Business Value Creation Through Project Management Based on Big Data Approach

Chennouk, Hamid; Ziyati, El Houssaine; Bhiri, Brahim El

doi:10.18280/isi.270516

Cited by 3 publications

(2 citation statements)

References 21 publications

(22 reference statements)

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“…The streaming staging area consists of a message managing system where a single unit of data is represented as a message and messages are organized under different topics [47].…”

Section: Data Generation and Collectionmentioning

confidence: 99%

Big Data Enabling Fish Farming Data-Driven Strategy

Aissi¹,

Benjelloun²,

Lakhrissi³

et al. 2022

ISI

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The last two decades have witnessed an exponential data generation in tremendous amounts. The digital transformation in many domains leads to massive amounts of heterogeneous data. In order to benefit from this generation, value is extracted through data processing. Meanwhile, the fish farming functioning activity generates data with such volume, speed, heterogeneous sources and structures but it is not fully exploited. The traditional data warehouse solutions are not able to manage complex data with these characteristics. Thus, the concept of the data lake has emerged for more flexible and powerful data exploitation. Indeed, big data technologies and techniques are used to extract, process and analyze data. Since big data technologies have proved their benefits in other domains, it is irrefutable that using them in the fish farming domain will help it to reach its full potential. For this purpose, we propose in this paper a dedicated data lake architecture for handling fish farming data to initiate the adoption of a data driven strategy.

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“…The streaming staging area consists of a message managing system where a single unit of data is represented as a message and messages are organized under different topics [47].…”

Section: Data Generation and Collectionmentioning

confidence: 99%

Big Data Enabling Fish Farming Data-Driven Strategy

Aissi¹,

Benjelloun²,

Lakhrissi³

et al. 2022

ISI

View full text Add to dashboard Cite

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“…In the swiftly transforming digital age, big data technology has been recognized as a crucial catalyst propelling advancements across various sectors (Awad et al, 2023;Chandrasekaran et al, 2021;Chennouk et al, 2022;Hasan et al, 2022;He et al, 2022;Kusrini et al, 2022;Lazarevska et al, 2022;Li and Gao, 2022;Zhang et al, 2023;Zhao et al, 2022). The agricultural industry, quintessential for sustaining human life, relies heavily on the efficiency and security of its supply chain.…”

Section: Introductionmentioning

confidence: 99%

Big data-driven risk decision-making and safety management in agricultural supply chains

Han,

Pan,

Zhang

2024

Qual. Assur. Saf. Crops Foods

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In the era of digitization, the integration of big data technologies has become instrumental in advancing agri-cultural supply chain management and bolstering risk decision-making processes. Agricultural supply chains, critical to ensuring food security and bolstering rural economies, face vulnerabilities stemming from a myriad of internal and external elements, including natural disasters and market dynamics. Consequently, the urgency to adopt effective risk management strategies is paramount. Contemporary studies have explored the utilization of big data in decision-making processes specific to agricultural supply chain risks, predominantly concentrat-ing on preliminary risk prediction and characterization. Nonetheless, there exists a shortfall in comprehensively analyzing the intricate interplay among risk factors and establishing a holistic risk management decision-making framework based on such analyses. This research addresses these deficiencies through two principal investigative components. First, this research explores the analysis of risk factors and their interrelationships in the agricultural supply chain based on a decision tree algorithm with a transition structure. This algorithm enhances decision-makers’ understanding of risk factors and their interrelationships, and guide the implementation of effective risk mitigation measures and the formulation of contingency plans. Subsequently, the research constructs a corresponding data-driven multi-criteria decision-making method, assisting managers in balancing different risk management strategies in a volatile supply chain environment, considering costs, benefits, and feasibility to formulate the optimal strategy. The innovation of this research lies in the development of a novel risk analysis tool based on the transition decision tree algorithm. This is the first time that such advanced algorithms are applied to agricultural supply chain risk management, filling a gap in the current research. The outcomes of this study not only contribute to enhancing risk management practices within agricultural supply chains but also offer novel insights and methodological tools that are applicable in research and practices across related domains.

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