Dimensions of Data Analytics in Supply Chain Management: Objectives, Indicators and Data Questions

Brandtner, Patrick; Udokwu, Chibuzor; Darbanian, Farzaneh; Falatouri, Taha

doi:10.1145/3450588.3450599

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…Still, we deem it important to further analyze the potential of descriptive and diagnostic approaches in SCM to extract important factors and features from the data. Related to the stages of a DA project, it might especially be useful for the earlier tasks of business and data understanding (Brandtner, Udokwu et al, 2021b). Future research might especially focus on the combination of different DA models across the levels of descriptive, predictive, and prescriptive analytics, as e.g.…”

Section: Combining Different Da Models Across Da Levelsmentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

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In recent years, there has been a growing surge of interest in the application of data analytics (DA) within the realm of supply chain management (SCM), attracting attention from both practitioners and researchers. This paper presents a comprehensive examination of recent implementations of DA in SCM. Employing a systematic literature review (SLR), we conducted a meticulous analysis of over 354 papers. Building upon a prior SLR conducted in 2018, we identify contemporary areas where DA has been applied across various functions within the supply chain and scrutinize the DA models and techniques that have been employed. A comparison between past findings and the current literature reveals a notable upsurge in the utilization of DA across most SCM functions, with a particular emphasis on the prevalence of predictive analytics models in contemporary SCM applications. The findings of this paper offer a detailed insight into the specific DA models and techniques currently in use across various SCM functions. Additionally, a discernible increase in the adoption of mixed or hybrid DA models is observed. However, several research gaps persist, including the need for more attention to real-time DA in SCM, the integration of publicly available data, and the application of DA to mitigate uncertainty in SCM. To address these areas and guide future research endeavors, the paper concludes by delineating six concrete research directions. These directions offer valuable avenues for further exploration in the field.

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Section: Combining Different Da Models Across Da Levelsmentioning

confidence: 99%

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Darbanian,

Brandtner,

Falatouri

et al. 2024

OSCM: An Int. Journal

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“…The international transportation and production of energy products may be affected by geopolitics, natural disasters, and other factors, causing disruptions and instability in the supply chain. Energy production and processing may generate large amounts of pollutants and greenhouse gas emissions, exacerbating problems such as environmental pollution and climate change (Brandtner et al, 2021). Therefore, while participating in global value chains, the energy industry must pay attention to environmental protection and sustainable development issues and take effective measures to reduce environmental risks.…”

Section: Overview Of Global Value Chains and International E-commerce...mentioning

confidence: 99%

Analysis of Energy International E-commerce Innovation Strategy Based on Global Value Chain

Bai

2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: With the drive of globalization and digitalization, the global energy industry is undergoing a brand new transformation. Energy international e-commerce is an emerging paradigm that continues to grow within the global value chain framework, bringing significant changes and opportunities to the energy sector.OBJECTIVES: This research examines the role of international e-commerce in advancing the energy industry's growth, maximizing the distribution of resources worldwide, and boosting market competitiveness. It does this by analyzing the innovation strategy of the sector based on the global value chain.METHODS: The basic concepts and characteristics of global value chain theory and energy international e-commerce are analyzed, and then the innovation strategies in technological innovation, international cooperation, supply chain optimization, and data-driven are explored in depth, and empirical analyses of these strategies are conducted through case studies.RESULTS: It is found that technological innovation not only promotes the development of international energy e-commerce but also gives rise to new business models; international cooperation and supply chain optimization effectively optimize the global resource allocation and market layout; and data-driven market expansion strategy improves the market competitiveness of enterprises. The case study results further validate the effectiveness and practicality of these strategies. CONCLUSION: Energy international e-commerce innovation strategies based on GVCs play an essential role in promoting the transformation and upgrading of the energy industry, optimizing resource allocation efficiency, and enhancing enterprises' market competitiveness.

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“…Although research works [12][13][14][15]17] show various SCM use cases and applications of ensemble modelling, they do not provide a general context of DA ensemble within the various processes in SCM. Brandtner et al [18] identified SCM processes such as customer activities, procurement, forecast and planning, packaging and handling, transportation, warehousing and inventory management with their data problems that can be addressed with ML analytics. The ensemble approaches in Inedi et al [13] and Haque et al [16] focused on forecast and planning SCM activities, while Safara [12] focused on customer activities.…”

Section: A Ensembles In Supply Chain Management (Scm)mentioning

confidence: 99%

“…This research gap is expanded on further in the literature review section. In line with the previous work in [18], we consider DA problems in SCM as business issues that occur in different processes of SCM which analytics and ML algorithms are employed to solve. Thus, the focus of the current paper is to systematically develop an ensemble modelling framework for addressing DA problems that occur in the context of SCM.…”

Section: Introductionmentioning

confidence: 99%

“…To answer the main research question, this paper examines related articles that have developed ensemble approaches for different problem types and use cases. In the paper, the main DA problems in SCM according to [18] are considered as the input for generating an ensemble modelling framework. Thus, the ensemble framework for SCM is developed by mapping the general context of DA problems and use cases to SCM contexts.…”

Section: Introductionmentioning

confidence: 99%

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Development of an Ensemble Modeling Framework for Data Analytics in Supply Chain Management

Udokwu,

Zimmermann,

Brandtner

et al. 2023

JAIT

Self Cite

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The application of Data Analytics (DA) in Supply Chain Management (SCM) provides a plethora of opportunities for improving the performance, efficiency, and resilience of organizations by predicting the behaviour of the supply chain network. In this regard, the use of complex DA approaches that involve the combination of multiple Machine Learning (ML) models such as the ensemble approaches have shown to be highly effective in improving prediction accuracy and model performance. Still, the application of these ensemble approaches has not yet been properly explored in SCM analytics. Thus, this paper presents an ensemble model framework specifically designed for SCM issues by exploring problem types and use cases where ensemble approaches can be applied in SCM. The developed framework enables the selection of the right ensemble models for specific SCM DA tasks. Thus, this paper contributes to the scientific body of knowledge by presenting a systematic approach to ensemble modelling in SCM analytics by outlining suitable ensemble model compositions for different SCM use cases and data analytics problems. Hence the results of this paper provide a potential tool for DA practitioners in SCM to further improve the performance of their Supply Chain (SC) networks by selecting appropriate ensemble elements for different SCM analytics problems.

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Dimensions of Data Analytics in Supply Chain Management: Objectives, Indicators and Data Questions

Cited by 12 publications

References 14 publications

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Data Analytics in Supply Chain Management: A State-of-the-Art Literature Review

Analysis of Energy International E-commerce Innovation Strategy Based on Global Value Chain

Development of an Ensemble Modeling Framework for Data Analytics in Supply Chain Management

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