scholarly journals Application of Statistical Process Control (SPC) in Manufacturing Industry in a Developing Country

Procedia CIRP ◽  
2016 ◽  
Vol 40 ◽  
pp. 580-583 ◽  
Author(s):  
Ignatio Madanhire ◽  
Charles Mbohwa
Keyword(s):  
Process Control ◽  
Developing Country ◽  
Statistical Process Control ◽  
Manufacturing Industry ◽  
Statistical Process

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>

scholarly journals Implementation of Statistical Process Control through PDCA Cycle to Improve Potential Capability Index of Drop Impact Resistance: A Case Study at Aluminum Beverage and Beer Cans Manufacturing Industry in Indonesia

2020 ◽  
Vol 24 (1) ◽  
pp. 104 ◽  
Author(s):  
Sunadi Sunadi ◽  
Humiras Hardi Purba ◽  
Sawarni Hasibuan
Keyword(s):  
Process Control ◽  
Statistical Process Control ◽  
Manufacturing Industry ◽  
Impact Resistance ◽  
Process Capability ◽  
Drop Impact ◽  
Nominal Group ◽  
Statistical Process ◽  
Capability Index ◽  
Pdca Cycle

<p><strong>Purpose:</strong> The purposes of this study are first, to analyze why the <em>process capability index </em>(<em>Cpk</em>) for drop impact resistance (DIR) does not meet the specification or less than 1.33, and second, to find out what improvements should be made to make it meet the specification.</p><p><strong>Methodology/Approach:</strong> The methodology used was Statistical Process Control (SPC) through the PDCA cycle, supporting with Cause and Effect Diagram (CED), Nominal Group Technique (NGT) and “why, what, where, when and how (5W1H)” method.</p><p><strong>Findings:</strong> With the above methods, the result of the study was given a positive impact on the company. The average of DIR was increased from 20.40 cm to 25.76 cm, increased by 26.27% and the standard deviation was reduced from 1.80 to 1.48, and then the<em> Cpk</em> index was increased from 0.48 to 1.79 it means the process is in control and capable.</p><p><strong>Research Limitation/implication:</strong> This research was limited only on the two-piece can aluminum cans manufacturing process, no for three-piece cans manufacturing. SPC through PDCA cycle is an interesting method for continuous improvement of process capability in the cans manufacturing industry.</p><strong>Originality/Value of paper:</strong> This study highlights the area of future research SPC through the PDCA cycle to analyze and optimize process capability. Therefore, this research is considered to promote and adopt high-valued methodologies for supporting industry to achieve global competitive advantages.

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.

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