How Digital Twins Could Transform the Supply Chain

AI in procurement and supply chain is a “why now” issue because spending has moved faster than confidence in outcomes. According to Consumer Goods Technology, 67% of supply chain digital investments are now allocated to artificial intelligence, yet more than half of chief supply chain officers remain uncertain about the ROI of those AI investments. That gap makes the current moment especially important: leaders are no longer deciding whether AI belongs in the supply chain technology stack; they are deciding how to prove it creates measurable business impact. The urgency is also structural. www.gep.com reports that AI is becoming procurement’s operating model rather than a bolted-on feature. In practice, that shifts AI from experimentation into the way teams source, plan, evaluate suppliers, manage risk, and support decisions. Once AI becomes embedded in operating workflows, weak governance, unclear metrics, or poorly defined use cases can scale quickly across the organization. Consumer Goods Technology also notes that organizations are investing heavily in AI while measuring business impact remains challenging. That combination creates pressure for procurement and supply chain leaders to move from broad AI ambition to disciplined execution: selecting use cases tied to specific outcomes, defining ROI measures before deployment, and ensuring teams can act on AI-generated recommendations. The opportunity is significant, but the timing matters because investment is already flowing. The next phase is about converting that investment into accountable performance, not simply adding more AI capability. The first major trend is not simply that manufacturers and procurement teams are adopting more AI. It is that AI value is shifting from model capability to data connectivity, because AI performance is becoming constrained by how well commercial, operational, and procurement data are connected. According to www.gep.com, most procurement data still lives in silos across supplier records, spend data, and contracts. That fragmentation matters because, as www.gep.com reports, a connected data layer lets AI reason, while fragmented data prevents real intelligence from forming. The same pattern is visible in manufacturing commerce. Oro-Commerce reports that most manufacturing production data has historically remained locked away from the commercial layer, limiting its use for buyer outcomes. In practical terms, the data needed to support better buying experiences may already exist, but it is often separated from the systems that shape quoting, pricing, product availability, and customer account context. AI cannot reliably improve buyer outcomes if it cannot see the relevant production and commercial signals together. This makes data readiness a core AI strategy rather than a back-office cleanup effort. Oro-Commerce recommends that organizations ensure product data is clean, account hierarchies across ERP, PIM, and CRM systems are unified, and pricing rules are consistent to achieve consistent AI results. That recommendation points to a broader shift: enterprises are moving from experimenting with isolated AI features toward building the data foundations required for repeatable, explainable, and commercially useful AI outputs. For manufacturers, distributors, and procurement organizations, the implication is clear. AI initiatives will be limited if supplier records, spend data, contracts, production data, product information, account structures, and pricing logic remain disconnected. The near-term competitive advantage will come from making those data layers coherent enough for AI systems to reason across them. In this trend, the winning investment is not only the AI tool itself, but the connected data environment that allows the tool to produce trustworthy results. Building on that foundation, the second trend is the move from reactive operations to simulation-led decision-making. Manufacturers are using AI not only to automate individual tasks, but to anticipate what may happen across equipment, orders, products, and supply chains before teams commit labor, inventory, or capital. According to Oro-Commerce, AI systems in manufacturing can flag equipment degradation, catch defects at line speed, and compress planning cycles. That shifts ecommerce and operations workflows from “detect and respond” toward “predict and prevent.” This matters because the same predictive layer can connect production signals with buyer behavior. Oro-Commerce reports that predictive models can flag potential reorders before buyers log in. In a B2B commerce environment, that means the platform can surface replenishment prompts, account-specific recommendations, or inventory guidance before a customer manually searches for a part or repeats an order. The experience becomes less like a static catalog and more like a planning interface tied to operational reality. Digital twins extend this trend further. Oro-Commerce describes digital twin technology as a way to simulate product behavior and supply chain dynamics in real time, enabling virtual testing before physical resources are committed. For manufacturers, that can support better decisions about product performance, fulfillment options, and supply readiness. Instead of waiting for a disruption to expose weak assumptions, teams can test scenarios earlier and adjust plans with less waste. The supply chain use case is especially important as volatility increases. www.gep.com says digital twins can simulate disruptions such as tariff shifts, port closures, and unreliable routes before they happen. That capability gives procurement, supply chain, and commerce teams a shared planning model for evaluating risk. If a route becomes unreliable or a tariff change alters landed cost, the business can model alternatives before the customer experience is affected. In practice, this trend pushes B2B ecommerce platforms closer to operational intelligence systems. The winning platforms will not simply display available products; they will help manufacturers forecast demand, anticipate replenishment, test supply scenarios, and guide buyers toward decisions that reflect real production and logistics constraints. However, digital twins can only transform supply chains if the simulation layer is fed by normalized, workflow-ready operational data. Stargo’s supply chain benchmarks show that AI-assisted vendor document normalization cut supplier onboarding cycle time by 11.4 days on average, underscoring that the path to better scenario planning often starts with fixing the document and approval flows that shape supplier readiness. In one anonymized deployment, Stargo also processed 18,000 purchase-order attachments in the first 30 days without increasing back-office headcount—evidence that supply chain AI value depends on execution capacity, not just predictive modeling. The next shift is not simply more automation; it is agentic AI moving from task automation to orchestration across B2B commerce. AI agents are beginning to coordinate work across procurement, sales, commerce, contracts, and supply chain systems. According to www.gep.com, agentic AI refers to systems that act rather than only answer: they can detect issues, evaluate options, and take steps without constant prompting. That matters because many B2B workflows are not single-step tasks. They involve RFQs, quotes, contract terms, purchase orders, approvals, exceptions, and supplier or customer communications. www.gep.com also distinguishes orchestration from basic automation: automation handles one isolated task at a time, while orchestration coordinates many tasks across the source-to-pay process. In practical terms, that means AI is being positioned less as a chatbot and more as a workflow participant that can move information, check requirements, surface exceptions, and trigger the next action across connected systems. Oro-Commerce reports that AI agents are expected to increasingly intermediate B2B buying processes, including triaging inbound RFQs, generating draft quotes for review, and reconciling purchase orders against contracts. That points to a more agent-led commercial operating model, where the buyer’s and seller’s systems may exchange structured requests, draft responses, and compliance checks before a human steps in for review or approval. A key enabler is interoperability. www.gep.com says shared technical standards allow AI agents to exchange information across vendors and systems. Without those standards, agents remain trapped inside individual platforms and can only optimize narrow parts of the process. With them, agentic AI can support cross-system coordination: a buying agent can interpret a need, a supplier-side agent can prepare a quote, and procurement systems can compare the order against contract terms. The strategic implication is that companies should evaluate AI not only by how well it completes a single task, but by how reliably it can orchestrate multi-step, multi-system workflows with appropriate human oversight.