Big data analytics for empowering milk yield prediction in dairy supply chains

<p>Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics. Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.</p>

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Using big data analytics to improve decision-making in apparel supply chains

Information Systems for the Fashion and Apparel Industry ◽

10.1016/b978-0-08-100571-2.00004-x ◽

2016 ◽

pp. 63-95 ◽

Cited By ~ 6

Author(s):

L. Banica ◽

A. Hagiu

Keyword(s):

Decision Making ◽

Big Data ◽

Supply Chains ◽

Data Analytics ◽

Big Data Analytics

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Assessment of rice yield prediction models based on big data analytics for better supply chain decision-making in Thailand

International Journal of Value Chain Management ◽

10.1504/ijvcm.2021.118289 ◽

2021 ◽

Vol 12 (3) ◽

pp. 221

Author(s):

Sumanya Ngandee ◽

Attaphongse Taparugssanagorn ◽

Chutiporn Anutariya ◽

John K.M. Kuwornu

Keyword(s):

Decision Making ◽

Big Data ◽

Supply Chain ◽

Data Analytics ◽

Prediction Models ◽

Rice Yield ◽

Big Data Analytics ◽

Yield Prediction

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Big Data Analytics in Sustainable Supply Chain Management: A Focus on Manufacturing Supply Chains

Sustainability ◽

10.3390/su13137101 ◽

2021 ◽

Vol 13 (13) ◽

pp. 7101

Author(s):

Joash Mageto

Keyword(s):

Big Data ◽

Supply Chain ◽

Supply Chain Management ◽

Supply Chains ◽

Data Analytics ◽

Social Issues ◽

Big Data Analytics ◽

Sustainable Supply Chain ◽

Chain Management ◽

Sustainable Supply Chain Management

Sustainable supply chain management has been an important research issue for the last two decades due to climate change. From a global perspective, the United Nations have introduced sustainable development goals, which point towards sustainability. Manufacturing supply chains are among those that produce harmful effluents into the environment in addition to social issues that impact societies and economies where they operate. New developments in information and communication technologies, especially big data analytics (BDA), can help create new insights that can detect parts and members of a supply chain whose activities are unsustainable and take corrective action. While many studies have addressed sustainable supply chain management (SSCM), studies on the effect of BDA on SSCM in the context of manufacturing supply chains are limited. This conceptual paper applies Toulmin’s argumentation model to review relevant literature and draw conclusions. The study identifies the elements of big data analytics as data processing, analytics, reporting, integration, security and economic. The aspects of sustainable SCM are transparency, sustainability culture, corporate goals and risk management. It is established that BDA enhances SSCM of manufacturing supply chains. Cyberattacks and information technology skills gap are some of the challenges impeding BDA implementation. The paper makes a conceptual and methodological contribution to supply chain management literature by linking big data analytics and SSCM in manufacturing supply chains by using the rarely used Toulmin’s argumentation model in management studies.

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Barriers to big data analytics in manufacturing supply chains: A case study from Bangladesh

Computers & Industrial Engineering ◽

10.1016/j.cie.2018.04.013 ◽

2019 ◽

Vol 128 ◽

pp. 1063-1075 ◽

Cited By ~ 30

Author(s):

Md. Abdul Moktadir ◽

Syed Mithun Ali ◽

Sanjoy Kumar Paul ◽

Nagesh Shukla

Keyword(s):

Big Data ◽

Supply Chains ◽

Data Analytics ◽

Big Data Analytics

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How big data analytics can help manufacturing companies strengthen supply chain resilience in the context of the COVID-19 pandemic

The International Journal of Logistics Management ◽

10.1108/ijlm-02-2021-0095 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Surajit Bag ◽

Pavitra Dhamija ◽

Sunil Luthra ◽

Donald Huisingh

Keyword(s):

Risk Management ◽

Big Data ◽

Supply Chain ◽

Supply Chains ◽

Data Analytics ◽

Big Data Analytics ◽

Hypothetical Model ◽

Content Type ◽

Supply Chain Resilience ◽

Management Capabilities

PurposeIn this paper, the authors emphasize that COVID-19 pandemic is a serious pandemic as it continues to cause deaths and long-term health effects, followed by the most prolonged crisis in the 21st century and has disrupted supply chains globally. This study questions “can technological inputs such as big data analytics help to restore strength and resilience to supply chains post COVID-19 pandemic?”; toward which authors identified risks associated with purchasing and supply chain management by using a hypothetical model to achieve supply chain resilience through big data analytics.Design/methodology/approachThe hypothetical model is tested by using the partial least squares structural equation modeling (PLS-SEM) technique on the primary data collected from the manufacturing industries.FindingsIt is found that big data analytics tools can be used to help to restore and to increase resilience to supply chains. Internal risk management capabilities were developed during the COVID-19 pandemic that increased the company's external risk management capabilities.Practical implicationsThe findings provide valuable insights in ways to achieve improved competitive advantage and to build internal and external capabilities and competencies for developing more resilient and viable supply chains.Originality/valueTo the best of authors' knowledge, the model is unique and this work advances literature on supply chain resilience.

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