scholarly journals Univariate statistical process control of super saver beans: a case of RMV supermarket, zimbabwe.

2015 ◽  
Vol 1 (3) ◽  
pp. 238-248
Author(s):  
Romeo Mawonik ◽  
Vinscent Nkomo

Statistical Process Control (SPC) uses statistical techniques to improve the quality of a process reducing its variability. The main tools of SPC are the control charts. The basic idea of control charts is to test the hypothesis that there are only common causes of variability versus the alternative that there are special causes. Control charts are designed and evaluated under the assumption that the observations from the process are independent and identically distributed (IID) normal. However, the independence assumption is often violated in practice. Autocorrelation may be present in many procedures, and may have a significant effect on the properties of the control charts.Thus, traditional SPC charts are inappropriate for monitoring process quality. In this study, wepresent methods for process control that deal with auto correlated data and a method based on time series ARIMA models (Box Jenkins Methodology). We apply the typical Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) charts as SPC techniques and the time-series method in determining packaging process quality.

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares

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.


2014 ◽  
Vol 16 (1) ◽  
pp. 138-158 ◽  
Author(s):  
Martin Kovářík ◽  
Libor Sarga ◽  
Petr Klímek

We will deal with corporate financial proceeding using statistical process control, specifically time series control charts. The article outlines intersection of two disciplines, namely econometrics and statistical process control. Theoretical part discusses methodology of time series control charts, and in research part, the methodology is demonstrated on two case studies. The first focuses on analysis of Slovak currency from the perspective of its usefulness for generating profits through time series control charts. The second involves regulation of financial flows for a heteroskedastic financial process by EWMA and ARIMA control charts. We use Box-Jenkins methodology to find models of time series of annual Argentinian Gross Domestic Product available as a basic index from 1951–1998. We demonstrate the versatility of control charts not only in manufacturing but also in managing financial stability of cash flows. Specifically, we show their sensitivity in detecting even small shifts in mean which may indicate financial instability. This analytical approach is widely applicable and therefore of theoretical and practical interest.


2017 ◽  
Vol 866 ◽  
pp. 379-382
Author(s):  
Unchalee Tonggumnead ◽  
Kittipong Klinjan

The monitoring of processes is a vital mechanism for ensuring that such processes remain safe and under control. The present research aims to solve problems associated with correlated data by applying the Box-Jenkins method integrated with statistical process control (SPC) tools, namely the Shewhart chart, the moving average chart, the cumulative sum (CUSUM) chart, and the exponentially weighted moving-average (EWMA) chart. The efficiency of the four SPC tools was also compared in terms of the false alarm rate (FAR) and the missed detection rate (MDR). The findings indicated that the EWMA chart was the most effective in detecting anomaly, the Shewhart chart and the moving average chart produced high MDR, and the CUSUM chart suffered the highest FAR.


Author(s):  
Mario Lesina ◽  
Lovorka Gotal Dmitrovic

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.


2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S475-S476
Author(s):  
Arthur W Baker ◽  
Ahmed Maged ◽  
Salah Haridy ◽  
Jason E Stout ◽  
Jessica L Seidelman ◽  
...  

Abstract Background Nontuberculous mycobacteria (NTM) are increasingly implicated in healthcare facility-associated (HCFA) outbreaks. However, systematic methods for NTM surveillance and outbreak detection are lacking and represent an emerging need. We examined how statistical process control (SPC) might perform in NTM outbreak detection. Methods SPC charts were optimized for surgical site infection surveillance and adapted to analyze 3 NTM outbreaks that occurred from 2013-2016 at a single hospital. The first 2 outbreaks occurred contemporaneously and consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition and cardiac surgery-associated extrapulmonary MABC infection, respectively. The third outbreak was a pseudo-outbreak of Mycobacterium avium complex (MAC) at a bronchoscopy suite. We retrospectively analyzed monthly rates of unique patients who had: 1) positive respiratory cultures for MABC obtained on hospital day 3 or later; 2) positive non-respiratory cultures for MABC; and 3) positive bronchoalveolar lavage (BAL) cultures for MAC collected at the bronchoscopy suite. For each outbreak, we used these rates to construct a standardized moving average (MA) SPC chart with MA span of 3 months. Rolling baselines were estimated from average rates for months 7 through 12 prior to each monthly data point. SPC detection was indicated by the first data point above the upper control limit (UCL) of 3 standard deviations. Traditional surveillance detection was defined as the time of outbreak detection by routine infection control procedures. Results SPC detection occurred 5, 4, and 9 months prior to traditional surveillance outbreak detection for the three outbreaks, respectively (Figures 1 and 2). Prospective NTM surveillance using the MA chart potentially would have prevented an estimated 19 cases of pulmonary MABC, 9 cases of extrapulmonary MABC, and 80 cases of BAL MAC isolation (Table). No data points exceeded the UCL during 95 cumulative months of post-outbreak surveillance, suggesting low burden of false positive SPC signals. Figure 1. Use of a moving average statistical process control (SPC) chart for early detection of hospital-associated outbreaks of pulmonary Mycobacterium abscessus complex (MABC) and cardiac surgery-associated extrapulmonary MABC infection. The pulmonary chart analyzes cases of hospital-onset respiratory isolation of MABC. The extrapulmonary chart analyzes cases of positive non-respiratory cultures for MABC. CL, center line; LCL, lower control limit; UCL, upper control limit. Figure 2. Use of a moving average statistical process control (SPC) chart for early detection of a pseudo-outbreak of Mycobacterium avium complex (MAC) that occurred at a bronchoscopy suite. The chart analyzes cases of MAC isolated from bronchoalveolar lavage cultures. CL, center line; LCL, lower control limit; UCL, upper control limit. Table. Estimated cases of hospital-associated nontuberculous mycobacteria that would have been prevented by prospective surveillance with a moving average statistical process control (SPC) chart. Conclusion A single MA SPC chart detected 3 HCFA NTM outbreaks an average of 6 months earlier than traditional surveillance. SPC has potential to improve NTM surveillance, promoting early cluster detection and prevention of HCFA NTM. Disclosures All Authors: No reported disclosures


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 1855.2-1855
Author(s):  
M. Stevens ◽  
N. Proudlove ◽  
J. Ball ◽  
C. Scott

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


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