A comparative study of automatic guidance signals and planting speeds for corn

Process variations are classified into common cause and assignable cause variations in the manufacturing and services industries. Common cause variations are inherent in a process and can be described implicitly or explicitly by stochastic methods. Assignable cause variations are unexpected and unpredictable and can occur before the commencement of any special events. Reducing process variations are critical for industries with a low tolerance for variability such as semiconductor manufacturing. While engineering process control (EPC) methods such as feedback/feedforward controllers are widely employed in continuous process industry to reduce common cause variations, statistical process control (SPC) methods have been successfully utilized in discrete parts industry through identification and elimination of the assignable cause of variations. Recently, integration of EPC and SPC methods has emerged in the semiconductor manufacturing industry and has resulted reducing manufacturing waste and improving process efficiency. This paper provides a review of various control techniques and develops a unified framework to model the relationships among these well-known methods in EPC, SPC, and integrated EPC/SPC. A case study centered on chemical mechanical planarization process demonstrates the utility of this framework.

Statistical Process Control (SPC) is the use of statistical methods to improve a production process in measuring production performance so that the process remains statistically controlled. This Statistic is based on the understanding that will never the same production and production costs obtained each year, then the automatic acquisition cost of goods sold will never be the same. Therefore, in this research will focus the Statistical Process Control on the cost of goods s old supervision process which during this time management may not be able to directly supervise the use of cost until the determination of the cost of goods sold. Moreover, t he existence of Statistical Process Control will assist management in overseeing the price stable CPO sales stakes and are in statistical control. This research was conducted at PT. Sumber Sawit Makmur which is a private plantation company oriented to the field of oil palm plantation equipped with its management plant. The purpose of this study is to (1) analyze and evaluate how the implementation of Statistical Process Control acts as a supervisory tool on CPO selling price, (2) reexamination of cost of goods sold outside statistical control, (3) to know how to apply Statistical Process Control at the Oil Palm Plantation Company. This research was conducted at Palm Oil Company in Medan City. The method of analyst technique was done by using descriptive method by using Statistical Process Control. Data obtained and collected will be analyzed using Statistical Process Control Methods on Individual X and MR control charts. T he results of research showed that the use of statistical control process at PT. Sumber Sawit Makmur to assess behavior on CPO cost of goods sold. The control limits that have been determined on the control chart X are: CL = 5.857, UCL=7.845, LCL= 3.869 and on the MR control chart that is CL=747, UCL=2.441, LCL=0. On the control chart X, CPO/Kg cost of goods sold at PT. Sumber Sawit Makmur is not in statistical control since from the 10 years data there are 3 years not in statistical control . Those are in 2005, 2006, and 2014. CPO/Kg cost of goods sold is not in statistical control on account of special cause s namely CPO/Kg production and cost of production. Meanwhile on the MR control chart, the CPO/Kg cost of goods sold is in statistical control.

According to W. Shewhart, process variation can be classified into assignable cause and common cause variations. Assignable cause variation can be eliminated by statistical process control (SPC) methods through identification and elimination of the root cause of the process shift. Common cause variation is inherent in the process and is generally difficult to reduce by SPC methods. However, if the common cause variation can be modeled by an autocorrelated process and physical variables are available to adjust the output, the common cause variation can be reduced by automatic process control (APC) methods through feedback/feedforward controllers. Integration of SPC and APC methods can result in major improvements in industrial efficiency.Most SPC monitoring methods and traditional APC process adjustment methods (such as those based on one-step-ahead minimum mean squared error predictors or proportional integral derivative controllers) involve one or both of the following two steps: (i) “whitening” the process by subtracting a predictor and (ii) monitoring the prediction errors with appropriate control limits. This paper reviews common process monitoring and adjustment methods for process control of autocorrelated data, including model-free methods based on batch means, and investigates the general relationships and properties of the underlying models.KeywordsControl ChartMinimum Mean Square ErrorExponentially Weighted Move AverageStatistical Process ControlExponentially Weighted Move Average ChartThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Objectives: To confirm that the mix-ELISA detects antibody against Salmonella serovars Typhimurium, Infantis, and Choleraesuis; to demonstrate that statistical process control (SPC) methods can be used to validate the mix-ELISA; and to demonstrate how SPC can be used to assess the Salmonella serologic status of swine. Methods: Three groups of pigs were inoculated with Salmonella Typhimurium, Salmonella Choleraesuis, or Salmonella Infantis (one serovar per group). Serologic responses were measured with the mix-ELISA and compared to responses of a group of uninfected pigs. Mix-ELISA results were evaluated using SPC methods to calculate a positive-negative cutoff value and to determine assay diagnostic sensitivity and specificity. The SPC results were compared to results of receiver operator characteristic (ROC) curve analysis. Results: Three cutoffs were determined from the SPC methods: group average (optical density [OD]% > 7.239), group range (OD% range >= 12.61), and individual (OD% >= 12). ROC curve analysis also showed optimized sensitivity (0.845) and specificity (1.00) when the individual cutoff was OD% >= 12.5. The OD% values were highest in pigs infected with Salmonella Typhimurium. Implications: The mix-ELISA detects antibody in pigs infected with Salmonella serovars, including Choleraesuis. Statistical process control methods can be used with mix-ELISA results to determine diagnostic cutoff values for assessing Salmonella serologic status. The degree of Salmonella exposure in swine can be assessed using SPC methods.

Purpose – The purpose of this paper is to develop a statistical control chart based model for earlier defect identification.Design/methodology/approach – The paper used statistical process control methods and an auto‐regression model to model the identification of the initiation point of a random defect. Conventional statistical process control (SPC) methods have been widely used in process industries for process abnormality detections. However, their practicability and achievable performance are limited due to the assumptions that a continuous process is operated in a particular steady state and that all variables are normally distributed. Because the case considered here does not meet the requirement of conventional SPC methods, we proposed adaptive statistical process control charts based on an autoregressive model to distinguish defects from normal changes in operating conditions. The method proposed has been tested on a set of vibration data of rolling element ball bearingsFindings – Several control ...

A pipe burst is a major water distribution system failure. Water escapes the network through the break increasing the total flow entering the network. These higher flows, in turn, increase the head losses in pipes and result in lower water pressures at customer taps. This study focuses on burst detection by seeking to identify anomalies in net system demand, pipe flow rates, and nodal pressure heads. Three univariate statistical process control (SPC) methods (the Western Electric Company rules, the cumulative sum (CUSUM) method, and the exponentially weighted moving average [EWMA]) and three multivariate SPC methods (Hotelling T2 method and multivariate versions of CUSUM and EWMA) are compared with respect to their detection effectiveness and efficiency. First, the three univariate methods are tested using real system burst detection and then the six SPC methods are compared using synthetic data. The real application using net system demand shows that burst flows are proportionally too small to be detected. Synthetic data analyses suggest that the univariate EWMA method using nodal pressure provides the highest detectability. The method's long record length helps detect small bursts and avoid false detection. SPC methods require consistent system operations for measurements beyond total area flow.

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. >

A positive patient safety culture in maternity units is linked to higher quality of care and better outcomes for mothers. However, safety culture varies across maternity units. Analyses of variation in safety culture using statistical process control (SPC) methods may help provider units to learn from each other's performance. This study aims to measure patient safety culture across maternity units in Oman using SPC methods. The 36-item Safety Attitude Questionnaire (SAQ) was distributed to all doctors, nurses, and midwifes working in ten maternity care units in Oman's hospitals and analysed using SPC methods. The SAQ considers six domains: job satisfaction, perception of management, safety climate, stress recognition, teamwork, and work condition. Of the 892 targeted participants, 735 (82%) questionnaires were returned. The overall percentage of positive safety responses in all hospitals ranged from 53% to 66%, but no hospital had the targeted response of above 75%. Job satisfaction had the highest safety score (4.10) while stress recognition was the lowest (3.17). SPC charts showed that the overall percentage of positive responses in three maternity units (H1, H7, and H10) was above and one (H4) was below the control limits that represent special cause variation that merits further investigation. Generally, the safety culture in maternity units in Oman is below target and suggests that considerable work is required to enhance safety culture. Several maternity units showed evidence of high/low special cause variation that may offer a useful starting point for understanding and enhancing safety culture.

The process of a customer replacing one provider of a service or merchandise for another is called a churn. In competitive business environments, such as telecommunications, insurance, banking, hotels and mail order, customers can easily leave one company—and they really do. Since the cost of recruiting new customers is higher than the cost of retaining them, it is crucial for companies in these trades to monitor their customer population in order to keep churn rates low. Statistical process control (SPC) methods are developed to cover the needs of monitoring industrial processes and intensive care patients. They are based on procedures where data are analysed automatically and on‐line. When results indicate that the process is out of control, an alarm alerts an engineer or physician, who can take corrective action in order to get the process back under control. This paper discusses the use of SPC methods as a means to enhance precision in detecting increasing churn rates. We show that SPC methods can give market analysts a powerful tool for tracking customer movements and churn. An early warning system (EWS), based on the same ideas as used in process industries, will give foresight and a longer time to react against churn, hence providing an advantage over competitors. In the examples discussed in this paper we monitor usage in order to detect decreasing volumes that indicate churn. Data were extracted from internal databases, and analysed and reported on‐line. We conclude that the potential improvement by using SPC methods in churn management is high. Copyright © 2004 John Wiley & Sons, Ltd.

Background and Aim:Consistency in producing raw milk with less variation in bulk tank milk somatic cell count (BMSCC) is important for dairy farmers as their profit is highly affected by it in the long run. Statistical process control (SPC) is widely used for monitoring and detecting variations in an industrial process. Published reports on the application of the SPC method to smallholder farm data are very limited. Thus, the purpose of this study was to assess the capability of the SPC method for monitoring the variation of BMSCC levels in milk samples collected from smallholder dairy farms.Materials and Methods:Bulk tank milk samples (n=1302) from 31 farms were collected 3 times/month for 14 consecutive months. The samples were analyzed to determine the BMSCC levels. The SPC charts, including the individual chart (I-chart) and the moving range chart (MR-chart), were created to determine the BMSCC variations, out of control points, and process signals for each farm every month. The interpretation of the SPC charts was reported to dairy cooperative authorities and veterinarians.Results:Based on a set of BMSCC values as well as their variance from SPC charts, a series of BMSCC data could be classified into different scenarios, including farms with high BMSCC values but with low variations or farms with low BMSCC values and variations. Out of control points and signals or alarms corresponding to the SPC rules, such as trend and shift signals, were observed in some of the selected farms. The information from SPC charts was used by authorities and veterinarians to communicate with dairy farmers to monitor and control BMSCC for each farm.Conclusion:This study showed that the SPC method can be used to monitor the variation of BMSCC in milk sampled from smallholder farms. Moreover, information obtained from the SPC charts can serve as a guideline for dairy farmers, dairy cooperative boards, and veterinarians to manage somatic cell counts in bulk tanks from smallholder dairy farms.

Radiation Portal Monitors (RPM) acquire sequential measurements of gamma activity that can be modeled as a sequence of Poisson distributed random numbers. These series can be analyzed with Statistical Process Control (SPC) methods to trigger alarms when the statistical distribution of the ambient dose during a transit changes. Some usual SPC methods have been compared and modified to narrow the time required to bound an alarm. Both the probabilities of detection and the Average Run Length (ARL) are used and fitted to curves to benchmark the SPC methods, which are ranked as: SR, EWMA, CUSUM, SPRT, FLR, and Shewhart.

Early recognition and response to increases in surgical site infections using optimised statistical process control charts—The early 2RIS trial: A multicentre stepped wedge cluster randomised controlled trial

To the Editor: The article by Spiegelhalter and colleagues [1] and the Counterpoint papers by Benneyan and Borgman [2], Lim [3], and Bolsin and Colson [4] deserve further comment. At least two issues should be raised, the first of which, the primacy of systems, is of crucial importance. Benneyan and Borgman state that ‘Fostering greater and more widespread use of these methods remains a significant challenge’. In Australia, statistical process control (SPC) methods were implemented with enthusiasm in the mid-1980s, as the Australian Council on Healthcare Standards (ACHS) embarked on widespread hospital accreditation. They quickly fell into disuse because they were found not to be useful. This occurred because the role of SPC was not understood. Thus we have waited nearly 20 years for their resurrection in hospitals, albeit with much improved methods. Unless we learn from the mistakes of the mid-1980s, these very valuable methods will once again be found wanting and they will once again fall into disuse. The problem is that processes must be brought into statistical control before SPC is useful; the message that control limits are useless unless the process is in control is so fundamental that it seems easily to be forgotten. It can, of course, be argued …

To investigate the γ pass rate limit of plan verification equipment for volumetric modulated arc therapy (VMAT) plan verification and its sensitivity on the opening and closing errors of multi-leaf collimator (MLC), 50 cases of nasopharyngeal carcinoma VMAT plan with clockwise and counterclockwise full arcs were randomly selected. Eight kinds of MLC opening and closing errors were introduced in 10 cases of them, and 80 plans with errors were generated. Firstly, the plan verification was conducted in the form of field-by-field measurement and true composite measurement. The γ analysis with the criteria of 3% dose difference, distance to agreement of 2 mm, 10% dose threshold, and absolute dose global normalized conditions were performed for these fields. Then gradient analysis was used to investigate the sensitivity of field-by-field measurement and true composite measurement on MLC opening and closing errors, and the receiver operating characteristic curve (ROC) was used to investigate the optimal threshold of γ pass rate for identifying errors. Tolerance limits and action limits for γ pass rates were calculated using statistical process control (SPC) method for another 40 cases. The error identification ability using the tolerance limit calculated by SPC method and the universal tolerance limit (95%) were compared with using the optimal threshold of ROC. The results show that for the true composite measurement, the clockwise arc and the counterclockwise arc, the descent gradients of the γ passing rate with per millimeter MLC opening error are 10.61%, 7.62% and 6.66%, respectively, and the descent gradients with per millimeter MLC closing error are 9.75%, 7.36% and 6.37%, respectively. The optimal thresholds obtained by the ROC method are 99.35%, 97.95% and 98.25%, respectively, and the tolerance limits obtained by the SPC method are 98.98%, 97.74% and 98.62%, respectively. The tolerance limit calculated by SPC method is close to the optimal threshold of ROC, both of which could identify all errors of ±2 mm, while the universal tolerance limit can only partially identify them, indicating that the universal tolerance limit is not sensitive on some large errors. Therefore, considering the factors such as ease of use and accuracy, it is suggested to use the true composite measurement in clinical practice, and to formulate tolerance limits and action limits suitable for the actual process of the institution based on the SPC method. In conclusion, it is expected that the results of this study can provide some references for institutions to optimize the radiotherapy plan verification process, set appropriate pass rate limit, and promote the standardization of plan verification.

A survey on multistage/multiphase statistical modeling methods for batch processes