Improving product quality in pulp mill using statistical process control (SPC)

This paper addresses the link between quality and sustainability in engineering systems and illustrates the role of quality tools in sustaining a high-level performance. It focuses on the application of Statistical Process Control (SPC) as a quality method at a PV (Photovoltaic) power plant that is set to generate the electricity from solar panels. In terms of performance, the PV plant has to meet certain contractual requirements including the level of delivered energy during the commissioning. Such level is highly impacted by the quality of the PV modules in terms of the solar energy generated from each module (based on the result of a flash test). The aim is to test the performance of two different PV technologies (i.e., thin film and crystalline silicon panels) that are supplied to the project from two different manufacturers during installation. To this end, SPC samples are drawn from the flash test results of the 39000 module supplied to the PV project. Results are analyzed and SPC control charts are developed to identify the special causes of process variability, explore variation patterns, and take remedial actions to ensure process stability. Results showed that even if the manufacturer supplied PV modules that are within the customer specification, it is not repress that the manufacturing process is statistically in control. SPC control charts have revealed multiple special causes in the process due to high variability in the power generated from the tested modules. These causes were carefully investigated and turned into opportunities for process improvement and stability. The study has highlighted the statistical challenges of the PV manufacturing process, as adopting SPC analyses will help manufacturer/suppliers to reduce the waste and improve the overall sustainability of the PV power plant.

Statistical process control is the application of statistical methods to the measurement and analysis of variation process. Various regulatory authorities such as Validation Guidance for Industry (2011), International Conference on Harmonisation ICH Q10 (2009), the Health Canada guidelines (2009), Health Science Authority, Singapore: Guidance for Product Quality Review (2008), and International Organization for Standardization ISO-9000:2005 provide regulatory support for the application of statistical process control for better process control and understanding. In this study risk assessments, normal probability distributions, control charts, and capability charts are employed for selection of critical quality attributes, determination of normal probability distribution, statistical stability, and capability of production processes, respectively. The objective of this study is to determine tablet production process quality in the form of sigma process capability. By interpreting data and graph trends, forecasting of critical quality attributes, sigma process capability, and stability of process were studied. The overall study contributes to an assessment of process at the sigma level with respect to out-of-specification attributes produced. Finally, the study will point to an area where the application of quality improvement and quality risk assessment principles for achievement of six sigma-capable processes is possible. Statistical process control is the most advantageous tool for determination of the quality of any production process. This tool is new for the pharmaceutical tablet production process. In the case of pharmaceutical tablet production processes, the quality control parameters act as quality assessment parameters. Application of risk assessment provides selection of critical quality attributes among quality control parameters. Sequential application of normality distributions, control charts, and capability analyses provides a valid statistical process control study on process. Interpretation of such a study provides information about stability, process variability, changing of trends, and quantification of process ability against defective production. Comparative evaluation of critical quality attributes by Pareto charts provides the least capable and most variable process that is liable for improvement. Statistical process control thus proves to be an important tool for six sigma-capable process development and continuous quality improvement.

The objective of this article is to apply Statistical Process Control (SPC) to guarantee the safety in the operation of a sterilization chamber with ethylene oxide gas in thermo-sensitive material. One of the most used in Mexico; but at the same time, one of the most unfavorable methods due to the risks it presents to exposed workers. Environmental and personal measurements were carried out to control the exposure limits. Once the data was collected, SPC tools such as variable control charts (X bar-R chart) were applied to verify that the process was under statistical control. The results of the environmental and personal measurements of the sterilization process carried out by the manufacturer of surgical and healing material showed the process is within the permitted exposure limits. Recommendations are extended in relation to the use of personal protection equipment and working environment conditions, to fulfill with the requirements of national regulatory frameworks.

Statistical Process Control (SPC) has become of great significance for software engineering organizations as more of them decide to implement quality improvement initiatives. The Capability Maturity Model Integration (CMMI-DEV 1.3) for example, proposes the use of statistical techniques at maturity level 4 to ensure some degree of process predictability. However, the nature of software products and processes poses many challenges to the application of SPC, mainly regarding the design of control charts, a key tool. These challenges have led to opposing views on the applicability of SPC to software processes. This article presents an industry experience report on the application of SPC in a Software Verification and Validation Unit at an Information Technology Division from a financial institution. We present the steps followed to implement SPC in this organization, describe the theoretical assumptions involved in selecting the appropriate control charts, and show a process improvement analysis of using SPC in the organization.

This paper explores some of the implications the statistical process control (SPC) methodology described by Pfadt and Wheeler (1995) may have for analyzing more complex performances and contingencies in human services or health care environments at an organizational level. Service delivery usually occurs in an organizational system that is characterized by functional structures, high levels of professionalism, subunit optimization, and organizational suboptimization. By providing a standard set of criteria and decision rules, SPC may provide a common interface for data-based decision making, may bring decision making under the control of the contigencies that are established by these rules rather than the immediate contingencies of data fluctuation, and may attenuate escalation of failing treatments. SPC is culturally consistent with behavior analysis, sharing an emphasis on data-based decisions, measurement over time, and graphic analysis of data, as well as a systemic view of organizations.

This paper reports on the findings of a questionnaire survey into the use of statistical process control (SPC) in a sample of United Kingdom automotive suppliers. All the 76 respondents had attended the Ford‐approved three‐day course on SPC held at the University of Manchester Institute of Science and Technology (UMIST). The survey investigated the following broad issues of SPC: motivation, implementation, application, suppliers and customers, and benefits. A number of findings surprised the authors; these included the number of organizations who had not appointed a SPC facilitator, organizations experiencing difficulties in deciding what type of control charts to use, control charts being ‘filled‐in’ by people not directly responsible for control of a process, charts being displayed away from work areas and the frankness of some suppliers in declaring that the vast majority of their manufacturing processes are not in a state of statistical control. If there is an overall message arising from the findings it is that the introduction and application of SPC should not be overcomplicated.

Port state control (PSC) inspection is a critical measure in maritime safety, preventing substandard ships from entering ports. However, the current inspection system lacks an effective tool to monitor ship inspections and to identify which factors cause abnormal instances. Although statistical process control (SPC) has been proven an effective tool in process control in various other industries, this study is a pioneer in exploring the application of SPC on maritime inspections. In this paper, the inspection records collected in Taiwan’s ports are utilized to construct control charts of inspection deficiencies so as to identify time-independent abnormal instances in inspections. Following that, a cause-and-effect diagram is conducted to trace the cause of those abnormal instances occurring at a specific time period. The results demonstrate that SPC is effective to monitor maritime inspections and is capable of identifying time-dependent abnormal instances. From a managerial and practical perspective, port administrations can use SPC to identify time-dependent abnormal instances and take proactive actions to mitigate the non-random phenomena in PSC inspections. To keep deficiency occurring in a stable and low profile, port administrations need persistent and consistent inspection requirements and avoid non-routine inspections. Meanwhile, inspectors with various specialties and backgrounds require consistent training.

As suggested by Box and Kramer [1] and others, it is possible to reduce both the special cause and common cause variations by applying Statistical Process Control (SPC) methods to monitor the output of an Automatic Process Control (APC) controlled process. In this paper, we develop an economic model for SPC monitoring of APC controlled processes. We also develop an economic loss-based criterion, the Average Quality Cost (AQC), to evaluate the performance of SPC charting methods. The AQC and the traditional average run length of three common SPC charts are investigated and compared.

The paper describes the application of statistical process control (SPC) methods to supervise the control of a noisy process. SPC charts form a supervisory unit that monitors process outputs and selectively applies terms of a conventional PID controller. The advantages of this method lie in the ability of the control loop to distinguish between process noise and genuine error signals. This stops the controller reacting unnecessarily reducing actuator work. Results are presented for the control of a steam/water heat exchanger rig and show the effectiveness of this supervisory unit to produce quieter control without creating a lag in the control loop. >

Traditional statistical process control (SPC) techniques of control charting are not applicable in many process industries because data from these facilities are autocorrelated. Therefore the reduction in process variability obtained through the use of SPC techniques has not been realized in process industries. Techniques are needed to serve the same functions as SPC control charts, which are to identify shifts in correlated parameters. Neural networks are a potential tool for identifying shifts in correlated process parameters, as data independence is not an assumption of neural network theory. In this research, a back-propagation neural network is utilized to identify shifts in process parameter values from AR (1) time series models with varying values of the autocorrelation coefficient φ. To find the appropriate number of input nodes for use in a neural network model, the all-possible-regression selection procedure is applied. In addition, time series residual control charts are also developed for the data sets for comparison. As the results reveal, networks were successful at separating data that were shifted one, two and three standard deviations from non-shifted data for generated process data. The SPC control charts were not able to identify the same process shifts. In the other words, the neural networks can be used to identify shifts in process parameters. Therefore, it is allowing improved control in manufacturing processes that generate correlated process data.

The control of the statistical process is to apply statistical techniques to monitor each stage of the production process and to achieve the purpose of improving and ensuring the quality of the product. The basic element of quality control is the control chart. Control chart is a statistical tool to control product quality in the production process, and is the most important method in quality control. Statistical process control is widely used in the quality management of energy, environment, chemical engineering and other fields, and plays a vital role in improving the process and product quality in related fields.

Semiconductor lithography manufacturing presents a major challenge for the application of classical Statistical Process Control (SPC) methodologies due to the complex nature of this process. For example, difficulties can occur due to inadequate data sampling, nonnormal error distributions, equipment or process instability and nonstationary random errors. Incorrect use of classical SPC techniques can result in the incorrect interpretation of process stability which can have a drastic impact on productivity. Photolithography provides additional SPC challenges due to the inherent multivariable nature of the output variables that are being controlled. This paper examines appropriate SPC and monitoring techniques for stepper control of overlay performance using in-process measurement and analysis equipment to address these issues. The average run length of three charting techniques is compared to quantify the ability of each technique to detect process mean shifts. Shewart, Exponentially Weighted Moving-Average (EWMA) and Cumulative-Sum (CUSUM) charts are analyzed for a baseline process and mean shifts of 0.42, 0.85 and 1.25 standard deviations. These results illustrate the superior performance of a CUSUM chart over Shewart and EWMA charts. In addition, the Shewart chart with Western Electric rules produced false mean shift alarms for the baseline case. The EWMA is also observed to be sensitive to the selection of weighting factors. The effectiveness of plotting individual wafers is compared with plotting lot means. The plotting of individual wafers outperforms lot means in the determination of baseline shifts because of the larger population size of the individual charts.

This article is the first in a two-part series discussing and illustrating the application of statistical process control (SPC) to processes often examined by hospital epidemiologists. The basic philosophical and theoretical foundations of statistical quality control and their relation to epidemiology are emphasized in order to expand mutual understanding and cross-fertilization between these two disciplines. Part I provides an overview of quality engineering and SPC, illustrates common types of control charts, and provides references for further information or statistical formulae. Part II discusses statistical properties of control charts, issues of chart design and optimal control limit widths, alternate possible SPC approaches to infection control, some common misunderstandings, and more advanced issues. The focus of both articles is mostly non-mathematical, emphasizing important concepts and practical examples rather than academic theory and exhaustive calculations.

BackgroundThis study seeks to increase clinical operational efficiency and accelerator beam consistency by retrospectively investigating the application of statistical process control (SPC) to linear accelerator beam steering parameters to determine the utility of such a methodology in detecting changes prior to equipment failure (interlocks actuated).MethodsSteering coil currents (SCC) for the transverse and radial planes are set such that a reproducibly useful photon or electron beam is available. SCC are sampled and stored in the control console computer each day during the morning warm-up. The transverse and radial - positioning and angle SCC for photon beam energies were evaluated using average and range (Xbar-R) process control charts (PCC). The weekly average and range values (subgroup n = 5) for each steering coil were used to develop the PCC. SCC from September 2009 (annual calibration) until two weeks following a beam steering failure in June 2010 were evaluated. PCC limits were calculated using the first twenty subgroups. Appropriate action limits were developed using conventional SPC guidelines.ResultsPCC high-alarm action limit was set at 6 standard deviations from the mean. A value exceeding this limit would require beam scanning and evaluation by the physicist and engineer. Two low alarms were used to indicate negative trends. Alarms received following establishment of limits (week 20) are indicative of a non-random cause for deviation (Xbar chart) and/or an uncontrolled process (R chart). Transverse angle SCC for 6 MV and 15 MV indicated a high-alarm 90 and 108 days prior to equipment failure respectively. A downward trend in this parameter continued, with high-alarm, until failure. Transverse position and radial angle SCC for 6 and 15 MV indicated low-alarms starting as early as 124 and 116 days prior to failure, respectively.ConclusionRadiotherapy clinical efficiency and accelerator beam consistency may be improved by instituting SPC methods to monitor the beam steering process and detect abnormal changes prior to equipment failure.PACS numbers: 87.55n, 87.55qr, 87.56bd

Assessing the evidence from the use of SPC in monitoring, predicting & improving software quality