scholarly journals Stability assessment and process capability analysis in a food pasta company

Author(s):  
Roxana González Álvarez ◽  
Aníbal Barrera García ◽  
Ana Beatriz Guerra Morffi ◽  
Juan Felipe Medina Mendieta

Statistical quality control is a set of tools and techniques that allows to verify, monitor and control the variability of processes to improve product quality and business competitiveness. The objective of this study was to evaluate the pasta production process of a company that belongs to the food industry sector in terms of stability and compliance of quality specifications. The Six Sigma improvement methodology was used, which focuses on identifying and eliminating the causes of variation in the processes. Data collection was accomplished by the use of different techniques, such as: interviews, brainstorming, review of documents, teamwork and direct observation. In addition, process documentation techniques and classical quality tools including Pareto chart, control charts, process capability analysis, histogram, Ishikawa diagram and experimental design were used. Multivariate data reduction techniques were also applied. The results showed for the quality characteristic Humidity that the process is out of statistical control and it is uncapable to meet the required specifications, for which the causes were investigated and improvement actions were proposed, achieving an increase in the sigma quality level.

2021 ◽  
Vol 25 (8) ◽  
pp. 1477-1482
Author(s):  
O.F. Odeyinka ◽  
F.O. Ogunwolu ◽  
O.P. Popoola ◽  
T.O. Oyedokun

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.


Author(s):  
Fernanda Siqueira Souza ◽  
Danilo Cuzzuol Pedrini ◽  
Carla Schwengber Ten Caten

Process capability analysis is extremely important for optimization and quality improvement. It verifies whether the process under analysis is capable of producing items within engineering and customers’ specifications. The use of capability indices when assumptions are not satisfied leads to erroneous conclusions, compromising the study and analysis of the process, jeopardizing the fulfillment of requirements from management or external customers. Aiming at filling a gap identified in the literature, the main contributions of this work are: (i) proposition of capability indices for processes monitored through control charts based on regression models, for symmetric and asymmetric specifications; and (ii) comparison of the proposed indices with traditional capability indices through a simulated process.


Author(s):  
Dereje Girma ◽  
Omprakash Sahu

Identifying the presence and understanding the causes of process variability are key requirements for well controlled and quality manufacturing. This pilot study demonstrates the introduction of Statistical Process Control (SPC) methods to the spinning department of a textile manufacturing company. The methods employed included X Bar and R process control charts as well as process capability analysis. Investigation for 29 machine processes identified that none were in statistical control. Recommendations have been made for a repeat of the study using validated data together with practical application of SPC and control charts on the shop floor and extension to all processes within the factory.


2020 ◽  
Vol 9 (1) ◽  
pp. 87-97
Author(s):  
Nathasa Erdya Kristy ◽  
Mustafid Mustafid ◽  
Sudarno Sudarno

In quality assurance of hexagonal paving block products, quality control is needed so the products that produced are in accordance with the specified standards. Quality control carried out involves two interconnected quality characteristics, that is thickness and weight of hexagonal paving blocks, so multivariate control chart is used. Improved Generalized Variance control chart is a tool used to control process variability in multivariate manner. Variability needs to be controlled because of in a production process, sometimes there are variabilities that caused by engine problems, operator errors, and deffect in raw materials that affect the process. The purpose of this study is to apply Improved Generalized Variance control chart in controlling the quality of hexagonal paving block products and calculating the capability of production process to meet the standards. Based on the assumption of multivariate normal distribution test, it can be seen that the data of quality characteristics of hexagonal paving blocks have multivariate distribution. While based on the correlation test between variables it can be concluded that the characteristics of the quality of thickness and weight correlate with each other. The result of the control using these control chart shows that the process is statistically in control. The results of process capability analysis show that the production process has been running according to the standard because the process capability index value is generated using a weighting of 0.5 for each quality characteristic that is 1.01517. Keywords: Paving Block, Quality Control, Variability, Improved Generalized Variance, Process Capability Analysis


2014 ◽  
Vol 670-671 ◽  
pp. 1588-1592
Author(s):  
Xiao Qing Wang ◽  
Bing Liu ◽  
Yong Gui Liu

This paper studies the convenient application of statistical control methods in quality control, which includes selecting Pareto chart, line chart, process capability analysis, correlation and regression analysis, orthogonal test and the control chart. The purpose, range and operation steps of these methods are in practical use in the textile industry. Furthermore, this paper describes the application of control chart by using standard operation procedure.


2010 ◽  
Vol 3 (S1) ◽  
pp. 531-534
Author(s):  
Maja Rujnić-Sokele ◽  
Mladen Šercer ◽  
Damir Godec

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