Journal of Japan Industrial Management Association
Online ISSN : 2187-9079
Print ISSN : 1342-2618
ISSN-L : 1342-2618
Volume 56 , Issue 2
Showing 1-18 articles out of 18 articles from the selected issue
  • Type: Cover
    2005 Volume 56 Issue 2 Pages Cover3-
    Published: June 15, 2005
    Released: November 01, 2017
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  • Type: Cover
    2005 Volume 56 Issue 2 Pages Cover4-
    Published: June 15, 2005
    Released: November 01, 2017
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  • Type: Index
    2005 Volume 56 Issue 2 Pages Toc2-
    Published: June 15, 2005
    Released: November 01, 2017
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  • Shoichi SATO, Mitsuyoshi HORIKAWA, Takashi KANAZAWA, Mitsumasa SUGAWAR ...
    Type: Article
    2005 Volume 56 Issue 2 Pages 65-73
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    There are few companies that are capable of balancing out supply and demand. Some companies have seasonal variations in the demand for products; for example, home appliances such as air-conditioners, leisure goods and apparel. Other companies have seasonal variations in raw materials; for example, food processors. It has been a critical and typical production problem to equalize the imbalance between supply and demand. Many companies have not yet worked out a solution for this problem. One of major reasons is that the balance itself can be easily influenced by environmental changes. In other words, the company must review standards for inventory levels, and innovate manufacturing system technologies with the measures devised to deal with the expected influence of these changes. It may even lead to restructuring production systems. This paper discusses a production system strategy created to manage seasonal variation in demand. A simulation-based investigation is conducted to analyze the impact of three methods in the production system. This paper first clarifies how the imbalance between demand and supply is equalized in the short term. There arises uncertainty when companies make to stock for smooth production. Therefore, an appropriate inventory level has to be decided. Second, how manufacturing flexibility affects a production system in the long term is clarified. One way of enhancing manufacturing flexibilities is to improve volume flexibility, which is the ability to operate profitably at a different output level. Also clarified is the way to improve the standardization/commonality for components and parts. There are some companies that have already implemented these improvements recently. The results provide manufacturers with better insight and guidelines for determining strategies for seasonal adjustments in production systems.
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  • Hidetaka MISAWA, Masakazu KANEZASHI
    Type: Article
    2005 Volume 56 Issue 2 Pages 74-83
    Published: June 15, 2005
    Released: November 01, 2017
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    This paper gives a practical solution method using a multiple objective genetic algorithms for a corrugated cardboard box production scheduling problem. This problem is formulated as a multiple objective combinational optimization problem that minimizes not only the setup time to exchange a block copy, but also the total loss of stencil paper. Usually, the solution for a multiple objective optimization problem is obtained as a set of Pareto optimal solutions. The decision-maker compares a certain Pareto optimal solution in a set with other Pareto optimal solutions in order to the desired solution. However, it is very difficult to obtain plural Pareto optimal solutions in one trial using the conventional methods. Therefore, we propose a method for obtaining a set of Pareto optimal solutions for a corrugated cardboard box production scheduling problem using multiple objective genetic algorithms. The genetic algorithms produce many possible solutions during the search process. By utilizing this advantage, the proposed method can efficiently obtain a set of Pareto optimal solutions. The proposed method is applied to a complex corrugated cardboard box production problem, and the results show the effectiveness of the proposed method.
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  • Aya ISHIGAKI, Yasuhiro HIRAKAWA
    Type: Article
    2005 Volume 56 Issue 2 Pages 84-91
    Published: June 15, 2005
    Released: November 01, 2017
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    The Toyota Kanban system is known as one of the most effective production systems for controlling materials flow in multi-stage manufacturing processes. Materials flow is triggered by the input flow from subsequent stages using Kanban-loops. The CONWIP system and the nested Kanban-loop system were proposed as improved versions of the Toyota Kanban system. However, these studies used different structures of Kanban-loops under different models. In multi-stage manufacturing processes, there are many points just before and after each stage that are able to trigger the flow. In this paper, a multiple Kanban-loop system is proposed to generalize the Kanban-loop structure in multi-stage manufacturing processes. The multiple Kanban-loop system has all the possible loops beginning from the output position of each stage and ending at the upstream stages. The Toyota Kanban system, the CONWIP system and the nested Kanban-loop system can be expressed as a special version of the multiple Kanban-loop system. Under the objective functions of system productivity and total work-in-process, it is shown that the nested Kanban-loop system can achieve optimal materials flow in multi-stage manufacturing processes.
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  • Masahide KAWASAKI, Kazuko MORIZAWA, Hiroyuki NAGASAWA
    Type: Article
    2005 Volume 56 Issue 2 Pages 92-100
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Circumstances for production planning/scheduling can change abruptly, and frequently. We therefore assume scenarios with occurrence probabilities, and generate a set of nondominated solutions to minimize a weighted sum of criterion vectors based on the occurrence probabilities of scenarios. Since the assumed occurrence probabilities may have estimation errors, it is necessary to consider that the assumed occurrence probabilities may be far from the real values. Therefore, the sensitivity of the set of nondominated solutions is analysed with respect to possible changes in occurrence probabilities. This paper adopts the following new concepts for "closeness": "Individual-solution-based Point Closeness (I-PC)," "Nondominated-solution-based Point Closeness (N-PC)" and "Set Closeness (SC)." If these values of the set of nondominated solutions are small enough for decision makers to accept, the set of nondominated solutions is called "robust." Otherewise, for a permissible value ε given by the decision maker, we generate robust sets of approximate nondominated solutions with the maximum closeness less than ε, such as the "ε-robust set of approximate nondominated solutions," the "ε-robust local set of approximate nondominated solutions" and the "[ε_1, ε_2]-robust set of approximate nondominated solutions." We propose a method for generating these robust sets of approximate nondominated solutions. The effectiveness of the proposed methods in generating robust approximate sets is shown by using a numerical example of a single-machine two-objective scheduling problem to minimize mean flow time and maximum tardiness.
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  • Kayoko TAKAMORI, Ippei NAKASE, Yasuhiko TAKEMOTO, Ikuo ARIZONO
    Type: Article
    2005 Volume 56 Issue 2 Pages 101-108
    Published: June 15, 2005
    Released: November 01, 2017
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    Recently, Cao et al. considered the mean availability of a system that has multiple failure modes. Thus led to the creation of a formula for the mean availability of systems with multiple failure modes (i.e., random and wear-out failure modes). The formula of the mean availability derived by Cao et al. requires an exact distribution function of failure-time distribution that wear-out failures follow. However, there are often cases where information such as average and standard deviation are only known, and the exact distribution function isn't specified. In this article, under limited information such as average and standard deviation about wear-out failure mode, we address a procedure for evaluating the mean availability of a system that has random failure and wear-out failure modes.
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  • Kenji KURASHIGE, Toshio HASHIMOTO, Yoshimasa KAMEYAMA
    Type: Article
    2005 Volume 56 Issue 2 Pages 109-120
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    The objective of nurse scheduling problems is to allocate some work shifts to each nurse which has many constraints and criteria. The head nurse of each hospital ward creates a nurse shift table once a month. This operation is a complicated job and takes much time. In recent years, the application of meta-heuristics (Genetic Algorithm, Tabu Search, Simulated Annealing, etc.) to combination optimization problems allows the task to be performed quickly. Some practical nurse scheduling systems have adopted meta-heuristics and make shift tables automatically. However, most of the systems are developed for specific hospitals and are inapplicable to others. The reason is that constraints and criteria differ widely between each hospital or hospital ward. It is therefore expected that alternations in program code or much customization is needed in order to apply the system to other hospitals. The above-mentioned factors cause nurse scheduling systems to become costly. In this research, a nurse scheduling system that can be used by many hospitals has been developed. It is a semi-automated and interactive system. The development of an automatic system for many hospitals is very difficult because there are too many constraints, and option settings are too complex. The objective of this development is the realization of flexibility and low cost at the sacrifice of convenience. Generally, all constraints are classified into two categories: common constraints for many hospitals, and local constraints for a few particular hospitals. The system takes into consideration the common constraints. There is a low probability that a satisfactory shift table can be obtained automatically. Therefore, it is supposed that head nurse should correct the unsatisfactory part of the schedule created.
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  • Sadahito ISHIBASHI
    Type: Article
    2005 Volume 56 Issue 2 Pages 121-128
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Halo error is considered by many to be the major psychometric error affecting multi-factor ratings. Discussion of the halo error can be traced back more than 80 years. Halo error is more general and abstract latent variables and may determine "first-order" latent variables that commit halo error when evaluating the behavior of ratee. In other words, the latent variables that commit halo error when evaluating the behavior of each ratee directly influences the rating of other latent variables that need not have a direct effect on the rating. This model is called second-order factor analysis. This paper suggests the use of second-order factor analysis to estimate halo error. A sampling manager collected rating data and rated each case study that was made using nine persons and four evaluation factors each (9×4=36 cases). The results of the experiment clearly show that the model is fitted to empirical data from an opinion questionnaire collected by the manager. Moreover, this paper shows each rater's halo error quantitatively with factor scores. It is useful for recognizing individual rating tendencies. It is also effective to feedback scores in rater-error training, showing that the goal is to reduce halo error and to be more accurate by using performance appraisal to familiarize raters with the existence of halo error.
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  • Hajime MIZUYAMA, Katsunobu ASADA, Kentaro YAMADA
    Type: Article
    2005 Volume 56 Issue 2 Pages 129-138
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Detecting causal factors of chronic quality defects is very important, and often the most critical step of improving manufacturing quality in a multi-stage production system. Since advanced information technology has enriched manufacturing databases, it is now the time to ask how to utilize databases to streamline the process of this causal factor detection. However, applying conventional multivariate statistical analysis methods or modern data mining approaches simply to a database does not always provide sufficient knowledge for revealing the key factors of chronic defects and how they cause the defects. Thus, the authors propose a novel framework for more sophisticated exploratory quality data analysis in order to support detection of the causal factors. The proposed data analysis framework is named the "multi-stage quality information model (MSQIM)", which, if possible, should establish some hypotheses on the causal factors and/or the defect-causing mechanisms, and should at least identify which process steps within the production system further efforts of causal factor detection should be focused on. MSQIM first divides a manufacturing database into several segments, each of which corresponds to a certain process step within the production system. It then traces how the amount of information on the resultant manufacturing quality varies along with the process steps so as to identify the relevant process steps that require further focus. The varying pattern of the quality information is also studied in a qualitative way so that it assists in hypothesis generation. This paper mainly discusses how to implement MSQIM based on logistic regression. It also demonstrates how the proposed approach works through an industrial example.
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  • Masayuki MATSUI, Hiroki UCHIYAMA, Hiroaki FUJIKAWA
    Type: Article
    2005 Volume 56 Issue 2 Pages 139-145
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    In modern enterprises, the problem of balancing supply and demand becomes important, and the supply chain management (SCM) becomes the focus of attention. Under on-demand SCM, it is important to reduce the risk of supply chain over/under-stocking in real time. To hedge the risks, the equation of flow control is re-considered, and a dynamic logic control is introduced. For the logistics of PC products, this paper develops a look-ahead control logic based on a progressive curve. For simplicity, it is assumed that the demand (output) is uncontrollable (given), and the supply (input) is controllable (manageable) on the basis of base-inventory only. First, the moving base-inventory is obtained from the formulation of a Newsboy problem. Next, this indicator is checked by a cumulative control chart in quality control, and supply action (input) is carried out timely, within the upper limit. In this study, this method is applied to the warehouse company A, and a cumulative inventory reduction of approximately 42% is attained.
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  • Type: Appendix
    2005 Volume 56 Issue 2 Pages 146-
    Published: June 15, 2005
    Released: November 01, 2017
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  • Type: Appendix
    2005 Volume 56 Issue 2 Pages App8-
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Download PDF (80K)
  • Type: Appendix
    2005 Volume 56 Issue 2 Pages App9-
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Download PDF (80K)
  • Type: Appendix
    2005 Volume 56 Issue 2 Pages App10-
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Download PDF (80K)
  • Type: Appendix
    2005 Volume 56 Issue 2 Pages App11-
    Published: June 15, 2005
    Released: November 01, 2017
    JOURNALS FREE ACCESS
    Download PDF (80K)
  • Type: Appendix
    2005 Volume 56 Issue 2 Pages App12-
    Published: June 15, 2005
    Released: November 01, 2017
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