Statistical Quality Control Tools Research Articles

An Assessment of the Quality of Services of Kenya Power (KP) Ltd in Restoring Supply After Unplanned Interruptions Using Statistical Quality Control

The purpose of this study was to apply statistical quality control (SQC) techniques and tools to assess the customer’s experience of quality as offered by Kenya Power Ltd. It also seeks to determine whether or not the services of the company were in statistical control. The study was designed as a descriptive survey. The population consisted of 65,830 customers within Nakuru town and its environs from which four hundred customers were sampled. The sample population was stratified so that 286 were domestic consumers while the remaining 114 were non-domestic consumers. Stratified random sampling was used. The collected data was coded and summarized in the form of tables and entered into the SPSS program. Customers’ experiences were obtained and used to draw control charts, which were analyzed. It was found that the customers’ experience of service as regards the restoration of supply after unplanned interruptions was not in statistical control for both domestic and non-domestic consumers. These results are an indictment of the qualities of service of Kenya Power. The implication for the managers of service processes at Kenya Power is that they must ensure optimal service quality in the firm. In particular, urgent steps must be taken to identify the root cause or causes of special variation that result in service invariability and instability. We strongly advocate for the intensified use of statistical quality control tools in the utility services sector and particularly at Kenya Power, as a means of monitoring the production and service quality, and enabling the firm to take timely and appropriate action to correct undesirable deviation in production quality.

Achieving Customer Specifications Through Process Improvement Using Six Sigma: Case Study of NutriSoil – Portugal

Tolerance limits are essential in production process management, as they determine consumer satisfaction. The use of statistical quality control tools allows for process improvement and the cpk index enables its measurement. Above all, the adoption by businesses of lean tools has been crucial in reducing the variation of a process or a product, satisfying the consumer's specifications, eliminating defects, reducing operating costs, and, in short, increasing profitability.The NutriSoil Company in Portugal, a small and medium-sized enterprise (SME), sells fertilizer in bags. The company has had problems with its filling process due to excess weight of the bags. Results show that by implementing Six Sigma combined with the 5S program, NutriSoil achieved an improvement in its cpk index for this process, which increased consumer satisfaction and a highly significant cost savings. This resulted in increased competitiveness.

The Use of Statistical Quality Control Tools to Quality Improving in the Furniture Business

The main aim of the article is to illustrate the use of tools of operative quality management to prevent a decrease in quality during production, supportive and operational processes by the furniture manufacturing. There are more tools for achieving operative quality management targets and the most frequent method is probably measurement and evaluation of the capability of processes through capability indexes. In addition to other histogram and Ishikawa diagram are the next frequently used tools for quality improvement processes.

IMPLEMENTATION OF STATISTICAL QUALITY CONTROL (S.Q.C.) IN WELDED STAINLESS STEEL PIPE MANUFACTURING INDUSTRY

Stainless steel welded pipe is manufacture by various methods like as shearing, bending, partial welding, full welding, nitriding, final length, expanding and buffing. This research is about to implement statistical quality control tools in pipe manufacturing industries. There are various defect found in stainless steel welded pipe manufacturing, due to these defects, pipes are rejected lot wise, and that is very unproductive result for each and every industries. Statistical quality control (SQC) tools, such as x bar chart, R-chart, C-chart, P-chart had been utilized to measure the variability in the process. We observed good result about the process, whether it is in control or not. These types of charts will also provide the fast result that will be used to take action immediately. In pipe manufacturing industries, rejection reduction is possible by proper shearing process, appropriate welding line, maintaining expanding process. This research helps to decide the process within the control limit or not, that would be decided by various control charts. This is also used for quality improvement. In S.Q.C, there are number of tools used such as Frequency distribution, Control charts, acceptance sampling, and analysis of the data. From all of these tools, we have to concentrate only on control charts. Ultimately, It has been used for any manufacturer to solve the rejection problem, enhancing the productivity and quality of the product. In apurvi industries, out of control process has been detected by Xbar and R chart and root cause analysis used for defect detection. C chart has also been used for depicted the number of defects in the particular lot. Using the C chart it can be easily decided whether the particular lots are accepted or not. Flow of this research is the beginning with the introduction, Literature review regarding the research area then Methodology followed by the result and discussion. Finally future scope of the research is presented.

Quality of cut and basecutter blade configuration for the mechanized harvest of green sugarcane

Quality control is used to evaluate processes and products, and is a powerful tool for reducing variability. The objective of this study was to evaluate the quality of green sugarcane (Saccharum spp.) cutting for mechanized harvest, using statistical quality control tools. Cutting height and damage to ratoon stalks caused by different blade and disc combinations of the basecutter mechanism were used as indicators of quality. Cutting height showed high variability with a controlled process for some treatments. However, these treatments were incapable of producing satisfactory results. The damage index was lower in treatments that used tilted blades, but above the target for all treatments, which caused significant damage to the ratoons. In general, the process of mechanized harvest as assessed by these indicators was found incapable of achieving targeted results and staying below specification limits, and thus requires corrective actions to improve quality.

Cost of quality management: An empirical study from Turkish marble industry

In this study, a quality cost model to measure and control costs of quality (COQ) occurring in marble plants was developed. In the marble industry, most properties associated with quality depend on uncontrolled and mostly immeasurable natural and geological conditions. And because of these naturerelated problems, it is difficult to apply commonly used quality cost models to the marble industry and there has not been any application of cost models to the marble industry in literature. For the first time, quality costs occurring in marble production systems are determined, classified and calculated. A computer program containing this model and statistical quality control tools was developed. The software was tested using data from marble plants located in the Diyarbakir (Turkey) Region that has a significant share of the Turkish natural stone industry. It was found that quality costs vary depending on product types in range from 9 to 34% of total production costs for three different stone types.

Managing A Quality Digitization Practice in Cultural Heritage Institutions: Statistical Quality Control Tools and Techniques

The appearance of user-friendly, scientifically informative, standards-based digital imaging targets within the Cultural Heritage community presents opportunities for establishing achievable and verifiable image capture specifications, for streamlining digitization hardware selection and optimal use, and for monitoring critical steps in digital imaging workflows. The success of these standards based quality control tools and procedures will require appropriate selection and adoption, in concert with a serious commitment on the part of management to their use.Crucial to this adoption effort is understanding the role that similar techniques have long played in those professional-level analog imaging and film processing facilities that were organizationally or physically set apart – “Black Boxed” – from the Cultural Heritage institutions that utilized their services and received their products. In this paper, we propose that Cultural Heritage institutions move to adopt industry standard statistical process control and quality improvement language, tools, and procedures. We propose this with some urgency because less rigorous and less effective quality measurement, monitoring, and process control & improvement procedures are currently being adopted by or developed in Cultural Heritage digital imaging and archiving facilities.

Statistical analysis in Turkish chromite mining

Many of the techniques developed by mathematical statisticians for the analysis of data are used in the control of product quality. Statistical Process Control (SPC) may be used to cover all uses of statistical techniques for this purpose. However, it often relates particularly to four separate but related techniques that constitute the most common working statistical tools in quality control. One important tool in statistical quality control is the Shewhart control chart. Control schemes are the most widely known and used methods among SPC methods. They can be used to determine whether the differences in quality resulted from coincidences of operation conditions or from artificial reasons for which the source can be determined. The aim of this study is the determination of the lower, upper and central control limits of chromite concentrates obtained from Mikromaden Co., Akmetal Co. and Pinar Mining Co. in the Aladag mountain region of Adana, in Turkey.

Optimum rectifying inspection plans

The rectifying inspection plan is a useful tool in statistical quality control (SQC) to assure the average product quality between several stages of a production line. Two optimum rectifying inspection plans are presented. They minimize either ATI (average total inspection) or AOQ (average outgoing quality) and use the other as a constraint. The definitions for both ATI and AOQ are generalized to handle the varying fraction defective of the incoming lots. A comprehensive study is conducted to compare the performance characteristics of the optimum rectifying inspection plans with several other plans. The results show that the optimum plans usually produce the best outcome and always satisfy the design specifications. Moreover, the results of a sensitivity study indicate that the optimum plans are fairly robust. Finally, a manufacturing example illustrates the applications and the managerial significance of the optimum rectifying inspection plans.

Deming’s red bead experiment and mechanical sampling

The red bead experiment (RBE) is very well‐known in total quality management. It was designed by Dr W.E. Deming to show managers several lessons in management and sampling. The RBE has evolved over several decades and now has an accepted standard format. The RBE also serves as an introduction to control charts which are an important tool in statistical quality control. Surprisingly very little is known about the statistical properties of the RBE. This is most likely due to the fact that the RBE involves mechanical sampling and so is much more complicated than probability sampling. New methods are given for statistical analysis when mechanical sampling is involved (e.g. mechanical sampling of non‐uniform output in a manufacturing environment). This approach has specific applications to control charts which are a major tool in quality control. In a rigorous analysis, it is shown that p, the sample proportion of red beads in the RBE, is an unbiased estimator of the RBE process defect rate. Expressions are also found for the variance of p, enabling the calculation of control chart limits. Some of the techniques used can be applied to other mechanical sampling situations which may at first viewing seem intractable.

Practical Application of Statistical Process Control with the Use of Decision Trees

Manufacturing industries are more and more moving toward using statistical quality control (SQC) tools. Statistical process control (SPC) is one of the SQC tools which enables users to control their processes. The problem in the use of such a tool is th..

Signal detection for human error correction in quality control

Acceptance sampling by attributes is now a universal and fundamental tool in statistical quality control. Among the various plans, single sampling has been recognized to offer significant cost savings and administrative simplicity. Single sampling by attributes is generally carried out or supervised by a human inspector. Unfortunately, human inspection error is another fact of life in industrial inspection. Two types of errors are possible. One is the Type 1 error, in which a good item is classified as defective (i.e., nonconforming); the other is the Type 2 error, in which a defective item is passed as good. Presently, little has been written on the effects of inspection errors on the outgoing quality and on how to compensate for these errors. The research objective of this paper is to show how a knowledge of the errors can be used to design compensating acceptance sampling plans for industrial quality control tasks. In contrast to the typical human inspection models in quality control, with fixed performance irrespective of incoming product quality, the model proposed here uses a form of signal detection theory (SDT) to predict inspector performance in order to improve system performance. The paper proposes the concept of lability, measured by a number ranging from 0 to 1, to characterize an inspector's ability to response to costs, rewards and probabilities involved in the inspection decision. This concept allows the modeling of both a constant-error inspector having a lability of 0, an SDT inspector having a lability of 1, and all those which fall within this range. Using this concept, the paper presents two models of the human inspector with considerations of: (a) constant Type 1 and Type 2 errors independent of the incoming fraction nonconforming, and (b) increasing Type 2 error and decreasing Type 1 error with increasing fraction nonconforming as specified by SDT. A general model covering these inspection errors is proposed. The paper shows that an imperfect inspector, who can detect the signal properly to adjust the decision criterion conservatively in the direction specified by the SDT, can compensate for the potential errors. A numerical example is proposed to illustrate the model application.

How to Use Continuous Quality Improvement Theory and Statistical Quality Control Tools in a Multispecialty Clinic

The management philosophy of continuous quality improvement (CQI) and the tools of statistical quality control (SQC) have the potential for advancing quality management in medicine as they have in industry. The authors report their favorable experience with the approach and explain how to adapt CQI principles and SQC charts and graphs, citing examples from their participation in a quality improvement effort in a multispecialty clinic serving a large hospital. The coupling of statistical techniques with modern approaches to outcome analysis may provide powerful tools not only for quality assurance and assessment but also for technology evaluation and resource allocation.

Quality-Control Practices in Lead-Acid Battery Manufacturing to Improve Quality, Cost, Reliability

This is a case study on the diagnosis of quality problems in a lead-acid battery plant. The study demonstrates the effectiveness of integrating statistical quality assurance programs with process and production control methods in improving the overall performance of the plant. It asserts that quality control is an integral part of process and production control methods. Process and production control methods had to be characterized in order to discover the correction needed for improving quality. The salient features of the study include: * adoption of operating schedules which maintain steady production * application of statistical quality control tools to monitor and direct trends in quality parameters * use of quality circles for diagnosing quality problems * the efficacy of simple devices for dissemination of quality standards to all production staff by posting specification control charts. Several quality improvements were achieved by paying attention to manufacturing process details rather than by sophisticated research methods. The outcome of the study led to reducing scrap and rework as well as improving battery performance and reliability in the field.