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

2020 ◽  
Vol 2 (5) ◽  
Author(s):  
Jonathan Simon Greipel ◽  
Gina Nottenkämper ◽  
Robert Heinrich Schmitt
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Small Sample ◽  
The Other ◽  
Sample Sizes ◽  
Performance Indices ◽  
Statistical Process ◽  
Production Processes ◽  
Volume Production ◽  
Low Volume

Abstract 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.

scholarly journals PEMODELAN KERANGKA ADAPTIVE THRESHOLD UNTUK MEMONITOR PRODUKSI MINYAK SAWIT NASIONAL BERBASIS STATISTICAL PROCESS CONTROL DAN ARTIFICIAL NEURAL NETWORK-BACKPROPAGATION

2016 ◽  
Vol 11 (2) ◽  
pp. 113-122
Author(s):  
Wahyu Widji Pamungkas ◽  
Syamsul Maarif ◽  
Tun Tedja Irawadi ◽  
Yandra Arkeman
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Process Control ◽  
Statistical Process Control ◽  
Palm Oil ◽  
Historical Data ◽  
Adaptive Threshold ◽  
The Other ◽  
Production Volume ◽  
Statistical Process

Indonesia is the largest exporter of palm oil in the world, as the largest producer Indonesia still havemany problems. The problem caused by incomparable between the growth of upstream and downstreampalm oil industries. This impact to low added value of palm oil, then Indonesia exports palm oil in crudeform. On the other hand, On the other hand , orientation export of this commodity is also prone of barrier,because Indonesia was not the price setter of this commodity in the international market. Therefore it isimportant to monitor and predict the development of national palm oil production volume in order to takegood anticipation. This research develop a framework model adaptive threshold to monitor the growing ofnational palm oil production volume with techniques of statistical process control (SPC) and back propagationartificial neural network (ANN - BP) methods. Historical data production volume period from 1967 to 2015was used as a base of the behavior as data to determine the threshold and prediction volume for nextperiods. The formation of the threshold value was based on the behavior of the historical data, which areoriented by the epicenter of the average value in the last two periods .Through mapping of data historicalperiod values, existing and forecast values with adaptive threshold can show tolerant level for the threshold.Furthermore, based on the analysis, it is known that the prediction of 2016 to 2018 period, there will behappen the dynamics production volume of national palm oil within tolerance threshold. The values of thesepredictions generated from the simulation model predictions of ANN-BP with the level very good of validationmodel, demonstrated the level of squared errors is very small1 in the MSE = 0.00021136 with a degree ofoutput correlation and the target is very strong2 with R Validation is 99.98 percent.Keywords: adaptive threshold, statistical process control, artificial neural network, national palm oilproduction.

Bayesian networks for statistical process control with attribute data

2019 ◽  
Vol 36 (2) ◽  
pp. 232-256 ◽  
Author(s):  
Barry Cobb ◽  
Linda Li
Keyword(s):  
Process Control ◽  
Bayesian Networks ◽  
Statistical Process Control ◽  
Average Run Length ◽  
Large Change ◽  
Small Sample ◽  
Statistical Process ◽  
Content Type ◽  
Average Percentage ◽  
Attribute Data

PurposeBayesian networks (BNs) are implemented for monitoring a process via statistical process control (SPC) where attribute data are available on output from the system. The paper aims to discuss this issue.Design/methodology/approachThe BN provides a graphical and numerical tool to help a manager understand the effect of sample observations on the probability that the process is out-of-control and requires investigation. The parameters for the BN SPC model are statistically designed to minimize the out-of-control average run length (ARL) of the process at a specified in-control ARL and sample size.FindingsThe BN model outperforms adaptivenpcontrol charts in all experiments, except for some cases where only a large change in the proportion of sample defects is relevant. The BN is particularly useful when small sample sizes are available and when managers need to detect small changes in the proportion of defects produced by the process.Research limitations/implicationsThe BN model is statistically designed and parameters are chosen to minimize out-of-control ARL. Future advancements will address the economic design of BNs for SPC with attribute data.Originality/valueThe BNs allow qualitative knowledge to be combined with sample data, and the average percentage of defects can be modeled as a continuous random variable. The framework of the BN easily permits classification of the system operation into two or more states, so diagnostic analysis can be performed simultaneously with statistical inference.

Using Statistical Process Control Charts to Study Stuttering Frequency Variability During a Single Day

2013 ◽  
Vol 56 (6) ◽  
pp. 1789-1799 ◽  
Author(s):  
Hamid Karimi ◽  
Sue O’Brian ◽  
Mark Onslow ◽  
Mark Jones ◽  
Ross Menzies ◽  
...  
Keyword(s):  
Process Control ◽  
Control Systems ◽  
Statistical Process Control ◽  
Control Charts ◽  
Communication Satisfaction ◽  
The Other ◽  
Marked Variation ◽  
Statistical Process ◽  
Process Control Charts ◽  
Statistical Process Control Charts

Purpose Stuttering varies between and within speaking situations. In this study, the authors used statistical process control charts with 10 case studies to investigate variability of stuttering frequency. Method Participants were 10 adults who stutter. The authors counted the percentage of syllables stuttered (%SS) for segments of their speech during different speaking activities over a 12-hr day. Results for each participant were plotted on control charts. Results All participants showed marked variation around mean stuttering frequency. However, there was no pattern of that variation consistent across the 10 participants. According to control charts, the %SS scores of half the participants were indicative of unpredictable, out-of-control systems, and the %SS scores of the other half of the participants were not. Self-rated stuttering severity and communication satisfaction correlated significantly and intuitively with the number of times participants exceeded their upper control chart limits. Conclusions Control charts are a useful method to study how %SS scores might be applied to the study of stuttering variability during research and clinical practice. However, the method presents some practical problems, and the authors discuss how those problems can be solved. Solving those problems would enable researchers and clinicians to better plan, conduct, and evaluate stuttering treatments.

scholarly journals Basic Developments of Quality Characteristics Monitoring

2013 ◽  
Vol 2013 ◽  
pp. 1-8
Author(s):  
Shahryar Sorooshian
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Quality Characteristics ◽  
Comprehensive Understanding ◽  
Statistical Process ◽  
Production Processes ◽  
Control Theories

Process control tools are a widely used approach in many operations and production processes. Process control chart ranks as one of the most important theories used in these disciplines. This paper reviewed the bias of quality characteristics monitoring. Specifically, this study tries to provide a comprehensive understanding of theories of process control. The text starts with a theoretical review of statistical process control theories and follows by a technical introduction to developed tools for process control.

scholarly journals Use of quality tools to evaluate forest seedlings production in the nursery stage

2021 ◽  
Vol 8 (2) ◽  
pp. 1425-1432
Author(s):  
Rafael Eloy de Souza ◽  
Alfredo José dos Santos Junior ◽  
Alan Henrique Marques de Abreu ◽  
Natália Dias de Souza ◽  
Ananias Francisco Dias Júnior
Keyword(s):  
Process Control ◽  
Root Growth ◽  
Production Process ◽  
Statistical Process Control ◽  
Production System ◽  
Statistical Process ◽  
Production Processes ◽  
Definition Of ◽  
Quality Tools

The control of production processes can assist in the standardization of variability, reducing waste, and improving the quality of a service or product. Thus, this study aimed to analyze the non-conformities in a production system of forest seedlings from Atlantic Forest aiming at the standardization of the production system and adjustments for field cultivation. The definition of the attributes was made through a technical visit to the forestry nursery to know the location and the production process of the seedlings. For the process evaluation, statistical process control tools were used. The non-conformities analyzed were: coiled root growth, disintegrated substrates of plants, presence of roots fixed to the ground, presence of phytopathogen attack symptoms and/or herbivory and symptoms of nutritional deficiency. In general, variability was detected in the production process, compromising the success in planting the seedlings in the field, as well as their quality.

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