Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

Chan, Hing Kai; Choi, Tsan‐Ming; Yue, Xiaohang

doi:10.1109/tii.2016.2558477

Cited by 19 publications

(5 citation statements)

References 19 publications

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“…On the other hand, Choi et al [66] asserts big data is a powerful tool that can be used effectively to solve problems related to operational and supply chain risk. Others acknowledge the similar need for big data applications in risk and operation management in industrial applications [67].…”

Section: Big Data Analytics and Risk Mitigationmentioning

confidence: 99%

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

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The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.In reviewing literature on big data analytics (BDA) and sustainability, Keeso [12] argues that the application of big data in sustainable operations is often slow to achieve desired outcomes. Although sustainable supply chain management has little history in academic literature, it has become a mainstream area for practitioners [13][14][15]. On the other hand, building on the knowledge-based perspective some suggest the need for BDA as a knowledge resource that can be harvested and retained. The knowledge-based view can be traced from Simon's [16] seminal work with significant extensions by Grant [17,18], Huber [19] and Levitt and March [20]. Further, the knowledge-based perspective suggests how big data can fuel the purposive search for market and resource innovation opportunities [21]; such resources can provide corporations a competitive advantage [22].While building knowledge-based resources, it is also imperative to predict and avoid risks causing potential disruptions [23]. Recently, supply chain risk management (SCRM) has become a priority because of its capability to avert potential disruption and recover more quickly from disruptions that cannot be averted [24]. There are various risks including material flow risk, information flow risk and financial flow risk that cause supply chain disruptions [25]. Among these risks, supply chain social issues can impose significant operational risks on the supply chain [26]. In the literature the linkage between supply chain social issues, and risk management has been established [26]. Further, Klassen and Vreecke [26] show that social issues can be managed through effective monitoring, innovation cap...

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Section: Big Data Analytics and Risk Mitigationmentioning

confidence: 99%

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

et al. 2017

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“…The question of whether retailers should sell green products first is critical in the era of big data [67]. Retailers can collect massive amounts of consumer demand data and instantly update demand forecasts for new products to improve their retail business performance and services [68]. If a company sells green products first, it will incur lower environmental costs.…”

Section: Social Responsibility Theorymentioning

confidence: 99%

Exploring the Key Elements of Sustainable Design from a Social Responsibility Perspective: A Case Study of Fast Fashion Consumers’ Evaluation of Green Projects

Fan

Chang

2023

Sustainability

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With the growing global awareness of sustainable development, the impact of “fast fashion” on the environment has drawn much attention. This study investigated 365 fast fashion consumers with higher-than-average spending on fast fashion garments in Taiwan and analyzed the data with the analytic network process. From the perspective of social responsibility theory, the triple bottom line framework, and the concept of cradle-to-cradle, this study conducted in-depth interviews with 10 experts in the field of clothing. Based on the results, this study constructed a model with 4 core dimensions and 12 key criteria of sustainable clothing design, with the enterprise, design, and consumer sides as the decision-making options. Moreover, it was found that consumers believe that the first decision-making key that affects sustainable design is the enterprise side, and the most important green item that affects sustainable design is “cherishing”. The findings of this study can provide an important reference standard for sustainable design in the apparel industry.

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“…It is important to note that given the BDA radical depart from traditional methods, current models for SCRM might have become obsolete (Kai Chan et al. , 2016).…”

Section: Introductionmentioning

confidence: 99%

“…Most of these models presupposed the formalization of input and output structures that are somewhat prearranged, which clashes with the dynamic reality and the availability of techniques to analyze unstructured data. Effective models need to allow real-time revisions, which can be hard to be done without adequate technological support (Kai Chan et al. , 2016; Choi et al.…”

Section: Introductionmentioning

confidence: 99%

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Big data analytics for supply chain risk management: research opportunities at process crossroads

Santos

Marques

2022

BPMJ

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PurposeThe purpose of this study is to map current knowledge on big data analytics (BDA) for supply chain risk management (SCRM) while providing future research needs.Design/methodology/approachThe research team systematically reviewed 53 articles published between 2015 and 2021 and further contrasted the synthesis of these articles with four in-depth interviews with BDA startups that provider solutions for SCRM.FindingsThe analysis is framed in three perspectives. First, supply chain visibility – i.e. the number of tiers in the solutions; second, BDA analytical approach – descriptive, prescriptive or predictive approaches; third, the SCRM processes from risk monitoring to risk optimization. The study underlines that the forefront of innovation lies in multi-tiered, multi-directional solutions based on prescriptive BDA to support risk response and optimization (SCRM). In addition, we show that research on these innovations is scant, thus offering an important avenue for future studies.Originality/valueThis study makes relevant contributions to the field. We offer a theoretical framework that highlights the key relationships between supply chain visibility, BDA approaches and SCRM processes. Despite being at forefront of the innovation frontier, startups are still an under-explored agent. In times of major disruptions such as COVID-19 and the emergence of a plethora of new technologies that reshape businesses dynamically, future studies should map the key role of such actors to the advancement of SCRM.

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Guest Editorial Big Data Analytics: Risk and Operations Management for Industrial Applications

Cited by 19 publications

References 19 publications

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain

Exploring the Key Elements of Sustainable Design from a Social Responsibility Perspective: A Case Study of Fast Fashion Consumers’ Evaluation of Green Projects

Big data analytics for supply chain risk management: research opportunities at process crossroads

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