The Impact of Nonconforming Items on (s, S) Inventory Model with Customer Order Reservation and Cancellation

Yasuhiko Takemoto,Ikuo Arizono 대한산업공학회 2009 Industrial Engineeering & Management Systems Vol.8 No.2

The ultimate goal of inventory management is to decide the timing and the quantity of ordering in response to uncertain demands. Recently, some researchers have focused upon an impact of distortions in the information, e.g., customer order cancellation, on an economical inventory policy. The customer order cancellation is considered a kind of distortions in demands, because a demand that is eventually cancelled is equivalent to a phony demand. Also, there are some additional distortions in the inventory information. For instance, the procurement of suppliers may include some nonconforming items as a result of imperfect production and inspection by the suppliers, and/or damage in transit. The nonconforming item should be considered a kind of distortions in the inventory information, because the nonconforming item is equivalent to a phony stock. In this article, we consider an inventory model under the situation that customers can cancel their orders and the procurement of suppliers may include some nonconforming items. Then, we introduce the customer order reservation into the inventory model for the purpose of avoiding the costly backlogs, because the customer order reservation gives retailers a period to fulfill customer’s requests. We formulate a periodic review (s, S) inventory model and investigate the economical operation under the situation mentioned above. Further, through the sensitivity analysis, we show the impact of these distortions and the effect of the customer order reservation on the inventory policy.

The Impact of Nonconforming Items on (s, S) Inventory Model with Customer Order Reservation and Cancellation

Takemoto, Yasuhiko,Arizono, Ikuo Korean Institute of Industrial Engineers 2009 Industrial Engineeering & Management Systems Vol.8 No.2

The ultimate goal of inventory management is to decide the timing and the quantity of ordering in response to uncertain demands. Recently, some researchers have focused upon an impact of distortions in the information, e.g., customer order cancellation, on an economical inventory policy. The customer order cancellation is considered a kind of distortions in demands, because a demand that is eventually cancelled is equivalent to a phony demand. Also, there are some additional distortions in the inventory information. For instance, the procurement of suppliers may include some nonconforming items as a result of imperfect production and inspection by the suppliers, and/or damage in transit. The nonconforming item should be considered a kind of distortions in the inventory information, because the nonconforming item is equivalent to a phony stock. In this article, we consider an inventory model under the situation that customers can cancel their orders and the procurement of suppliers may include some nonconforming items. Then, we introduce the customer order reservation into the inventory model for the purpose of avoiding the costly backlogs, because the customer order reservation gives retailers a period to fulfill customer's requests. We formulate a periodic review (s, S) inventory model and investigate the economical operation under the situation mentioned above. Further, through the sensitivity analysis, we show the impact of these distortions and the effect of the customer order reservation on the inventory policy.

Estimation of Change Point in Process State on CUSUM ($\bar{x}$, s) Control Chart

Takemoto, Yasuhiko,Arizono, Ikuo Korean Institute of Industrial Engineers 2009 Industrial Engineeering & Management Systems Vol.8 No.3

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM ($\bar{x}$, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM ($\bar{x}$, s) control chart are considered.

Discrimination of Out-of-Control Condition Using AIC in (x, s) Control Chart

Takemoto, Yasuhiko,Arizono, Ikuo,Satoh, Takanori Korean Institute of Industrial Engineers 2013 Industrial Engineeering & Management Systems Vol.12 No.2

The $\overline{x}$ control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the ($\overline{x}$, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the ($\overline{x}$, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

Discrimination of Out-of-Control Condition Using AIC in (x, s) Control Chart

Yasuhiko Takemoto,Ikuo Arizono,Takanori Satoh 대한산업공학회 2013 Industrial Engineeering & Management Systems Vol.12 No.2

The x control chart for the process mean and either the R or s control chart for the process dispersion have been used together to monitor the manufacturing processes. However, it has been pointed out that this procedure is flawed by a fault that makes it difficult to capture the behavior of process condition visually by considering the relationship between the shift in the process mean and the change in the process dispersion because the respective characteristics are monitored by an individual control chart in parallel. Then, the (x, s) control chart has been proposed to enable the process managers to monitor the changes in the process mean, process dispersion, or both. On the one hand, identifying which process parameters are responsible for out-of-control condition of process is one of the important issues in the process management. It is especially important in the (x, s) control chart where some parameters are monitored at a single plane. The previous literature has proposed the multiple decision method based on the statistical hypothesis tests to identify the parameters responsible for out-of-control condition. In this paper, we propose how to identify parameters responsible for out-of-control condition using the information criterion. Then, the effectiveness of proposed method is shown through some numerical experiments.

Estimation of Change Point in Process State on CUSUM (x, s) Control Chart

Yasuhiko Takemoto,Ikuo Arizono 대한산업공학회 2009 Industrial Engineeering & Management Systems Vol.8 No.3

Control charts are used to distinguish between chance and assignable causes in the variability of quality characteristics. When a control chart signals that an assignable cause is present, process engineers must initiate a search for the assignable cause of the process disturbance. Identifying the time of a process change could lead to simplifying the search for the assignable cause and less process down time, as well as help to reduce the probability of incorrectly identifying the assignable cause. The change point estimation by likelihood theory and the built-in change point estimation in a control chart have been discussed until now. In this article, we discuss two kinds of process change point estimation when the CUSUM (x, s) control chart for monitoring process mean and variance simultaneously is operated. Throughout some numerical experiments about the performance of the change point estimation, the change point estimation techniques in the CUSUM (x, s) control chart are considered.

Variable Sampling Inspection with Screening When Lot Quality Follows Mixed Normal Distribution

Suzuki, Yuichiro,Takemoto, Yasuhiko,Arizono, Ikuo Korean Institute of Industrial Engineers 2009 Industrial Engineeering & Management Systems Vol.8 No.3

The variable sampling inspection scheme with screening for the purpose of assuring the upper limit of maximum expected surplus loss after inspection has been proposed. In this inspection scheme, it has been assumed that a product lot consists of products manufactured through a single production line and lot quality characteristics follow a normal distribution. In the previous literature with respect to inspection schemes, it has been commonly assumed that lot quality characteristics obey a single normal distribution under the condition that all products are manufactured in the same condition. On the other hand, the production line is designed in order that the workload of respective processes becomes uniform from the viewpoint of line balancing. One of the solutions for the bottleneck process is to arrange the workshops in parallel. The lot quality characteristics from such a production line with the process consisting of some parallel workshops might not follow strictly the single normal distribution. Therefore, we expand an applicable scope of the above mentioned variable sampling inspection scheme with screening in this article. Concretely, we consider the variable sampling inspection with screening for the purpose of assuring the upper limit of average outgoing surplus quality loss in the production lots when the lot quality follows the mixed normal distribution.

Variable Sampling Inspection with Screening When Lot Quality Follows Mixed Normal Distribution

Yuichiro Suzuki,Yasuhiko Takemoto,Ikuo Arizono 대한산업공학회 2009 Industrial Engineeering & Management Systems Vol.8 No.3

The variable sampling inspection scheme with screening for the purpose of assuring the upper limit of maximum expected surplus loss after inspection has been proposed. In this inspection scheme, it has been assumed that a product lot consists of products manufactured through a single production line and lot quality characteristics follow a normal distribution. In the previous literature with respect to inspection schemes, it has been commonly assumed that lot quality characteristics obey a single normal distribution under the condition that all products are manufactured in the same condition. On the other hand, the production line is designed in order that the workload of respective processes becomes uniform from the viewpoint of line balancing. One of the solutions for the bottleneck process is to arrange the workshops in parallel. The lot quality characteristics from such a production line with the process consisting of some parallel workshops might not follow strictly the single normal distribution. Therefore, we expand an applicable scope of the above mentioned variable sampling inspection scheme with screening in this article. Concretely, we consider the variable sampling inspection with screening for the purpose of assuring the upper limit of average outgoing surplus quality loss in the production lots when the lot quality follows the mixed normal distribution.

Proposal of Approximation Analysis Method for GI/G/1 Queueing System

Fangfang Kong,Ippei Nakase,Ikuo Arizono,Yasuhiko Takemoto 대한산업공학회 2008 Industrial Engineeering & Management Systems Vol.7 No.2

There have been some approximation analysis methods for a GI/G/1 queueing system. As one of them, an approximation technique for the steady-state probability in the GI/G/1 queueing system based on the iteration numerical calculation has been proposed. As another one, an approximation formula of the average queue length in the GI/G/1 queueing system by using the diffusion approximation or the heuristics extended diffusion approximation has been developed. In this article, an approximation technique in order to analyze the GI/G/1 queueing system is considered and then the formulae of both the steady-state probability and the average queue length in the GI/G/1 queueing system are proposed. Through some numerical examples by the proposed technique, the existing approximation methods, and the Monte Carlo simulation, the effectiveness of the proposed approximation technique is verified.

Comparison of Rigorous Design Procedure with Approximate Design Procedure for Variable Sampling Plans Indexed by Quality Loss

Ishii, Yoma,Arizono, Ikuo,Tomohiro, Ryosuke,Takemoto, Yasuhiko Korean Institute of Industrial Engineers 2016 Industrial Engineeering & Management Systems Vol.15 No.3

Traditional acceptance sampling plans have focused on the proportion of nonconforming items as an attribute criterion for quality. In today's modern quality management under high quality production environments, the reduction of the deviation from a target value in a quality characteristic has become the most important purpose. In consequence, various inspection plans for the purpose of reducing the deviation from the target value in the quality characteristic have been investigated. In this case, a concept of the quality loss evaluated by the deviation from the target value has been accepted as the variable evaluation criterion of quality. Further, some quality measures based on the quality loss have been devised; e.g. the process loss and the process capability index. Then, as one of inspection plans based on the quality loss, the rigorous design procedure for the variable sampling plan having desired operating characteristics (VS-OC plan) indexed by the quality loss has been proposed by Yen and Chang in 2009. By the way, since the estimator of the quality loss obeys the non-central chi-square distribution, the rigorous design procedure for the VS-OC plan indexed by the quality loss is complicated. In particular, the rigorous design procedure for the VS-OC plan requires a large number of the repetitive and complicated numerical calculation about the non-central chi-square distribution. On the other hand, an approximate design procedure for the VS-OC plan has been proposed before the proposal of the above rigorous design procedure. The approximate design procedure for the VS-OC plan has been constructed by combining Patnaik approximation relating the non-central chi-square distribution to the central chi-square distribution and Wilson-Hilferty approximation relating the central chi-square distribution to the standard normal distribution. Then, the approximate design procedure has been devised as a convenient procedure without complicated and repetitive numerical calculations. In this study, through some comparisons between the rigorous and approximate design procedures, the applicability of the approximate design procedure has been confirmed.