Social Media Big Data Analytics for Demand Forecasting

2022 ◽  
pp. 902-920
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

scholarly journals Social Media Big Data Analytics for Demand Forecasting

2020 ◽  
Vol 28 (1) ◽  
pp. 103-120 ◽  
Author(s):  
Rehan Iftikhar ◽  
Mohammad Saud Khan
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply Chain ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Predictive Modelling ◽  
Product Demand ◽  
Emotion Model ◽  
Improving Accuracy

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

2019 ◽  
pp. 70-99
Author(s):  
Murat Ozemre ◽  
Ozgur Kabadurmus
Keyword(s):  
Big Data ◽  
International Trade ◽  
Supply Chain ◽  
Data Analytics ◽  
Open Data ◽  
Big Data Analytics ◽  
Global Supply Chain ◽  
Factors Affecting ◽  
Export Volume

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

Using Big Data Analytics to Forecast Trade Volumes in Global Supply Chain Management

2022 ◽  
pp. 921-944
Author(s):  
Murat Ozemre ◽  
Ozgur Kabadurmus
Keyword(s):  
Big Data ◽  
International Trade ◽  
Supply Chain ◽  
Data Analytics ◽  
Open Data ◽  
Big Data Analytics ◽  
Global Supply Chain ◽  
Factors Affecting ◽  
Export Volume

As the supply chains become more global, the operations (such as procurement, production, warehousing, sales, and forecasting) must be managed with consideration of the global factors. International trade is one of these factors affecting the global supply chain operations. Estimating the future trade volumes of certain products for specific markets can help companies to adjust their own global supply chain operations and strategies. However, in today's competitive and complex global supply chain environments, making accurate forecasts has become significantly difficult. In this chapter, the authors present a novel big data analytics methodology to accurately forecast international trade volumes between countries for specific products. The methodology uses various open data sources and employs random forest and artificial neural networks. To demonstrate the effectiveness of their proposed methodology, the authors present a case study of forecasting the export volume of refrigerators and freezers from Turkey to United Kingdom. The results showed that the proposed methodology provides effective forecasts.

Improving emergency response operations in maritime accidents using social media with big data analytics: a case study of the MV Wakashio disaster

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Carine Dominguez-Péry ◽  
Rana Tassabehji ◽  
Lakshmi Narasimha Raju Vuddaraju ◽  
Vikhram Kofi Duffour
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Big Data ◽  
Emergency Response ◽  
Early Warning System ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Warning System ◽  
Content Type

PurposeThis paper aims to explore how big data analytics (BDA) emerging technologies crossed with social media (SM). Twitter can be used to improve decision-making before and during maritime accidents. We propose a conceptual early warning system called community alert and communications system (ComACom) to prevent future accidents.Design/methodology/approachBased on secondary data, the authors developed a narrative case study of the MV Wakashio maritime disaster. The authors adopted a post-constructionist approach through the use of media richness and synchronicity theory, highlighting wider community voices drawn from social media (SM), particularly Twitter. The authors applied BDA techniques to a dataset of real-time tweets to evaluate the unfolding operational response to the maritime emergency.FindingsThe authors reconstituted a narrative of four escalating sub-events and illustrated how critical decisions taken in an organisational and institutional vacuum led to catastrophic consequences. We highlighted the specific roles of three main stakeholders (the ship's organisation, official institutions and the wider community). Our study shows that SM enhanced with BDA, embedded within our ComACom model, can better achieve collective sense-making of emergency accidents.Research limitations/implicationsThis study is limited to Twitter data and one case. Our conceptual model needs to be operationalised.Practical implicationsComACom will improve decision-making to minimise human errors in maritime accidents.Social implicationsEmergency response will be improved by including the voices of the wider community.Originality/valueComACom conceptualises an early warning system using emerging BDA/AI technologies to improve safety in maritime transportation.

scholarly journals A Study on an Extensive Hierarchical Model for Demand Forecasting of Automobile Components

2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Cisse Sory Ibrahima ◽  
Jianwu Xue ◽  
Thierno Gueye
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Data Analytics ◽  
Demand Forecasting ◽  
Big Data Analytics ◽  
Support Vector ◽  
Chain Management ◽  
Wide Range ◽  
Auto Parts

Demand forecasting and big data analytics in supply chain management are gaining interest. This is attributed to the wide range of big data analytics in supply chain management, in addition to demand forecasting, and behavioral analysis. In this article, we studied the application of big data analytics forecasting in supply chain demand forecasting in the automotive parts industry to propose classifications of these applications, identify gaps, and provide ideas for future research. Algorithms will then be classified and then applied in supply chain management such as neural networks, k-nearest neighbors, time series forecasting, clustering, regression analysis, support vector regression and support vector machines. An extensive hierarchical model for short-term auto parts demand assessment was employed to avoid the shortcomings of the earlier models and to close the gap that regarded mainly a single time series. The concept of extensive relevance assessment was proposed, and subsequently methods to reflect the relevance of automotive demand factors were discussed. Using a wide range of skills, the factors and cofactors are expressed in the form of a correlation characteristic matrix to ensure the degree of influence of each factor on the demand for automotive components. Then, it is compared with the existing data and predicted the short-term historical data. The result proved the predictive error is less than 6%, which supports the validity of the prediction method. This research offers the basis for the macroeconomic regulation of the government and the production of auto parts manufacturers.

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