On stochastic ordering and control charts for traﬃc intensity

For sequentially monitoring and controlling average and variability of an online manufacturing process, x¯ and s control charts are widely utilized tools, whose constructions require the data to be real (precise) numbers. However, many quality characteristics in practice, such as surface roughness of optical lenses, have been long recorded as fuzzy data, in which the traditional x¯ and s charts have manifested some inaccessibility. Therefore, for well accommodating this fuzzy-data domain, this paper integrates fuzzy set theories to establish the fuzzy charts under a general variable-sample-size condition. First, the resolution-identity principle is exerted to erect the sample-statistics’ and control-limits’ fuzzy numbers (SSFNs and CLFNs), where the sample fuzzy data are unified and aggregated through statistical and nonlinear-programming manipulations. Then, the fuzzy-number ranking approach based on left and right integral index is brought to differentiate magnitude of fuzzy numbers and compare SSFNs and CLFNs pairwise. Thirdly, the fuzzy-logic alike reasoning is enacted to categorize process conditions with intermittent classifications between in control and out of control. Finally, a realistic example to control surface roughness on the turning process in producing optical lenses is illustrated to demonstrate their data-adaptability and human-acceptance of those integrated methodologies under fuzzy-data environments.

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Performance comparison between on-line sensors and control charts Performance comparison between on-line sensors and control charts in manufacturing process monitoring

IIE Transactions ◽

10.1080/07408179908969918 ◽

1999 ◽

Vol 31 (12) ◽

pp. 1181-1190 ◽

Cited By ~ 2

Author(s):

KWEI TANG ◽

WILLIAM W. WILLIAMS ◽

WUSHONG JWO ◽

LINGUO GONG

Keyword(s):

Process Monitoring ◽

Manufacturing Process ◽

Control Charts ◽

Performance Comparison ◽

On Line ◽

And Control

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Estimation of production consistency of diphtheria, tetanus, and pertussis components of the DTP vaccine using Shewhart charts

BIOpreparations Prevention Diagnosis Treatment ◽

10.30895/2221-996x-2021-21-4-256-265 ◽

2021 ◽

Vol 21 (4) ◽

pp. 256-265

Author(s):

I. A. Alekseeva ◽

O. V. Perelygina ◽

E. D. Kolyshkina

Keyword(s):

Control Charts ◽

Large Population ◽

National Standards ◽

Statistical Control ◽

Technological Processes ◽

Shewhart Control ◽

Dtp Vaccine ◽

The Stability ◽

And Control ◽

Shewhart Charts

The Russian Federation puts special emphasis on vaccination-related issues, in accordance with the WHO recommendations. The fact that vaccination, in particular with the diphtheria, tetanus, and pertussis vaccine (DTP vaccine), covers large population groups, accounts for the relevance of research aimed at improving the quality of vaccines. One of the ways to produce vaccines of assured quality is to maintain consistent manufacturing processes that ensure consistency of product characteristics. The stability of the technological processes may be assessed using Shewhart charts. The aim of the study was to assess the production consistency of diphtheria, tetanus, and pertussis components of DTP vaccine using Shewhart control charts. Materials and methods: the study used data from 60 batch summary protocols of a Russian-produced DTP vaccine that were submitted to the Testing Centre of the Scientific Centre for Expert Evaluation of Medicinal Products from September 2017 until April 2020. The study assessed one of the main vaccine quality characteristics—specific (protective) activity of diphtheria, tetanus, and pertussis components. Shewhart charts for the diphtheria and tetanus components were constructed based on the manufacturer’s summary protocols, while Shewhart charts for the pertussis component were constructed based on both summary protocols and the results obtained by the Testing Centre during certification of the product batches. The Shewhart charts were used in accordance with the national standards GOST R 50779.42-99 and GOST R ISO 7870-2-2015. Results: a retrospective analysis of R- and X-charts covering a 2.5-year period revealed some characteristic trends in special-cause criteria. The most alarming situation was observed for the production of the diphtheria component. The technological processes were somewhat safer in the case of the tetanus and pertussis components. The production process lacked due statistical control, which is confirmed by the lack of correlation between the results of the pertussis component activity assessment obtained by the manufacturer and the Testing Centre. Conclusions: during the analysed period, the production of the diphtheria, tetanus, and pertussis components of the DTP vaccine was not always consistent. This highlights the need to conduct research aimed at standardisation of both production processes and control test conditions.

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Combining Capability Indices and Control Charts in the Process and Analytical Method Control Strategy

10.5772/intechopen.91354 ◽

2020 ◽

Author(s):

Alexis Oliva ◽

Matías Llabrés

Keyword(s):

Control Strategy ◽

Analytical Method ◽

Control Charts ◽

Process Capability ◽

Real Data ◽

Process Capability Indices ◽

Specification Limits ◽

Capability Indices ◽

And Control ◽

Control Limits

Different control charts in combination with the process capability indices, Cp, Cpm and Cpk, as part of the control strategy, were evaluated, since both are key elements in determining whether the method or process is reliable for its purpose. All these aspects were analyzed using real data from unitary processes and analytical methods. The traditional x-chart and moving range chart confirmed both analytical method and process are in control and stable and therefore, the process capability indices can be computed. We applied different criteria to establish the specification limits (i.e., analyst/customer requirements) for fixed method or process performance (i.e., process or method requirements). The unitary process does not satisfy the minimum capability requirements for Cp and Cpk indices when the specification limit and control limits are equal in breath. Therefore, the process needs to be revised; especially, a greater control in the process variation is necessary. For the analytical method, the Cpm and Cpk indices were computed. The obtained results were similar in both cases. For example, if the specification limits are set at ±3% of the target value, the method is considered “satisfactory” (1.22<Cpm<1.50) and no further stringent precision control is required.

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Process Capability Analysis in a Polypropylene Bag Manufacturing Company

Journal of Applied Sciences and Environmental Management ◽

10.4314/jasem.v25i8.30 ◽

2021 ◽

Vol 25 (8) ◽

pp. 1477-1482

Author(s):

O.F. Odeyinka ◽

F.O. Ogunwolu ◽

O.P. Popoola ◽

T.O. Oyedokun

Keyword(s):

Control Charts ◽

Process Capability ◽

Production Plant ◽

Specification Limit ◽

Process Capability Analysis ◽

Capability Analysis ◽

Vertically Aligned ◽

Lower Specification Limit ◽

And Control

Process capability analysis combines statistical tools and control charts with good engineering judgment to interpret and analyze the data representing a process. This work analyzes the process capability of a polypropylene bag producing company. The case study organization uses two plants for production and data was collected over a period of nine months for this study. Analysis showed that the output spread of plant 1 was greater than the specification interval spread which implies poor capability. There are non-conforming parts below the Lower Specification Limit (LSL: 500,000 metres) and above the Upper Specification Limit (USL: 600,000 metres) and that the output requires improvement. Similarly, the capability analysis of plant 2 shows that the overall output spread is greater than the specification interval spread (poor capability). The output centre in the specification and overall interval are vertically aligned, thus specifying that the output from plant 2 is also process centered and requires improvement. Recommendations were made to improve the outputs from each production plant.

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