This study predicts bankruptcy by making predictions after separating the entire industry into individual industries via the decision tree method and support vector machines (SVMs), and then it compares two method, decision tree and SVM. This research predicts bankruptcy in two steps. The first step is to predict using decision tree method and the second step is to predict using support vector machine for 11 industries. The financial statements of the listed companies in the Tokyo Stock Exchange, the Osaka Securities Exchange, and other stock exchanges were used as data in this study. The data of 244 bankrupt companies that went bankrupt between 1991 and 2014 are used. On the other hand, data of 64708 non-bankrupt companies that did not go bankrupt between 1991 and 2014 for 24 years are used. The data is acquired from Nikkei NEEDS database. In the decision tree, the multi-step branching process is stratified, and the bankruptcy prediction is performed in the tree diagram. In SVM, prediction of bankruptcy is almost perfectly conducted to discriminate the companies for each industry.

In workplaces developing map database for car navigation systems, the issue of specification changes occurs frequently. Geographical conditions, traffic restrictions, and various other conditions differ in each part of the world, making it difficult to identify all specification changes in advance. Moreover, specification changes occurring in one project may affect other projects developed around the same time. This paper looks at how to manage and run software development projects with many specification changes to achieve greater stability. This paper suggests a change point management and solution for determining how to adjust the initial development schedules and allocate development staff each time a specification changes, to ensure the stabilization of the project as a whole.

This paper considers a model of emergency facility construction problem with given emergency facility under the block-wisely difference accident occurrence probabilities and difference construction costs. That is, we consider the following problem. There exists a polygonal area X where new ambulance service station should be located beside a given one (hereafter we call this facility as old one) and there exist m hospitals, We assume that possible candidates are in a finite number of blocks that construction costs are different block-wisely. If an accident occurs , the ambulance cars in the nearer station among either given or new one rushes to the scene of accident and bring the injured persons to the nearest hospitals as soon as possible. Demand points (possible accident occurrence points) are distributed with block-wisely uniform probabilities in X. Weighted sum of A-distances of the route from the nearer station among old and new to the hospital via accident point is considered. For the weighted distance from the nearer station to each block with uniform accident occurrence probability, maximum expected distance is considered with respect to this distance. We also consider the construction cost where in possible site of each block it is constant. The expected distance should be minimized and construction cost be minimized. Above setting, we formulate the model with bi-objectives. After definition of non-dominated site, we propose some solution procedure to find some non-dominated sites. Finally we conclude the results and discuss further research problems.

The development of the Internet of Things (IoT) is indispensable for the spread of Industry 4.0. To develop IoT research, this article analyzes the trends in IoT research from the viewpoint of the importance of IoT research in responding to the global IoT scenario. In this analysis, we perform network analysis and investigate the characteristics of researchers who are related to IoT research. We study the fusion of different fields, such as chemistry and engineering, in IoT research. We also propose four ways of increasing IoT research activities in Japan.

Rhoades & Eisenberger [1] stated supervisors act as agent of the organization, then employees develop global view concerning the degree to which supervisors value their contributions and care about their well-being. Eisenberger & Stinglehamber [2] indicated strength of influence on the perceived organization support (POS) follows to the status of supervisors. Zagenczyk et al. [3] reported coworkers’influence on POS through social network. But there are few studies on perceived supervisor support (PSS) depending on hierarchy and perceived coworker support ( PCS). This studies investigated difference of influence on affective commitment (AC) and citizenship behavior (OCB) from PSS and PCS. Subject of research are technical employees working in steel product factories located in Japan. We analyzed data by regression analysis and covariance structure analysis. We found that (1) Influence of PSS on AC and OCB does not depends on hierarchical order of the organization. (2) PCS influence on AC and OCB more than that from factory manager and immediate supervisor. (3) Affective commitment (AC) mediate between perceived coworker and supervisor support and citizenship behavior. Study suggested that perceived support of coworker and powerful boss have strong influence on employees.

Among the existing body of research focused on business models are studies by Osterwalder (2010), Slywotzky (2002), Yamada (2012), and Matsubara (2014). However, these involved the qualitative classification of company business models. This study quantifies business models and uses data analysis to clarify the key factors that change the business model of a company. Moreover, it uses data to analyze business models in the manufacturing industry and proposes an approach to such business models.

Recent years, have witnessed rapid developments in the digital archive, communication, and sensing technologies. Manufacturing industries, governments, etc., can now utilize data generated either from the public or Internet of Things devices for decision making purposes. Thus, big data analysis techniques are becoming increasingly important. However, contamination of the source data with noise often leads to erroneous analysis results. Data cleansing has been one of the main challenges in carrying out data analysis because it is difficult to remove noise data from the real data especially when there is no welldefined metrics for distinguishing between the noise data and the real data. Hence, the purpose of this study is to propose an effective method for cleaning the data before they are processed. This method involves generating the metadata and simulation model according to the purpose of application and finally conducting the simulation. We validated the experimental data cleansing method using the probe person survey data. As a result, even if the necessary data items are lacking they are can be compensated for by setting a formula to estimate them. The results of our experiments show that this method gives relatively accurate results.

While diversified J-REITs that invest in multiple property sectors retain the largest share since the inception of the market, specialized J-REITs have gradually gained presence. The increasing share of specialized J-REITs could be a rational movement if we can verify their superior performance against diversified J-REITs. This research tries to compare diversified and specialized J-REITs through operational asset-focused J-REITs to determine the presence of different risk-return relationships between them using actual market data of 48 months. We choose five J-REITs from three sub-asset categories for our data analyses. From actual market data, we find no evidence of superior abnormal returns in either hotel- or healthcare-focused J-REITs vis-à-vis diversified J-REITs. For systematic risk, we find significantly lower results with respect to the three sub-asset categories, with no significant difference between them. Furthermore, we construct a simulated diversified J-REIT using five specialized J-REITs, and find that dynamic asset allocation has the potential to outperform existing diversified J-REITs. These results are not fully consistent with those in the previous studies in the United States, but they may indicate a unique phenomenon, at least in the given period, in the J-REIT market.

Package designs that successfully unite labels and the physical form of a plastic bottle drink often provide a good indication of whether customers will purchase plastic bottle drinks. Therefore, enterprises strive to develop attractive package designs. To develop these designs, enterprises must first determine their underlying purpose for improving package designs. Then, they can determine the images they need to project to convey this purpose, and the enterprises can incorporate the company image into package designs. In this way, customers can get a sense of the image of the company through the package design. However, there is currently no methodology to identify company images and develop package designs that reflect these images. Package design usually proceeds by a process of trial and error, based on designers’subjective perceptions and experiences. This study aims to propose a method to improve the package design of a plastic bottle drink. We determined the images that the company should project by setting a purpose. Also, we attempted to understand the images that customers perceive from the current package design of products. Then, we compare the company’s intended images with the customers’ perceived images to identify the images that should be reflected in the new package design. Furthermore, we discussed a method for incorporating these images into package design. We studied the relationship between images and design elements using a multivariate regression/quantification method of the first type. In so doing, we were guided toward improving the present package design. Finally, we proposed a concrete procedure to develop the package design, based on the regression equation.

The concept of principal points has been the subject of numerous theoretical and application studies. There are also the studies of principal points for multivariate binary distributions. Moreover, , recently, principal points for multivariate binary distribution considering two classes using an external criterion has studied. For example binary questionnaire data for products or services are classified based on the external criterion of customer satisfaction. In previous studies, principal points were located by minimizing the expected squared distance between the principal points and a multivariate binary distribution within a class, and maximizing the squared distance between the two classes. However, this approach to finding principal points was very time-consuming. In this study, we propose an algorithm for finding principal points considering an external criterion for multivariate binary distributions. Moreover, we apply our method to real binary data that is divided into two classes by the binary criterion, and we demonstrate the effectiveness of our approach.

In this paper, the problem of the allocation and location of automated external defibrillators (AEDs) is considered. An AED is a portable device that has been developed to allow for emergency treatment against sudden heart seizures such as myocardial infarction by electric shock in the absence of healthcare workers. This emergency treatment by AED must be administered as soon as possible following heart seizure. Therefore, it should be easily accessible. We discuss the problem of placing a fixed number of AEDs in a given set of buildings. For this problem, we determine both the number of AEDs that should be allocated to each building and the location of each AED in the building. We formulate this combination problem by considering the rectilinear distance and developing an algorithm to obtain a good approximation solution.

Recently, assurance cases standardized by ISO have received a lot of attention from researchers. A previous study showed that it is appropriate to use assurance case to demonstrate the improved feasibility of accomplishing management vision and management strategy using four models (Management vision model, Management strategy model, Business process model, and IT system model). However, in the previous study, concrete description methods were not discussed.

Filling this gap of knowledge, this study aims to show a detailed four-layered assurance case description method using D-Case approach to connect multiple layers of the hierarchical structure of an assurance case. This is an improvement from the previous in that it now focuses on one layer for improving the feasibility of accomplishing both management vision and management strategy. As a result, this study contributes to the existing literature by adding a description method for a four-layered assurance case that includes 30 steps and uses D-Case. This study concludes with future research topics.

The present study attempts to examine the impact of smartphone-based healthcare technology (SHT) adoption in public healthcare services. System dynamics is used to understand the substantial impact of SHT adoption among patients and doctors would improve the discharge rate and patient’s medical costs. The results show that the changes in the proportion of SHT adoption among patients seem to be significant to the perceived medical expenses and the patient discharge rate from public hospitals. This is due to the fact that the increase in the proportion of patient using SHT would reduce the proportion of basic medical expenses incurred by patients and increase inpatient discharge rate as well. On the other hand, the proportion of both medical doctors and patients adoption of SHT for medical consultation is found to be vital to improve the number of patients being discharged from hospital wards. This impact is implicitly shown from the model through the effectiveness of health consultation using common available social networking apps. In conclusion, the findings of this work are hoped to provide an apprehension to the hospital management’s decision makers and doctors that smartphone devices are considered vital to improve healthcare delivery in Malaysian government hospital and to reduce the burden of medical costs.

Managing marketing funds in order to maximize profits while minimizing costs is an inherent objective of any business. A key to optimizing this relation is to select a subset of customers to target marketing expenses. An approach is presented, which helps identifying such a subset at an early stage of their life cycle. We suggest a measure to rate the users' value relative to others, and present an approach how to predict the user rating. This work compares and evaluates two algorithms (naive Bayes and random forest) which predict the user rating. Two groups were used, the first representing the top rated users, which are the part of the subset on whom the marketing funds should be focused, and a second one containing the users which may be neglected. The dataset is skewed, due to the amount of users of interest being low. It is shown how the algorithms can deal with such an imbalanced set. The algorithms are evaluated with a precisionrecall curve. The framework is compared to other prediction models and metrics such as recency, frequency,monetary model and customer lifetime value. Since the framework is flexible, it encourages including and expanding other established metrics.

When a material breaks due to a collision in a manufacturing process, the more expensive the material is, the greater the cost incurred by the manufacturer. As a result, estimating collision probability is very important. A method for computing collision probability in an in-line machines model already exists. In this paper, in order to create a general manufacturing model, we focus on parallel machines, and present a framework for computing collision probability in a parallel machines model. We first define the collision probability in the parallel machines model, and offer an effective method of computing whether a collision occurs or not when a job is fed into the line. This method searches for an idle machine if no collision occurs,and is applied in order to compute the collision probability from computer simulations. The effect of the method presented is then confirmed through implementation of this framework.

Having multiple viewpoints are an indispensable capability for persons who can lead the development and operation of large-scale complex technological and social systems. In addition, the previous study describes as follows.”Citizens are diverse because our society has become more complex in recent years. Therefore how to evaluate, how to satisfy the required level of accountability, and how to reach a good agreement are problems that must be addressed.” This study therefore aims to evaluate whether Goal Structuring Notation (GSN) is a notation method structuralizing with multiple viewpoints for agreement formation. Through the evaluation, this study intends to clarify what it is to structuralize with multiple viewpoints by using GSN. A quantitative evaluation method and a qualitative evaluation method are used for data analysis. This study discusses structuralizing with multiple viewpoints. Concretely, this study showed that structuralizing with multiple viewpoints: 1) Connects multiple viewpoints, creates new boundaries, and enables structuralizing what was described by using GSN, when multiple Strategy nodes are used. 2) Enables expressing a combination of a viewpoint that is selected from multiple viewpoints aligned in the horizontal direction, and a viewpoint that is selected from multiple viewpoints aligned in the vertical direction, when one Strategy node is used. 3) Enables discovering an unexpected viewpoint through a conversation using GSN. Finally, this study concludes with future research topics.

In recent years, third party enterprises are going into the various maintenance business actively. These firms enter into the maintenance market of the information systems, marine engines, automobile, construction machineries and elevators/escalators in Japan. However, current state of the markets isn't desirable for manufacturers and customers, since there are problems in the maintenance quality provided by third party enterprises. The supply of inexpensive and low-quality maintenance service has a negative influence on the business ecosystem which consists of manufacturers, customers and third party enterprises. The negative influence damages the reliability of the whole products. Thus, this research elucidates the business ecosystem in the maintenance business and derives the activities each player should carry out for quality improvement. As a result of system dynamics and case analysis, we found the importance of the exchange of quality information between manufacturer and third party enterprises. When quality information exchange is carried out, it is expected that the business ecosystem of maintenance service will expand.

This paper examines the effect of restricting the elderly’s driver’s licenses or encouraging elderly drivers to forfeit their licenses on reducing the number of traffic accidents. Considering the pedestrians and drivers who participate in compulsory automobile liability insurance and voluntary insurance we model a traffic accident scenario that incorporates the number of elderly, the insurance rates, population density, income, and pavement extensions by prefecture in Japan. Furthermore, we model a simultaneous equation that considers the occurrence of traffic accident and insurance participation. The study found that when strict liability is applied to a driver, drivers are not likely to avoid accidents beforehand. Notably, it is possible that elderly people possessing a driver's license induce more traffic accidents. It is difficult to expect the effect of deterring an accident in the insurance market unless there is a mechanism to give pedestrians incentives to avoid accidents.