Improving supply chain risk management - can additional data help?

Urciuoli, Luca; Hintsa, Juha

doi:10.1504/ijlsm.2018.10013175

Cited by 4 publications

(3 citation statements)

References 38 publications

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“…The prevailing paradigm of government regulation and management of cross-border trade in goods has been incrementally impacted since the inception of the 21st century by several interrelated factors driving change. Change drivers range from heightened policy concerns about security and safety risks in international supply chains (Urciuoli, 2018) to the increasingly sophisticated use of prearrival data collection and IT automation by governments. Innovation in national border clearance processes may also be driven by bilateral and multilateral arrangements, notably the Revised Kyoto Convention (RKC) (WCO, 2006) and the treaties administered by the World Trade Organization (WTO), including the recent WTO Agreement on Trade Facilitation (TFA) (WTO, 2017).…”

Section: Contemporary Border Goods Clearancementioning

confidence: 99%

Re-examining Border Clearance in the Age of E-Commerce

Blegen

2020

World Customs Journal

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Traditional customs clearance processes for goods arriving in ports or at border crossings involve declaration, inspection, revenue collection, and release procedures focused in time and space at the point of arrival. This paradigm for government management of cross-border trade in goods has been incrementally impacted in the 21st century by change drivers ranging from heightened concerns about security and safety risks in international supply chains to the increasingly sophisticated use of prearrival data collection and IT automation in border management processes. Over the past decade, the rapid growth of online retailing, sometimes referred to as e-commerce, has led to an exponential increase in goods, often individually packaged, shipped directly from the seller to consumer. Existing border clearance models for international goods shipments were established in the context of an environment dominated by business-to-business commercial shipments of cargo for further distribution (for example, by retailers) in the country of destination. The rapidly expanding volume of cross-border direct-to-consumer shipments generates large numbers of small package consignments, presenting a challenge for existing goods clearance models in many countries. Contemporary border clearance processes may not be ideally suited to the rapidly expanding volumes of such shipments, and the limited reporting requirements often associated with such consignments may make effective risk management and revenue collection very difficult for governments. Border management modernisation initiatives spearheaded by multilateral organisations have generated some clarity in confirming that e-commerce’s effects do challenge traditional border clearance models, highlighting many of the underlying issues, but they have not generated a clear path forward on policy solutions. As a result, individual countries have been re-examining their national border clearance processes and are starting to take actions to tackle the e-commerce border clearance challenge on their own, highlighting the need for further multilateral work in this area to develop a consensus on policy measures to address the border management concerns identified.

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Section: Contemporary Border Goods Clearancementioning

confidence: 99%

Re-examining Border Clearance in the Age of E-Commerce

Blegen

2020

World Customs Journal

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“…Recently, many disastrous events, such as terrorist attacks, pandemics, natural disasters, etc., have hugely interrupted supply chains. As Urciuoli & Hintsa (2018) explained, changing business trends, globalization, complexity, and specialization immensely drive risk and gradually decrease managerial power to control operations (Urciuoli & Hintsa, 2018).…”

Section: Introduction To Supply Chain Managementmentioning

confidence: 99%

Sustainable/Robust Supplier Selection in the Post-Pandemic Era: Using Data Envelopment Analysis

Zahedi-Seresht,

Athaudage,

Lee

et al. 2023

AJIBM

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With the existence of COVID-19, the whole economy experienced an unprecedented challenge. Organizations must be resilient to the ever-changing and unanticipated market to avoid being out of the fierce competition. In an era of information explosion, managers require a systematic, explicable, comparative, and traceable approach to evaluate and choose suppliers. In recent years, procurement strategies have been revamped due to the disruption in the global supply chain by the pandemic and war in Europe. A wrong supplier selection decision seriously damages the Company's supply chain, operations, and reputation. Therefore, partnering with a sustainable supplier is a prerequisite for business success. With the rising importance of sustainability, choosing a competent supplier is one of the significant strategic management decisions. A sustainable supplier impacts business operations and accelerates long-term growth, enhancing efficiency and effectiveness. In the post-pandemic era, it is expected to have new approaches to define inputs and outputs to rank suppliers and logistics firms. This study uses Data Envelopment Analysis (DEA) to identify a sustainable supplier. Our approach involves selecting suitable inputs and outputs, improving the accuracy and relevance of the study to find sustainable/robust suppliers. The results of this research have been implemented in the business intelligence system of a company.

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“…Conversely, its development limitations are usually related to IT infrastructure, human resources and knowledge, and openness to information exchange in the supply chain . Urciuoli and Hintsa (2018) argue the importance of data to support risk management initiatives and any information sharing barrier may jeopardise all benefits of information sharing between companies.…”

Section: Literature Reviewmentioning

confidence: 99%

Algoritmos inteligentes aplicados à predição de atrasos em transporte aéreo

Dayrell Mendonça

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The air freight cargo industry is increasingly facing operational challenges due to tougher global competition and higher service level requirements from customers. Machine Learning techniques are consequently being applied as an aviation supply chain risk management (SCRM) approach in order to predict delays, reduce operational uncertainty and reduce costs. The objective of the research is to evaluate whether the use of machine learning techniques contributes to a better prediction of air transport shipment delays in order to optimize the performance of the international supply chain control capacity. For that, different classes of algorithms are tested in the data mining phase of KDD (Knowledge Discovery in Databases): Support Vector Machine, Random Forest, Artificial Neural Networks and K-Nearest Neighbors. The Random Forest algorithm achieved the best result with accuracy of 86% in the cross validation test scenario after a combined class balancing procedure. Overall, the research also adds to the current literature as both transport and supplier data are used in a specific machine learning application.

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Improving supply chain risk management - can additional data help?

Cited by 4 publications

References 38 publications

Re-examining Border Clearance in the Age of E-Commerce

Re-examining Border Clearance in the Age of E-Commerce

Sustainable/Robust Supplier Selection in the Post-Pandemic Era: Using Data Envelopment Analysis

Algoritmos inteligentes aplicados à predição de atrasos em transporte aéreo

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