As an emerging technology, the knowledge graph (KG) has been successfully applied in various industries. Though some potential benefits of the KG have been identified, there is still little work on implementing the KG in supply chain risk management (SCRM). This study develops a KG-based risk management framework to improve the resilience of Supply Chains (SCs). Specifically, the construction of the SC knowledge graph (SC-KG) framework, including the implementation steps, is presented in detail for the purpose of SC knowledge retrieval, data visualization analysis, risk monitoring, early warning, and decision support. Furthermore, the SC-KG is well constructed to build a scenario-based SCRM framework under consideration of the severity of disruptions. Especially during long-term disruptions, the continuity of SCs is maintained through the employment of a product change strategy and a structurally scalable and dynamically adapted network design method. The findings of the study are instructive for SC managers in adopting digital technologies for SC mitigation and recovery under disruptions. Finally, a practical SC-KG containing over 2.5 million entities and 11 types of relationships has been developed and its basic functions have been implemented, which contributes to improving the quality of SC management.

Dynamics in supply chains (SCs) can trigger risks due to the changing and propagating nature. In the context of COVID-19, this article presents an assessment-to-control decision-making support scheme to tackle propagation effect uncertainties of SCs considering product changes. First, a new decision model is proposed for risk warnings, with the potential advantages that: 1) propagation effects can be assessed generally and objectively and 2) permitting control theory to integrate and identify the interrelations between propagation effects. More specifically, the bullwhip effect (BE) with operational and behavioral causes is quantified as cascading amplified inventory fluctuations. The ripple effect (RE) from large-scale supplier disruptions driven by COVID-19 is quantified as increased entropy rates (ERs). Then, the system studied is integrated as a closed-loop control system under provided change control. Moreover, some criteria are derived for the existence of controller gains/decision coefficients to stabilize the closed-loop system with the BE mitigation under the RE. Finally, a mask SC case study under COVID-19 is performed for examining the effectiveness of the proposed scheme.

The product and its supply chain (SC) are two key parts of corporate competitiveness under mass production or customization. Nevertheless, they are frequently isolated in manufacturing field evolution. At the operational decision level, this article studies uncertainty processing in the coevolution of products and SCs using a measure-to-control supported approach. A decision system framework that can incorporate macrochange interactions between products and SCs is presented. From the systematic view on coevolution, an interface for measure and control of uncertainty propagation is provided, which has two potential benefits: 1) facilitating concurrent measures of risks with multiple causes and consequences; and 2) allowing control theory to tackle multiple dynamics and identify their interrelations. Specifically, via effective value and entropy rate measures of bullwhip effect and ripple effect, cost-guaranteed control methods are proposed for attenuating risks of the product-SC uncertainty system. Finally, a mask SC application verifies the robustness and resilience of the proposed approach.

This paper deals with the problems of networked modelling and memory-event-triggering $H_{\mathrm{\infty }}$ reliable control for steel jacket-type platforms in network environments. First, a networked model of the jacket platform against earthquakes and probabilistic actuator faults is established. Based on this model, a memory-based event-triggering communication scheme is introduced to account for the constrained communication resources. A distinct feature of the proposed triggering scheme is that it has great potential to identify and trigger the significantly changed data than the existing ones without ‘memory’. Then, by formulating the network-based closed-loop platform system as a stochastic delay system, some sufficient conditions are derived for co-designing the desired controller and the triggering scheme. Finally, comparative simulation results are provided to demonstrate the effectiveness and merits of the proposed triggering and control co-design method. It is shown that compared with some existing event-triggering control methods, the proposed memory-event-triggering $H_{\mathrm{\infty }}$ reliable controller is effective to mitigate vibrations of the jacket platform and is potentially advantageous to save more network bandwidth and control cost.