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.

Enhancing the process capability is a must in quality improvement of process manufacturing in the industry. Usually, Statistical Process Control (SPC) is applied to measure the activity of quality improvement. In this case, the statistical process control is used to measure the process capability of molding machine in regard to force the result of inner contact rubber button in the plastic industry. The forces of inner contact rubber button which are produced by molding machine already become the critical point as a judgment of malfunctions or not. Meanwhile, the forces problem still is found in molding machine that affects to the inner contact rubber button as defect product. Furthermore, as the show of SPC, the process capability of molding machine is 0. 63. This means the process capability is needed for improvement. Seeing that the result of quality improvement is made by applying Poka-Yoke, the process capability of molding machine is improved to 1. 65.

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.

The two most significant sources of uncertainty are randomness and incomplete information. In real systems, we wish to monitor processes in the presence of these two kinds of uncertainty. This paper aims to construct a fuzzy statistical control chart that can explain existing fuzziness in data while considering the essential variability between observations. The proposed control chart avoids defuzzification methods such as fuzzy mean, fuzzy mode, fuzzy midrange, and fuzzy median. The out-of-control states are determined based on a fuzzy in-control region and a simple and precise graded exclusion measure that determines the degree to which fuzzy subgroups are excluded from the fuzzy in-control region. The proposed chart is illustrated with a numerical example.

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.

Purpose: In many industrial processes, there are situations where simultaneous monitoring and control of two or more dependent variables are necessary. In such cases, univariate control of quality characteristics can be misleading when considered independently. In the classical approach, when a quality characteristic falls outside the specified technical limits, the quality loss is regarded as a cost. All products within the technical limits of the quality characteristic are assumed to have similar quality, regardless of the deviation of the quality characteristic from its target value. However, it is essential to distinguish between products that fall within the technical limits of the quality characteristic, as any deviation from the target value incurs a proportional loss.  Methodology: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. In summary, this study focuses on the statistical-economic design of a multivariate T²-Hotelling control chart with multivariate variable sampling intervals in the presence of a reflected normal multivariate loss function (RNLMVSIT²). Additionally, sensitivity analysis is employed to understand the effects of time and cost parameters on design parameters and the average cost. Findings: The results demonstrate the satisfactory performance of the proposed models. Originality/Value: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated.