scholarly journals Application of Statistical Quality Control in Monitoring the Production, Packaging and Marketing Process of Sachet Water

2020 ◽  
pp. 32-45
Author(s):  
Terna Godfrey Ieren ◽  
Samson Kuje ◽  
Abraham Iorkaa Asongo ◽  
Innocent Boyle Eraikhuemen
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Statistical Quality Control ◽  
Statistical Control ◽  
Statistical Process ◽  
Marketing Process ◽  
Product Sales ◽  
Sachet Water

Statistical process control is a technique employed to enhance the quality and productivity of processes and the distribution or marketing of its products. Sachet water is a product that has become popular and is being used as a replacement for lack of potable water. It is an alternative that is readily available, affordable but with questions about its purity, production and marketing processes. The objective of this study is to apply statistical control charts in monitoring the production, packaging and distribution or marketing processes of sachet water in Nigeria. This paper employed statistical quality control approach to monitor process stability in a Table Water manufacturing company. Quality control tools such as p-chart, u-chart, X-bar and R charts as well as process capability chart were use to observed field data obtained from the sachet water manufacturing company on important processes of sachet water production and marketing for 30 working days. This was done to check if the processes were in control or out of control and to verify the capability of the marketing process of the product meeting preset specifications. With this, the statistical control charts suitable for the processes were constructed using package “qcc” in R software version 3.6.1. The results from p-chart and u-chart showed that the production and packaging process of the product is not in control and hence the need for further investigations and corrective measures to prevent variability in the process and thus allowing improvement in the quality of the product. Also, the results from X-bar and R charts showed that the marking process was in statistical process control in respects of the product sales recorded by the four independent marketers, with no assignable cause of variation. It also revealed that, the product marketing process has low capability of successfully attending the preset specification limits in respect of the product sales and hence generating low profit for the company.

Inspection in Process Control

1998 ◽  
Vol 42 (16) ◽  
pp. 1170-1174
Author(s):  
Somchart Thepvongs ◽  
Brian M. Kleiner
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Signal Detection ◽  
Control Chart ◽  
Control Charts ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Statistical Quality Control ◽  
Total Quality ◽  
Statistical Process

Consistent with the precepts of total quality control and total quality management, there has been a resource shift from incoming and outgoing inspection processes to statistical quality control of processes. Furthermore, process control operators are responsible for their own quality, necessitating the in-process inspection of components. This study treated the statistical process control task of “searching” control charts for out-of-control conditions as an inspection task and applied the Theory of Signal Detection to better understand this behavior and improve performance. Twelve subjects participated in a research study to examine how the portrayal of control chart information affected signal detection theory measures. The type of display did not have a significant effect on the sensitivity and response criterion of subjects. These results are discussed in terms of the applicability of Signal Detection Theory in control chart decision making as well as implications on display design.

scholarly journals Improving Process Performance of Cotton Spinning by Using Statistical Process Control Techniques

2020 ◽  
pp. 15-22
Author(s):  
Dereje Girma ◽  
Omprakash Sahu
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Shop Floor ◽  
Statistical Control ◽  
Statistical Process ◽  
Process Capability Analysis ◽  
Capability Analysis ◽  
R Process ◽  
And Control

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.

PACS Monitoring

2010 ◽  
pp. 244-263
Author(s):  
Carrison K.S. Tong ◽  
Eric T.T. Wong
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Continuous Process ◽  
Descriptive Statistics ◽  
Statistical Techniques ◽  
Statistical Control ◽  
Executive Management ◽  
Statistical Process

The present study advocates the application of statistical process control (SPC) as a performance monitoring tool for a PACS. The objective of statistical process control (SPC) differs significantly from the traditional QC/QA process. In the traditional process, the QC/QA tests are used to generate a datum point and this datum point is compared to a standard. If the point is out of specification, then action is taken on the product and action may be taken on the process. To move from the traditional QC/QA process to SPC, a process control plan should be developed, implemented, and followed. Implementing SPC in the PACS environment need not be a complex process. However, if the maximum effect is to be achieved and sustained, PACSSPC must be implemented in a systematic manner with the active involvement of all employees from line associates to executive management. SPC involves the use of mathematics, graphics, and statistical techniques, such as control charts, to analyze the PACS process and its output, so as to take appropriate actions to achieve and maintain a state of statistical control. While SPC is extensively used in the healthcare industry, especially in patient monitoring, it is rarely applied in the PACS environment. One may refer to a recent SPC application that Mercy Hospital (Alegent Health System) initiated after it implemented a PACS in November 2003 (Stockman & Krishnan, 2006). The anticipated benefits characteristic to PACS through the use of SPC include: • Reduced image retake and diagnostic expenditure associated with better process control. • Reduced operating costs by optimizing the maintenance and replacement of PACS equipment components. • Increased productivity by identification and elimination of variation and outof- control conditions in the imaging and retrieval processes. • Enhanced level of quality by controlled applications. SPC involves using statistical techniques to measure and analyze the variation in processes. Most often used for manufacturing processes, the intent of SPC is to monitor product quality and maintain processes to fixed targets. Hence besides the HSSH techniques, the proposed TQM approach would include the use of SPC. Although SPC will not improve the reliability of a poorly designed PACS, it can be used to maintain the consistency of how the individual process is provided and, therefore, of the entire PACS process. A primary tool used for SPC is the control chart, a graphical representation of certain descriptive statistics for specific quantitative measurements of the PACS process. These descriptive statistics are displayed in the control chart in comparison to their “in-control” sampling distributions. The comparison detects any unusual variation in the PACS delivery process, which could indicate a problem with the process. Several different descriptive statistics can be used in control charts and there are several different types of control charts that can test for different causes, such as how quickly major vs. minor shifts in process means are detected. These control charts are also used with service level measurements to analyze process capability and for continuous process improvement efforts.

Application of Statistical Process Control Tools to Improve Product Quality in Manufacturing Processes

2011 ◽  
Vol 110-116 ◽  
pp. 4023-4027
Author(s):  
Omar Bataineh ◽  
Abdullah Al-Dwairi
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Charts ◽  
Process Capability ◽  
Manufacturing Processes ◽  
Statistical Process ◽  
Free Products ◽  
Drug Companies

Quality control and improvement at the process level is a vital activity for the achievement of defect-free products in various manufacturing processes. This study employs statistical process control (SPC) tools such as control charts and process capability ratio for quality control and improvement. The control charts employed are , R and the cumulative-sum (CUSUM). The process capability ratio used is the so called process capability index (PCI). These tools have been implemented with the aid of Minitab® statistical software. In this study, the manufacturing process of gelatin capsules is investigated in terms of quality of the capsules, which are produced and shipped for use by various drug companies. As a result of implementation of SPC tools, an expected reduction in the number of defective capsules by 29% relative to the stage before implementation was achieved.

scholarly journals Tokat-Kazova’da Mısır Yetiştiriciliğinde Ekim Kalitesinin Değerlendirilmesinde İstatistiksel Proses Kontrol Yaklaşımı

2020 ◽  
Vol 8 (12) ◽  
pp. 2664-2676
Author(s):  
Engin Özgöz ◽  
Ebubekir Altuntas ◽  
Abdullah Kasap
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Agricultural Production ◽  
Statistical Quality Control ◽  
Plant Spacing ◽  
Statistical Process ◽  
Process Plant ◽  
Plant Emergence

In this study, it was aimed to evaluate of the quality of the sowing process applied by 10 different producers in their fields in Tokat / Kazova, in the cultivation of grain corn, by Statistical Process Control. In determining of the quality of the sowing process, plant spacing uniformity, seeding depth uniformity and ratio of plant emergence were used. Acceptable plant spacing, the multiples ratio and the miss ratio were determined for planting spacing uniformity on the row. Plant emergence ratio values varied between 59.60% (D field) and 93.13% (B field) in the trial areas. Acceptable plant spacing, the multiples ratio and the miss ratio values ranged from 65.56% - 90.24%, 4.02 - 15.69% and 5.11% - 12.91%, in the trial areas, respectively. It was concluded that the sowing process is generally ‘Moderate’ and ‘Inadequate’ according to the parameters of plant spacing uniformity. Similarly, individual and moving range quality charts prepared for statistical quality control of plant spacing uniformity and seeding depth uniformity showed that the sowing process was not under control. According to these results, it is necessary to take precautions regarding the factors that negatively affect the quality of the sowing process for sustainable agricultural production.

scholarly journals Analisis Pengendalian Kualitas Pada Pengolahan Produk Kulit Dengan Metode Statistical Process Control Pada UMKM Paris Leather

2021 ◽  
Vol 20 (2) ◽  
pp. 167
Author(s):  
Dimas Ari Nuryanto ◽  
Widya Setiafindari
Keyword(s):  
Quality Control ◽  
Process Control ◽  
Statistical Process Control ◽  
Statistical Quality Control ◽  
Statistical Process ◽  
Statistical Quality ◽  
Quality Control Check ◽  
Reward And Punishment

<p class="Abstrak">Pada proses produksi kulit pada UMKM Paris Leather masih terdapat kecacattan saat proses pewarnaan kulit dan proses pembuatan produk kulit, dimana proses produksi kulit tidak sesuai dengan standar kecacatan produk yang telah ditentukan oleh UMKM Paris Leather. Dari data produksi produk kulit pada bulan Januari s.d Februari 2021 mencapai 933 produk lembaran kulit, 173 produk tas dan 155 produk jaket, masih terdapat produk cacat pada produksi dengan jumlah rata-rata cacat produk kulit lembaran 5 % per hari, produk tas 4 % per hari dan produk jaket 3 % per hari dengan jumlah produk cacat yang terjadi perlu dilakukan usulan perbaikan kualitas untuk mengurangi cacat pada produk kulit, maka dalam meyelesaikan masalah tersebut perlu menganalisis apa saja jenis cacat dominan yang terjadi pada produksi kulit, kemudian mencari penyebab cacat pada produk dan pewarnaan yang terjadi di UMKM Paris Leather. Analisis data dilakukan dengan menggunakan teknik Statistical Quality Control, Check Sheet, Stratifikasi, Peta Kontrol, Diagram Pareto, Diagram Sebab-Akibat, Diagram Pencar, dan Histogram. Hasil dari penelitian ini menunjukan jenis kerusakan yang paling banyak terjadi pada proses produksi kulit adalah cacat jahit tidak rapi sebesar 50 % produk tas, 40 % produk jaket, dan proses perwarnaan kulit cacat lubang sebesar 50 %. Usulan perbaikan sebaiknya dilakukan adalah mengkaji ulang proses pengoperasian mesin, menggunakan check sheet, melaksanakan training, memberlakukan sistem reward and punishment, pengecekan material dan mesin, dan bekerja sesuai SOP.</p><div><hr align="left" size="1" width="33%" /></div><p class="Abstrak"><strong>Kata kunci</strong>: Statistical Quality Control1, Produk Kulit2, UMKM Paris Leather3</p>

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