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.

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.

Using Data Envelopment Analysis for Green Supplier Selection in Manufacturing under Vague Environment

2012 ◽  
Vol 622-623 ◽  
pp. 1682-1685 ◽  
Author(s):  
Atefeh Amindoust ◽  
Ahmed Shamsuddin ◽  
Ali Saghafinia
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Decision Makers ◽  
Central Component ◽  
Data Envelopment ◽  
Linguistic Terms ◽  
Green Supplier Selection ◽  
Growth Of Knowledge ◽  
Using Data

In these days, considering the growth of knowledge about environmental protection and green issues in manufacturing, green supplier selection would be the central component in the management of supply chain. This paper intends to apply data envelopment analysis for supplier selection considering environmental merits. The suppliers’ performances with respect to criteria are not pure numbers and considered in linguistic terms according to decision makers’ opinion. To handle the subjectivity of decision makers’ assessments, fuzzy logic has been applied. A case study is done to present the application of the method.

Multidimensional Model Design using Data Mining

2017 ◽  
Vol 13 (1) ◽  
pp. 1-35 ◽  
Author(s):  
Sandro Bimonte ◽  
Lucile Sautot ◽  
Ludovic Journaux ◽  
Bruno Faivre
Keyword(s):  
Data Mining ◽  
Rapid Prototyping ◽  
Data Warehouse ◽  
Decision Maker ◽  
Decision Makers ◽  
Model Design ◽  
Multidimensional Model ◽  
Mapping Process ◽  
Using Data

Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.

scholarly journals Metaheuristic Approaches for Solving Truck and Trailer Routing Problems with Stochastic Demands: A Case Study in Dairy Industry

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Seyedmehdi Mirmohammadsadeghi ◽  
Shamsuddin Ahmed
Keyword(s):  
Sensitivity Analysis ◽  
Service Providers ◽  
Memetic Algorithm ◽  
Stochastic Demand ◽  
Routing Problem ◽  
Benchmark Instances ◽  
Using Data ◽  
Number Of Customers ◽  
Sizeable Number

Manufacturers and service providers often encounter stochastic demand scenarios. Researchers have, thus far, considered the deterministic truck and trailer routing problem (TTRP) that cannot address ubiquitous demand uncertainties and/or other complexities. The purpose of this study is to model the TTRP with stochastic demand (TTRPSD) constraints to bring the TTRP model closer to a reality. The model is solved in a reasonable timeframe using data from a large dairy service by administering the multipoint simulated annealing (M-SA), memetic algorithm (MA), and tabu search (TS). A sizeable number of customers whose demands follow the Poisson probability distribution are considered to model and solve the problem. To make the solutions relevant, first, 21 special TTRPSD benchmark instances are modified for this case and then these benchmarks are used in order to increase the validity and efficiency of the aforementioned algorithms and to show the consistency of the results. Also, the solutions have been tested using sensitivity analysis to understand the effects of the parameters and to make a comparison between the best results obtained by three algorithms and sensitivity analysis. Since the differences between the results are insignificant, the algorithms are found to be appropriate and relevant for solving real-world TTRPSD problem.

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
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