Excel Logistics Services

2017 ◽  
pp. 1-8
Author(s):  
Sunil Chopra
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Department Store ◽  
Distribution Center ◽  
Statistical Process ◽  
Logistics Services ◽  
And Control ◽  
Control Limits

Looks at the introduction of statistical process control (SPC) into a distribution center servicing a department store chain. Focuses on the receiving process in the distribution center and describes the introduction of SPC methodology. Discusses run charts, pareto diagrams, and control limits.To introduce statistical process control.

An Integrated Statistical Process Control and Wavelet Transformation Model for Detecting QRS Complexes in ECG Signals

2010 ◽  
Vol 1 (2) ◽  
pp. 1-20
Author(s):  
Wen-Hung Yang ◽  
Bernard C. Jiang
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Wavelet Transformation ◽  
Transformation Model ◽  
Change Points ◽  
Frequency Noise ◽  
Statistical Process ◽  
Ecg Signals ◽  
And Control ◽  
Control Limits

In this study, the authors propose an approach for detecting R-wave of electrocardiogram (ECG) signals. A statistical process control chart is successfully integrated with wavelet transformation (WT) to detect R-wave locations. This chart is a graphical display of the quality characteristic measured or computed from samples versus the sample number or time from the production line in a factory. This research performed WT at the signal preprocessing stage; the change points and control limits are then determined for each segment and the R-wave location is rechecked by spreading the points at the decision stage. The proposed procedures determine the change points and control limits for each segment. This method can be used to eliminate high-frequency noise, baseline shifts and artifacts from ECG signals, and R-waves can be effectively detected. In addition, there is flexibility in parameter value selection and robustness over wider noise ranges for the proposed QRS detection method.

A Wireless Text Messaging System Improves Communication for Neonatal Resuscitation

2016 ◽  
Vol 32 (3) ◽  
pp. 307-312 ◽  
Author(s):  
Colleen A. Hughes Driscoll ◽  
Jamie A. Schub ◽  
Kristi Pollard ◽  
Dina El-Metwally
Keyword(s):  
High Risk ◽  
Process Control ◽  
Amniotic Fluid ◽  
Statistical Process Control ◽  
Text Messaging ◽  
Neonatal Resuscitation ◽  
Statistical Process ◽  
Time Control ◽  
Control Limits ◽  
Resuscitation Team

Handoffs for neonatal resuscitation involve communicating critical delivery information (CDI). The authors sought to achieve ≥95% communication of CDI during resuscitation team requests. CDI included name of caller, urgency of request, location of delivery, gestation of fetus, status of amniotic fluid, and indication for presence of the resuscitation team. Three interventions were implemented: verbal scripted handoff, Spök text messaging, and Engage text messaging. Percentages of CDI communications were analyzed using statistical process control. Following implementation of Engage, the communication of all CDI, except for indication, was ≥95%; communication of indication occurred 93% of the time. Control limits for most CDI were narrower with Engage, indicating greater reliability of communication compared to the verbal handoff and Spök. Delayed resuscitation team arrival, a countermeasure, was not higher with text messaging compared to verbal handoff ( P = 1.00). Text messaging improved communication during high-risk deliveries, and it may represent an effective tool for other delivery centers.

scholarly journals Dynamic Risk Analysis in Metro Construction Using Statistical Process Control

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yichen Wang ◽  
Hong Zheng ◽  
Xinyue Lu
Keyword(s):  
Risk Analysis ◽  
Process Control ◽  
Dynamic Analysis ◽  
Statistical Process Control ◽  
Control Measures ◽  
Risk Level ◽  
Time Interval ◽  
Statistical Control ◽  
Statistical Process ◽  
And Control

Metro construction is normally carried out in complex engineering geological environment, so it can generate various risk events. In the process of metro construction, a scientific risk dynamic analysis is indispensable to reduce and control risks. In order to analyze the risk in metro construction more scientifically and reasonably, in this study, a new risk dynamic analysis method for metro construction is proposed using statistical process control. The method can analyse the risk level according to the process’s capacity index and identify the characteristics of risk variation according to the statistical control chart. The risk level and the characteristic of risks may vary with dynamical updating of monitoring data, so the conclusion of risk evaluation for a time interval can be drawn and corresponding safety measures can be ascertained. The method ushers statistical process control, so the random factors in risk evolution can be considered fully. Then, the method is applied to the risk analysis of shield construction under the Beijing-Tianjin intercity railway in Beijing Metro Line 8, a typical risk problem in the traffic construction. The variation of the risk level and the characteristic of risks can be evaluated reasonably because the dynamical randomness is considered. Moreover, whether risk control measures should be taken and what the effective measures are can be ascertained explicitly.

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.

Statistical Process Control Technology Applied in ERP System

2011 ◽  
Vol 421 ◽  
pp. 461-464 ◽  
Author(s):  
Ying Ji Li ◽  
Wei Xi Ji
Keyword(s):  
Process Control ◽  
Production Process ◽  
Statistical Process Control ◽  
Control Chart ◽  
Nuclear Power ◽  
Analytical Data ◽  
Quality Data ◽  
Control Technology ◽  
Statistical Process ◽  
And Control

For the high and strict quality requirement in the manufacturing process of nuclear power parts, this paper is based on the combination of Statistical Process Control technology and the ERP quality management and control the production quality based on the control chart. PowerBuilder 9.0 and SQL Server2000 were used to design and develop the system while PowerBuilder 9.0 as front-end development tool and SQL Server2000 as back-end DBMS respectively. Firstly, collect the quality data of the production process (some important processes). Then, analysis these data and form control chart. Real-time monitor production process by the control charting to ensure the process is stability. Organic combination of SPC and ERP to improve and control the quality, not only enrich the analytical data of SPC, but also make up the ERP data to analysis and control quality data.

STATISTICAL PROCESS CONTROL FOR LOW NONCONFORMITY PROCESSES

1995 ◽  
Vol 02 (01) ◽  
pp. 15-22 ◽  
Author(s):  
T.N. GOH ◽  
M. XIE
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Exact Probability ◽  
Statistical Process ◽  
Shewhart Control Charts ◽  
Shewhart Control ◽  
Cumulative Count ◽  
Pattern Recognition Approach ◽  
Control Limits

Statistical process control of high quality products is an important issue in modern quality control applications because of the success of continuous improvement efforts worldwide. The conventional Shewhart control charts based on 3-sigma control limits tend to encounter certain practical and theoretical problems as “zero-defect” is approached. In this paper, we describe some general approaches to solving this problem, focusing on the control charts for nonconformities or defects. We suggest that for a moderate nonconformity process, the exact probability limits should be used. For a lower non-conformity process, a “pattern recognition” approach can be applied. Finally, for a near-zero nonconformity process, a modified approach based on the cumulative count of nonconformities can be used.

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.

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