Cloud computing can provide scalable solutions for data storage and sharing that make it easier to collaborate and exchange information at every stage of the supply chain. Achieve transformative results through scalable infrastructure, advanced data analytics, robust security, targeted advertising and cutting-edge generative AI. Two ARC Advisory Group white papers on the next stage of AI in supply chain operations.

Accenture Invests in Aera Technology to Fuel AI-Enabled Supply Chains

Finally, our analysis underscored the transformative potential of integrating digital twins with edge analytics. Future studies were recommended to build empirical frameworks that harnessed real‐time sensor data to feed into digital twin models, thereby enabling instantaneous, data‐driven decision‐making. Comparative studies examining centralised versus distributed digital twin architectures could have provided crucial insights into how these technologies impacted response times, cost efficiency, and overall resilience.

How to Optimize A&D Intralogistics Operations With Automated Asset Tracking

Attacks disrupt ordering systems, track-and-trace platforms, and sometimes entire production operations. Climate-related events are increasing, making them a growing threat to global supply chain stability. Companies that prepare in advance face less damage and recover faster when disasters strike without warning. A global food giant partnered with Accenture to revolutionize their supply chain and operations using AI, data and automation, resulting in significant efficiency gains.

The Impact of Geopolitical Events on Global Supply Chains

Second, they employed machine learning or advanced statistical techniques to improve the robust optimisation model for supply chain resilience. The literature increasingly references emerging technologies like digital twins and edge analytics, but only a handful of studies offer concrete examples of their combined use in supply chain resilience. Digital twin models, which replicate physical operations in real time, offer exceptional situational awareness, while edge analytics enables instantaneous data processing close to the source, reducing latency in decision-making. Future research could build frameworks where real‐time sensor data (e.g. from factories, warehouses, or transport nodes) feed into digital twin simulations, supplemented by edge analytics that filter or aggregate data locally to reduce bandwidth demands. By exploring “what‐if” scenarios (e.g. labour shortages, component failures) in a virtual environment, managers can swiftly reconfigure production lines, re‐route shipments, or allocate resources to mitigate potential disruptions. Comparative studies evaluating distinct digital twin architectures (e.g. centralised cloud vs. distributed edge systems) and their impact on response times and cost‐efficiency would offer actionable insights for practitioners.

The interplay between the external environment and supply chain networks is fundamental to the operations of businesses and society (Ivanov, 2018). The interconnectedness of supply chains and the environment with human culture and the global economy have made the interactions within these ecosystems exceedingly nuanced and complex (Paul et al., 2023; Tarigan et al., 2021). Such interdependencies amplify vulnerabilities while also creating opportunities for adaptive and resilience strategies, thus necessitating a multidimensional understanding of systemic risk and resilience (Berger et al., 2025; Yang et al., 2023). This complexity creates a challenging situation where many disciplines promote resilience as a coping tool (Alikhani et al., 2025).

The four pillars of resilient supply chains

These buyers are explicitly codifying procurement frameworks with an emphasis on supplier redundancy. Retail pharmacy chains are using the same approach, particularly in markets where consumer demand surges can quickly reduce inventory levels. From a segment perspective, gel-based formulations are most https://callmeconstruction.com/news/beyond-the-highway-how-trucker-dating-is-steering-romance-into-a-new-era/ vulnerable to supply chain volatility because of their dependence on multiple chemical inputs and emulsifying agents. Patch-based systems, although more advanced in terms of technology, are also exposed to the same risk due to a shortage of sources of adhesive material. The spray and roll-on formats are somewhat more resilient due to simpler formulation structures, but they are not immune to API scarcity. Hospitals and health systems are deliberately splitting volume allocations among several vendors to minimize dependency risk.

• Second, supply-demand imbalances, particularly in more mature chip process nodes and technologies, could emerge, leading to the second-guessing of recent investments.
• As a futurist, he is dedicated to exploring how these innovations will shape our world.
• COVID-19 is present again, indicating research focus on managing disruptions due to such unforeseen events.
• Governments aim to ensure that products use eco-friendly designs and minimize counterfeits.
• This extensive reach ensures that businesses can shift or reroute operations quickly in response to unexpected disruptions, reducing the risk of dependence on a single region.

We use your contact information to respond to your inquiries or to provide information on products or services to you. C5MI implements, standardizes, and sustains SAP Digital Core systems — delivering the performance the investment was designed to produce, and maintaining it long after go-live. At Stage 4, real-time data stops being observed and starts driving action — dynamic task reallocation, exception-triggered responses, workforce adjustments made in minutes rather than hours. The operation develops the capability to adapt to what the live data is telling it, not what a shift debrief reveals afterwards. Robotics, IoT, edge computing, and automation are changing Supply Chains into intelligent ecosystems that anticipate and adapt.

Evidently, the food, automotive, and manufacturing industries were emphasised in the literature analysis since recognising the implications of these industries is crucial. Even though many papers have varied case studies and themes, the food, automotive, and manufacturing sectors are highlighted as keywords in 31 of them. This underscores their prominence in the scholarly literature on supply chain resilience. Given their susceptibility to disruptions and risks, these industries are the focus of numerous researchers. These platforms allow for real-time data access, integrate seamlessly with other technologies and can help improve collaboration across global supply chains. Companies are also adopting automated inventory management tools to mitigate supply chain bottlenecks and ensure material availability.

Financial and operational risk

Therefore, ML equips supply chain management professionals with the knowledge and tools required to make better decisions and enhance the overall efficacy of their operations. By employing ML models, practitioners may better comprehend client preferences, optimise processes, and improve customer service. Designing a supply chain network represents a strategic decision where supply chain management and resilience converge. Specifically, supply chain network design (#47) integrates key elements such as risk management (#35) and the proactive handling of disruptions (#57) that affect markets, industries, and organisations. Notably, recent events like the COVID-19 pandemic (#38) have starkly revealed that many supply chains lack the resilience (#138) necessary to withstand localised outages and the cascading loss of connectivity across multiple tiers (Aldrighetti et al., 2021).

Visibility and Coordination Become Strategic

They are exploring both “nearshoring” options in Canada and Mexico and “reshoring” options in the United States. The share of US trade in goods with China has declined from 21.2% in 2018 to 13.9% in 2023. The p-robust approach integrates two objectives, specifically the minimisation of expected costs and the minimisation of the worst-case costs, by reducing the expenses scheduled while constraining the relative regret in each scenario. The solutions obtained by p-robust, ensure that the close regret of the solutions does not surpass 100% P in any given scenario (Snyder & Daskin, 2006). In other words, the stochastic p-robust optimisation model is a viable method for optimisation under conditions of uncertainty.

Elevating sustainability objectives has also redefined the role of supply chain managers, who now face the task of identifying and mitigating environmental and social risks across multifaceted, global, networks (Gouda & Saranga, 2018). For example, variability in supplier performance or market demand can disrupt production schedules, leading to either excess inventory or costly shortages (Paull et al., 2025). For example, supply chain uncertainties have made remanufacturing more complex and less efficient, hindering sustainable enterprises’ growth and the circular economy (Peng et al., 2020). Furthermore, the ambiguity in uncertainty raises the probability that risks, and causes adverse operational outcomes (Gital & Bilgen, 2024). Furthermore, the presence of uncertainty can elevate the probability of https://pankisi.info/the-beginners-guide-to-20/ a risk materialising, whereby risk is a manifestation of ambiguity, specifically, the lack of certainty regarding future events.

Turning to ML, the application of ML algorithms has been highlighted as a possible means of enhancing supply chain management processes. ML algorithms may utilise large volumes of data to anticipate future events and give insights into the efficacy of present operations. This can aid supply chain management practitioners in identifying improvement opportunities, optimising operations, and making better-informed decisions. To utilise ML for supply chain management goals, practitioners must have adequate data to develop reliable models.