In this paper the delivery times in the delivery chains is modeled and monitored Statistically. The model is developed based on a double warning control chart using Lorenzen and Vance cost function and it is solved by using the Genetic Algorithm approach. The model is tested using the TNT post services data in the United States of America.

Shewhart control charts are widely accepted as standard tools monitoring manufacturing Statistical processes. The control charts have not applied, when the process distribution is not normal. The bootstrap is one of the resampling methods that can be used in Statistical quality control without normality assumption. In most of papers, only the percentile bootstrap confidence interval is used for control limits. In this paper, we apply percentile bootstrap, bootstrap-t, bias corrected accelerated (BCa) and approximate bootstrap confidence interval (ABC) for mean control limits of Statistical process. Then, the bootstrap confidence intervals are used and compared for mean control limits in simulation study. Finally, the bootstrap control limits are used for mean of CO2 data in Isfahan Zamzam factory.

In Statistical process control, traditionally, the assumption is made that successive observations of a quality characteristic are independently distributed. However, in practice, the observations are often serially correlated. This point has received considerable attention in the literature in the last decade and the modified Shewhart and the residuals charts have been proposed to deal with this situation. In this paper, it is investigated how well these control charts are able to detect a shift in the mean of AR (1) and AR (2) data. It is shown that for negative auto-Correlations, the residuals chart is the better of these two and, for positive auto-Correlation, it is better to choose the modified shewhart chart. Then, a modification of the residuals chart was made that outperforms both charts in the case of positive autoCorrelation. Moreover, in order to improve the performance of the existing control charts, three kinds of control charts namely modified EWMA, EWMA residuals and EWMA of modified residuals were developed, Through a simulation study, it is shown that, in terms of negative auto -Correlation, the EWMA residuals chart is the best and for positive auto-Correlation it is best to choose the EWMA chart of the modified residuals. Finally, an algorithm was proposed to control AR (1) and AR (2) data. This algorithm was implemented to a real world problem and the results were reported.

In this research, application of Statistical quality control process in Kishwood industrial company has been investigated. Statistical quality control process includes control charts and acceptance samplings. In this study, qualitative and quantitave characteristics of raw material and work in progress was investigated using standard tables and control charts (X 􀴥 , R, P, U) were designed for grooving, drilling, laminating and edge banding processes. In the raw material and work in progress, medium density fiberboard (16mm) lots, HPL veneer, purchased ABS stripe and the distance of the double hole from the edge of the piece in terms of quantative characteristic were investigated by MIL-STD-414 tables. In addition, purchased veneered particle board lot and the accuracy of the stripe in cabinet bottom parts in terms of qualitative characteristic were investigated by MIL-STD-105E tables. All the results were compatible with the acceptable standards of the company. Since lots data were located in the control limits, we can conclude that the whole process had been under control during the research time and can be used to control the future of the process. By calculating the process capability index (CP), it was found that grooving and drilling process have a very high capability, so the percentage of waste in these two processes is very low.

In this research, applications of Statistical quality control process in Sima choob Company have been investigated. Sima Choob produces various wood products per order only. Statistical quality control process includes all control investigations and diagrams. In this research, work in progress and the finished products were studied, using standard MIL-STD tables, current Statistical techniques and control diagrams (X, R, C, and U) of qualitative and quantitative characteristics of their raw materials. The results obtained from Statistical quality control process showed that during the research time and in the raw material section, the quality of twin wheels lot, 40Cm rail, Tran's rod, laminated particleboard, and medium density fiberboard was not compatible with the acceptable standards of the company. This leads to return of the raw material as well as imposing more control on the suppliers of such products. On the other hand, work in progress lot and finished products were compatible with the company's standards. Since data lots were located in the control limits of assembling, painting and other sections indicated that the whole process had been under control during the research time. In general, it can be concluded that using Statistical quality control process for the raw materials, work in progress, and the finished products would enhance the quality and the useful life of the products.

Background: This study aims to perform quality control (QC) practices for setup reproducibility during radiotherapy for nasopharyngeal carcinoma (NPC) using Statistical process control (SPC) tools. Materials and Methods: A total of 480 fractional images from 48 NPC patients with the first 10 fractions of the treatment were collected. In QC practices, setup errors were described using the histogram and normal curve, cumulative frequencies of absolute setup errors and 3D Euclidean Distance (Eu) were analyzed,the X ̅,-S chart and process capability index (Cpk) with the variable Eu were utilized to identify whether the outlier occurred and to evaluate the QC process. Results: The translational setup error distributions were almost normal in Lateral, Longitudinal and Vertical directions and were narrower in Lateral and Vertical directions. Vertical translational errors and Eu with a larger magnitude sag appeared the most frequently. Between the couch sag and no sag, the Eu mean of 7 to 7 NPC patients with the same 3 patients was out of control and the standard deviation of Eu of nil to 2 patients was outlier based on the X ̅,-S chart, and the Cpk was 1. 05 and 1. 36 respectively, when the specification limit of translational errors was ±, 3 mm. Conclusion: Daily imaging is necessary to increase setup reproducibility for NPC patients and more measures should be taken to facilitate quality assurance procedures. SPC is better applied to QC practices depending on the reliable data and the acceptable tolerance levels in further studies.

Introduction: Statistical process control (SPC) is a handy and powerful tool for monitoring quality assurance (QA) programs in radiotherapy. This study explains the institutional experience in monitoring weekly output constancy QA and patient-specific quality assurance (PSQA) using SPC tools. Material and Methods: Prospective monitoring of output constancy has been demonstrated by the simultaneous usage of Shewhart's I-MR charts and time-weighted control charts. PSQA results were retrospectively analysed in a combined γ,and dose volume histogram (DVH) based analysis using control charts and process capability indices. A PSQA analysis method has been illustrated in which the site-specific action limits (AL) and control limits (CL) for γ,and DVH based analysis were obtained using SPC. Results: The simultaneous use of different control charts indicated a systematic error in the output constancy of Linac as successive measurement points fell above the CL. The reason for failure was found and process was monitored further. The obtained AL and CL for γ,and DVH based analysis were used to decide pass or fail criteria in PSQA. Among the analysed treatment plans, fourteen plans of different treatment sites failed the PSQA analysis. Cause-and-effect analysis of these failed treatment plans in PSQA pointed out six primary potential sources of errors in the results. Conclusion: SPC tools can be adopted among institutions for consistent and comparable QA programs. If the QA process monitored using SPC falls outside the CL, cause-and-effect diagrams can be used to extract all possible contributing factors that lead to such a process state.

An integrated model of maintenance planning and Statistical process control is developed for a production process. The process has two operational states including an in-control state and an out-of-control state, where the process failure mechanism is supposed as a general continuous distribution with non-decreasing failure rate. Based on the information obtained from the control chart, three types of maintenance actions may be implemented in the process. The integrated model optimally determines the parameters of the control chart and maintenance actions so that the expected cost per time unit is minimized. To evaluate the performance of the integrated model, a stand-alone model is developed. In the stand-alone model, only maintenance planning is considered. Finally, a real case study is presented to clarify the performances of these models.