Extracting supply chain maps from news articles using deep neural networks

Wichmann, Pascal; Brintrup, Alexandra; Baker, Simon; Woodall, Philip; McFarlane, Duncan

doi:10.1080/00207543.2020.1720925

Cited by 71 publications

(60 citation statements)

References 29 publications

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“…The standardisation capability assists in gathering, analyzing and assessing control information of potential risk triggers that could materialize into systematic disruptions (Heckmann et al, 2015). Challenges of collaborative approaches to build resilience during a pandemic point to the need for addressing fears on consequence of increased visibility across collaborating HCSC partners: fears over disintermediation of the HCSC; the need to protect a supplier from outside intervention; decision blocking, malicious intent (SC cyberattacks); and incompatible performance criteria (Wichmann et al, 2020). Behavioral uncertainty has a significant negative effect on SCCRM antecedents such as trust and commitment (Chao et al, 2013).…”

Section: 2mentioning

confidence: 99%

A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic: capabilities for enabling health-care supply chain resilience

Friday

Savage

Melnyk

et al. 2021

JHLSCM

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PurposeInventory management systems in health-care supply chains (HCSC) have been pushed to breaking point by the COVID-19 pandemic. Unanticipated demand shocks due to stockpiling of medical supplies caused stockouts, and the stockouts triggered systematic supply chain (SC) disruptions inconceivable for risk managers working individually with limited information about the pandemic. The purpose of this paper is to respond to calls from the United Nations (UN) and World Health Organization (WHO) for coordinated global action by proposing a research agenda based on a review of current knowledge and knowledge gaps on the role of collaboration in HCSCs in maintaining optimal stock levels and reinforcing resilience against stockout disruptions during pandemics.Design/methodology/approachA systematic review was conducted, and a total of 752 articles were analyzed.FindingsCollaborative planning, forecasting and replenishment practices are under-researched in the HCSC literature. Similarly, a fragmented application of extant SC collaborative risk management capabilities undermines efforts to enhance resilience against systematic disruptions from medical stockouts. The paucity of HCSC articles in humanitarian logistics and SC journals indicates a need for more research interlinking two interdependent yet critical fields in responding to pandemics.Research limitations/implicationsAlthough based on an exhaustive search of academic articles addressing HCSCs, there is a possibility of having overlooked other studies due to search variations in language controls, differences in publication cycle time and database search engines.Originality/valueThe paper relies on COVID-19's uniqueness to highlight the limitations in optimization and individualistic approaches to managing medical inventory and stockout risks in HCSCs. The paper proposes a shift from a fragmented to holistic application of relevant collaboration practices and capabilities to enhance the resilience of HCSCs against stockout ripple effects during future pandemics. The study propositions and suggestion for an SC learning curve provide an interdisciplinary research agenda to trigger early preparation of a coordinated HCSC and humanitarian logistics response to future pandemics.

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Section: 2mentioning

confidence: 99%

A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic: capabilities for enabling health-care supply chain resilience

Friday

Savage

Melnyk

et al. 2021

JHLSCM

View full text Add to dashboard Cite

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“…A hybrid technique that combines machine learning and simulation was developed and used to examine its applicability in decision-making support in SCP and resilient supplier selection (Cavalcante et al 2019 ). Furthermore, Wichmann et al ( 2020 ) discussed automated supply chain mapping to maintain supply chain structural visibility by using deep learning which facilitated automatic extraction of the supplier–buyer relation from natural language text. It enabled firms to validate existing supply chain maps, generate fundamental supply chain maps automatically, and enhance existing maps with further information of the suppliers.…”

Section: Emerging Research Themes Of Ai and Supply Chain Resiliencymentioning

confidence: 99%

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

et al. 2021

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The challenging situations and disruptions that occurred due to the outbreak of the COVID-19 pandemic have created a severe need for supply chain resiliency (SCR). There has been a growing interest among researchers to investigate the resiliency in supply chain operations to overcome risks and disruptions and to achieve successful project management. The supply chain of every business requires innovative projects to accomplish competitive advantage in the market. This study was conducted to identify the significance of artificial intelligence (AI) for creating a sustainable and resilient supply chain, and also to provide optimum solutions for supply chain risk mitigation. A systematic literature review has been conducted to examine the potential research contribution or directions in the field of AI and SCR. In total, 162 articles were shortlisted from the SCOPUS database in the chosen field of research. Structural Topic Modeling (STM), a big data-based approach, was employed to generate several thematic topics of AI in SCR based on the shortlisted articles, and all topics were discussed. Furthermore, the bibliometric analysis was conducted using R-package to investigate the research trends in the area of AI in SCR. Based on the conducted review of literature, a research framework was proposed for AI in SCR that will facilitate researchers and practitioners to improve technological development in supply chain firms. The purpose is to combat sudden risks and disruptions so that project management will perform well Post COVID-19. The study will be also helpful for future researchers and practitioners to identify research directions based on existing literature covered in this paper in the field of SCR. Future research directions are proposed for AI-enabled resilient supply chain management. This study will also provide several implications for supply chain managers to achieve the required resilience in their supply chains post COVID-19 by focusing on the elements of the proposed research framework.

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“…Hence companies need to be more attentive to their networks and gather data to form an understanding of their positioning. Data from independent intermediaries could be used as well as supporting new technologies such as supply chain mining (Wichmann et al 2020) or supply chain link prediction . Modelling and simulation may help investigate the effect of structural change, relate network characteristics to performance potentially in wider industrial settings.…”

Section: Conclusion and Managerial Implicationsmentioning

confidence: 99%

How does the position of firms in the supply chain affect their performance? An empirical study

Arora

Brintrup

2021

Appl Netw Sci

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The relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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Extracting supply chain maps from news articles using deep neural networks

Cited by 71 publications

References 29 publications

A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic: capabilities for enabling health-care supply chain resilience

A collaborative approach to maintaining optimal inventory and mitigating stockout risks during a pandemic: capabilities for enabling health-care supply chain resilience

Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research

How does the position of firms in the supply chain affect their performance? An empirical study

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