This research studies on the demand forecasting for service parts considering parts life cycle, that gets relatively less attentions in the field of forecasting. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods, then we propose the new demand forecasting method by using these findings and reinforcement leaning technique. Using simulation experiments, we proved that the proposed forecasting method is better than the existing methods under various experimental environments.

In this research, we propose efficient demand forecasting scheme for intermittent demand. For this purpose, we first extensively analyze the drawbacks of the existing forecasting methods such as Croston method and Syntetos-Boylan approximation, then using these findings we propose the new demand forecasting method. Our goal is to develop forecasting method robust across many situations, not necessarily optimal for a limited number of specific situations. For this end, we adopt combining forecasting method that utilizes unbiased forecasting methods such as simple exponential smoothing and simple moving average. Various simulation results show that the proposed forecasting method performed better than the existing forecasting methods.

Various inventory control theories have tried to modelling and analyzing supply chains by using quantitative methods and characterization of optimal control policies. However, despite of various efforts in this research filed, the existing models cannot afford to be applied to the realistic problems. The most unrealistic assumption for these models is customer demand. Most of previous researches assume that the customer demand is stationary with a known distribution, whereas, in reality, the customer demand is not known a priori and changes over time. In this paper, we propose a reinforcement learning based adaptive echelon base-stock inventory control policy for a multi-stage, serial supply chain with non-stationary customer demand under the service level constraint. Using various simulation experiments, we prove that the proposed inventory control policy can meet the target service level quite well under various experimental environments.

Polyurethane is an elastomer polymer, which is flexible, tough and resistant. This material is widely used various fields such as automobile, aeroplane, textiles, construction industries. Currently there exists more than 1,000 manufacturing companies in Korea that are closely related to Polyurethane. In spite of a number of Polyurethane manufacturing companies, there are little attention in academia as well as industries to study the safety and manufacturing process improvement on Polyurethane. In this paper, we consider a case study for the Polyurethane manufacturer to improve productivity by using safety management and manufacturing process reengineering. After careful analysis, we derive three enhancements to increasing the safety and productivity for the target company. Especially, we achieve about 16% productivity improvement in roller manufacturing process by replacing manual stirring job with automated mixing machine.

Today's fierce competition and global economic recession make most of manufacturing companies in the world difficult to gain a profit. In order to survive such a environment and increase competitiveness, manufacturing companies have to continuously eliminate their wasteful factors through an efficient process analysis, improve quality of products, increase the flexibility of manufacturing processes. In this paper, we consider a case study for the Shanghai New Auto which is a subcontractor of MOBIS in China, to improve productivity by using therblig method, one of the motion analysis, to minimize the work-in-process inventories and to shorten the manufacturing cycle times. We also try to relocate the facility layout to increase the efficiency and flexibility of manufacturing processes.

In a real-life supply chain environment, demand forecasting is usually represented by probabilistic distributions due to the uncertainty inherent in customer demands. However, the customer demand used for an actual supply chain planning is a single deterministic value for each of periods. In this paper we study the choice of single demand value among of the given customer demand distribution for a period to be used in the supply chain planning. This paper considers distributed multi-echelon supply chain and the objective function of this paper is to minimize the total costs, that is the sum of holding and backorder costs over the distribution network under the service level constraint, by using demand selection scheme. Some useful findings are derived from various simulation-based experiments.

In reality, distribution planning for a supply chain is established using a certain probabilistic distribution estimated by forecasting. However, in general, the demands used for an actual distribution planning are of deterministic value, a single value for each of periods. Because of this reason the final result of a planning has to be a single value for each period. Unfortunately, it is very difficult to estimate a single value due to the inherent uncertainty in the probabilistic distribution of customer demand. The issue addressed in this paper is the selection of single demand value among of the distributed demand estimations for a period to be used in the distribution planning. This paper proposes an efficient demand selection scheme for minimizing total inventory costs while satisfying target service level under the various experimental conditions.

The aim of this study is to establish an efficient distribution planning for a capacitated multi-stage supply chain. We assume that the demand information during planning horizon is given a deterministic form using a certain forecasting method. Under such a condition, we present a cost effective heuristic method for minimizing chain-wide supply chain inventory cost that is the sum of holding and backorder costs by using look-ahead technique. We cope with the capacity restriction constraints through look-ahead technique that considers not only the current demand information but also future demand information. To evaluate performance of the proposed heuristic method, we compared it with the extant research that utilizes echelon stock concept, under various supply chain settings.

The main focus of this study is to investigate the performance of a clark-scarf type multi-echelon serial supply chain operating with a base-stock policy and to optimize the inventory levels in the supply chains so as to minimize the systemwide total inventory cost, comprising holding and backorder costs as all the nodes in the supply chain. The source of supply of raw materials to the most upstream node, namely supplier, is assumed to have an infinite raw material availability. Retailer faces random customer demand, which is assumed to be stationary and normally distributed. If the demand exceeds on-hand inventory, the excess demand is backlogged. Using the echelon stock and demand quantile concepts and an efficient simulation technique, we derive near optimal inventory policy. Additionally we discuss the derived results through the extensive experiments for different supply chain settings.

  Due to recent growing interest in autonomous software agents and their potential application in areas such as electronic commerce, the autonomous negotiation become more important. Evidence from both theoretical analysis and observations of human intera

기계적으로 합금처리한 Mg-18wt.%Ni 혼합물의 수소저장특성이 조사되었다. 1h, 3h, 그리고 6h 동안 기계적으로 합금처리한 혼합물들 중에서 6h동안 기계적으로 합금처리한 혼합물(MA 6h sample)이 가장 좋은 활성화, 수소화물 형성.분해 특성을 보인다. 수소화물 형성.분해 cycling을 시킴에 따라 Mg2Ni상이 형성된다. MA 6h sample은 비교적 쉽게 활성화되며, 순수한 Mg나 Mg-10wt.%Ni 합금보다 수소화물 형성속도가 높으나, Mg2Ni 합금보다는 수소화물 형성속도가 약간 낮다. MA 6h sample은 Mg2Ni 합금에 비해 낮은 수소화물 분해속도를 보이지만, 순수한 Mg나 Mg-25wt.%Ni 합금보다는 높은 수소화물 분해속도를 보인다. MA 6h sample은 순수한 Mg나 다른 합금들보다 큰 수소저장용량을 가지고 있다.