Search Results for « Supply Chain »

While consolidation strategies form the backbone of many supply chain optimisation problems, exploitation of multi-tier material relationships through consolidation remains an understudied area, despite being a prominent feature of industries that produce complex made-to-order products. In this paper, we propose an optimisation framework for exploiting multi-to-multi relationship between tiers of a supply chain. The resulting formulation is flexible such that quantity discounts, inventory holding, and transport costs can be included. The framework introduces a new trade-off between tiers, leading to cost reductions in one tier but increased costs in the other, which helps to reduce the overall procurement cost in the supply chain. A mixed integer linear programming model is developed and tested with a range of small to large-scale test problems from aerospace manufacturing. Our comparison to benchmark results shows that there is indeed a cost trade-off between two tiers, and that its reduction can be achieved using a holistic approach to reconfiguration. Costs are decreased when second tier fixed ordering costs and the number of machining options increase. Consolidation results in reduced inventory holding costs in all scenarios. Several secondary effects such as simplified supplier selection may also be observed.

Most of the theories that have dominated supply chain management (SCM) take a reductionist and static view on the supply chain and its management, promoting a global hunt for cheap labor and resources. As a result, supply chains tend to be operated without much concern for their broader contextual environment. This perspective overlooks that supply chains have become both vulnerable and harmful systems. Recent and ongoing crises have emphasized that the structures and processes of supply chains are fluid and interwoven with political‐economic and planetary phenomena. Building on panarchy theory, this article reinterprets the supply chain as a social–ecological system and leaves behind a modernist view of SCM, replacing it with a more contemporary vision of “dancing the supply chain.” A panarchy is a structure of adaptive cycles that are linked across different levels on scales of time, space, and meaning. It represents the world’s complexities more effectively than reductionist and static theories ever could, providing the basis for transformative SCM.

This paper systematizes knowledge about secure software supply chain patterns. It identifies four stages of a software supply chain attack and proposes three security properties crucial for a secured supply chain: transparency, validity, and separation. The paper describes current security approaches and maps them to the proposed security properties, including research ideas and case studies of supply chains in practice. It discusses the strengths and weaknesses of current approaches relative to known attacks and details the various security frameworks put out to ensure the security of the software supply chain. Finally, the paper highlights potential gaps in actor and operation-centered supply chain security techniques

Building a strong supply chain is essential for business success. But when it comes to improving their supply chains, few companies take the right approach. Many businesses work to make their chains faster or more cost-effective, assuming that those steps are the keys to competitive advantage. To the contrary: Supply chains that focus on speed and costs tend to deteriorate over time. The author has spent 15 years studying more than 60 companies to gain insight into this and other supply chain dilemmas. His conclusion: Only companies that build supply chains that are agile, adaptable, and aligned get ahead of their rivals. All three components are essential; without any one of them, supply chains break down. Great companies create supply chains that respond to abrupt changes in markets. Agility is critical because in most industries, both demand and supply fluctuate rapidly and widely. Supply chains typically cope by playing speed against costs, but agile ones respond both quickly and cost-efficiently. Great companies also adapt their supply networks when markets or strategies change. The best supply chains allow managers to identify structural shifts early by recording the latest data, filtering out noise, and tracking key patterns. Finally, great companies align the interests of the partners in their supply chains with their own. That's important because every firm is concerned solely with its own interests. If its goals are out of alignment with those of other partners in the supply chain, performance will suffer. When companies hear about the triple-A supply chain, they assume that building one will require increased technology and investment. But most firms already have the infrastructure in place to create one. A fresh attitude alone can go a long way toward making it happen.

Large Language Models (LLMs) have recently enabled natural language interfaces that translate user queries into executable SQL, offering a powerful solution for non-technical stakeholders to access structured data. However, one of the limitation that LLMs do not natively express uncertainty makes it difficult to assess the reliability of their generated queries. This paper presents a case study that evaluates multiple approaches to estimate confidence scores for LLM-generated SQL in supply chain data retrieval. We investigated three strategies: (1) translation-based consistency checks; (2) embedding-based semantic similarity between user questions and generated SQL; and (3) self-reported confidence scores directly produced by the LLM. Our findings reveal that LLMs are often overconfident in their own outputs, which limits the effectiveness of self-reported confidence. In contrast, embedding-based similarity methods demonstrate strong discriminative power in identifying inaccurate SQL.

Supply chain vulnerability has become a fashionable area of management research. The purpose of this paper is to provide a critique of the extant canon and to review of the positioning of research in the field, together with literature drawn from several relevant and overlapping fields of research and practice. The aim is to foster a more explicit understanding of the relationships between supply chain vulnerability, risk and supply chain management, and in turn their relevance to related fields such as corporate governance, business continuity management, security and emergency planning. The ultimate objective is to clarify the agenda for further research. The paper begins with an examination of the concept of a “supply chain” and the scope and nature of supply chain management (SCM), then the fusion of SCM with the many and varied interpretations of “risk” and its faltering relationship to risk management. It is argued that attitudes to risk and approaches to risk management vary greatly within SCM and between related disciplines. It is concluded that although more work is needed within the SCM discipline, the issue of supply chain risk and vulnerability should not be addressed solely from a functional SCM perspective. Supply chain vulnerability is a concern for many other managerial disciplines. Research agendas should therefore strive to inform and support the needs of all those with a legitimate interest in supply chain risk and vulnerability management.

Supply Chain Management requires addressing a variety of complex decision-making challenges, from sourcing strategies to planning and execution. Over the last few decades, advances in computation and information technologies have enabled the transition from manual, intuition and experience-based decision-making, into more automated and data-driven decisions using a variety of tools that apply optimization techniques. These techniques use mathematical methods to improve decision-making. Unfortunately, business planners and executives still need to spend considerable time and effort to (i) understand and explain the recommendations coming out of these technologies; (ii) analyze various scenarios and answer what-if questions; and (iii) update the mathematical models used in these tools to reflect current business environments. Addressing these challenges requires involving data science teams and/or the technology providers to explain results or make the necessary changes in the technology and hence significantly slows down decision making. Motivated by the recent advances in Large Language Models (LLMs), we report how this disruptive technology can democratize supply chain technology - namely, facilitate the understanding of tools' outcomes, as well as the interaction with supply chain tools without human-in-the-loop. Specifically, we report how we apply LLMs to address the three challenges described above, thus substantially reducing the time to decision from days and weeks to minutes and hours as well as dramatically increasing planners' and executives' productivity and impact.

Since the late 1950s it has been recognised that the systems used internally within supply chains can lead to oscillations in demand and inventory as orders pass through the system. The uncertainty generated by these oscillations can result in late deliveries, order cancellations and an increased reliance on inventory to buffer these effects. Despite the best efforts of organisations to stabilise the dynamics generated, industry still experiences a high degree of uncertainty from this source. The “supply chain complexity triangle” describes the interaction of deterministic chaos, parallel interactions and demand amplification. It provides a framework for understanding the generation of uncertainty within supply chains. The implications for supply chain strategy and manufacturing logistics are discussed.

Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limitations come up when commercial discrete event simulation software are used for modeling complex real world-systems (i.e. supply chains, industrial plants). The objective of this paper is twofold: first a state of the art on commercial discrete event simulation software and an overview on discrete event simulation models development by using general purpose programming languages are presented; then a Supply Chain Order Performance Simulator (SCOPS, developed in C++) for investigating the inventory management problem along the supply chain under different supply chain scenarios is proposed to readers.

In today's uncertain and turbulent markets, supply chain vulnerability has become an issue of significance for many companies. As supply chains become more complex as a result of global sourcing and the continued trend to “leaning‐down”, supply chain risk increases. The challenge to business today is to manage and mitigate that risk through creating more resilient supply chains.