CONTROLE ESTATÍSTICO DE PROCESSO (CEP) APLICADO NA COLHEITA MECANIZADA DO SORGO

2018 ◽  
Vol 17 (3) ◽  
pp. 490
Author(s):  
JOÃO PAULO BARRETO CUNHA ◽  
MILA SOUZA CASTRO ◽  
ANDERSON GOMIDE COSTA ◽  
MURILO MACHADO DE BARROS ◽  
TULIO ALMEIDA MACHADO ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Maximum Level ◽  
Statistical Process ◽  
Forage Sorghum ◽  
Randomized Design ◽  
Completely Randomized Design ◽  
And Control ◽  
The Ideal

RESUMO - A colheita, sendo uma das principais etapas no processo produtivo, precisa manter as perdas dentro de um controle aceitável para que seja possível atingir o máximo nível de qualidade e produtividade. No presente estudo, objetivou-se avaliar as perdas quantitativas durante a colheita mecanizada do sorgo forrageiro por meio do controle estatístico de processo (CEP). O experimento foi arranjado em delineamento inteiramente casualizado (DIC), em que foi realizada a análise de variância para a verificação do efeito significativo da declividade e da velocidade operacional nas perdas, e, quando significativos, foi submetida ao teste de comparação de médias de Tukey a 5% de significância. Cartas sequenciais e cartas de controle para valores individuais e de amplitude móveis foram utilizadas como ferramentas de controle estatístico de processo para verificar o efeito da velocidade operacional nas perdas. Com base nos resultados obtidos é possível indicar que a faixa de velocidade operacional de 4 a 5 km h-1 apresentou a menor variação dos dados, não apresentando nenhum ponto fora do limite de controle, o que lhe conferiu a condição de faixa ideal para colheita. Com base na análise estatística houve maiores perdas no transporte à medida que se aumenta a faixa de declividade do terreno.Palavras-chave: colheita mecanizada, forragicultura, carta de controle, velocidade operacional. STATISTICAL PROCESS CONTROL (SPC) APPLIED IN THE MECHANIZED HARVEST OF SORGHUM  ABSTRACT - Harvesting is one of the main steps in the production process and it is necessary to keep the losses under control in order to reach the maximum level of quality and productivity. The present study aimed to evaluate the quantitative losses during the mechanized harvesting of forage sorghum using the statistical process control (SPC). The experiment was arranged in a completely randomized design (DIC), and analysis of variance was performed to verify the significant effect of declivity and operational velocity on losses, and the significant was submitted to the Tukey test at 5% significance. Sequential charts and control charts for individual and mobile amplitude values, composed of upper and lower control and average limits, were used as statistical process control tools to verify the effect of operational speed on losses. Based on the results obtained it is possible to indicate that the operational velocity range from 4 to 5 km h-1 presented the lowest variation of data, presenting no point outside of the control limit, being the ideal range for harvest. The statistical analysis showed higher losses in transportation as the slope of the terrain increased.Keywords: Mechanized harvesting, forage farming, control charts, operational speeds.

scholarly journals STATISTICAL PROCESS CONTROL (SPC) OF DAMAGE METRICS IN THE IMPEDANCE-BASED STRUCTURAL HEALTH MONITORING

2021 ◽  
Vol 39 (1) ◽  
pp. 7
Author(s):  
Quintiliano Siqueira Schroden NOMELINI ◽  
José Waldemar Da SILVA ◽  
Carlos Alberto GALLO ◽  
Roberto Mendes FINZI NETO ◽  
José Dos Reis Vieira de MOURA JUNIOR ◽  
...  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Error Detection ◽  
Control Charts ◽  
Statistical Process ◽  
Randomized Design ◽  
Processing Step ◽  
Sources Of Variation ◽  
Repeatability And Reproducibility ◽  
Completely Randomized Design

Statistical Process Control (SPC) stands out for the use of control charts and for repeatability and reproducibility (R&R) techniques. This work aimed at its applications in the aspects of pre-processing of structural monitoring. The experiment was carried out in a completely randomized design (CRD) with two sources of variation: eight aluminum beams with piezoelectric patches and five types of damage (D1 = baseline, D2 = 0.6g, D3 = 1.1g, D4 = 1.6g, D5 = 2.2g). All measurements were gathered at 30oC and with 20 repetitions for each condition case, producing a damage metric. In the R&R study, a low variation of repetition was observed (9.84%), but a high reproducibility (72.39%), representing that the damage metrics were similar for each situation, but a high variation among beams and damages. Based on this evaluation, the control charts helped to verify in which beams and damages these greatest variabilities were found. Concluding, the control charts for mean and individual measures as well as the R&R study were interesting tools for raw data pre-processing step for measurement error detection.

scholarly journals Surveillance of Cyclospora cayetanensis Epidemics in USA from Long-Term National Outbreaks Reporting System-Based Monitoring: An Observational Study Using Statistical Process Control Methodologies

2019 ◽  
Vol 2 (1) ◽  
pp. 4-9
Author(s):  
Mostafa Essam Ahmed Mostafa Eissa ◽  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Reporting System ◽  
Statistical Process ◽  
Pareto Analysis ◽  
Cyclospora Cayetanensis ◽  
The Usa ◽  
One Year ◽  
And Control

Cyclosporiasis epidemics are caused primarily by food contaminated essentially with Cyclospora cayetanensis protozoa from Phylum Apicomplexa. National Outbreaks Reporting System (NORS) provides comprehensive monitoring and records for outbreaks in the USA. The pattern of the microbial epidemics could be investigated using statistical process control (SPC) techniques including Pareto analysis and control charts. The incidence of this outbreak is higher in some states more than others, especially Florida and transmitted mainly through herbal food constituents as a vehicle. Process-behavior charts show disease patterns and trends with the rate of occurrence per day 14.4%. Most of illness cases tend to occur in the summer environment except for March in one-year due spiking in the number of affected individuals during a solitary outbreak episode.

scholarly journals Improving Process Performance of Cotton Spinning by Using Statistical Process Control Techniques

2020 ◽  
pp. 15-22
Author(s):  
Dereje Girma ◽  
Omprakash Sahu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Shop Floor ◽  
Statistical Control ◽  
Statistical Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
R Process ◽  
And Control

Identifying the presence and understanding the causes of process variability are key requirements for well controlled and quality manufacturing. This pilot study demonstrates the introduction of Statistical Process Control (SPC) methods to the spinning department of a textile manufacturing company. The methods employed included X Bar and R process control charts as well as process capability analysis. Investigation for 29 machine processes identified that none were in statistical control. Recommendations have been made for a repeat of the study using validated data together with practical application of SPC and control charts on the shop floor and extension to all processes within the factory.

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

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.

scholarly journals 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

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

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

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
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.

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