Clustering of similar processes for the application of statistical process control in small batch and job production

Wiederhold, Michael; Greipel, Jonathan; Ottone, Renato; Schmitt, Robert

doi:10.1051/ijmqe/2016018

Cited by 8 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An annual schedule for both internal and external verification of critical equipment has been set up [27]. For example: weighing-scales identified as critical are periodically checked in-house with calibration weights and control charts [28][29][30][31][32][33]. The weighing-scales are also verified annually by an external service-provider.…”

Section: Machinery and Measurementmentioning

confidence: 99%

An overview of Quality Management System implementation in a research laboratory

Molinéro-Demilly

Charki

Jeoffrion

et al. 2018

Int. J. Metrol. Qual. Eng.

View full text Add to dashboard Cite

Abstract. The aim of this paper is to show the advantages of implementing a Quality Management System (QMS) in a research laboratory in order to improve the management of risks specific to research programmes and to increase the reliability of results. This paper also presents experience gained from feedback following the implementation of the Quality process in a research laboratory at INRA, the French National Institute for Agronomic Research and details the various challenges encountered and solutions proposed to help achieve smoother adoption of a QMS process. The 7Ms (Management, Measurement, Manpower, Methods, Materials, Machinery, Mother-nature) methodology based on the Ishikawa 'Fishbone' diagram is used to show the effectiveness of the actions considered by a QMS, which involve both the organization and the activities of the laboratory. Practical examples illustrate the benefits and improvements observed in the laboratory.

show abstract

Section: Machinery and Measurementmentioning

confidence: 99%

An overview of Quality Management System implementation in a research laboratory

Molinéro-Demilly

Charki

Jeoffrion

et al. 2018

Int. J. Metrol. Qual. Eng.

View full text Add to dashboard Cite

show abstract

“…Change point models [5], control charts with greater sensitivity [6], self-starting charts [7], pre-control-charts [8], Bayesian approaches [9], control charting process and parameters instead of product features [10] are exclusively for control charts. Normalizing and grouping of similar features (grouping) [11] as well as bootstrapping [12] are methods for the general application of SPC. This is advantageous, as we cannot only use the enlarged dataset for control charts but also for more informative process capability analyses with more reliable confidence limits due to an enlarged sample size.…”

Section: Introductionmentioning

confidence: 99%

“…SPC is obliged to a great number of data of the corresponding products to achieve significant statements on which we can base decisions. Therefore, for low volume production, grouping is a solution to achieve the required data [14]. When grouping data, we add a third type of variation: variation due to grouping.…”

Section: Introductionmentioning

confidence: 99%

“…• Similar features are features of the same type produced by processes of the same technology and have quality characteristics with the same distribution, homogenous spread and location [11]. • Grouping of similar features means combining similar features descending from different products [11].…”

Section: Introductionmentioning

confidence: 99%

“…• Grouping algorithms automatically identify and combine similar features to increase the database by using historical measurement data of similar features and statistical methods [11].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparison of grouping algorithms to increase the sample size for statistical process control

2020

View full text Add to dashboard Cite

In this study, we present and compare four grouping algorithms to combine samples from low volume production processes. This increases their sample sizes and enables the application of Statistical Process Control (SPC) to low volume production processes. To develop the grouping algorithms, we define different grouping criteria and a general grouping process. To identify which algorithm is optimal, we deduct following requirements on the algorithms from real production datasets: their ability to handle different amount of characteristics and sample sizes within each characteristic as well as being able to separate characteristics possessing distributions with different spreads and locations. To check the fulfillment of these requirements, we define two performance indices and conduct a full-factorial Design of Experiments. We achieve the performance indices for each algorithm by using simulations with artificial data incorporating the aforementioned requirements. One index rates the achieved group sizes and the other one the compactness within groups and the separation between groups. To validate the applicability of grouping algorithms within SPC, we apply real production data to the grouping algorithms and control charts. The result of this analysis shows that the grouping algorithm based on cluster analysis and splitting exceeds the other algorithms. In conclusion, the grouping algorithms enable the application of SPC to small sample sizes. This provides companies, which produce in low volumes, with new means of reducing scrap, generating process knowledge and increasing quality.

show abstract

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Kostyszyn

Brandstätter

Vollmer

et al. 2021

Quality & Reliability Eng

Self Cite

View full text Add to dashboard Cite

With ISO 7870-8, a standardized application of charting techniques for short runs and small mixed batches was presented in 2017. Similar to various scientific approaches, it requires that sample values from grouped processes follow nearly identical distributions. In practice, however, there tend to be differences between distribution parameters. Moreover, equal parameters do not ensure that distributions are properly aligned to the center line and control limits of the chart. These facts can lead to undesired control chart performances which can be expressed by average run lengths (ARL) during in-control and out-of-control conditions. In this work, a statistical test for sufficient control chart performances during monitoring of grouped processes based on preliminary samples is proposed. Control chart performances are defined as sufficient when they deviate within acceptable ranges from usual performances during single process monitoring in mass production. The ARL resulting from estimated distributions and planned production sequences is used as test statistic and calculated via the Markov chain approach. Exemplary tests are executed for scenarios with individuals and cumulated sum (CUSUM) charts. A simulative determination of error rates resulting from the ARL-based testing demonstrates its effectiveness in testing for sufficient control chart performances compared to an indirect testing with Levene's test and a oneway analysis of variance (ANOVA). K E Y W O R D Saverage run length, control chart performances, grouped processes monitoring, ISO 7870-8, short run SPC This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Clustering of similar processes for the application of statistical process control in small batch and job production

Cited by 8 publications

References 13 publications

An overview of Quality Management System implementation in a research laboratory

An overview of Quality Management System implementation in a research laboratory

Comparison of grouping algorithms to increase the sample size for statistical process control

Statistical testing for sufficient control chart performances during monitoring of grouped processes

Contact Info

Product

Resources

About