Evaluation of Statistical Process Control Techniques in Monitoring Weekly Body Condition Scores as an Early Warning System for Predicting Subclinical Ketosis in Dry Cows

The management of body condition score (BCS) during the dry period is associated with the postpartum health outcomes of dairy cows. However, the difference between the actual BCS and the fixed ideal value is not able to accurately predict the occurrence of postpartum diseases. This study aimed to use statistical process control (SPC) technology to monitor the BCS of dry cows, to evaluate the effect of control charts on nutritional strategies, and to explore the utility of SPC in predicting the incidence of postpartum subclinical ketosis (SCK). The BCS and SCK data of 286 cows from the dry off period to 60 days postpartum were collected to set up the early warning function. Three control charts, including a control chart for the average BCS of the herds, for the BCS of each dry cow, and for individual BCS, were established. The early warning signs for postpartum SCK development were: (1) an individual BCS more than 3.5 that remained unchanged for six weeks; (2) a capability index (CPK), an SPC tool, greater than −0.52. Using these parameters, the early warning signs of SCK development were verified in 429 dry cows. The results showed that the accuracy of early warning signal was 0.64 and the precision was 0.26. The control chart showed that the average BCS of dry cows was consistently higher than the expected upper limit of BCS during the experimental period, and that the addition of new cows to the herds increased the average BCS. In summary, the application of SPC technology to monitor the BCS of dry cows was not a good tool for the prediction of postpartum SCK occurrence but was an appropriate tool for guiding positive nutrition strategies.

Download Full-text

Monitoring of mechanical sugarcane harvesting through control charts

Engenharia Agrícola ◽

10.1590/1809-4430-eng.agric.v35n6p1079-1092/2015 ◽

2015 ◽

Vol 35 (6) ◽

pp. 1079-1092 ◽

Cited By ~ 5

Author(s):

Murilo A. Voltarelli ◽

Rouverson P. da Silva ◽

Cristiano Zerbato ◽

Carla S. S. Paixão ◽

Tiago de O. Tavares

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Moving Average ◽

Grinding Wheel ◽

Individual Values ◽

Statistical Process ◽

Minas Gerais State ◽

Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

Download Full-text

A study on the application of control chart in healthcare

ITM Web of Conferences ◽

10.1051/itmconf/20213601001 ◽

2021 ◽

Vol 36 ◽

pp. 01001

Author(s):

Yee Kam Seoh ◽

Voon Hee Wong ◽

Mahboobeh Zangeneh Sirdari

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Manufacturing Industry ◽

Positive Impact ◽

Healthcare Management ◽

Healthcare Sector ◽

Statistical Tool ◽

Statistical Process

The most concerning issues in the healthcare system will always be quality control and quality improvement as they are significant to the health condition of the patient. A quality statistical tool such as statistical process control (SPC) charts will be efficient and highly effective in reducing the sources of variation within the healthcare process and in monitoring or controlling improvement of the process. The control chart is a statistical process control methodology designed to evaluate the process improvement or change in the manufacturing industry and is being implemented gradually in the healthcare sector. This will enable healthcare organizations to prevent unnecessary investment or spending in any changes that sound good but do not have any positive impact on real progress or improvement. When there is greater participation of humans in healthcare, the risks of error are also greater. Control charts help determine the source of error by differentiating the common and special cause of variation, each requiring a different response from healthcare management. This paper intends to deliver an overview of SPC theory and to explore the application of SPC charts by presenting a few examples of the implementation of control charts to common issues in the healthcare sector. After a brief overview of SPC in healthcare, the selection and construction of the two widely used control charts (Individuals and Moving Range chart, U chart) were adopted and illustrated by using the example from healthcare.

Download Full-text

PACS Monitoring

Governance of Picture Archiving and Communications Systems ◽

10.4018/978-1-59904-672-3.ch015 ◽

2010 ◽

pp. 244-263

Author(s):

Carrison K.S. Tong ◽

Eric T.T. Wong

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Continuous Process ◽

Descriptive Statistics ◽

Statistical Techniques ◽

Statistical Control ◽

Executive Management ◽

Statistical Process

The present study advocates the application of statistical process control (SPC) as a performance monitoring tool for a PACS. The objective of statistical process control (SPC) differs significantly from the traditional QC/QA process. In the traditional process, the QC/QA tests are used to generate a datum point and this datum point is compared to a standard. If the point is out of specification, then action is taken on the product and action may be taken on the process. To move from the traditional QC/QA process to SPC, a process control plan should be developed, implemented, and followed. Implementing SPC in the PACS environment need not be a complex process. However, if the maximum effect is to be achieved and sustained, PACSSPC must be implemented in a systematic manner with the active involvement of all employees from line associates to executive management. SPC involves the use of mathematics, graphics, and statistical techniques, such as control charts, to analyze the PACS process and its output, so as to take appropriate actions to achieve and maintain a state of statistical control. While SPC is extensively used in the healthcare industry, especially in patient monitoring, it is rarely applied in the PACS environment. One may refer to a recent SPC application that Mercy Hospital (Alegent Health System) initiated after it implemented a PACS in November 2003 (Stockman & Krishnan, 2006). The anticipated benefits characteristic to PACS through the use of SPC include: • Reduced image retake and diagnostic expenditure associated with better process control. • Reduced operating costs by optimizing the maintenance and replacement of PACS equipment components. • Increased productivity by identification and elimination of variation and outof- control conditions in the imaging and retrieval processes. • Enhanced level of quality by controlled applications. 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. Hence besides the HSSH techniques, the proposed TQM approach would include the use of SPC. Although SPC will not improve the reliability of a poorly designed PACS, it can be used to maintain the consistency of how the individual process is provided and, therefore, of the entire PACS process. A primary tool used for SPC is the control chart, a graphical representation of certain descriptive statistics for specific quantitative measurements of the PACS process. These descriptive statistics are displayed in the control chart in comparison to their “in-control” sampling distributions. The comparison detects any unusual variation in the PACS delivery process, which could indicate a problem with the process. Several different descriptive statistics can be used in control charts and there are several different types of control charts that can test for different causes, such as how quickly major vs. minor shifts in process means are detected. These control charts are also used with service level measurements to analyze process capability and for continuous process improvement efforts.

Download Full-text

The Use of Statistical Process Control Tools for Analysing Financial Statements

Folia Oeconomica Stetinensia ◽

10.1515/foli-2017-0010 ◽

2017 ◽

Vol 17 (1) ◽

pp. 129-137

Author(s):

Janusz Niezgoda

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Financial Statements ◽

Arithmetic Mean ◽

Statistical Process ◽

Shewhart Control Charts ◽

Shewhart Control ◽

Financial Analyses

Abstract This article presents the proposed application of one type of the modified Shewhart control charts in the monitoring of changes in the aggregated level of financial ratios. The control chart x̅ has been used as a basis of analysis. The examined variable from the sample in the mentioned chart is the arithmetic mean. The author proposes to substitute it with a synthetic measure that is determined and based on the selected ratios. As the ratios mentioned above, are expressed in different units and characters, the author applies standardisation. The results of selected comparative analyses have been presented for both bankrupts and non-bankrupts. They indicate the possibility of using control charts as an auxiliary tool in financial analyses.

Download Full-text

STATISTICAL PROCESS CONTROL IMPLEMENTATION AS EARLY WARNING SIGNAL FOR SAFETY INTERVENTION IMPROVEMENT AT MINING OPERATION

Journal of Engineering and Management in Industrial System ◽

10.21776/ub.jemis.2020.008.02.4 ◽

2020 ◽

Vol 8 (2) ◽

pp. 32-55

Author(s):

Arya Nugraha ◽

◽

Gatot Yudoko

Keyword(s):

Process Control ◽

Real Time ◽

Statistical Process Control ◽

Early Warning ◽

Control Charts ◽

Warning Signal ◽

Statistical Process ◽

Leading Indicator ◽

Early Warning Signal ◽

Safety Intervention

As the frequency, severity, and costs of safety risks continue to become a challenge for mining industry, the company understood that the existing safety analytic does not provide adequate information, as it has been relying predominantly on collecting and evaluating aggregated data of lagging indicators about past accidents. This method has been negatively driving the organization to carry out repetitive cycle of accident analysis and problem solving, and therefore, undertaking reactive responses. This paper investigated how statistical process control, in particular control charts, can be applied to hazards data, as the leading indicator of accidents, to detect statistically trends in safety process and safety behavior, aiming to control the safety process in real-time manner before the occurrence of accidents. The result showed that the latest iteration of control limits development in Phase 3 is suitable as the control chart for safety process in one of case study mine operation site. Furthermore, the implementation of control charts to hazards data not only it helps the organization to transition its safety analytic to leading indicator analysis, it enables the organization to control safety process in real-time practice and to carry out timely safety intervention long before the potential occurrence of severe accidents, in which within this case, the first early warning signal was triggered 49 days before the occurrence of the fatality accident.

Download Full-text

Interval Type-2 Fuzzy Standardized Cumulative Sum Control Charts in Production of Fertilizers

Mathematical Problems in Engineering ◽

10.1155/2021/4159149 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Nur Hidayah Mohd Razali ◽

Lazim Abdullah ◽

Zabidin Salleh ◽

Ahmad Termimi Ab Ghani ◽

Bee Wah Yap

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Fuzzy Numbers ◽

Cusum Chart ◽

Statistical Process ◽

Interval Type

Statistical process control is a method used for controlling processes in which causes of variations and correction actions can be observed. Control chart is one of the powerful tools of statistical process control that are used to control nonconforming products. Previous literature suggests that fuzzy charts are more sensitive than conventional control charts, and hence, they provide better quality and conformance of products. Nevertheless, some of the data used are more suitable to be presented in interval type-2 fuzzy numbers compared to type-1 fuzzy numbers as interval type-2 fuzzy numbers have more ability to capture uncertain and vague information. In this paper, we develop an interval type-2 fuzzy standardized cumulative sum (IT2F-SCUSUM) control chart and apply it to data of fertilizer production. This new approach combines the advantages of interval type-2 fuzzy numbers and standardized sample means which can control the variability. Twenty samples with a sample size of six were examined for testing the conformance. The proposed IT2F-SCUSUM control chart unveils that 15 samples are “out of control.” The results are also compared to the conventional CUSUM chart and type-1 fuzzy CUSUM chart. The conventional chart shows that 13 samples are “out of control.” In contrast, the type-1 fuzzy CUSUM chart shows that the process is “out of control” for 14 samples. In the analysis of average run length, the proposed IT2F-SCUSUM chart outperforms the other two CUSUM charts. Thus, we can conclude that the IT2F-SCUSUM chart is more sensitive and takes lesser number of observations to identify the shift in the process. The analyses suggest that the IT2F-SCUSUM chart is a promising tool in examining conformance of the quality of the fertilizer production.

Download Full-text

APSS – Software Support for Decision Making in Statistical Process Control

Quality Innovation Prosperity ◽

10.12776/qip.v22i3.1141 ◽

2018 ◽

Vol 22 (3) ◽

pp. 19

Author(s):

Darja Noskievicova

Keyword(s):

Active Learning ◽

Process Control ◽

Statistical Process Control ◽

Control Chart ◽

Control Charts ◽

Statistical Process ◽

Software Support ◽

Research Activities ◽

Problem Solving Process ◽

Selection Of

Purpose: SPC can be defined as the problem solving process incorporating many separate decisions including selection of the control chart based on the verification of the data presumptions. There is no professional statistical software which enables to make such decisions in a complex way.Methodology/Approach: There are many excellent professional statistical programs but without complex methodology for selection of the best control chart. Proposed program in Excel APSS (Analysis of the Process Statistical Stability) solves this problem and also offers additional learning functions.Findings: The created SW enables to link altogether separate functions of selected professional statistical programs (data presumption verification, control charts construction and interpretation) and supports active learning in this field.Research Limitation/implication: The proposed SW can be applied to control charts covered by SW Statgraphics Centurion and Minitab. But there is no problem to modify it for other professional statistical SW.Originality/Value of paper: The paper prezents the original SW created in the frame of the research activities at the Department of Quality Management of FMT, VŠB-TUO, Czech Republic. SW enables to link altogether separate functions of the professional statistical SW needed for the complex realization of statitical process control and it is very strong tool for the active learning of statistical process control tasks.

Download Full-text

APPLICATION OF SPC AND GROWTH MODELS ON THE ECONOMIC TRAJECTORY OF THE LEATHER AND FOOTWEAR INDUSTRY

NORDSCI Conference proceedings, Book 2 Volume 3 ◽

10.32008/nordsci2020/b2/v3/01 ◽

2020 ◽

Author(s):

Mario Lesina ◽

Lovorka Gotal Dmitrovic

Keyword(s):

Human Capital ◽

Process Control ◽

Statistical Process Control ◽

Control Charts ◽

Growth Models ◽

Pearson Correlation ◽

Small Companies ◽

Statistical Process ◽

Production Staff ◽

Footwear Industry

The paper shows the relation among the number of small, medium and large companies in the leather and footwear industry in Croatia, as well as the relation among the number of their employees by means of the Spearman and Pearson correlation coefficient. The data were collected during 21 years. The warning zone and the risk zone were determined by means of the Statistical Process Control (SPC) for a certain number of small, medium and large companies in the leather and footwear industry in Croatia. Growth models, based on externalities, models based on research and development and the AK models were applied for the analysis of the obtained research results. The paper shows using the correlation coefficients that The relation between the number of large companies and their number of employees is the strongest, i.e. large companies have the best structured work places. The relation between the number of medium companies and the number of their employees is a bit weaker, while there is no relation in small companies. This is best described by growth models based on externalities, in which growth generates the increase in human capital, i.e. the growth of the level of knowledge and skills in the entire economy, but also deductively in companies on microeconomic level. These models also recognize the limit of accumulated knowledge after which growth may be expected. The absence of growth in small companies results from an insufficient level of human capital and failure to reach its limit level which could generate growth. According to Statistical Process Control (SPC), control charts, as well as regression models, it is clear that the most cost-effective investment is the investment into medium companies. The paper demonstrates the disadvantages in small, medium and large companies in the leather and footwear industry in Croatia. Small companies often emerge too quickly and disappear too easily owing to the employment of administrative staff instead of professional production staff. As the models emphasize, companies need to invest into their employees and employ good production staff. Investment and support to the medium companies not only strengthens the companies which have a well-arranged technological process and a good systematization of work places, but this also helps large companies, as there is a strong correlation between the number of medium and large companies.

Download Full-text

AB1131 STATISTICAL PROCESS CONTROL AND PROCESS MAPPING QUANTIFY THE EFFECTS OF HISTORICAL CHANGES TO THE CONNECTIVE TISSUE DISEASE TESTING ALGORITHM AND IDENTIFY AREAS FOR FUTURE IMPROVEMENT IN A LARGE DIAGNOSTIC IMMUNOLOGY SERVICE

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.438 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 1855.2-1855

Author(s):

M. Stevens ◽

N. Proudlove ◽

J. Ball ◽

C. Scott

Keyword(s):

Process Control ◽

Statistical Process Control ◽

Control Charts ◽

Similar Proportion ◽

Limiting Factor ◽

List Type ◽

Process Mapping ◽

Statistical Process ◽

Current Testing ◽

Testing Algorithm

Background:Pathology test turnaround times (TATs) are a limiting factor in patient flow through rheumatology services. Quality improvement (QI) methodologies such as Lean use tools including statistical process control (SPC) and process mapping to study the performance of the whole of a clinical pipeline, expose unnecessary complexity (non-value-adding activity), and streamline processes and staff roles.Objectives:Understand effects of changes made to CTD testing algorithm over last 12 years by measuring some of the effects on TATs. Model current processes and suggest changes to workflow to improve TAT.Methods:High-level flow diagrams of the current testing algorithm, and low-level process maps of analyser and staff processes were drawn.Activity and TATs (working days between report and booking date) for ANA, ENA, DNA and CCP tests were plotted as XmR control charts.Results:Finding 1: Largest referral laboratory does not currently operate a separate DNA monitoring workstream, resulting in unnecessary ANA and ENA testing (figure 1).Figure 1.Current testing strategy (left) and suggested improvement (right)Finding 2:Samples are handed off between 3 different lab benches, each of which may be staffed by a different staff member on a different day, and results processing involves handoff to a further 2 different staff members.Finding 3:ANA demand is close to capacity, ENA demand exceeds current capacity (table 1).Table 1.Demand for ANA, ENA and DNA tests, compared to capacityTestMedian Demand(tests/ day)Approx. Capacity(tests/ day)NotesANA74100Close to 80% recommended by the ILGsENA3836*Less capacity than demand!!DNA34100PlentyFinding 4:Stopping screening DNA requests on ANA result increased the number of DNA tests performed by about 10 samples per day (30%), but decreased turnaround time by a similar proportion (3.3 to 2.3 days, figure 2). It also reduced turnaround times of ANA and ENA tests.Figure 2.Control chart of average TAT of dsDNA antibodies by request dateConclusion:Typically for a QI project, the initially simple CTD testing pipeline has accumulated many changes made without consideration of whole system performance, and is now a struggle to run.Improvement ideas to be explored from this work include:Liaising with main referral lab to develop a DNA monitoring workstream to reduce unnecessary ANA and ENA testingReduce handoffs, sample journey around lab analysers, and staff hands-on time by:changing ANA test methodology to same as DNAcreating new staff roles (analyser operators to perform validation/ authorisation steps)Create more capacity for ENA testing by increasing the frequency of this test on the weekly rotaCreate more capacity for service expansion by running analysers at weekends (staff consultation required)Reduce demand on service by engaging and educating requestorsImprove TAT for DNA by:processing samples the day they are booked in, instead of 1 day laterauto-validating runs…using control charts to measure improvementDisclosure of Interests:None declared

Download Full-text