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Exposing potential weaknesses in a supply chain before they become problems allows for a solutions-based approach. Technology can help organizations strategically prepare for the future's inevitable challenges while minimizing the impact of those disruptions as much as possible. Whether the outside stressor is a trade policy shift, increased regulations or component shortages, a technology stack designed to meet these threats head-on offers alternatives — different suppliers, components, logistics routes, or even different processes to deliver the products your customers expect. 

After careful consideration of the business needs, teams should look to solutions that empower resilience and implement the appropriate stack effectively. Successful completion of this aim results in proactive and reactive response, compliance, scalability, and efficiency. 

How a technology stack achieves the intended benefits is diverse and varied. Multiple categories of solutions exist that serve to strengthen supply chain resilience. Here are five supply chain technologies companies should consider implementing to prepare for the next disruption.

One of the major causes of recent supply chain planning challenges has been unpredictable swings in demand for physical goods — particularly goods that require electronic components and chips. To keep up with these demand shifts, lead times are up to 18 to 24 months before requested delivery, instead of the traditional six months or less. 

OEMs, in turn, must get a handle on their anticipated demand for a time well into the future and build the possibilities for disruption into that forecast. Demand planning and forecasting technology provides component and product alternatives to reduce inventory cost, allowing supply chain managers to make fast, high-quality decisions. 

The shift toward AI-powered forecasting is accelerating rapidly.  predicts that 70% of large organizations will adopt AI-based supply chain forecasting to predict future demand by 2030. AI-based forecasting can dynamically detect complex patterns across time series data, enabling more frequent and granular forecasts, and can learn from diverse datasets to make more reliable predictions on new product introductions or promotions where historical data is limited.  found that 94% of companies plan to use AI or GenAI for decision support — underscoring how rapidly this capability has moved from aspiration to expectation. 

It's important to remember that technology should just be one piece of a resilient supply chain strategy. At 91社区, we look at it through the lens of people first, then processes and technology. So, solid demand planning and forecasting starts with people — ensuring you're picking the right supplier partner that will be able to meet your organization's needs in your timeframe. Once your supplier is in place, collaborate to standardize your processes and implement technology. 

There are many worries that might keep supply chain leaders up at night; I don't think a demand forecast should be one of them. Though you absolutely need to create one, you can rest assured that it will be incorrect to some degree. Whether you get it wrong, or your customer gets it wrong, it's never going to be completely accurate. 

I think about demand forecasts this way: I drove myself to work today, so I have a car that's working. I've got my updated phone and my laptop, and last time I was at the store, there was food on the shelf. This all points to the fact that supply chains are working even though forecasting will never be accurate. But it's important to double down on investments in demand planning technology to ensure you have your arms around your forecasts to the best of your ability. 

One area of the supply chain where you can count on technology to deliver more precise data is the factory floor. Thanks to next-generation connectivity, supply chain teams can more clearly connect the dots between demand for a component, its procurement, and its use in manufacturing. 

With the advancements in 5G that cloud computing enables, which in turn vastly expands the possibilities of the Internet of Things, it's more possible than ever to keep supply chains in constant contact with the factories they're serving. According to , AI (59%) and cloud (56%) are the top technologies in active use among operations and supply chain leaders, and nearly all respondents say those capabilities are delivering value. IoT adoption remains lower — only 33% of leaders are using IoT-enabled supply chain capabilities — but of those who have deployed it, 52% say IoT has been very effective in creating value. The return on targeted IoT investment is clear; the gap is in implementation at scale. 

It's critically important to have an enterprise resource planning (ERP) system that leverages this ability to connect to the factories. Factories are where the action happens, generating data you can turn into actionable insights, intelligence and decisions that drive better supply chain outcomes. 

An ERP can integrate into broader manufacturing capabilities that rely on IoT connectivity, like artificial intelligence, machine learning, augmented reality, and virtual reality. These tools also orchestrate and provide visibility into the movement of materials once they enter the factory, a view of the supply chain that commodity managers traditionally may have lacked. 

Edge computing — done in decentralized processors away from traditional data centers or clouds but closer to the device requiring the connection — facilitates the function of these powerful, ultra-low latency IoT devices at the core of this connected future where supply chains and factories work in harmony. With edge computing, data is kept close to the source, reducing the potential for connectivity challenges or security threats. Data travels faster and more reliably on the edge, making it easier for supply chain leaders in warehouse and logistics operations to understand which products are in their facility and make real-time, analytical decisions. 

Logistics operations can also benefit from processing on the edge. The artificial intelligence and machine learning used by smart robots in warehouses depends on the highly reliable connections that edge computing provides. According to the  conducted by MHI, Peerless Research Group and The Robotics Group, 48% of organizations were using robots in their plants and warehouses in 2025, up from 23% just three years earlier — and 32% plan to adopt them within the next three years. As adoption accelerates, the edge computing infrastructure that connects those systems to broader supply chain operations becomes a critical planning consideration, not an afterthought. Automation is a piece of an optimized logistics technology stack. However, it requires connected infrastructure to function. 

Again, the goal of adding these systems to your technology stack is end-to-end visibility. Transportation and warehouse management systems should be integrated with your automated ERP to keep information about the location and movement of materials and products before and after they are in your factory all in one place for all stakeholders to find at any given time. 

At ports, dockworkers and truck drivers could have detailed information about how much is in each container for a particular OEM or that would be going to a particular warehouse. Meanwhile, warehouse supply chain managers would know exactly how many shipments arrive per day and per hour to schedule workers efficiently. 

An intelligent TMS should consolidate orders and freight to optimize each shipment, saving money and reducing the environmental footprint of each. Land, sea, and air routes can be created more efficiently, saving carbon emissions, labor costs, and time from order to delivery.

Supply chain leaders are making meaningful investments across a wide array of technologies, and the pace of adoption is accelerating. Here are a few examples of what organizations are exploring and deploying over the next five years.

Artificial intelligence and machine learning

Only 28% of supply chain leaders report using AI today, according to the . But that number is expected to nearly triple — reaching 82% — within five years. AI and machine learning are already adding value in inventory management, demand planning, logistics and disruption management. As connected factories are more fully realized, AI can be used to capture and analyze supply chain data right at the manufacturing line level, allowing supply chain managers to make highly informed, quick decisions.  research reinforces the urgency: top-performing supply chain organizations invest in AI and machine learning at more than twice the rate of their lower-performing peers. 

Robotics

The role of automation is growing rapidly throughout the supply chain, from autonomous mobile robots in warehouse environments to automated ports and long-haul transportation.  April 2026 prediction that half of new warehouses in developed markets will be human-optional by 2030 reflects the structural shift already underway: as labor availability tightens and AI improves robot coordination, companies are redesigning warehouses around robotic fleets rather than retrofitting for them. The automation imperative extends beyond the warehouse as well, with autonomous vehicles and drone delivery on the near-term deployment horizon for logistics operations. 

Supply chain traceability and data integrity

End-to-end traceability — a tamperproof, real-time view of all activities in a given supply chain, from payments and orders to inventory and compliance — is increasingly a strategic priority and, in some industries and regions, a regulatory requirement. New supply chain due diligence laws in the EU and elsewhere require companies to demonstrate compliance across multiple supplier tiers. By 2030, forecasts that SCM software with agentic AI will grow from under $2 billion in 2025 to $53 billion, with 60% of enterprises using SCM software having adopted agentic AI features by that point, up from 5% in 2025. The organizations investing in traceability infrastructure and intelligent orchestration now are better positioned to meet those requirements without emergency remediation when regulations tighten further. 

This should be the goal of any organization's supply chain technology stack: visibility. Supply chain roadmaps, and the technology stack used to create them, must be continually reevaluated for efficiency, effectiveness and relevancy in an ever-shifting landscape. A strong supply chain technology stack can help an OEM move toward a position of cost leadership and offer a competitive advantage for their customers. 

With a clearer view of the current ecosystem and potential hazards ahead, organizations can then leverage their people and processes to build supply chain resiliency and navigate disruptions as they come.