STATISTICAL PROCESS CONTROL USING CONTROL CHARTS

2011 ◽  
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Statistical Process

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.

scholarly journals Application of Statistical Process Control on Port State Control

2020 ◽  
Vol 8 (10) ◽  
pp. 746
Author(s):  
Chien-Chung Yuan ◽  
Wu-Hsun Chung ◽  
Cunqiang Cai ◽  
Shu-Te Sung
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Time Dependent ◽  
Inspection System ◽  
State Control ◽  
Statistical Process ◽  
Critical Measure ◽  
Low Profile ◽  
Port State Control

Port state control (PSC) inspection is a critical measure in maritime safety, preventing substandard ships from entering ports. However, the current inspection system lacks an effective tool to monitor ship inspections and to identify which factors cause abnormal instances. Although statistical process control (SPC) has been proven an effective tool in process control in various other industries, this study is a pioneer in exploring the application of SPC on maritime inspections. In this paper, the inspection records collected in Taiwan’s ports are utilized to construct control charts of inspection deficiencies so as to identify time-independent abnormal instances in inspections. Following that, a cause-and-effect diagram is conducted to trace the cause of those abnormal instances occurring at a specific time period. The results demonstrate that SPC is effective to monitor maritime inspections and is capable of identifying time-dependent abnormal instances. From a managerial and practical perspective, port administrations can use SPC to identify time-dependent abnormal instances and take proactive actions to mitigate the non-random phenomena in PSC inspections. To keep deficiency occurring in a stable and low profile, port administrations need persistent and consistent inspection requirements and avoid non-routine inspections. Meanwhile, inspectors with various specialties and backgrounds require consistent training.

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