scholarly journals Application of Statistical Process Control Chart in Food Manufacturing Industry

2021 ◽  
Vol 4 (5) ◽  
pp. 82-87
Author(s):  
Noora Shrestha
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Manufacturing Industry ◽  
Statistical Process ◽  
Food Manufacturing ◽  
Food Manufacturing Industry

scholarly journals A study on the application of control chart in healthcare

2021 ◽  
Vol 36 ◽  
pp. 01001
Author(s):  
Yee Kam Seoh ◽  
Voon Hee Wong ◽  
Mahboobeh Zangeneh Sirdari
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Manufacturing Industry ◽  
Positive Impact ◽  
Healthcare Management ◽  
Healthcare Sector ◽  
Statistical Tool ◽  
Statistical Process

The most concerning issues in the healthcare system will always be quality control and quality improvement as they are significant to the health condition of the patient. A quality statistical tool such as statistical process control (SPC) charts will be efficient and highly effective in reducing the sources of variation within the healthcare process and in monitoring or controlling improvement of the process. The control chart is a statistical process control methodology designed to evaluate the process improvement or change in the manufacturing industry and is being implemented gradually in the healthcare sector. This will enable healthcare organizations to prevent unnecessary investment or spending in any changes that sound good but do not have any positive impact on real progress or improvement. When there is greater participation of humans in healthcare, the risks of error are also greater. Control charts help determine the source of error by differentiating the common and special cause of variation, each requiring a different response from healthcare management. This paper intends to deliver an overview of SPC theory and to explore the application of SPC charts by presenting a few examples of the implementation of control charts to common issues in the healthcare sector. After a brief overview of SPC in healthcare, the selection and construction of the two widely used control charts (Individuals and Moving Range chart, U chart) were adopted and illustrated by using the example from healthcare.

scholarly journals Statistical Process Control with Intelligence Based on the Deep Learning Model

2019 ◽  
Vol 10 (1) ◽  
pp. 308 ◽  
Author(s):  
Tao Zan ◽  
Zhihao Liu ◽  
Zifeng Su ◽  
Min Wang ◽  
Xiangsheng Gao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Deep Learning ◽  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Manufacturing Industry ◽  
Short Term Memory ◽  
Statistical Process ◽  
Control Chart Pattern ◽  
Chart Patterns

Statistical process control (SPC) is an important tool of enterprise quality management. It can scientifically distinguish the abnormal fluctuations of product quality. Therefore, intelligent and efficient SPC is of great significance to the manufacturing industry, especially in the context of industry 4.0. The intelligence of SPC is embodied in the realization of histogram pattern recognition (HPR) and control chart pattern recognition (CCPR). In view of the lack of HPR research and the complexity and low efficiency of the manual feature of control chart pattern, an intelligent SPC method based on feature learning is proposed. This method uses multilayer bidirectional long short-term memory network (Bi-LSTM) to learn the best features from the raw data, and it is universal to HPR and CCPR. Firstly, the training and test data sets are generated by Monte Carlo simulation algorithm. There are seven histogram patterns (HPs) and nine control chart patterns (CCPs). Then, the network structure parameters and training parameters are optimized to obtain the best training effect. Finally, the proposed method is compared with traditional methods and other deep learning methods. The results show that the quality of extracted features by multilayer Bi-LSTM is the highest. It has obvious advantages over other methods in recognition accuracy, despite the HPR or CCPR. In addition, the abnormal patterns of data in actual production can be effectively identified.

Effectiveness Indices for Statistical Process Control Chart Performance

2019 ◽  
Vol 27 (1) ◽  
pp. 221-226
Author(s):  
Siyuan F. Yang ◽  
Wei-Ting K. Chien
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Statistical Process

scholarly journals Monitoring of mechanical sugarcane harvesting through control charts

2015 ◽  
Vol 35 (6) ◽  
pp. 1079-1092 ◽  
Author(s):  
Murilo A. Voltarelli ◽  
Rouverson P. da Silva ◽  
Cristiano Zerbato ◽  
Carla S. S. Paixão ◽  
Tiago de O. Tavares
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Control Chart ◽  
Control Charts ◽  
Moving Average ◽  
Grinding Wheel ◽  
Individual Values ◽  
Statistical Process ◽  
Minas Gerais State ◽  
Weighted Moving Average

ABSTRACT Statistical process control in mechanized farming is a new way to assess operation quality. In this sense, we aimed to compare three statistical process control tools applied to losses in sugarcane mechanical harvesting to determine the best control chart template for this quality indicator. Losses were daily monitored in farms located within Triângulo Mineiro region, in Minas Gerais state, Brazil. They were carried over a period of 70 days in the 2014 harvest. At the end of the evaluation period, 194 samples were collected in total for each type of loss. The control charts used were individual values chart, moving average and exponentially weighted moving average. The quality indicators assessed during sugarcane harvest were the following loss types: full grinding wheel, stumps, fixed piece, whole cane, chips, loose piece and total losses. The control chart of individual values is the best option for monitoring losses in sugarcane mechanical harvesting, as it is of easier result interpretation, in comparison to the others.

scholarly journals Pengendalian Kualitas Produksi dengan Statistical Process Control (SPC)

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Mohamad Solihudin
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Industry ◽  
Product Quality Control ◽  
Standard Size ◽  
Statistical Process ◽  
Analysis Process ◽  
Keywords Quality Assurance

<p>PT. Surya Toto Indonesia Tbk. is one of the processing industry (manufacturing insudtry) with export and domestic market share. To keep the share, the company provides high quality assurance for products offered to consumers and the improvement of quality has always been the main point of fulfilling the production process. This study aims to determine what factors cause the occurrence of reject products are not standard sizes (UTS) and How can quality control with application approach Statitical Process Control (SPC) in machining section PT. Surya Toto Indonesia Tbk. to overcome reject nonstandard size (UTS). The results of this study indicate that the product quality control in machining section on the BNC-1machine, there are the highest reject percentage (11.80%) on part number S23059 with the highest type of rejection is not a standard size (uts) at a size of 9 ± 0,05mm. From the results of field observation and brainstorming, the factors that cause bad part (reject) is a factor of BNC-1 machines are old, worn collet locking bolt and fastener tool insert screws loose. The corrective action taken is to move the process of part number S23059 in production in BNA-DHY2 machine, after analysis process by the method of Statistical Process Control (SPC) concluded for capabilty process (CP) BNA-DHY2 machine excellent is 1,85.</p><p>Keywords: Quality assurance, manufacturing industry, quality control, Statistical Process Control</p>

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