In supply chain, there are a variety of different uncertainties including demand, service time, lead time, and so forth. The uncertainty of demand has been commonly studied by researchers or practitioners in the field of supply chain. However, the uncertainty of upstream supply chain has also increased. A problem of uncertainty in the upstream supply chain is the fluctuation of the lead time. The stochastic lead time sometimes causes to happen so called the order crossover which is not the same sequences of the order placed and the order arrived. When the order crossover happens, ordinary inventory policies have difficult to find the optimal inventory solutions. In this research, we investigate the lead time distribution in case of the order crossover and explore the resolutions of the inventory solution with the order crossover.

During the shift from gasoline vehicles to electric ones, auto parts manufacturing companies have realized the importance of improvement in the manufacturing process that does not require any layout changes nor extra investments, while maintaining their current production rate. Due to these reasons, for the auto part manufacturing company, I-company, this study has developed the simulation model of the PUSH system to conduct a process analysis in terms of production rate, WIP level, and logistics work’s utilization rate. In addition, this study compares the PUSH system with other three manufacturing systems -KANBAN, DBR, and CONWIP- to compare the performance of these production systems, while satisfying the company’s target production rate. With respect to lead-time, the simulation results show that the improvement of 77.90% for the KANBAN system, 40.39% for the CONWIP system, and 69.81% for the DBR system compared to the PUSH system. In addition, with respect to WIP level, the experimental results demonstrate that the improvement of 77.91% for the KANBAN system, 40.41% for the CONWIP system, and 69.82% for the DBR system compared to the PUSH system. Since the KANBAN system has the largest impacts on the reduction of the lead-time and WIP level compared to other production systems, this study recommends the KANBAN system as the proper manufacturing system of the target company. This study also shows that the proper size of moving units is four and the priority allocation of bottleneck process methods improves the target company’s WIP and lead-time. Based on the results of this study, the adoption of the KANBAN system will significantly improve the production process of the target company in terms of lead-time and WIP level.

This study suggests a model of production information system that can reduce manufacturing lead time and uniformize quality by using DNC S/W as a part of constructing production information management system in the industrial field of the existing marine engine block manufacturing companies. Under the effect of development of this system, the NC machine interface device can be installed in the control computer to obtain the quality information of the workpiece in real time so that the time to inspect the process quality and verify the product defect information can be reduced by more than 70%. In addition, the reliability of quality information has been improved and the external credibility has been improved. It took 30 minutes for operator to obtain, analyze and manage the quality information when the existing USB memory is used, but the communication between the NC controller computer and the NC controller in real time was completed to analyze the workpiece within 10 seconds.

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.

This paper is to analyze the picking lead time for picking batch size in a warehouse system and to get minimum picking batch size that is the warehouse system feasible. The warehouse system consists of aisles and racks, which two racks face each other through aisle. The products are picked from the storage locations by batch size. The probability that items are picked in the each row of the rack in the aisle for order picking activity is derived. The picking lead time for picking batch size is the time passed from the first picking location to arrival at starting location in aisle picking all items included in a batch size. The picking lead time for picking batch size in an aisle is analyzed. The picking lead time for picking batch size in the whole warehouse system is obtained. The warehouse system is feasible if all items that customers order are picked from the storage locations for same period. The picking batch size that is the warehouse system feasible is obtained. The problem is analyzed, a solution procedure is developed, and a numerical example is shown to explain the problem.

As customers' demands for diversified small-quantity products have been increased, there have been great efforts for a firm to respond to customers' demands flexibly and minimize the cost of inventory at the same time. To achieve that goal, in SCM perspective, many firms have tried to control the inventory efficiently. We present an mathematical model to determine the near optimal (s, S) policy of the supply chain, composed of multi suppliers, a warehouse and multi retailers. (s, S) policy is to order the quantity up to target inventory level when inventory level falls below the reorder point. But it is difficult to analyze inventory level because it is varied with stochastic demand of customers. To reflect stochastic demand of customers in our model, we do the analyses in the following order. First, the analysis of inventory in retailers is done at the mathematical model that we present. Then, the analysis of demand pattern in a warehouse is performed as the inventory of a warehouse is much effected by retailers' order. After that, the analysis of inventory in a warehouse is followed. Finally, the integrated mathematical model is presented. It is not easy to get the solution of the mathematical model, because it includes many stochastic factors. Thus, we get the solutions after the stochastic demand is approximated, then they are verified by the simulations.

제조업체의 성공은 고객의 요구를 파악하는 능력과 이들 요구를 만족시키면서, 얼마나 최소비용을 투자하여 제품화를 신속히 개발하는가에 달려있다. 기업의 목표를 달성하기 위해서 제조 판매 및 마케팅 그리고 제품 디자인 및 개발기간과 같은 여러 요소들이 복합적으로 적절히 조화를 이루어야만 한다. 이 논문에서는 여러 요소 중 신제품 개발기간에 초점을 맞추어 현재 각 기업에서 많이 사용되고 있는 6시그마 기법을 적용하여 새로운 제품의 개발기간을 단축시키기 위한 방법론을 제시한다.

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application.

기업은 고객이 원하는 시기에 원하는 제품을 구매할 수 있도록 항상 준비가 되어 있어야 한다. 고객의 수요를 만족시키기 위하여 기업은 다양한 수요예측방법을 통하여 적절한 재고 수준과 수요예측을 하고 있다. 제조 기업의 경우에는 다른 산업에 비하여 정확한 수요예측과 낮은 재고 수준의 유지가 비용과 직접적인 연관이 있기 때문에 제조 기업은 경제적인 주문량 결정(Economic Order Quantity: EOQ)이 매우 중요한 문제이다. 주문량을 결정하는 방법에는 여러 가지가 있지만, 본 논문에서는 고객 지연을 방지하기 위하여 경제적 주문량 결정에 고객 지연과 관련된 비용을 포함시키는 것은 물론 고객 지연이라는 상황을 방지하는 노력의 한 방법으로 가격 할인(discount system)을 이용하고자 한다. 가격 할인을 이용하여 고객으로 하여금 빠른 주문을 유도하고 그로 인하여 고객 지연 상황의 발생을 줄여보려고 한다.

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application.

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

In this paper MIP(mean inventory penod) Model and OMMIP decision flow have been developed MIP model can cal-culate mean inventory period which is subject to the order quantity alternative plan OMMIP decision flow leads how can decide the most minimized or

In this paper MIP(Mean Inventory Period) Model and OMMIP decision flow have been developed. MIP model can calculate mean inventory period Which is subject to the order quantity alternative plan. OMMIP decision flow leads how can decide the most minimized order quantity in mean inventory period among various order quantity alternatives. This paper also suggests how to select the order quantity with minimum inventory period as optimal order quantity by means of comparison each mean inventory period with other mean inventory period,after simulating EOQ and order quantity of OMMIP calculated in MIP model.

This research fundamentally deals with an analysis of service level for a multi-level inventory distribution system which is consisted of a central distribution center and several branches being supplied stocks from the distribution center, Under continuous review policy, the distribution center places an order for planned order quantity to an outside supplier, and the order quantity is received after a certain lead time. Also, each branch places an order for particular quantity to its distribution center, and receives the order quantity after a lead time. In most practical distribution environment, demands and lead times are generally not fixed or constant, but variable. And these variabilities make the analysis more complicated. Thus, the main objective of this research is to suggest a method to compute the service level at each depot, that is, the distribution center and each branch with variable demands and variable lead times. Further, the model will give an idea to keep the proper level of safety stocks that can attain effective or expected service level for each depot