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General introduction
Published in Adedeji B. Badiru, Handbook of Industrial and Systems Engineering, 2013
The relationship between EPC and SPC through prediction has been recently explored in many industrial applications. To make an appropriate selection between the two approaches in practice, it is important to identify disturbance structures and strengths of the two control methods to influence the process. Figure 42.2 presents four categories of ongoing research and application of the two quality-control approaches. If a process is not correlated, there is no need to employ EPC schemes. Traditional SPC control charts should be used for identifying assignable cause variations.When data are correlated, the possibility of employing EPC techniques should be examined. SPC control charts are called for to monitor autocorrelated processes if no feasible EPC controller exists.If appropriate controllers are available, EPC control schemes can be employed to compensate for the autocorrelated disturbance. However, no single EPC controller system can compensate for all kinds of potential variations.
Implementing Solutions
Published in James William Martin, Lean Six Sigma for the Office, 2021
There are other control tools that are effective for maintaining process performance. An example is Statistical Process Control (SPC), which uses different types of control chart to detect excess process variation of key variables documented in the control plan. Statistical Process Control will now be discussed in detail becomes it has several important attributes relevant for effective control.
Making the DMAIC Model More Lean and Agile: Analyze
Published in Terra Vanzant Stern, Lean and Agile Project Management, 2017
Statistical analysis is often called SPC. The primary tool used in SPC is the control chart. SPC involves using statistical techniques to measure and analyze the variation in processes. Most often used for manufacturing processes, the intent of SPC is to monitor product quality and maintain processes to fixed targets.
Application of Statistical Process Control to Monitor Underground Coal Mine Fires Based on CO Emissions
Published in Combustion Science and Technology, 2022
Nilufer Kursunoglu, Huseyin Ankara
Statistical process control (SPC) is a method or tool used to continuously monitor a process and identify the conditions that cause variability (instability) in the process. For quality measurement, the SPC requires statistical sampling, which consists primarily of small piles of units collected at sequential, regular intervals. The primary objective of the SPC is to identify and resolve problems with assignable causes. The objective of the SPC is to maintain process stability by removing specific causes of variation. A controlled process consistently produces a product within its inherent tolerances by monitoring and eliminating specific causes of change. Benefits of the SPC include the reduction of control activities, the ability to monitor process capability, and the identification of corrective and preventive action requirements (Montgomery 2009). The SPC is a tool used to ensure production conformity to predetermined quality specifications, target standard adherence, and minimize the production of nonconforming products. Thus, it affords the opportunity to make decisions based on data in order to initiate corrective and preventive actions. (Figure 1).
Bayesian cross-product quality control via transfer learning
Published in International Journal of Production Research, 2022
Quality is a key determinant of business success in modern industries, the control of which has become an important operation strategy for almost all organisations. Statistical process control (SPC), which originated in the 1920's, has developed into a popular and useful quality technique to maintain and improve process stability (Montgomery 2009). Unlike managerial or engineering efforts, SPC is a data-driven method with statistical fundamentals to model and monitor process variations (Yin et al. 2014; Bahria et al. 2019). Typically, SPC consists of two phases in implementation. Phase I is an offline retrospective stage where a set of historical reference data are collected to characterise an in-control (IC) process state, while Phase II deploys a control chart for online prospective process monitoring and detection of any potential process shift from the predefined IC state. When an out-of-control (OC) state is detected, appropriate actions can be taken immediately for process remedy.
Nonparametric adaptive CUSUM chart for detecting arbitrary distributional changes
Published in Journal of Quality Technology, 2021
Statistical process control (SPC) applies statistical methods to the monitoring and control of a process in order to detect abnormal variations of the process. One of the most popular SPC tools is the control chart, which plots a statistic that measures a feature of the process over time. When the charting statistic is within the predetermined control limits, it indicates that the process is in a state of statistical control (hereafter in-control). When this charting statistic goes beyond the control limits, it triggers an alarm to indicate that the process is likely experiencing abnormal variations (hereafter out-of-control). Control charts are easy to visualize and interpret, therefore they have been applied to applications across many different industries, including fraud detection, disease outbreak surveillance, network traffic monitoring, and others (see, for example, Tsung, Zhou, and Jiang 2007; Woodall 2006; Jeske et al. 2009).