Improving Efficiency for Retail Warehouse Using Data Envelopment Analysis

Kusrini, Elisa; Prakoso, Indro; Hidayatuloh, Syarif

doi:10.18280/mmep.090132

Cited by 3 publications

(2 citation statements)

References 20 publications

(25 reference statements)

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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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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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“…Data warehouses store large amounts of data, enabling organizations to quickly access and analyze the information [5], [6]. This data can be employed to identify trends, understand customer behavior, and optimize processes [7]. Additionally, data warehouses are also used to track performance metrics, helping organizations identify areas of improvement and make data-driven decisions [8].…”

Section: Introductionmentioning

confidence: 99%

One Data Indonesia Policy Adoption for Telkom University Data Warehouse Framework

Gozali,

Romadhony,

2023

regist. j. ilm. teknol. sist. inf.

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The Indonesian government has implemented a data warehouse named One Data (Satu Data) Indonesia (ODI) to support its operations since 2019. However, the implementation of this concept in universities has been limited, with only a few universities adopting it. Telkom University is one of the few universities in Indonesia that has already taken steps to implement ODI at the university level. The adoption of ODI at Telkom University is known as the One Data Telkom University (ODTU) project. This project aims to create a platform for universities to share data and collaborate more effectively. This paper thoroughly examines the implementation of the ODI policy and data warehouse framework at Telkom University, focusing on the ODTU data warehouse design and architecture. This paper discusses the implementation of ODTU into several applications, including the One Data Portal, One Data Dashboard, and One Data Market. Moreover, it identifies the challenges encountered during the implementation process, such as data integration, data privacy and security, standardized data models, and the promotion of a shared vision among stakeholders with varying levels of data literacy. Our analysis results demonstrate the effectiveness of the ODTU framework in improving data management practices at Telkom University. The customer satisfaction index (CSI) shows that across key reliability, assurance, and responsiveness measures, Telkom University experienced average score improvements of 3-6% after implementing ODTU. This study contributes to the existing literature on ODI policy adoption in the context of higher education institutions, providing insights for institutions seeking to improve their data management practices.

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