How to use predictive analytics and AI in supply chain transformation

Supply chain leaders face challenges with siloed data. A unified data model and predictive analytics pave the way for an AI-driven future.

Three questions to ask

• How can organizations effectively integrate disparate data sources to achieve real-time visibility in their supply chains?
• What foundational elements support the implementation of a unified data model?
• In what ways can organizations leverage advanced data platforms to enhance decision-making and operational efficiency?

This article was authored by: Raj Jaasthi, Principal, Business Consulting, Ernst & Young LLP

In a world where natural disasters cripple factories and trade wars reshape global supply flows, executives see disruption in headlines every day — yet they can’t precisely evaluate the impact on their supply chains due to unresolved challenges: fragmented data, outdated systems and organizational inertia. This subverts not only end-to-end visibility for today but also the potential of generative and agentic artificial intelligence (AI) for tomorrow. The goal is a proactive supply chain with agility, but leaders are fighting just to be reactive.

The solution lies in a unified data model, powered by modern platforms, real-time feeds and predictive analytics. Together, these elements form the cornerstone of resilient supply chains, fostering agility today and unlocking actionable insights for tomorrow, while serving as the foundation to master our impending AI future. However, organizations face the challenge of transforming their supply chains from cost centers into engines of growth that deliver customer satisfaction and competitive advantage.

EY research from late 2024 illustrates this conflict. It shows that 25% of supply chain leaders admit their organizations are unprepared for geopolitical tensions, such as wars or tariffs. Nearly a quarter lack readiness for managing transportation disruptions, and 23% are vulnerable to another health crisis like a pandemic. It’s not surprising that so many organizations can’t handle extreme weather, sudden shortages or health crises — all common developments that can disrupt or halt manufacturing. One reason for the continued challenges, despite years of technology investments, is the reliance on traditional tools that are unsuited for today’s business environment and are failing under pressure.

For instance, legacy enterprise resource planning (ERP) systems, designed for batch processing, are ill-suited for the real-time demands of modern supply chains, which must be agile enough for sudden demand spikes or geopolitical shifts. Data lakes, meant to centralize information, often become siloed across functions, rendering them ineffective. (EY research shows that 17% of companies have a supply chain data lake but fail to use it.) The financial burden is equally daunting: maintaining legacy ERP systems and underutilized data lakes drains budgets that could be redirected to innovation.

The result is a fractured landscape where critical insights are buried in spreadsheets or locked in incompatible systems, leading to cascading problems, as reflected in our research:

• Thirty-eight percent of supply chain leaders cite fragmented data and a lack of integrated platforms as their top barrier to tracking key performance indicators (KPIs).
• Another 34% struggle with the absence of upstream or downstream data, leaving them blind to supplier or customer dynamics.
• For 22% of organizations, supply chain collaboration remains primitive, reliant on emails and shared spreadsheets — a method that invites errors and delays.

Yet glimmers of progress are emerging: 42% of companies are embarking on digital journeys, adopting cloud-based tools to streamline supply chain functions, and 18% have migrated most of their data to the cloud, though many have yet to fully leverage the data and tools to generate insights. These steps, while encouraging, are merely the foundation. Below we outline steps to make strides on your digital supply chain transformation journey.

The power of a unified data model and AI

The catalyst for supply chain transformation is a unified data model, which integrates disparate sources into a single coherent view. By weaving together near-real-time feeds from Internet of Things (IoT) devices, sensors and cloud platforms, this model delivers a dynamic, end-to-end picture of the supply chain. It fosters collaboration by providing teams with a shared, accurate view of operations, from supplier performance to customer satisfaction, and it slashes the time required to gather and analyze KPIs, turning a once-arduous task into a seamless process.

Enhanced visibility sharpens risk management, allowing companies to use predictive analytics to anticipate disruptions rather than scramble in their wake. For example, real-time data can flag a weather-related delay at a key port, enabling rerouting before bottlenecks form. Forecast accuracy and metrics like inventory turns, overall equipment effectiveness and on-time in-full (OTIF) delivery become not just measurable but actionable, updated instantly to reflect the latest realities. OTIF use cases, as well as those in sales and operations execution, illustrate the power of end-to-end integration, revealing how a single decision reverberates across the supply chain. For instance, adjusting production to meet a sudden demand surge can be modeled to ensure it doesn’t compromise delivery timelines or inflate costs.

Over time, this clarity cultivates a culture of continuous improvement, where insights drive iterative gains in efficiency and resilience. Crucially, a unified data model is also the bedrock for future AI programs. Without trusted, integrated data, AI initiatives falter. But with it, companies can deploy tools like demand sensing to predict market shifts, predictive maintenance to pre-empt equipment failures, or digital twins to simulate supply chain scenarios. Generative AI can help automate complex tasks, such as supplier negotiations, while agentic AI — capable of autonomous decision-making — beckons as the next frontier.

Actions to take

The path to this future demands deliberate action:

1. Rigorously assess data capabilities, identifying gaps in integration and visibility. This audit is both technical and strategic, aligning infrastructure with long-term goals.
2. Invest in a unified data model and supporting platforms that enable predictive analytics. These systems must prioritize real-time insights, leveraging IoT, cloud technologies and scalable architectures to ensure agility. For example, a global retailer might integrate IoT data from warehouse sensors with supplier feeds to optimize inventory in real time.
3. Foster a data-driven culture. Dismantle silos, train teams to embrace analytics and embed collaboration into the core of the supply chain.
4. Define a clear AI roadmap, starting with foundational tools like machine learning-based forecasting, predictive maintenance and progress toward autonomous systems.

Each step builds momentum, creating a virtuous cycle of insight and innovation. But the cost of inaction is steep. Organizations that cling to fragmented data and analog processes risk being outpaced by competitors who wield real-time insights with precision — becoming not just lagging but irrelevant and unable to meet customer demands or weather the next crisis. Conversely, those who embrace a unified data model and predictive analytics in their supply chains can navigate disruption with agility, delight customers with precision and unlock efficiencies that fuel growth. A unified view of the supply chain helps transform it from a cost center into a strategic asset, capable of driving competitive advantage.