供应链网络中可追溯性技术的采纳
Traceability Technology Adoption in Supply Chain Networks
AI总结：
针对供应链网络中可追溯性技术采纳的种子集合选择难题，提出基于线性规划的固定参数可处理算法，并利用大规模实验揭示网络结构对扩散的影响，为管理者优化扩散策略提供依据。
摘要：
现代可追溯性技术有望通过简化召回、提高可见性或验证可持续的供应商实践来改善供应链管理。主导实施可追溯性技术的倡议必须选择成本最低的企业集合——即种子集合——作为早期采纳的目标。选择这个种子集合具有挑战性，因为企业是相互关联的复杂网络中的供应链的一部分，从而产生固有的供应链效应：从可追溯性中获得的好处取决于产品供应链中一部分企业对技术的采纳。我们证明了在供应链网络中选择成本最低的种子集合的问题难以解决，甚至难以在多项式对数因子内近似。尽管如此，我们提供了一种新颖的基于线性规划的算法来识别成本最低的种子集合。该算法在供应链网络的树宽上是固定参数可处理的，我们证明在现实世界的供应链网络中树宽较低。该算法还使我们能够推导出易于计算的关于选择最优种子集合成本的界限。最后，我们利用我们的算法进行大规模数值实验，提供关于供应链网络结构如何影响扩散的见解。这些见解可以帮助管理者优化其技术扩散策略。
英文摘要：
Modern traceability technologies promise to improve supply chain management by simplifying recalls, increasing visibility, or verifying sustainable supplier practices. Initiatives leading the implementation of traceability technologies must choose the least-costly set of firms - or seed set - to target for early adoption. Choosing this seed set is challenging because firms are part of supply chains interlinked in complex networks, yielding an inherent supply chain effect: benefits obtained from traceability are conditional on technology adoption by a subset of firms in a product's supply chain. We prove that the problem of selecting the least-costly seed set in a supply chain network is hard to solve and even approximate within a polylogarithmic factor. Nevertheless, we provide a novel linear programming-based algorithm to identify the least-costly seed set. The algorithm is fixed-parameter tractable in the supply chain network's treewidth, which we show to be low in real-world supply chain networks. The algorithm also enables us to derive easily-computable bounds on the cost of selecting an optimal seed set. Finally, we leverage our algorithms to conduct large-scale numerical experiments that provide insights into how the supply chain network structure influences diffusion. These insights can help managers optimize their technology diffusion strategy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
