An Economic Approach to the Management of High-Quality Processes (original) (raw)
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A compound control chart for monitoring and controlling high quality processes
European Journal of Operational Research, 2014
In the present article, we propose a new control chart for monitoring high quality processes. More specifically, we suggest declaring the monitored process out of control, by exploiting a compound rule couching on the number of conforming units observed between the (i À 1)th and the ith nonconforming item and the number of conforming items observed between the (i À 2)th and the ith nonconforming item. Our numerical experimentation demonstrates that the proposed control chart, in most of the cases, exhibits a better (or at least equivalent) performance than its competitors.
A CASE STUDY OF QUALITY CONTROL CHARTS IN A MANUFACTURING INDUSTRY
collection of problem solving tools and the most sophisticated useful method in achieving process stability and improving the process capability through the reduction of variability. In the manufacturing process, every product doesn't meet the desired range of quality consistently with the customer specification. This inconsistency occurs due to several sources of variations such as machines, operators, materials etc. The Ultimate target of control chart is to monitor the variations, and subsequently control the process. On account of applying SPC methods, this study deals with the control and improvement of the quality of bolt by inspecting the bolt's height, diameter and weight from a bolt manufacturing company. In this inspection, we have developed X bar chart, S and Range control chart for each three variables. Furthermore, we have also focused on Estimated Weighted Moving Average (EWMA) for detecting small process shifts and multivariate Hotelling's T 2 for simultaneous monitoring of height and diameter of bolt. These inspections show that either the process is in control or out of control. For the out of control situation, the assignable reasons behind it should be identified and prevented by taking necessary steps.
Analytical Dimension to Quality Check in Production Process through Control Charts
2018
Quality control is of paramount importance to any company in improving the product quality. Due to changing industry standards and competitive issues, embracing quality engineering techniques for strong operations support has become of prime importance to maintain and sustain competitive advantage. In this paper researcher intend to analyze the production line of a product, detect assignable variations in process and calculate the capability of the process using statistical Process Control. For plotting data points using control charts Sample size of 50 measurements with subgroup size 5 is considered. Since this is a variable data with subgroup size between 2 to 10, data is plotted with the help of X bar and R chart. Also to conclude on the capability of the process and check instability and level shift Process Capability and Process Capability Index is calculated. In this study it is found that process mean has shifted though there was a complete absence of assignable causes of var...
Economical control chart with supplementary rules to monitor the average number of defects
The International Journal of Advanced Manufacturing Technology, 2014
This study proposes a procedure for an on-line process control system to monitor the average number of defects using a Shewhart-like chart with two sets of limits (viz., control and warning limits). After the production of m units, the mth item is inspected. If the number of defects exceeds the upper control limit or if, in a sequence of the last h inspections, all inspected items exhibit a number of defects between the warning and control limits, then the process is stopped for adjustment; otherwise, production continues. The properties of an ergodic Markov chain are used to obtain an expression for the average cost per item produced. The inspection interval (m), warning and control limits (W and C, respectively), and the sequence size (h) are determined by minimizing the average cost per produced item. A numerical example illustrates the proposed procedure.
Practices on the Structure and Operation of Quality Control Charts
International Journal of Disciplines Economics & Administrative Sciences Studies, 2022
Statistical Process Control is regarded as a field that provides statistical instruments to detect changes in processes and monitor data flows. By means of Statistical Process Control, which has a dynamic structure, possible problems are determined by following the process; the factors causing the problem are established and solutions are produced. Quality Control, as the control process applied in the production stages, is a crucial function in order to maximize consumer satisfaction and it is used in all phases of statistical methods; design, production and service activities. The primary goal of quality control is to maintain the rate of defective products at a minimum level. Being one of the most effective tools in quality control, Control Charts are a significant method of statistical process control, which is referred to in the phase of improving the production process. Fundamentally, Control Charts are used to specify the factors that cause variability. In this study, it is presented by which methods possible deviations from the center line are detected, the structure and types of Control Charts used to improve the process are evaluated; thereby the Quantitative and Qualitative Control Charts are explained theoretically. Additionally, applications on Quantitative and Qualitative Control Charts are performed and control methods used for control charts are explained and an application for calculating acceptance probabilities are included.
Control Engineering Practice, 2013
Processes with very low rate of nonconformities are frequently observed in practice. These processes are known as ''high quality processes''. Traditionally, the study of the rate of nonconformities was carried out using the conventional 3-sigma p control chart (Shewhart), constructed by the normal approximation. But this p chart suffers a serious inaccuracy in the modeling process and in control limits specification when the true rate of nonconforming items is small. This paper shows that, with simple adjustments to the control limits of the p-chart, achieving equal or even better improvement while still working on the original data scale, is feasible. In particular, an improved p chart which can provide a large improvement over the usual p chart for attributes is presented. This new chart, based on the Cornish-Fisher quantile correction, is also better than a previous simpler correction proposed in the literature. The improved p chart is compared with both, normal-based chart and modified p chart with one correction term and the benefits of including a new term of correction for monitoring high-quality processes is illustrated with real data.
Reduction of non conforming through statistical process control charts
Research Paper, 2022
The textile industry of Pakistan is a growing sector that contributes to the economy. Pakistan exports depend heavily upon textile goods. A minor defect in the finished good can cause a major loss of the export goods. Due to the involving the number of workers checking the product repeatedly can be very expensive therefore the quality engineering techniques of Statistical Control Charts are used in the textile industry. This research study aims at developing process control charts for the textile industry in Pakistan. For this purpose, the textile industry was taken into consideration. P-chart was developed to monitor the variation in the process with a Six Sigma standard deviation. The collection of data was for six months from various departments of the textile industry. The attribute data were collected for the analysis from 4 different units of the industry. The construction of the P-Chart includes the Control Limits (CL), Upper Control Limits (UCL), 3 sigma deviations from the mean Control Limit (CL), Lower Control Limits (LCL), –3 sigma deviation from the mean Control Limit (CL). The result showed that the processes of the production units were under control, however, the mean was not centered which was due to some common cause of the process which is acceptable. The P-chart can serve as a standard for developing the new process.
Comparison between four Methods to Construction Number of Defectives Control Chart
Journal of Arts, Literature, Humanities and Social Sciences, 2019
A quality control chart (also called process chart) is a graph that shows average for the data (output) or the product fall within the common or normal range of variation if the process is under statistical control. The existing study draws Comparison between four methods to construct Number of Defectives Chart. We compare methods to obtain the Number of Defectives chart based on Six sigma, which this makes the method much better than other methods, because the distance between control limits for Number of Defectives chart is based on six sigma which that has the smallest comparison properties than other methods, and that the process is out of control for all charts. It is proposed that the six sigma should be used when monitoring and detecting out of control cases.