Impact of Predictive Analytics of Big Data in Supply Chain Management on Decision-Making

Patrick, Wafula M.; Anselemo, Peters I.; Ronoh, Richard; Mbugua, Prof. Samuel

doi:10.32628/cseit228423

Cited by 4 publications

(4 citation statements)

References 17 publications

(3 reference statements)

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“…The use of AI and ML in various sectors, including supply chain management, is expected to continue growing in importance in the coming years (Naved, 2022). Furthermore, the impact of predictive analytics of Big Data in supply chain management on decision-making processes has been studied, highlighting the significance of leveraging predictive analytics for informed decision-making in supply chain operations (Patrick et al, 2022).…”

Section: Future Trends and Developmentsmentioning

confidence: 99%

Reviewing the use of big data in supply chain optimization in the USA

Israel Osejie Okoduwa,

Bankole Ibrahim Ashiwaju,

Jeremiah Olawumi Arowoogun

et al. 2024

World J. Adv. Res. Rev.

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This paper provides a succinct overview of the review conducted on the utilization of Big Data in supply chain optimization within the United States. The advent of Big Data analytics has revolutionized traditional supply chain management, offering unprecedented opportunities for enhancing efficiency, reducing costs, and improving overall operational performance. This comprehensive review examines the current state of Big Data applications in supply chain optimization, focusing specifically on the context of the United States. The review begins by outlining the fundamental role of Big Data in transforming supply chain processes. It delves into various aspects, including data collection, processing, and analysis, emphasizing their impact on decision-making and strategic planning. Notably, the study highlights real-world examples of successful Big Data implementations within the U.S. supply chain landscape, showcasing how companies have leveraged data-driven insights to streamline operations and gain a competitive edge. Furthermore, the paper explores the challenges associated with the adoption of Big Data in supply chain management. It addresses concerns related to data security, privacy, and the need for advanced technological infrastructure. Additionally, the review considers the organizational and cultural shifts required for successful integration, emphasizing the importance of a holistic approach to implementation. The paper concludes by pointing towards future trends and potential developments in the use of Big Data for supply chain optimization in the U.S. It underscores the evolving nature of technology and its continued impact on reshaping supply chain strategies. The insights derived from this review contribute to a deeper understanding of the current landscape, offering valuable implications for practitioners, researchers, and policymakers engaged in optimizing supply chain processes through the harnessing of Big Data analytics.

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Section: Future Trends and Developmentsmentioning

confidence: 99%

Reviewing the use of big data in supply chain optimization in the USA

Israel Osejie Okoduwa,

Bankole Ibrahim Ashiwaju,

Jeremiah Olawumi Arowoogun

et al. 2024

World J. Adv. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…In recent years, the exploitation of big data is recognized as an important lever for boosting businesses as data from big data makes it possible to make a forecast in the supply chain. Wafula et al [1] studied the supply chain system of Kenyan companies with regard to decision-making. Design Science research is the methodology used to obtain the results of his research.…”

Section: Big Data Effect On Value Creationmentioning

confidence: 99%

“…"Value creation" and "Value capture" are the endogenous latent variables. Those variables are summarized by Equation (1). The relationship between those variables is described in Table 1.…”

Section: The Structural Modelmentioning

confidence: 99%

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Value Creation and Capture with Big Data in Smart Phones Companies

2022

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With the advent of social media, the volume of data generated on the internet has exploded due to the growing number of social network users and their interactions on the internet. Given that, in the age of the digital economy, data has become raw material in terms of decision making, it is important and urgent to conduct a study to understand the effect of big data on value creation and value capture. The goal of the current research is to study the share of big data in value creation and capture in the companies Apple and Samsung. The main question addressed by this article is whether the increasing volumes of data in the digital age can improve the creation and capture of value. To achieve this goal, we considered active users of the three main social networks that Samsung and Apple companies use for their advertising to describe “big data”. We measure the “value creation” through the hardware component of the smartphone, such as the battery, camera, CPU speed, RAM (Random-Access Memory), screen size, and storage. Profit, satisfaction, and unit of phone sold are the three manifest variables considered to measure “Value capture”. As a methodology, we used partial least squares and structural equation modeling to obtain the results. The pattern enables us to measure the effect of big data on value creation and value capture. The results indicate that CPU speed, RAMs, and battery capacity are the principal variables that impact the “value creation” in terms of customer need and satisfaction.

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Big Data Analytics for Supply Chain Optimization

Vaghani

2024

Revolutionizing Supply Chains Through Digital Transformation

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Despite the extensive discussion on the benefits of BDA in supply chains, there remains a lack of systematic understanding of how BDA can be effectively linked to Supply Chain Optimization (SCO). This document explores the cumulative published literature from 2014 and 2024, so as to ascertain the impact of big data analytics on supply chain optimization. 64 articles were selected from diverse academic journals, and from these articles, information was gathered, analyzed, and organized. The results shows that Big data is often used for making predictions in SCO; big data analytics has a lot of difficulties dealing with large amounts of data related to managing the supply chain, which include too much data, problems with data quality, keeping data safe and private, difficulty in analyzing data, ability to grow as needed, and complicated supply chains. The study recommends that big data projects should begin with specific goals instead of just hoping people will use them. Starting with small test projects can help show the benefits of big data analytics.

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Impact of Predictive Analytics of Big Data in Supply Chain Management on Decision-Making

Cited by 4 publications

References 17 publications

Reviewing the use of big data in supply chain optimization in the USA

Reviewing the use of big data in supply chain optimization in the USA

Value Creation and Capture with Big Data in Smart Phones Companies

Big Data Analytics for Supply Chain Optimization

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