STAR: Shining Light on Space Supply Chain Risk

Ronald Birk, Lori W. Gordon, and Eleanor Mitch outline the factors behind the need for a system that dynamically updates space supply chain information. Along with higher demand, there is competition among sectors, such as medical device and auto makers, for certain commodities and many rare earth elements. The authors propose a distributed ledger technology (DLT) system called “Space supply chain Topology for Assessing Risk (STAR)” that would create a nexus for all stakeholders in the space supply chain community.

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A Maturity Model for supply chain risk management

Purpose

Supply chains are among the most important, complex and risky systems in the modern world. Thus, managing risk is no longer an option, but a fundamental process in organizations. Given the lack of pathways that guide companies toward supply chain risk management (SCRM), the purpose of this study is to provide a conceptual reference, in the form of a maturity model, to support them in the evolution and improvement of this process.

Design/methodology/approach

The proposal covered a broad literature review, a survey and a multiple case study. The research was conducted in the aerospace industry and included companies from the supply chain of a leading aircraft manufacturer.

Findings

The model elaborated with the research results has eight attributes and four levels, addressing critical issues for SCRM to achieve its scope and purposes. The attributes include the structuring and scope of the SCRM process, the importance it receives within the organization, the resources used and the qualification of employees, the role of leadership and the inter-organizational collaboration.

Practical implications

Managing risk along supply chains is particularly challenging, demands resources and knowledge and requires a continuous effort. The proposed model offers a reference for improvement, helping to identify areas that need to be strengthened and practices to be implemented. Thus, it can guide the focus and efforts in a more efficient and systematic way, in addition to support evaluations and comparisons.

Originality/value

Although maturity models are abundant in different fields and several are available for risk management, models specifically developed for SCRM are scarce. This study broadens the understanding of SCRM with novel insights about how to improve this process in an evolutionary way. While many researchers focused their efforts on the SCRM process steps, this study identified critical issues that transcend these steps. The research was carried out in a sector with a long tradition in risk management and included companies belonging to a same supply chain, that is, using an approach still little explored in studies on SCRM or risk management maturity models.

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AI-based evaluation system for supply chain vulnerabilities and resilience amidst external shocks: An empirical approach

The study focuses on the intricacies and vulnerabilities inherent in supply chains, which are often influenced by external disruptions such as pandemics, conflict scenarios, and inflation. The aim is to devise an AI-driven system that can accurately appraise these intricacies within the domain and mitigate their vulnerabilities effectively. The work employs an empirical approach utilizing datasets from various studies for developing Machine Learning (ML) and Deep Learning (DL) models. 

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Developing Supply Chain Capabilities Through Digitalization and Viability for Controlling the Ripple Effect

The COVID-19 pandemic affected all industries and presented manufacturing firms with enormous challenges, with considerable changes in consumer demand for goods and services. Supply chain management disruption caused by the COVID-19 outbreak resulted in several socio-economic roadblocks. The slow propagation of disruption risk results in a ripple effect along the entire chain. The lack of resilience and risk management capability is the prime cause, attributed to the unavailability of digital resources, skills, and knowledge. 

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Manipulating Supply Chain Demand Forecasting With Targeted Poisoning Attacks

Demand forecasting (DF) plays an essential role in supply chain management, as it provides an estimate of the goods that customers are expected to purchase in the foreseeable future. While machine learning techniques are widely used for building DF models, they also become more susceptible to data poisoning attacks. In this article, we study the vulnerability of targeted poisoning attacks for linear regression DF models, where the attacker controls the behavior of forecasting models on a specific target sample without compromising the overall forecasting performance.

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Manufacturer’s Contexts, Supply Chain Risk Management, and Agility Performance

he dynamism of the current business environment emanates significant challenges and disruption risks for supply chains. These vulnerabilities in contemporary supply chains have motivated a substantial academic focus on supply chain risk management (SCRM). In the empirical literature on SCRM, a firm’s external environment is conceptualized as a source of risk, and various organizational and technological factors are discussed as influencers of SCRM. 

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