論文

This study addresses the problem of the make-or-buy decision for products manufactured in multistage serial production processes. With a focus on the capital investment required to transition from external to internal manufacturing, the present paper proposes a method for determining the economic make-or-buy partition using the payback period. In this proposed approach, the economic make-or-buy partition is determined based on the estimated production period and production volume of the products. In addition, for multi- product scenarios where make-or-buy partitions are considered for each product, the present paper elucidates a process for determining the economic make-or-buy partition while adhering to capital investment budget constraints.

Digital transformation (DX) has a significant impact on the supply chain, and this could promote the utilization of data and digital technologies in all systems. In this study, a storage location assignment problem was investigated utilizing a DX method to improve the efficiency of picking operations in distribution centers by focusing on the schedules of stored items. Storage assignment policies utilizing the arrival and shipping information of items to determine the optimal or near-optimal storage allocations to shorten the travel distance were considered. First, it was assumed that all arrival and shipping dates of items in the planning horizon are known. Then, greedy policies focusing on arrival dates, shipping dates, and the storage durations of items were compared. The greedy policy based on shipping dates showed the highest performance. Next, the ways in which these properties can be utilized to obtain the optimal solution efficiently were investigated. Finally, under the assumption that information on the arrival and shipping dates of all items is incomplete, policies to improve the greedy policy using arrival dates were considered.

A problem has been recognized that even though the economic activity of a proportion of the service industry in Japan has recently been increasing, productivity remains low. In order to overcome this problem, a large number of improvents in the service industry have been reported based on scientific and technological aspects which have conventionally been used applying into practice on-site experiences and intuition. This research reports a case study that implements improvements in efficiency of a Japanese restaurant franchise that sells carry-out side dishes and lunch-box meals in addition to providing eat-in meals. In order to analyze the amount of sales lost due to a long queue, a queueing model has been built that utilizes a discrete event simulation tool. Based on the derived analysis results, we examined measures for improving the operational efficiency of the restaurant based on the results of this work study, which have been advocated as traditinal industrial engineering methods. Since conventional work studies generally assume that the work to be analyzed is repeatedly performed, it is difficult to analyze operations that multi-tasking workers support on an irregular basis. The point of this research is that the simulation tool and the work study are utilized in combination to investigate the effectivenesses of the support workers and for considering plans to realize improvements. This research may be extended to other systems since the utilization of support workers helps not only in services systems, but also in logistics and production systems. Through this case study, our aim is to expand the possibility of the practical use of industrial engineering method.

© 2018 Japan Industrial Management Association. All rights reserved. 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.

© 2020 Japan Industrial Management Association. All rights reserved. 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 class-based 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.

© 2019 Japan Industrial Management Association. All rights reserved. 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.

© 2020 In the future smart factory, the production system will further head in the direction of on-demand production. Products will be assembled one by one based on different specifications of customers. In these recognitions, this paper considers a method for raising productivity of the robot work cell. Under the assumption that the dual-arm robot assembles products in the work cell where one robot is in charge of all steps of assembling the product, we propose a computational algorithm for searching the efficient assembly sequence and work assignment to the robot hands utilizing reinforcement learning. Furthermore, we intend to use past learning results to determine work plans of robots more effectively. The proposed methods can eliminate or decrease the workload of the robot teaching. In addition, they can contribute to shorten the assembly time of products by giving the efficient work plan. In this research, the basic theory for automating the work planning of actual assembled products is considered using a building block model.