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Due to the rapid growth of electronic commerce via the Internet, there is a need to streamline the supply chain in order to reduce product distribution costs and shorten delivery lead times. Among these, the role of distribution centers that connect physical and commercial distribution has become more important. Improvement in work efficiency in the distribution centers has also become an important theme. Aware of such problems, we focus on order picking operations where products are collected based on the customer order since it is one of the most labor-intensive activities in the distribution center. We consider methods to shorten the average traveling distance of order-picking operators from two perspectives: 1) Improving the floor layout of the distribution center, and 2) introducing better management rules to determine the storage position of products. In the first half of the paper, the mathematical model to evaluate the average traveling distance of the picker is proposed. The model proposed assumes that the width of the aisle and the size of the shelf are sufficiently small, and obtains the value of the traveling distance analytically. In addition, improvement measures are considered from the two perspectives stated above, and are evaluated by comparing with the case in conventional Traditional Layout with horizontal and vertical aisles. In the second half of the paper, two improvement measures combining the two perspectives are proposed based on the results of the first half of the paper. Both measures move the accumulation point of products to the center of the lower side of the floor. In addition, the first measure adopts the addition of a diagonal aisle on the floor and classbased management to determine the storage position of products. The second measure executes class-based storage in an area where the work floor is divided using diagonal straight lines, which means being equidistant from the accumulation point of products. As a result, this paper clarifies that the two improvement measures proposed in the second half of the paper shorten the average traveling distance by 25% or more compared to case in conventional Traditional Layout. [ABSTRACT FROM AUTHOR]

With the recent diversification of product preferences and increasing competition among companies, uncertainty of product demand is increasing. Product market life becomes shorter and precise prediction of selling price, sales volume and market life of products is becoming difficult. As an analysis on investment for product manufacturing, it is insufficient to only calculate the profit / loss under their expected values. There is a need for a method to quantitatively evaluate the risk of investment proposals. Within such a social background, this paper considers a method for evaluating the profitability and safety of manufacturing investment alternatives under the assumption that product selling price, sales volume and market life are uncertain. In the research, the concept of safety is first defined as the change in the rate of uncertain factors until the profit becomes zero. Next, the paper proposes a method for visualizing the profitability and safety of investment alternatives. In the method proposed, manufacturing investment alternatives are represented as triangles in the X-Y plane indicating the initial investment amount, annual fixed cost and unit variable cost. This method makes it possible to observe superiority or inferiority of the investment alternatives from the viewpoint of profitability and safety against uncertain change of product selling price, sales volume and market life. In addition, the proposed method by using the X-Y plane can help to examine new investment alternatives with improved profit and safety. For example, it can consider improved alternatives which raise safety against shortening market life while maintaining the target profit by reducing the initial investment amount and increasing the unit variable cost. [ABSTRACT FROM AUTHOR]

In the printer manufacturing industry, it is known that business impact is especially large because of the due date penalty or opportunity loss (including sales loss of toner cartridge and drum cartridge) when an inventory shortage occurs. Furthermore, in this industry, due to occurrence of sudden and large demand, it is not clear how to analyze demand distribution from historical sales data and how much inventory should be kept. We reviewed various literature regarding the demand forecasting method for sudden and large demand, but we were unable to find a proper method, and therefore decided to develop a new method ourselves. In this research, we propose a new demand forecasting method based on extreme value distribution. In the method proposed, we first classify historical sales data, then we estimate the parameters of the extreme value distribution, and finally forecast future demand. Additionally, we tested our method using real business data. As a result, we found that the method proposed performs better than the current method being used by businesses and can avoid inventory shortages when sudden and large demand occurs. [ABSTRACT FROM AUTHOR]

In recent years, the movement to introduce robots in production systems with the aim of replacing manual labor or combining manual and robot labor has started. In particular, dual-arm robots, which have two robotic arms, are gaining much attention in view of their ability to replace manual laborers. However, planning an efficient work system beforehand and teaching the lean movements to the robots are essential for using them more effectively. In assembly processes, the result of selecting the assembly order greatly influences the productivity of the production process. In such a background, this paper examines a method for determining an assembly order with high work efficiency. Under the assumption that dual-arm robots assemble the products that can be assembled easily (i.e., stacking toy blocks is used here), we propose a computational model for searching a highly efficient assembly order utilizing reinforcement learning. We also consider a method for using the results of previous learning model studies to effectively find solutions for new assembly models. Regression analysis is utilized to transfer the past learning results. [ABSTRACT FROM AUTHOR]

In the area of materials stock-flow analysis, the cumulative graph is an effective method to visualize and analyze flow patterns and embedded problems, leading to identifying ways to improve material flows in production and service systems. However, since the conventional approach of cumulative flow graphs assumes a tandem flow shoptype one-way flow, the graph cannot be used in complicated flow patterns where each stage is comprised of parallel processes and materials flow back-and-forth between these stages. This paper proposes a method to apply a cumulative flow graph to the case of such parallel flow pattern, where a single stage is comprised of a main system and a subsystem, and materials repeatedly flow between these systems before finally flowing out from the process. For the purpose of identifying fundamental problems in the material flow, the method proposed focuses on depicting stock quantity in each system on the cumulative flow graph. The paper finally applies the method proposed to the actual case of a physical distribution system comprised of the main and secondary warehouses, and the validity of the method proposed is discussed and clarified using practical examples. [ABSTRACT FROM AUTHOR]