Information sharing is one important issue of supply chain management. Sharing buyer's information, a supplier can obtain great benefits such as, quicker and more accurate response to the market, earlier procurement, etc. CRP (continuous replenishment planning) and VMI (vendor managed inventory) are typical models of this type of information sharing, where the upstream stage shares the downstream stage's information. In this paper, we consider another type of information sharing in which a downstream stage shares the upstream stage's information. We construct a model with information sharing and several reference models without information sharing. Through computer simulation, we evaluate the value of information sharing in terms of reduction of average inventory and ratio of stockout. Simulation results show that the model with limited production capacity and information sharing had a smaller ratio of stockout compared to the model with infinite production capacity and no information sharing, while the models yielded almost the same average inventory. A further evaluation comparing the well known nested property (or synchronous ordering) shows that the model with information sharing had a smaller ratio of stockout while the models yielded almost the same average inventory. A brief discussion on the effects of number of buyers with information sharing is presented.

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This study deals with problems in the design of a production seat system. Using the production seat booking system, a production department, with advance agreement of the production capacity, can achieve the delivery inquiry from individual customers. In recent years, supply chain management (SCM) requires immediate and correct answers of delivery date and reduction of stock. The production seat system, which aims to realize these demands, is thought to be one of the current important production systems. This system, however, is not supposed to draw up the production plan for all types of production processes; therefore, we need to set up a margin to all the possible factors that prevent production activities beforehand. This paper formulates a production-planning model for a production seat system for a two-stage flow shop. Two kinds of variables are set as the policy in the model: one is a variable for the frequency of machine trouble and the other is a variable for the estimated error between the actual processing time and the standard time for the plan. We clarify how much factors such as machine trouble and estimated error influence manufacturing performance. This paper presents a basis for designing a production seat booking system through a series of numerical experiments.

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A linear consecutive-k-out-of-n:F system consists of n components in a line. The system fails if and only if k or more consecutive components fail. A great deal of research work in this topic has been conducted since the beginning of the 1980s. The consecutive-k-out-of-r-from-n:F system is an extended system of a linear consecutive-k-out-of-n:F system. This system similarly consists of n linearly ordered components. The system fails if and only if there are at least k failed components among any r consecutive components. So, this system can represent quality control problems and inspection procedures, radar detection problems, and so on. The optimal arrangement problem in the linear consecutive-k-out-of-r-from-n:F system is to obtain the arrangement (optimal arrangement) that provides maximum system reliability within all the arrangements of components when all components don't necessarily have the same failure probability. In this paper, we propose an efficient algorithm based on a branch and bound method, for the optimal arrangement problem in the linear consecutive-k-out-of-r-from-n:F system. Our proposed algorithm conducts the following procedures (1) Searches only the arrangement that satisfies the necessary conditions for optimal arrangement, (2) Obtains reliability of the system and subsystems efficiently by "Malinowski and Preuss (1995)," and (3) Removes searches for arrangements that are no useful. We executed numerical experiments for solving an optimal arrangement problem in order to evaluate the proposed algorithm. From the results of actually solving the optimal arrangement using a computer, we showed the efficiency of our proposed algorithm, although the conclusions within the range of our executed experiments. We also show combinations of component failure probabilities that the proposed algorithm can solve more efficiently.

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In traditional reliability theory, both the system and its components are allowed to take only two possible states: working or failed. In a multi-state system, both the system and the components are allowed to be in M+1 states: 0, 1, 2,…, M, where M is a positive integer which represents a system or unit in perfect functioning state, while zero is complete failure state. A multi-state system reliability model provides more flexibility for the modeling of equipment conditions. Huang et al. (2003) proposed more general definitions of the multi-state consecutive-k-out-of-n:F and G systems and then provide an exact algorithm for evaluating the system state distribution of decreasing multi-state consecutive-k-out-of-n:F systems. Another algorithm is provided to bound the system state distribution of increasing multi-state consecutive-k-out-of-n:F and G systems. The multi-state consecutive-k-out-of-n:F system is applicable to, for example, quality control problems. In this paper, we provide two theorems and a recursive algorithm which evaluate the system state distribution of a multi-state consecutive-k-out-of-n:F system using the theorems. These recursive formulas are useful for any multi-state consecutive-k-out-of-n:F system, including the decreasing multi-state F system, the increasing multi-state F system and other non-monotonic F systems. We calculate the order of computing time and memory capacity of the proposed algorithm and show that, in cases when the number of components n is large, the proposed algorithm is more efficient than other algorithms. A numerical experiment shows that when n is large, the proposed method is efficient for evaluating the system state distribution of multi-state consecutive-k-out-of-n:F systems.

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In this paper we propose a new approach to regression analysis. In the proposed approach the basic model is transformed into several independent partial models through orthogonal transformations of dependent variable and regression coefficients. In the transformed model, we treat the coefficient components, which are defined in a set of linear combinations of the regression coefficients, as the parameters instead of the regression coefficients. Because every coefficient component is included in an independent partial model, each can be manipulated independently. Hence, model building no longer must be considered a combination of coefficient components. This not only reduces the number of conceivable candidate models, making model selection easier, but it also leads to an improvement in estimates of parameters and the possibility of finding better models. We also analyze the relationship between the productivity of rice farming and the temperature in Hokkaido as an illustrative application of the proposed method. The estimation results suggest the advantages of the proposed approach.

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This study shows the advantages of job enlargement for aging workers using modern information technology. The concept of this production model, which attempts to balance productivity and workers' individuality, is described. A method using digital simulation for the construction of this production model and an actual case are presented. This production model focuses on manual procedures, and job enlargement thus takes a step back from of the trend towards automation. The actual examination proved the following. The productivity of the new production system became tantamount to that of the conventional flow line system; productivity exceeding 150% in three weeks. With respect to the speed in carrying a screw to a target object, the job-enlargement system requires less time than the flow line-system. This shows that subjects did not tighten screws in the rhythm of the work in the flow-line system. The job-enlargement system allows workers to adjust the production volume per unit time and the motion speed on their own initiative and thus, the workers are thought to be able to carry out tasks at the appropriate rhythm for themselves. Comparison of the motion loci of the flow line system and the job-enlargement system made it clear that when the aging workers used the job-enlargement system there was a smoother loci of motion in carrying the screw to the target (positioning). In the job-enlargement system, the enlarged work contents make it easier for the worker to adapt the speed and locus of the motion to his/her rhythm and it is believed to give the individual a sense of responsibility as well as a sense of achievement and satisfaction in the work. It is presumed that the worker was engrossed in his/her work, and did not find the work tedious.

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This paper focuses on multi-product sewing shops, in which there are various products and the operations combination for completing each product differs largely among products, and in which skill levels differ largely among workers. As basic research, this paper discusses one sub-problem of multi-product line operation planning: the single-product line operation planning problem, which includes not only the operation assignment problem but also the worker allocation problem for a product. Firstly, from a survey of the literature and investigation of real sewing shops, we clarify that there are two typical line operation planning methods. Both of them determine the above two problems separately, and the difference between them is the solving procedure: one method first assigns operations and then allocates workers to the workstations of the line (Method A), while the other takes the opposite procedure (Method B). Secondly, we carry out numerical experiments and clarify that Method A will cause large line balance loss when the varieties of operation and worker ability are large. Thirdly, in order to implement Method B under the new production environment, algorithms for worker allocation are proposed and discussed. The effects of Method B on decreasing line balance loss and cycle time are confirmed. We also point out that Method B can still be improved. In order to obtain a good operation plan for such lines, methods which can solve these two problems together should be developed, instead of solving them separately.

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