Supply Chain Risk Management: an Interactive Simulation Model in a Big Data Context

Vieira, Antônio; Dias, Luís M. S.; Santos, Maribel Yasmina; Pereira, Guilherme; Oliveira, José A.

doi:10.1016/j.promfg.2020.02.035

Cited by 13 publications

(11 citation statements)

References 12 publications

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“…The results outlined earlier suggest that if an organization is able to effectively harness its BDA capabilities and develop tools using these capabilities, the firm will be able to develop competitive capabilities in areas that can have a significant impact on organizational performance. The relevance of this finding lies in the fact that they tend to provide further support to the emerging stream of literature that attempts to show the relationship between firm strategy, Big Data and competitive capabilities (Singh, 2022;Talwar et al, 2021;Vieira et al, 2020) Hypothesis 1, 2 and 3 argue that the BDA-focused infrastructure, human capital and knowledge management capabilities positively impact the performance of an automated supply chain disruption risk alert tool. However, in contrast to the findings by Terziovski (2010), the results suggest that the effectiveness of an automated supply chain disruption identification tool is less dependent on the presence of a BDA-focused infrastructure capability or human capital capability, but more on the existence of an effective knowledge management capability.…”

Section: Discussion Of Resultsmentioning

confidence: 53%

“…BDA, therefore, is a technology that Predicting supply chain risks specifically impacts these antecedents, improving their effectiveness and therefore positively improving an organization's risk management capabilities (Spieske and Birkel, 2021). Building on these ideas Vieira et al (2020) develop a risk alert tool. Using simulations, they argue that a big data warehouse structure supported by big data tools can be effectively used to predict potential supply chain disruption events, and therefore enable firms to develop SCR (Vieira et al, 2020).…”

Section: Bij 305mentioning

confidence: 99%

“…Building on these ideas Vieira et al (2020) develop a risk alert tool. Using simulations, they argue that a big data warehouse structure supported by big data tools can be effectively used to predict potential supply chain disruption events, and therefore enable firms to develop SCR (Vieira et al, 2020). Therefore, from a supply chain risk management and resilience development perspective, BDA capabilities, if adopted by an organization, enhance the 'environmental sensing' capability of a firm enabling it to improve its risk prediction capabilities.…”

Section: Bij 305mentioning

confidence: 99%

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Predicting supply chain risks through big data analytics: role of risk alert tool in mitigating business disruption

Park

Singh²

2022

BIJ

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PurposeAs organizations globalize, they are facing twin challenges of (1) how to develop actionable intelligence from the vast amount of data flowing into their organization and (2) how to effectively manage the increasing risks to their supply chain. Therefore, the purpose of this paper is to bring these two issues on a single platform to understand how firms can effectively predict supply chain risk by developing and using BDA capabilities, through an automated risk alert tool.Design/methodology/approachThe authors used a questionnaire-based survey methodology supported by secondary data to collect information related to managerial perceptions on how firms can develop a risk alert tool by improving BDA capabilities. A database of 213 senior and middle-level managers was developed and used to test the proposed hypothesis. Using econometric techniques, the authors identify the conditions necessary for such an automated risk management tool to be effective.FindingsThe results suggest that if organizations focus on developing an effective IT infrastructure supported by a strong BDA capability, they will be able to leverage these capabilities to develop an effective risk management tool. Moderating influences of Upstream and Downstream Supply Chain IT Infrastructure capabilities were also observed on different types of BDA capabilities within a firm. In conclusion, it was argued that the effectiveness of a risk alert tool is dependent on how well firms harness big data analytics capability.Originality/valueThe value of the research stems from the fact that it uses managerial surveys to identify specific BDA capabilities that can enable firms to develop risk resilience capabilities. In addition, the article is one of the few empirical studies that aims to identify how firms can use BDA capabilities within a supply chain context to develop an automated risk alert tool. The article, therefore, contributes to the literature that identifies the value of BDA capabilities within the context of supply chain risk management.

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Section: Discussion Of Resultsmentioning

confidence: 53%

Section: Bij 305mentioning

confidence: 99%

Section: Bij 305mentioning

confidence: 99%

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Predicting supply chain risks through big data analytics: role of risk alert tool in mitigating business disruption

Park

Singh²

2022

BIJ

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“…Ali et al ( 2020a ) utilised DES for investigating the effect of lead time uncertainty on the bullwhip effect in a multi-product, multi-echelon decentralised and centralised supply chain. Vieira et al ( 2020 ) and Vieira et al ( 2019 ) developed DES models for aiding decision-making by testing various supply chain risk and disruption scenarios. Rachih et al ( 2019 ) utilised DES for evaluating the performance of a supply chain system by determining the impact of inventory level on costs.…”

Section: Results Of the Systematic Literature Reviewmentioning

confidence: 99%

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Wofuru-Nyenke

Briggs

Aikhuele

2022

Process Integr Optim Sustain

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Managers at various echelons of supply chains are continuously faced with problems of examining and improving supply chain processes with the aim of improving productivity and customer service level, while reducing emissions and total costs simultaneously. This study is aimed at presenting the trends in sustainable manufacturing supply chain modelling in order to establish the modelling approaches which have predominantly been used for improving manufacturing supply chains from years 2010 to 2020. The study employs the systematic literature review methodology for reviewing articles published within the 11-year period. We proffer a classification approach for manufacturing supply chain models, grouping these models into mathematical models, simulation models, hybrid models, and their subcategories. The results showed that though there is a rising trend in the use of simulation and hybrid models, mathematical models have been used more for sustainable manufacturing supply chain modelling. The rise in the use of simulation and hybrid models can be explained by the fact that these models tend to handle uncertain and stochastic data better than mathematical models, which perform better with deterministic data. This research will aid other researchers in recognising the current gaps in manufacturing supply chain modelling in order to identify future research directions.

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“…Costa and Santos [25] developed a BDW for smart cities using technologies such as Hive, Cassandra, HDFS, and Presto, among others. Vieira et al [26] developed a tool using Big Data technologies and a simulation model to assess the impact of disruptions in the performance of the supply chain.…”

Section: Related Workmentioning

confidence: 99%

Advancing Logistics 4.0 with the Implementation of a Big Data Warehouse: A Demonstration Case for the Automotive Industry

et al. 2021

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The constant advancements in Information Technology have been the main driver of the Big Data concept’s success. With it, new concepts such as Industry 4.0 and Logistics 4.0 are arising. Due to the increase in data volume, velocity, and variety, organizations are now looking to their data analytics infrastructures and searching for approaches to improve their decision-making capabilities, in order to enhance their results using new approaches such as Big Data and Machine Learning. The implementation of a Big Data Warehouse can be the first step to improve the organizations’ data analysis infrastructure and start retrieving value from the usage of Big Data technologies. Moving to Big Data technologies can provide several opportunities for organizations, such as the capability of analyzing an enormous quantity of data from different data sources in an efficient way. However, at the same time, different challenges can arise, including data quality, data management, and lack of knowledge within the organization, among others. In this work, we propose an approach that can be adopted in the logistics department of any organization in order to promote the Logistics 4.0 movement, while highlighting the main challenges and opportunities associated with the development and implementation of a Big Data Warehouse in a real demonstration case at a multinational automotive organization.

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Supply Chain Risk Management: an Interactive Simulation Model in a Big Data Context

Cited by 13 publications

References 12 publications

Predicting supply chain risks through big data analytics: role of risk alert tool in mitigating business disruption

Predicting supply chain risks through big data analytics: role of risk alert tool in mitigating business disruption

Advancements in Sustainable Manufacturing Supply Chain Modelling: a Review

Advancing Logistics 4.0 with the Implementation of a Big Data Warehouse: A Demonstration Case for the Automotive Industry

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