The future of traceability: from IIoT to AI and blockchain

Driving forces of change

This technological evolution is not happening in a vacuum. It is being accelerated by a perfect storm of economic, social, and regulatory pressures that demand a smarter, more accountable approach to supply chains.

• The 'conscious consumer': today's consumers demand radical transparency. They want to know the ‘story’ behind their products: where the raw materials came from, the conditions under which items were made, and the carbon footprint of their journey. This pressure for provenance is a major catalyst for innovation.
• Regulatory pressure: governments worldwide are enacting stringent regulations. From the FDA's ‘New Era of Smarter Food Safety’ to the EU's proposed ‘Digital Battery Passport’ regulations are mandating granular, end-to-end traceability to ensure safety, combat counterfeiting, and enforce environmental standards.
• Sustainability and ESG mandates: corporations are under intense pressure to meet environmental, social, and governance (ESG) goals. Traceability is no longer just operational – it is the fundamental mechanism for proving ethical sourcing, verifying recycled content, and accurately calculating supply chain carbon emissions.

From reactive to predictive

The combination of IIoT and AI is elevating traceability from an operational tool to a core strategic asset for creating agile, intelligent manufacturing systems. This synergy is transforming the fundamental question that traceability answers, from 'What happened?' to 'What is likely to happen, and what should we do about it?'.

Industrial internet of things (IIoT)

The industrial internet of things (IIoT) refers to the network of interconnected sensors, smart devices, and industrial machinery that collect and share data within a manufacturing environment. IIoT acts as the central nervous system for real-time traceability. While traditional systems rely on discrete data capture events (like a barcode scan at a workstation), IIoT enables a continuous stream of rich, contextual data.

Sensors embedded in machinery can monitor critical process parameters like temperature, pressure, and vibration in real-time. This data is automatically linked to the specific product or batch being processed, creating a dynamic and incredibly detailed digital history. This moves traceability from a simple record of movement to a comprehensive log of the exact conditions a product experienced at every moment of its creation.

Edge computing

The sheer volume of data generated by IIoT sensors creates a significant bottleneck. Sending this data deluge to a central cloud for analysis introduces latency – a delay that is unacceptable for mission-critical production decisions.

Edge computing solves this by performing data processing and AI-driven analysis directly at the source of data generation (eg on the machine itself or at a local gateway). This enables:

• Instantaneous action: an AI model running on an edge device can detect a micro-vibration that predicts a tool break and halt the machine in milliseconds, before a defect is even created.
• Reduced data load: the edge device processes the raw data stream and sends only relevant insights and summaries to the central system, reducing bandwidth and storage costs.
• Operational resilience:local systems can continue to run and make intelligent decisions even if the central cloud connection is lost.

Digital twin

As IIoT sensors and edge devices generate this continuous, pre-processed data stream, a new, powerful concept becomes possible – the digital twin. A digital twin is a high-fidelity, virtual replica of a physical asset, process, or even an entire supply chain.

This is not a static 3D model – it is a living, dynamic entity, continuously updated with real-time data from its physical counterpart. For traceability, the digital twin of a product or batch becomes its living history. Instead of querying a static database, stakeholders can interact with this virtual proxy to:

• Monitor real-time condition: see the exact status and environmental conditions of a product right now.
• Replay its history: visually and empirically review the exact journey and all process parameters a product experienced.
• Run ‘what-if’ scenarios: simulate how a change in logistics or production (eg a different shipping route) might impact product quality or shelf life, moving traceability into the realm of simulation and optimisation.

AI and machine learning (ML)

If IIoT is the nervous system and the digital twin is the living model, then AI and ML are the brain that processes and makes sense of the massive volumes of data generated. AI algorithms can analyse this real-time and historical data to identify complex patterns, predict future outcomes, and even recommend or automate corrective actions.

• Predictive quality control: instead of waiting for a defect to be found by a downstream inspection, AI can analyse process data to identify subtle deviations and anomalies that are precursors to quality issues. For example, an ML model might learn that a specific pattern of temperature fluctuations in an injection moulding machine often leads to a cosmetic defect. By flagging this pattern in real-time, the system can alert operators to make an adjustment before any bad parts are produced.
• Predictive maintenance: by analysing data from sensors on equipment (eg vibration, temperature, and power consumption), AI can predict when a machine is likely to fail. This allows maintenance to be scheduled proactively, preventing costly unplanned downtime and its associated impact on production schedules and quality.
• Supply chain optimisation and risk prediction: by analysing traceability data alongside external data sources (eg weather forecasts, shipping lane congestion, and geopolitical news), AI models can predict potential supply chain disruptions and recommend alternative sourcing or logistics strategies, building a more resilient and agile supply network.
• Dynamic demand forecasting: By linking granular traceability data (what's being made and sold) with external signals (like social media trends or holiday schedules), AI can forecast demand shifts with greater accuracy, allowing for proactive adjustments in production and inventory.

Explainable AI (XAI)

As AI takes on a greater role in decision-making, ensuring that its processes are transparent and understandable becomes critical, especially in regulated and safety-critical industries. Explainable AI (XAI) is a set of techniques and methods designed to address the 'black box' problem, where the inner workings of complex algorithms are opaque to human users.

XAI is crucial for building trust and confidence in AI-powered traceability systems. It provides methods to trace and explain how an AI model arrived at a specific conclusion or prediction. Techniques like DeepLIFT, which shows the links between activated neurons, and decision understanding ensure that AI-driven actions are auditable, accountable, and trustworthy, which is essential for both regulatory compliance and user adoption.

Blockchain technology

While IIoT and AI enhance the intelligence of traceability systems within and between trusted partners, blockchain technology addresses a different, but equally critical, challenge: building trust in multi-party environments where participants do not inherently trust one another.

What is blockchain, and why does it matter for supply chains?

At its core, a blockchain is a decentralised, distributed, and immutable digital ledger.

• Decentralised/distributed: instead of being stored in one central location, the ledger is copied and spread across a network of computers. This eliminates a single point of failure and control.
• Immutable: once a transaction (or 'block') is recorded and added to the chain, it is cryptographically linked to the previous one. Altering a block would require altering all subsequent blocks and gaining consensus from the majority of the network, making it virtually tamper-proof.

These features make blockchain a powerful tool for creating a shared, single version of the truth in a supply chain, enhancing integrity and accountability among all participants.

Key benefits of blockchain for traceability

• Enhanced data integrity and security: the immutable nature of the blockchain provides a high degree of confidence that the traceability data has not been altered or falsified after being recorded. This is a powerful tool against fraud and data tampering.
• Increased transparency and auditability: all permissioned stakeholders in the supply chain (eg supplier, manufacturer, logistic provider, regulator) can view the same ledger, creating an unprecedented level of transparency. This provides a clear, time-stamped, and unchangeable audit trail of every transaction and movement of a product.
• Automation with smart contracts: blockchains can host 'smart contracts' – self-executing contracts with the terms of the agreement directly written into code. These can automate business processes, such as triggering a payment to a supplier automatically upon verified receipt of goods, or releasing a shipment once quality control data is uploaded, reducing administrative friction and delays.

Real-world applications and a balanced perspective

Blockchain is already being applied to solve real-world traceability challenges. Walmart has famously used it to trace the provenance of pork in China and leafy greens in the U.S., reducing the time it takes to trace a food's origin from days to mere seconds. De Beers uses it to track diamonds, assuring customers they are conflict-free. Shipping giant Maersk has used it to track global shipping containers, streamlining complex international logistics.

However, it is important to maintain a balanced perspective. Blockchain is not a panacea for all traceability challenges. Widespread adoption has been hindered by significant hurdles:

• Scalability and performance: public blockchains can be slow and consume large amounts of energy, though private and consortium blockchains used for enterprise applications are often more efficient.
• Interoperability: a lack of universal standards makes it difficult to connect different blockchain platforms and integrate them with existing legacy systems like ERP and MES.
• Cost and complexity: implementing blockchain solutions remains a complex and expensive undertaking, requiring specialised expertise.
• Data privacy: the transparent nature of blockchain raises valid concerns about sharing sensitive business data. This has led to the dominance of private or consortium blockchains, where access is restricted to a select group of vetted participants.

Solving the privacy paradox with zero-knowledge proofs

One of the most promising solutions to the blockchain privacy challenge is the rise of Zero-Knowledge Proofs (ZKPs). ZKPs are a cryptographic technique that allows one party to prove to another that a specific statement is true, without revealing any of the underlying data used to make that proof.

In a supply chain, this means a supplier could prove to a manufacturer that a component meets a specific quality or organic certification standard without revealing their proprietary process, formula, or other sensitive business data. This allows for ‘trustless’ verification while maintaining commercial confidentiality, solving a key barrier to blockchain adoption.

Augmented reality (AR)

All this data is useless if it cannot be accessed by a human operator at the point of action. This is where augmented reality comes in. AR devices, such as smart glasses or mobile tablets, overlay digital information directly onto an operator's view of the physical world.

This technology is a game-changer for traceability:

• Hands-free information: a warehouse worker wearing smart glasses can simply look at a pallet, and its digital twin data – origin, destination, handling requirements – is instantly displayed in their field of vision.
• Guided operations: a maintenance technician can look at a machine and see its entire history, recent sensor readings, and AI-predicted failure points, along with step-by-step digital instructions for the repair.
• Visualising quality: an inspector can point a camera at a component and see a real-time overlay of its expected measurements versus its actual state, instantly flagging deviations invisible to the naked eye.

Enabling the circular economy

Perhaps the most profound impact of advanced traceability is its role as the central enabler of the circular economy. The traditional ‘take-make-dispose’ linear model is no longer sustainable. The future demands a circular model based on designing out waste, keeping products and materials in use, and regenerating natural systems.

This is impossible without granular traceability. To refurbish, remanufacture, or recycle a complex product, you must know exactly what it is made of, its usage history, and its condition. Advanced traceability creates a ‘material passport’ for every single item. This passport:

• identifies components by providing a detailed bill of materials for effective disassembly and sorting;
• logs usage and wear that allows for accurate assessment of a product's remaining useful life; and
• verifies provenance to prove that recycled content is genuine, creating a trusted market for secondary materials.

Traceability, therefore, provides the foundational data layer upon which a global circular economy can be built.

The converged ecosystem: the future is integrated

The true future of traceability lies not in any single one of these technologies, but in their powerful convergence. They form a synergistic loop that creates a truly intelligent ecosystem:

1. IIoT acts as the sensory layer, capturing high-fidelity, real-time data from the physical world.
2. Edge computing processes this data locally, enabling instantaneous, real-time decisions.
3. This data feeds the digital twin, creating a living, virtual model of the product and its journey.
4. AI and ML algorithms act as the brain, continuously analysing the digital twin to detect patterns, predict outcomes, and prescribe actions.
5. Augmented reality (AR) provides the human-machine interface, visualising this data for operators at the point of action.
6. Blockchain serves as the system of record – an immutable, decentralised notary (using ZKPs for privacy) that securely logs every event, making the entire history verifiable and trustworthy for all parties.

The ultimate vision is a supply chain that is no longer a rigid, reactive chain of events, but an adaptive, intelligent, and autonomous organism. It's a system that can pre-emptively reroute shipments before a storm hits, automatically reject a raw material batch based on its sensor-logged history, and provide a consumer with an immutable, verifiable record of a product's entire lifecycle and circular potential, all with the scan of a code.

From vision to reality: connecting your supply chain

This is where Pagero provides the 'engineering certainty' to make it happen. While this article outlines the what and the why, we build the how.

We deliver end-to-end automation solutions that form the central nervous system of any traceability system. From intelligent production lines and high-precision robotic workstations to advanced EOL (end-of-line) testers that integrate inspection and traceability, we build the physical systems that capture the data with unmatched precision and reliability.

Don't just plan for the future of traceability. Build it.