Mitigating Vibration Induced Drillstring Failures Using Data Analytics: Workflow and Case Study

2016 ◽  
Author(s):  
Ankit Mirani ◽  
Robello Samuel
Keyword(s):  
Data Analytics ◽  
Using Data

Data analytics dilemma at Alpen Hotel

2019 ◽  
Vol 9 (2) ◽  
pp. 58-63
Author(s):  
Tammy Wee ◽  
Arif Perdana ◽  
Detlev Remy
Keyword(s):  
Customer Relationship Management ◽  
Data Analytics ◽  
Data Privacy ◽  
Hospitality Industry ◽  
Service Providers ◽  
Relationship Management ◽  
Customer Relationship ◽  
Using Data ◽  
Privacy And Confidentiality

Data analytics is currently the buzzword for the hospitality industry to stay ahead of their competitors. Service providers use data analytics to ensure their brand remains relevant for customers. Using data analytics in customer relationship management is a relatively novel initiative for the hospitality industry to enhance the efforts of customer relationship management. Obtaining customers’ data (i.e. customers’ hotel stay and preferences) provides both opportunity and challenges for the hospitality industry. Data analytics helps the hospitality industry to quickly, effectively, and efficiently pursue data-driven decision-making. At the same time, acquiring relevant customers’ data is a challenge, for example, data privacy and confidentiality. This case study is based on Alpen Hotel (pseudonym), a luxury hotel in Singapore with a good standing in the hospitality industry. This case is focused on the issues they experienced in implementing data analytics as part of the hotel’s customer relationship management efforts. This case study aims to highlight data analytics dilemma at the hotel and may create an opportunity for hospitality educators to work interdisciplinary with faculties from an information systems or technology discipline. Finally, the case study may enhance knowledge and minimise the practice gap between industry and academia.

Optimal Drawdown for Woodford and Mayes in the Anadarko Basin Using Data Analytics

2021 ◽  
pp. 1-11
Author(s):  
A. Alzahabi ◽  
A. Alexandre Trindade ◽  
A. A. Kamel ◽  
A. Harouaka ◽  
W. Baustian ◽  
...  
Keyword(s):  
Return On Investment ◽  
Data Analytics ◽  
Anadarko Basin ◽  
Well Production ◽  
Dependent Variables ◽  
New Strategy ◽  
Using Data ◽  
Well Log Analysis ◽  
Predictive Relationship

Summary One of the enduring pieces of the jigsaw puzzle for all unconventional plays is drawdown (DD), a technique for attaining optimal return on investment. Assessment of the DD from producing wells in unconventional resources poses unique challenges to operators; among them the fact that many operators are reluctant to reveal the production, pressure, and completion data required. In addition to multiple factors, various completion and spacing parameters add to the complexity of the problem. This work aims to determine the optimum DD strategy. Several DD trials were implemented within the Anadarko Basin in combination with various completion strategies. Privately obtained production and completion data were analyzed and combined with well log analysis in conjunction with data analytics tools. A case study is presented that explores a new strategy for DD producing wells within the Anadarko Basin to optimize a return on investment. We use scatter-plot smoothing to develop a predictive relationship between DD and two dependent variables—estimated ultimate recovery (EUR) and initial production (IP) for 180 days of oil—and introduce a model that evaluates horizontal well production variables based on DD. Key data were estimated using reservoir and production variables. The data analytics suggested the optimal DD value of 53 psi/D for different reservoirs within the Anadarko Basin. This result may give professionals additional insight into more fully understanding the Anadarko Basin. Through these optimal ranges, we hope to gain a more complete understanding of the best way to DD wells when they are drilled simultaneously. Our discoveries and workflow within the Woodford and Mayes Formations may be applied to various plays and formations across the unconventional play spectrum. Optimal DD techniques in unconventional reservoirs could add billions of dollars in revenue to a company’s portfolio and dramatically increase the rate of return, as well as offer a new understanding of the respective producing reservoirs.

scholarly journals Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics

2020 ◽  
Vol 10 (16) ◽  
pp. 5585
Author(s):  
Jutamat Jintana ◽  
Apichat Sopadang ◽  
Sakgasem Ramingwong
Keyword(s):  
Monte Carlo Simulation ◽  
Association Rule ◽  
Data Analytics ◽  
Recommendation System ◽  
Decision Makers ◽  
Courier Service ◽  
Effective Cost ◽  
Using Data ◽  
Number Of Customers

The purpose of this research was to create a Matching Consignees/Shippers Recommendation System (MCSRS). We used the association rule to identify product associations, the clustering technique to group shippers and consignees according to behaviors when receiving goods from similar shipper groups, and the decision tree to identify possible matches between shippers and consignees. Finally, Monte Carlo simulation was used to estimate potential revenue. The case study is a courier company in Thailand. The results showed that garment products and clothes were the products with the highest association. Shippers and consignees of these products were segmented according to recency, frequency, monetary factors, number of customers, number of product items, weight, and day. Three rules are proposed that enabled the assignment of 8 consignees to 56 shippers with an estimated increase in revenue by 36%. This approach helps decision-makers to develop an effective cost-saving new marketing, inclusive strategy quickly.

scholarly journals Data Discovery with Tableau: A Case Study Using Data Analytics Tool

Helix ◽  
2018 ◽  
Vol 8 (5) ◽  
pp. 3849-3852
Author(s):  
Amresh Kumar
Keyword(s):  
Data Analytics ◽  
Data Discovery ◽  
Using Data

The framework of talent analytics using big data

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Arnold Saputra ◽  
Gunawan Wang ◽  
Justin Zuopeng Zhang ◽  
Abhishek Behl
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Design Methodology ◽  
Big Data Analytics ◽  
Talent Management ◽  
Content Type ◽  
Using Data ◽  
Business Challenges ◽  
Practical Implications

PurposeThe era of work 4.0 demands organizations to expedite their digital transformation to sustain their competitive advantage in the market. This paper aims to help the human resource (HR) department digitize and automate their analytical processes based on a big-data-analytics framework.Design/methodology/approachThe methodology applied in this paper is based on a case study and experimental analysis. The research was conducted in a specific industry and focused on solving talent analysis problems.FindingsThis research conducts digital talent analysis using data mining tools with big data. The talent analysis based on the proposed framework for developing and transforming the HR department is readily implementable. The results obtained from this talent analysis using the big-data-analytics framework offer many opportunities in growing and advancing a company's talents that are not yet realized.Practical implicationsBig data allows HR to perform analysis and predictions, making more intelligent and accurate decisions. The application of big data analytics in an HR department has a significant impact on talent management.Originality/valueThis research contributes to the literature by proposing a formal big-data-analytics framework for HR and demonstrating its applicability with real-world case analysis. The findings help organizations develop a talent analytics function to solve future leaders' business challenges.

Diagnosis of quality management systems using data analytics – A case study in the manufacturing sector

2020 ◽  
Vol 115 ◽  
pp. 103183 ◽  
Author(s):  
Rafael Sanchez-Marquez ◽  
José Miguel Albarracín Guillem ◽  
Eduardo Vicens-Salort ◽  
José Jabaloyes Vivas
Keyword(s):  
Quality Management ◽  
Data Analytics ◽  
Manufacturing Sector ◽  
Management Systems ◽  
Quality Management Systems ◽  
Using Data
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