Electronic books, or e-books, offer several benefits over traditional books: e-book devices are easily portable, easily accessible, and are able to store thousands of books without taking up physical space. Despite these merits, in 2013, e-books had a market share of only six percent of the book market in Japan. This prompted us to seek suggestions on promoting the usage of e-books among consumers. We carried out a fivestep study as follows: (i) A questionnaire-based survey was conducted with 1,000 consumers to gauge their behaviors and attitudes toward paper books, e-books, information sharing, degree of the desire to use e-books, and the frequency of e-book usage. (ii) We then extracted seven factors by applying exploratory factor analysis to the data collected. The factors included users' awareness of the advantages and disadvantages of e-books, love of paper books, interest in book contents, and gathering and sharing information through the Internet. (iii) Hypotheses were set that indicate the relationship among the extracted seven factors, degree of the desire to use e-books, and frequency of e-book usage. (iv) A confirmatory factor analysis was conducted. Then, we constructed a consumer e-book usage intention model and a consumer e-book usage frequency model using structural equation modeling (SEM). (v) After analyzing the models, suggestions were made to enterprises dealing with e-book, such as publishers, e-book stores and authors, for promoting consumer usage of e-book. The suggestions included raising consumers' interest in the contents of e-books, approaching consumers who actively disseminate information through social media and encouraging them to promote e-books, helping consumers to understand the advantages of e-books, and easing consumers' concern about the disadvantages of e-books.
In this paper, we consider the problem of finding a supply chain configuration with minimum risk and investment for risk mitigations. We have developed a simulation model considering disruption. We use this simulation model to evaluate supply chain risk. We propose three risk mitigation strategies: duplication, stabilization, and absorption. We used simulated annealing to find the optimal combinations of these mitigation plans. In a case study, we applied our model to a digital camera supply chain, and show how our model can be applied to obtain a proper supply chain configuration.
On March 11, 2011, the Great East Japan Earthquake hit Japan. Through the earthquake, awareness of disaster prevention has recently been increasing in Japan. Additionally, in the field of logistics, many studies on transportation and delivery during disasters are being conducted; the subject being relief goods not reaching the affected area. In this study, from the point of flow of goods, we focus on the relationship between the shelter and municipal-level collection point. The purpose of this study is to propose a logistics model that minimizes unsatisfied demand for the victims during the chaos of a disaster. In particular, since there is a variety of goods in the shelter, meeting victim's demands is required. We therefore propose a model using the idea of a multi-commodity network flow problem. In addition, to meet victim's demands equally, we incorporate a constraint on equality. In order to confirm the proposed logistics model, a case study is carried out. We focused on the number of evacuees in Soma City, Fukushima Prefecture, an area damaged by the Great East Japan Earthquake. Finally, we perform numerical experiments and confirm the effectiveness of the proposed model.
The Mahalanobis-Taguchi (MT) method is a standard method of multivariate analysis for detecting anomalies or recognizing patterns. A number of case studies using the MT method have been reported. However, good performance is only obtained when a sufficient number of samples can be ensured; if the number of samples is insufficient, this method has a large probability bias. In this paper, we first analyze the existing measures of methods, in which performing dimension reduction, such as using variable selection, is common, and show that there are some problems with testing for unknown data. Secondly, we propose two analytical procedures for small sample data in which the detection capability with respect to unknown data is taken into account. In these proposed procedures, when the number of data samples is small compared to the dimensions of the variables, the detection measure in the MT method is replaced by a measure derived through approximating correlation matrices based on probabilistic principal component analysis (PPCA) or by introducing ensemble learning. Finally, based on raw data analysis using the KDDCup99 dataset and simulation results, we consider how the proposed procedures should be applied when multicollinearity occurs and which of these two procedures should be applied according to the data pattern.
The bullwhip effect is one of the most important issues in supply chain management. This phenomenon describes the way in which fluctuation in demand increases upstream in the supply chain. Inaccurate demand forecasting accompanying a lack of communication is well known as a cause of the bullwhip effect. Nevertheless, there are few arguments as to the influence of the bullwhip effect on demand forecasting as generated through communication with business contacts. In this paper, we propose the framework and simulation model of a supply chain system based on demand forecasting built through communication with business contacts. Here, a simulation model was constructed by modeling past transaction data as knowledge sharing with business contacts. A recurrent neural network that is excellent in time-series prediction was used for demand forecasting in this simulation. We confirmed the effectiveness of our proposal through comparative experiments using inventory management simulation on conventional models and the proposed model.
We develop an interactive support system for generating schedules of a thorough physical medical checkup (ningendoku in Japanese) to reduce the waiting time of medical examinees. Improving the efficiency of the medical checkup is an important problem to be solved for both examinees and medical institutions. For examinees, long waiting time is a waste of personal time. Additionally, unexpected delays in schedule times make examinees to readjust their schedules. These are burdens on the examinees. For medical institutions, reducing examine waiting time for medical checkups under the constraints of doctors and medical facilities will increase their ability to increase the number of examinees. This will result in raising the income of the medical institutions. In this paper, we show the basic idea of implementing a scheduling support system for medical checkups, a 0-1 integer programming formulation of the medical checkup scheduling problem included in the system, and the results of applying the system to real data of a medical institution. In addition to this, we apply the Shortest Processing Time (SPT) rule to the order of examinees by considering the problem as a variation of the job shop scheduling problem. We solve the examine rescheduling problem using the SPT rule. As the result, the schedule obtained has a much shorter waiting time than the real one. We also propose an appointment scheme method for the medical institution to apply the SPT rule approximately. The effectiveness of the scheme is shown by numerical examples.
This study focuses on the engineer-to-order (ETO) manufacturing firms that are required to design an entire product to match the requests of their customers. Typical products include machine tools such as machining centers and d rilling machines. Since the customers are not familiar with all product functional specifications, the value of each product functional specification is proposed by the sales staff according to his/her experience. As a result, our target ETO manufacturing firms now face the big problem that the proposed product functional specifications need to be frequently changed not only during the quotation stage, but also after the contract is signed. These changes lead to not only additional costs, but also time loss for design/engineering, production and parts supply departments of the firm. In order to fix the product specifications as soon as possible, in this paper, a product functional structure model is proposed for accurately determining product functional specifications. The model is built by defining customer requirement specifications, product functional specifications, functional structure elements, and evaluating the relationships among the three kinds of specifications. In the first report of this research, drilling machines are taken as an example, and detailed definitions for customer requirement specifications and product functional specifications are determined. Moreover, a constraint detection system is developed to support the sales staff to gather necessary information from the customers and propose accurate product functional specifications to them. By using the product functional structure model, customers’ requirements can be defined clearly and impossible specifications of a product can be avoided.