Demand forecasting model development through big data analysis

2019 ◽  
Author(s):  
Seungjung Yang ◽  
Heajong Joo ◽  
Sekyoung Youm
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Demand Forecasting ◽  
Model Development ◽  
Big Data Analysis ◽  
Forecasting Model ◽  
Demand Forecasting Model

The Potential of Big Data Analysis in the Shipbuilding Industry: A Way of Increasing Competitiveness

2021 ◽  
pp. 53-74
Author(s):  
Andrea Cappelli ◽  
Iacopo Cavallini
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Value Chain ◽  
Information Needs ◽  
Demand Forecasting ◽  
Big Data Analysis ◽  
Production Costs ◽  
Shipbuilding Industry ◽  
Analysis Methods

It is possible to exploit potentials of Big Data in the shipbuilding industry in order to increase efficiency and company performance. Big Data analysis will probably have a great impact on strengthening the competitiveness in the whole sector, providing various types of benefits and effective support to the decision-making system. Academics maintain that analysis methods and algorithms can offer spe-cific guidelines to managers and practitioners in order to satisfy their information needs. Even though it is recognized that the techniques for Big Data analysis are relevant, only a few studies provide practical guidelines on how to apply these techniques in specific industries like shipbuilding. This preliminary study aims to develop a conceptual framework of Big Data anal-ysis based on the value chain approach. By using a deductive methodology, the framework is built taking into consideration four phases of the value chain in the shipbuilding industry - i.e. pre-production, design, production, and post-production. For its relevance, the study considers the pre-production phase, trying to classify data sources, analysis methods, and algorithms for the main activities of this node and also providing various suggestions to shipbuilding managers and practitioners. The researchers develop the framework by considering secondary data collected from the literature analysis. Our results can successfully support decision making in shipbuilding companies, making processes and operations more cost-effective and helping companies be more competitive. Specifically, in the pre-production node this will lead to real-time demand forecasting and a more reliable estimation of initial production costs.

Insights into seismic hazard from big data analysis of ground motion simulations

2019 ◽  
Vol 9 (1) ◽  
pp. 01-12 ◽  
Author(s):  
Kristy F. Tiampo ◽  
Javad Kazemian ◽  
Hadi Ghofrani ◽  
Yelena Kropivnitskaya ◽  
Gero Michel
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Big Data Analysis ◽  
Ground Motion Simulations

A Social Big Data Analysis on Sport Participation

2020 ◽  
Vol 25 (2) ◽  
pp. 18-30
Author(s):  
Seung Wook Oh ◽  
Jin-Wook Han ◽  
Min Soo Kim
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Sport Participation ◽  
Big Data Analysis ◽  
Social Big Data
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