order quantity and reorder point for optional components can be determined by minimizing the total cost that includes setup cost, inventory holding cost, and shortage cost. The performance of the proposed approach is tested by numerical examples. By using sensitivity analysis, we conclude that, as the reorder point for semi-finished product increases, the order quantity for semi-finished product increases, whereas the order quantity and reorder point of optional components decreases.

본 연구에서는 편의점에서 고객이 처음 원하는 제품의 재고가 없을 때 다른 제품으로의 대체구매가 존재하는 상황에서 수익을 극대화하는 제품들의 진열공간 크기와 보충시점 결정을 위한 5가지 재고관리 해법을 제안한다. 그리고 고객수요의 불확실성과 마케팅 요소를 반영한 시뮬레이션 모형을 구축하여 제안된 해법들의 성능을 평가한다.

If service level is increased, then service cost such as labor cost and facility cost will be increased. This service cost is included in the budget constraint in this paper. This service cost makes the problem difficult to solve. The purpose of this research is to develop an efficient approach for a continuous review inventory system with budget constraint when the semi-finished product and optional components are required to be assembled. Assuming that the reorder point for semi-finished product is given, order quantity for semi-finished product and order quantity and reorder point will be determined to minimize total cost that includes setup cost, inventory holding cost, and shortage cost. The performance of the proposed approach is checked by using an example.

This paper makes a detailed comparison between two metrics designed for measuring customer’s satisfaction in the retail industry. The first metric, which is called the customer service level, has not been widely used due to the intrinsic requirement on the parameter assumption(s) of the demand distribution. Unlike the customer service level metric the in stock ratio metric does not require any requirements on the demand distribution. And the in stock ratio metric is also very easy to understand the meaning. To develop the detailed planning activities for business with the in stock ratio metric on hand one should collect some information as following : 1) POS (Point of sales) data, 2) Inventory Data 3) Inventory Trend.

Industrial business environments have rapidly changed and face severe competitive challenges. The effective inventory system enables to product and deliver the products quickly for meeting due date of customer's order in this environment. This study have developed a web-based inventory system using RFID for an injection molding industry. The system analysis inventory problem issues such as inventory planning, warehouse assignment and assist to develop production scheduling. In this study, web-based inventory system using Java language and RFID technology is proposed and implemented. As the result of implementation of the system, we expected that it manages to inventory planning continually and systematically.