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.

Statistical process control application on service quality using SERVQUAL and QFD with a case study in trains’ services

2016 ◽  
Vol 28 (2) ◽  
pp. 195-215 ◽  
Author(s):  
Hadi Akbarzade Khorshidi ◽  
Sanaz Nikfalazar ◽  
Indra Gunawan
Keyword(s):  
Process Control ◽  
Service Quality ◽  
Statistical Process Control ◽  
Control Charts ◽  
Quality Function Deployment ◽  
Process Capability ◽  
Statistical Process ◽  
Content Type

Purpose – The purpose of this paper is to implement statistical process control (SPC) in service quality using three-level SERVQUAL, quality function deployment (QFD) and internal measure. Design/methodology/approach – The SERVQUAL questionnaire is developed according to internal services of train. Also, it is verified by reliability scale and factor analysis. QFD method is employed for translating SERVQUAL dimensions’ importance weights which are derived from Analytic Hierarchy Process into internal measures. Furthermore, the limits of the Zone of Tolerance are used to determine service quality specification limits based on normal distribution characteristics. Control charts and process capability indices are used to control service processes. Findings – SPC is used for service quality through a structured framework. Also, an adapted SERVQUAL questionnaire is created for measuring quality of train’s internal services. In the case study, it is shown that reliability is the most important dimension in internal services of train for the passengers. Also, the service process is not capable to perform in acceptable level. Research limitations/implications – The proposed algorithm is practically applied to control the quality of a train’s services. Internal measure is improved for continuous data collection and process monitoring. Also, it provides an opportunity to apply SPC on intangible attributes of the services. In the other word, SPC is used to control the qualitative specifications of the service processes which have been measured by SERVQUAL. Originality/value – Since SPC is usually used for manufacturing processes, this paper develops a model to use SPC in services in presence of qualitative criteria. To reach this goal, this model combines SERVQUAL, QFD, normal probability distribution, control charts, and process capability. In addition, it is a novel research on internal services of train with regard to service quality evaluation and process control.

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.

Application of statistical process control in service industry

2016 ◽  
Vol 11 (3) ◽  
pp. 763-782 ◽  
Author(s):  
Nima Mirzaei ◽  
Sadegh Niroomand ◽  
Rahim Zare
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Service Sector ◽  
Service System ◽  
Statistical Process ◽  
Pareto Chart ◽  
Content Type ◽  
Number Of Customers

Purpose This study aims to apply statistical process control (SPC) techniques to improve the quality and efficiency of the processes in a restaurant. Design/methodology/approach SPC tools such as check sheet, cause-and-effect analysis, Pareto chart, control charts and SERVQUAL methodology is adapted to measure and improve the quality of the system. Findings At the end, some suggestions for improving the quality of service system are proposed in this study to complete the research. Research limitations/implications The most difficult part of this study was data collection. Because of the situation of the restaurant, the number of customers does not exceed 60 every day. Another limitation of this study is that the samples have been collected from the same population each day, and it may affect the final result. Practical implications The research is based on the present service system at a restaurant, located at a university campus in Cyprus. Social implications A similar study can be applied in the social sector to evaluate and improve service quality. Originality/value In this paper, for the first time, SPC and SERVQUAL are used to evaluate and improve quality in the service sector.

scholarly journals Critical observations on the statistical process control implementation in the UK food industry

2017 ◽  
Vol 34 (5) ◽  
pp. 684-700 ◽  
Author(s):  
Sarina Abdul Halim Lim ◽  
Jiju Antony ◽  
Zhen He ◽  
Norin Arshed
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Food Industry ◽  
Statistical Tests ◽  
Future Research ◽  
Manufacturing Companies ◽  
Statistical Process ◽  
Food Manufacturing ◽  
Content Type ◽  
The Uk

Purpose Statistical process control (SPC) is widely applied for control and improve processes in manufacturing, but very few studies have reported on the successful application of SPC in the food industry, in particular. The purpose of this paper is to critically assess the status of SPC in the UK food manufacturing industry and to suggest future research avenues. Design/methodology/approach A research project was carried out in the UK food manufacturing sector through questionnaires. The results of the study were analysed using descriptive statistics and statistical tests to be applied in the hypothesis testing. Findings Findings revealed that 45 per cent of the respondents are SPC users and x ¯ -R and x ¯ -S charts are the most commonly applied SPC charts in this industry. It was determined that top management commitment is the most critical factor, while lack of SPC training is the most alarming challenge, and lack of awareness of SPC and its benefits are the main reasons for the food companies not implementing SPC. Research limitations/implications The study considered only the food manufacturing companies. Future research could be addressed toward the food service and food supply chain. Practical implications The paper provides information to food companies in the UK on most common practiced and important quality tools, SPC charts and critical success factors in the food industry. Moreover, the most challenging factors of SPC implementation in the food industry are presented. Originality/value This study depicted the current state of SPC practices in the food industry and the process performance in SPC and non-SPC companies is compared.

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