scholarly journals IOT Based Analyzation of Press Machine in Automation Industry

2020 ◽  
pp. 602-606
Author(s):  
Jagdish Patel ◽  
Komal Murtadak ◽  
Sayali Deore ◽  
Vaishnavi Thorat
Keyword(s):  
Process Control ◽  
Data Management ◽  
Continuous Improvement ◽  
Business Intelligence ◽  
Data Analytics ◽  
Process Industry ◽  
Valuable Resource ◽  
Modern Economy ◽  
Efficient Data ◽  
Asset Data

They say that companies that do not understand the importance of Analyzation are less likely to survive in the modern economy. Your data is your most valuable asset. Data management is important because the data your organization create is a very valuable resource. The last thing you want to do is spend time and resources collecting data and business intelligence, only to lose or misplace that information. In that case, you would then have to spend time and resources again to get that same business intelligence you already had. However, only well prepared and analyzed data leads to process knowledge and finally, to process control and continuous improvement. Thus, a robust and efficient data analytics strategy is one of the most valuable concepts for the process industry.

Mahakam Field Characterization Using Production Type-Curve With Business Intelligence Application

2020 ◽  
Author(s):  
P. Noverri
Keyword(s):  
Business Intelligence ◽  
Data Gathering ◽  
Production Data ◽  
Type Curve ◽  
Type Curves ◽  
Production Type ◽  
Efficient Data ◽  
Production Behavior ◽  
Factor Type ◽  
Mahakam Delta

Delta Mahakam is a giant hydrocarbon block which is comprised two oil fields and five gas fields. The giant block has been considered mature after production for more than 40 years. More than 2,000 wells have been drilled to optimize hydrocarbon recovery. From those wells, a huge amount of production data is available and documented in a well-structured manner. Gaining insight from this data is highly beneficial to understand fields behavior and their characteristics. The fields production characterization is analyzed with Production Type-Curve method. In this case, type curves were generated from production data ratio such as CGR, WGR and GOR to field recovery factor. Type curve is considered as a simple approach to find patterns and capture a helicopter view from a huge volume of production data. Utilization of business intelligence enables efficient data gathering from different data sources, data preparation and data visualization through dashboards. Each dashboard provides a different perspective which consists of field view, zone view, sector view and POD view. Dashboards allow users to perform comprehensive analysis in describing production behavior. Production type-curve analysis through dashboards show that fields in the Mahakam Delta can be grouped based on their production behavior and effectively provide global field understanding Discovery of production key information from proposed methods can be used as reference for prospective and existing fields development in the Mahakam Delta. This paper demonstrates an example of production type-curve as a simple yet efficient method in characterizing field production behaviors which is realized by a Business Intelligent application

TEM Sample Prep Tool Matching—DualBeam FIB Cut Look and Measure Monitoring for Process Control and Development in a 300 mm IC Fab

2016 ◽  
Author(s):  
Jangbae Jeon
Keyword(s):  
Process Control ◽  
Sample Preparation ◽  
Continuous Improvement ◽  
Process Time ◽  
Operational Monitoring ◽  
Thickness Control ◽  
Running Time ◽  
End Products ◽  
Novel Method

Abstract This work presents a novel method of continuous improvement for faster, better and cheaper TEM sample preparation using Cut Look and Measure (CLM). The improvement of the process is executed by operational monitoring of daily beam conditions, end products, bulk thickness control, recipe usage and tool running time. This process produces a consequent decrease in rework rate and process time. In addition, it also increases throughput with better quality TEM samples.

FDM: Fuzzy-Optimized Data Management Technique for Improving Big Data Analytics

2021 ◽  
Vol 29 (1) ◽  
pp. 177-185
Author(s):  
Gunasekaran Manogaran ◽  
P. Mohamed Shakeel ◽  
S. Baskar ◽  
Ching-Hsien Hsu ◽  
Seifedine Nimer Kadry ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Technique

Lessons Learned from Usace Seepage Barrier Wall Construction: Wolf Creek To Present

2018 ◽  
Vol 24 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Georgette Hlepas ◽  
Vanessa Bateman
Keyword(s):  
Data Management ◽  
Continuous Improvement ◽  
Lessons Learned ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Management Processes ◽  
Verification Methods ◽  
Efficiency And Effectiveness ◽  
Wall Construction ◽  
Barrier Wall

Abstract The U.S. Army Corps of Engineers (USACE) maintains a lessons-learned goal for all major projects to capture knowledge gained. The focus of the formal lessons-learned process is to share knowledge and experience nationwide improving USACE contracting methodologies, reducing overall costs, and improving designs. This continuous improvement can be seen in the evolution of USACE barrier wall construction designs and contracting methods. From the first Wolf Creek Dam barrier wall installed in the 1970s to the more recent Bolivar and East Branch Dam barrier wall projects, documentation and sharing of lessons learned in areas such as grouting, data management, and quality assurance procedures have increased the efficiency and effectiveness of barrier wall designs, monitoring, and contract specifications. Contractual philosophy, use of pre-grouting treatment, verification methods, and data management processes have all changed due to lessons learned and have enabled the USACE to improve the overall end product of barrier wall projects.

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