PLM Trends 2026: What CIOs in Manufacturing, Pharma and Retail Need to Know
Key Takeaways PLM investment in most organisations has outpaced PLM outcomes. The platform exists. The product definition stops at engineering sign-off. What happens between sign-off and the shopfloor is where value leaks. The biggest PLM failures in 2026 are coordination failures, not technology failures. The product definition moves. The business context behind it does not. AI cannot surface useful answers inside PLM unless engineering changes are connected to bills of materials, production routings, supplier records and quality history. Most organisations have not built that connection yet. Deploying AI before fixing it produces confident wrong answers. The digital thread breaks at functional boundaries. Not because the data is missing, but because each function works from its own version of the product with no shared process language connecting them. Supply chain volatility in 2026 is a product design problem, not a procurement problem. The organisations that respond fastest to disruption are the ones where PLM supports rapid requalification across sourcing, compliance and production simultaneously. Why does PLM keep failing to deliver outcomes despite significant investment? PLM has been a fixture in enterprise technology conversations for two decades. It keeps returning in 2026 not because organisations have avoided investing in it, but because the investment has not produced the operational outcomes that were originally expected. CIOs are no longer being asked whether they have a PLM platform. They are being asked whether a product decision made in that platform is still legible six weeks later, across three functions, in live operations. That is a harder question. For many organisations, the honest answer is no. The gap is not in the software. Oracle Fusion Cloud PLM provides a structured product record, change management workflows, compliance traceability and integration across the Oracle stack. The gap is in what happens after the platform does its job. Whether the product truth it holds continues to inform planning decisions, manufacturing conditions, quality judgements and supplier conversations, or whether it stops at engineering and gets reconstructed manually everywhere else. That is the conversation CIOs need to be having in 2026. What is actually changing in PLM in 2026? PLM was built to store product data and manage revisions. That original purpose still holds. What has changed is the expectation of what the platform must connect to and what it must carry into live operations. HCLTech published a PLM trends piece in February 2026 describing PLM moving toward becoming the central nervous system of the modern product enterprise. The framing is accurate. PLM is no longer a passive system sitting beside the engineering function. It is being asked to sit inside the operating model, connecting product strategy to supply chain execution, manufacturing performance, quality governance and service continuity. That shift has direct consequences for CIOs. Platform choices, governance structures and integration decisions made about PLM today will determine whether AI, sustainability compliance and supply resilience are buildable on top of that foundation in the next three years. PLM decisions in 2026 carry a longer consequence than most platform decisions do. Why does the digital thread keep breaking in complex organisations? The digital thread is the right concept. A connected flow of product data from design intent through planning, manufacturing, quality and field performance is what complex product businesses genuinely need. The problem is that most digital threads break the moment work crosses a functional boundary. Engineering approves a change. Procurement receives a partial signal. Manufacturing works from an older version of the specification. Quality resolves a deviation at site level without knowing whether the same pattern has appeared elsewhere. Service inherits the result without understanding what changed upstream or why. Each function is likely doing the right thing within its own frame. The break appears when the business context behind one decision fails to travel into the next. A design change approved for good commercial reasons arrives in manufacturing as an instruction with no explanation attached. A sourcing substitution that solved an immediate supply problem creates a process stability issue two weeks later that nobody connects back to its origin. This is not a data problem. Most complex organisations have more data than they can usefully act on. It is a process language problem. Engineering, supply chain, manufacturing and quality each speak their own operational language. PLM, as it is currently deployed in most organisations, has not been set up to translate between them. It stores the product record. It does not carry the reasoning behind it. The organisations getting this right in 2026 are the ones where a product definition change still carries its business context into planning, into the plant and into quality review, without someone having to manually reconstruct that context from three different systems. What does AI actually need to work inside PLM? AI is arriving inside every major PLM platform in 2026. Copilots, agentic change workflows, component recommendation engines, impact analysis tools. The vendor roadmaps are ambitious and some of the early capabilities are genuinely useful. There is a precondition most vendors are not being direct about. AI in PLM cannot return reliable answers unless the process layer beneath it is clean and structurally connected. Asking an AI to surface the impact of an engineering change is only meaningful if the engineering change is connected to the bill of materials, which is connected to the production routing, which is connected to the supplier qualification record, which is connected to the quality event history. If those connections are incomplete, and in most organisations they are, the AI surfaces partial answers with high confidence. That is a worse outcome than no answer, because teams act on it. HCLTech’s position on this is worth noting. AI in PLM must be treated as a controls problem, not a novelty. The guardrails matter more than the model. What decisions can be delegated safely, what the cost of error is, how auditability and rollback are handled. Without those guardrails, agentic PLM workflows create liability faster than they create value. CIOs evaluating AI-enabled PLM investments need to answer one question before
Why Product Lifecycle (PLM) Problems Keep Returning in Manufacturing
Key Takeaways Product lifecycle problems in manufacturing usually begin when product definitions, plant decisions, quality events, and supply changes stop staying connected across the business. Most manufacturers already have PLM, ERP, MES, and quality systems in place, yet product truth still weakens when decisions move from one function to another. On the shopfloor, this shows up as repeated trade offs, slower issue resolution, unstable quality, and teams solving the same problem with partial context. The real challenge is not system presence alone. It is whether engineering intent still informs planning, manufacturing, quality, and service when operating conditions change. Oracle Fusion Cloud Product Lifecycle Management helps create a structured product record, but manufacturers still need stronger continuity between that product truth and day to day execution. Connected Intelligence helps close this gap by linking product, supply, plant, and quality signals so decisions can be made with fuller business context. A product starts as one definition and gets reworked across the business A product starts with clarity. It has a specification, a cost target, a performance expectation, a route to market, and a reason to exist. At that point, the business still sees it as one commercial and operational object. The trouble starts when that definition begins to pass through different teams, different systems, and different constraints. Engineering works from design intent. Procurement works from supplier capacity, cost pressure, and lead times. Manufacturing works from routings, line conditions, yield targets, downtime, and output commitments. Quality works from compliance, traceability, and deviation discipline. Service teams inherit the result later, often after a series of upstream changes have already altered the conditions under which the product will perform. Each function may be doing the right thing within its own frame. The weakness appears when the product is no longer being managed with one shared business understanding behind it. That is why lifecycle management still matters, but only when it is treated as an operating discipline rather than a software label. The real weakness sits between systems Most manufacturers are not short of applications. PLM exists. ERP exists. MES exists. Quality systems, planning tools, spreadsheets, and site-level workarounds also exist. On paper, the stack looks complete. Yet the same problems return. A design revision is approved, but the plant may still be operating from older assumptions. A supplier substitution solves an immediate materials issue while changing scrap, yield, or process stability on the line. A deviation is closed at site level, but nobody can say with confidence whether the same pattern has shown up elsewhere. The problem is not that the business lacks systems. The problem is that the meaning of one decision often fails to travel into the next. McKinsey has written about the need for critical data elements such as engineering data, manufacturing data, and bills of material to move across ERP, PLM, and MES in a digital thread from engineering to servicing. Gartner’s public MES definition says much the same in more operational language, describing MES as a class of production software that manages, monitors, and synchronizes real time physical processes and coordinates execution with ERP, PLM, and quality management systems. The gap appears when that coordination does not carry enough business context into execution. On the shopfloor, lifecycle theory meets production pressure This is where the issue becomes expensive. The shopfloor does not work from lifecycle diagrams. It works from customer commitments, material shortages, machine downtime, labour availability, changeover pressure, and quality thresholds that do not move just because conditions have changed. Plant teams make decisions to keep output moving. They re sequence work, substitute materials, adjust settings, hold or release batches, and solve problems with the information available at that moment. Those decisions are often sound in local terms. The issue is that they are not always made with enough upstream and downstream context. A line adjustment that protects throughput today may create a quality issue tomorrow. A sourcing change that protects supply this week may affect process behaviour in production next week. A resolved deviation may return in another site because the earlier case remained local knowledge instead of becoming shared operating knowledge. Manufacturing needs context at the point of decision Many transformation programmes answer fragmentation with more visibility. More dashboards are built. More alerts are created. More reports are pushed into the business. Yet most plants are already rich in data. What they often lack is usable context when decisions have to be made fast. A production team needs more than a work order and a status screen. It needs to know what changed in the product definition, whether similar cases have occurred before, how supplier variability may affect the current situation, and what the likely planning and quality consequences are. Without that context, each team ends up solving the problem again from the beginning. This is where Oracle Fusion Cloud Product Lifecycle Management (PLM) fits naturally into the discussion. Oracle says its PLM platform helps standardize and structure the data and processes used to innovate, develop, and commercialize products and services. In its product material, Oracle also describes PLM as helping development and supply chain teams unify processes and manage data more effectively. That is the right foundation. The harder business question is whether that product truth continues to inform planning, manufacturing, quality, and service when work reaches live operations. Where Connected Intelligence fits For InspireXT, this is where Connected Intelligence becomes commercially useful. The issue in many manufacturing environments is not a shortage of data. It is that product, plant, supply, and quality decisions are still being made inside separate functional frames, even though the product itself moves across all of them at once. Connected Intelligence addresses that by bringing together the signals that usually sit apart, including product definitions, engineering changes, supply constraints, plant conditions, quality events, and downstream operating effects. The value is not just clearer reporting. The value is better judgement under production pressure. When teams can see how one decision will affect the next part of the chain, they can reduce
From Recipe to Retail: Accelerating New Product Introduction (NPI) in Food & Beverage
Key Takeaways Accelerate R&D Product launches slow when formulation, packaging and sourcing sit in different systems. Moving from Agile PLM to Cloud PLM begins to bring these pieces into one working rhythm, allowing teams to move without waiting on each other. Ensure compliance at the source Allergen data and packaging details need to be right long before production begins. A Specright implementation brings this into one place, replacing scattered files with a single, dependable view of product specifications. Protect margins through execution Plans hold value only when they carry through to procurement and supply. When specification data flows into an Oracle Fusion implementation, buying, inventory, and financial planning begin to reflect what was actually designed. The Digital Divide in Food Manufacturing The Food and Beverage industry moves on narrow margins and accelerating launch cycles than ever before. Consumer preferences shift quickly, and regulatory expectations continue to tighten around sourcing, allergens, and packaging. Yet the moment a new recipe leaves the test kitchen, something changes. What was clear and contained begins to scatter. Formulations remain tied to legacy lifecycle systems. Packaging details find their way into spreadsheets. Procurement and inventory sit inside ERP environments that rarely see the full picture. Each system holds a part of the truth, but none of them carry it end to end. This is where most new product introductions begin to slow, not because the idea lacks strength, but because the path it must travel is broken in quiet places. A connected digital thread changes that journey. It allows the data behind a product to move as one, from the first formulation to the moment it reaches the shelf, without being rewritten or reinterpreted along the way. What follows is how leading food manufacturers are beginning to build that continuity across their systems. How specification data anchors compliance and product execution Before a product reaches production, its definition is already set—ingredients, allergens, packaging, and regulatory attributes. The issue is not the absence of this information, but where it sits and how consistently it travels. In most food organisations, formulation data is managed in PLM, packaging details are maintained separately, and compliance information often lives across documents or manual records. Each dataset is maintained with care, yet they rarely come together at the point where decisions are made. This creates a recurring operational gap: When a formulation changes, packaging and labellFing must be revalidated manually. When a compliance query is raised, teams assemble information across systems under time pressure. When products move from development into sourcing, specifications are often reinterpreted, introducing variation into procurement and production. This pattern is not incidental. A recent analysis by McKinsey & Company highlights that fragmented data environments remain one of the primary causes of inefficiency across manufacturing supply chains, particularly where product data does not flow consistently across functions. Similarly, research from Deloitte points to continued reliance on manual data handling and disconnected systems as a key driver of compliance and traceability risk in food and beverage operations. A strategic Specright implementation addresses this issue at the source by structuring specification data, ingredient composition, allergen profiles, packaging attributes, and supplier details all within a single system. Instead of being referenced across documents, this data becomes the primary input for downstream processes. This changes execution in practical terms. When a formulation is updated, downstream packaging and labelling requirements reflect that change without requiring revalidation. Compliance checks are based on defined data rather than assembled inputs. Product definitions move into sourcing and procurement without being recreated, reducing interpretation at each stage. The impact shows up in execution, where rework reduces, regulatory responses become faster and more consistent, and product data remains aligned as it moves through development, sourcing, and production. Regulatory expectations reinforce this shift. The U.S. Food and Drug Administration continues to emphasise traceability, data integrity, and the ability to demonstrate control across manufacturing and quality processes, requirements that become difficult to meet when specification data is fragmented or manually assembled. Managing specifications as a connected dataset allows organisations to meet these expectations with consistency rather than effort. However, defining the product correctly is only one part of the system. The next challenge is ensuring that this definition carries through into procurement, inventory, and financial planning without being altered or re-entered. How product definition translates into execution with Oracle Fusion Defining a product correctly is only the starting point. The real test begins when that definition moves into procurement, inventory, and financial planning, where even small inconsistencies begin to carry cost. In many food organisations, this transition is where alignment breaks. Product specifications defined during development do not consistently carry through into ERP systems, which means procurement teams often source against outdated inputs, inventory positions begin to reflect assumptions rather than actual product requirements, and financial models continue to rely on cost structures that no longer match what is being produced. This misalignment rarely appears as a single failure. It builds gradually across cycles, as each function works from a slightly different version of the product. Procurement negotiates based on one set of specifications, planning operates on another, and finance closes the loop using data that has already shifted. Over time, this creates a steady drift between what was designed and what is executed. The operational impact is measurable. Planning cycles extend because data must be reconciled across systems before decisions can be made. Inventory builds against outdated or incomplete specifications, increasing working capital pressure. Costing accuracy declines as product changes are not reflected in real time, leading to margin leakage that is often identified only after production. McKinsey highlights that supply chain performance improves significantly when planning and execution are synchronised across functions, rather than operating in isolation. An Oracle Fusion implementation begins to address this by acting as the operational backbone where product, supply chain, and financial data converge. When integrated with upstream systems such as Cloud PLM and specification platforms like Specright, it allows product definitions to move directly into procurement, inventory management, and planning processes without being
From Disruption Response to Signal-Led Supply Chains
Key Takeaways Batch release delays often begin days earlier, in supplier deviations, missed deliveries, or quality flags that never came together in one view. Each function optimises its own metric, yet no one sees how today’s decision reshapes tomorrow’s execution. By the time a planner adjusts supply or a plant reschedules, the impact has already moved downstream into service levels and working capital. Most firefighting on the floor is the system compensating for decisions taken without full network context. Control returns only when demand, supply, and execution signals meet at the same decision point, not after the outcome is visible. The System Fails Before the Disruption Does What looks like a sudden disruption rarely begins where it is first seen. A missed delivery, a production delay, or a quality issue at release often carries signals that were already present days, sometimes weeks, earlier—inside supplier performance shifts, logistics variability, or capacity strain. These signals exist, but they remain scattered, held within functions, and never brought together in time to influence a decision. By the time the issue is recognised, it has already moved across tiers and into execution. The response then becomes reactive, not because the organisation is slow, but because the system was never designed to act earlier. What appears as disruption is simply the moment the system can no longer absorb what it failed to see. Fragmented visibility creates misaligned decisions The underlying issue is structural. Demand, supply, logistics, and risk signals are captured across different systems and functions, each operating with its own view of the network. While individual functions may have visibility, the business does not. As a result, decisions are taken based on partial information, and their downstream impact is only understood after execution begins. This is where supply chains lose control—not at the point of disruption, but at the point of decision. Small shifts are amplified because the system does not respond in a coordinated way. Supply chains need to operate on signals, not events Responding to disruption after it occurs is no longer sufficient. The operating model needs to shift from event-driven response to signal-led coordination, where inputs from across the network directly inform decisions before disruption materialises. This requires that demand changes, supplier risks, logistics constraints, and inventory positions are understood together at the point of action. When decisions are made in isolation, variability propagates. When they are made in context, variability is contained. The difference is not in the speed of response, but in when and how decisions are taken. When signals and decisions operate together A supply chain that cannot align signals at the point of decision will continue to react to its own outcomes. What changes in a signal-led model is not the presence of disruption, but the timing and coherence of response. When demand, supply, and execution signals are brought into the same operating context, decisions begin to reflect how the network is actually behaving, not how it was last reported. Actions taken in one part of the system no longer create unintended consequences elsewhere because their impact is already understood at the point they are made. This is where a connected view becomes operational rather than analytical. It ensures that decisions are not revisited after execution, but held through it. The difference is not in how quickly disruption is managed. It is in how rarely it is allowed to propagate.
From Data Availability to Decision Alignment: Rethinking Information Flow in Manufacturing
Key Takeaways Information lag in manufacturing is structural, created by how data moves across functions rather than by a lack of data itself. Decisions slow down because demand, capacity, material, and financial signals are not understood together at the point of action. Centralised reporting improves visibility, but does not eliminate the gap between what the system knows and what the business acts on. A connected view of operations allows decisions to be taken with downstream impact in mind, rather than corrected after the fact. Embedding intelligence into planning and execution roles is what enables decision cycles to move at the speed of operations. When data exists but decisions still lag Manufacturing today is not short of data. Across production systems, supply networks, sales channels, and financial platforms, information is generated continuously. Yet in most Businesses, decisions continue to lag behind what the data is already indicating. By the time performance is reviewed, the state of operations has already moved on. This delay is not caused by the absence of data, but by how it is structured and consumed. Information sits within functions—production, supply chain, finance, commercial—each operating with its own cadence of updates, reporting cycles, and visibility. As a result, what appears as a complete view is often a consolidation of partial perspectives, assembled after the fact rather than understood in the moment. The consequence is not just slower decisions. It is decisions taken without full context, where actions in one part of the system create unintended effects elsewhere, only becoming visible when they need to be corrected. Why information lag is a structural problem Most manufacturing organizations have attempted to solve this through centralisation, bringing data into warehouses and dashboards to create a single version of the truth. While this improves reporting consistency, it does not resolve the underlying issue. Manufacturing decisions are inherently interconnected. Demand influences production, production shapes inventory, inventory affects working capital, and all of these evolve continuously. When each function captures and updates its data at different intervals, the business loses synchronisation. Visibility moves at different speeds, and decisions are made on snapshots that no longer reflect the current state. What this creates is a structural lag between what the system knows and what the business acts upon. The more complex the operation becomes, the wider this gap grows, and the more effort is required to bridge it manually. From reporting systems to a connected operational view Addressing this requires a shift in how information flows through the business. Data can no longer be treated as something that is consolidated periodically and reviewed retrospectively. It needs to move continuously, owned by the domains that generate it, and available in a form that can be acted upon across functions. This is where a connected view becomes critical. Demand signals, production constraints, material availability, and financial impact need to be understood together, not in sequence. When these signals are aligned, decisions can be made with awareness of their downstream implications, rather than being corrected later. In practice, this takes shape through an operating layer that brings together sensing, processing, and response into a single loop. Often described as a digital nervous system, it connects data ingestion, signal interpretation, and decision-making across the business. The objective is not visibility alone, but coordination—ensuring that decisions reflect the state of the system as it exists, not as it was last reported. What changes when decisions move at the speed of operations When information begins to flow in this way, the effect is not limited to faster reporting, but to how the business operates. Decision cycles compress because signals are available when they are needed. Planning and execution become more closely aligned because they operate on the same view of demand and constraints. The need for manual reconciliation reduces, as the system itself carries context across functions. This is the basis of Connected Intelligence—where data, process context, and decision workflows operate together, allowing the business to anticipate impact, coordinate actions, and maintain stability even as conditions change. Technology plays a role in enabling this shift. Platforms that support domain-driven data ownership, real-time data movement, and integrated decision environments allow organizations to move beyond fragmented dashboards toward a unified command layer. Within such an environment, signals from production, supply chain, commercial, and finance are not reviewed separately, but understood together, allowing leadership and teams to act earlier and with greater confidence. At that point, performance is no longer defined by how quickly reports are produced or how often decisions are revisited. It is defined by how consistently the business can act on a shared, real-time understanding of its operations.
From Planned Schedules to Responsive Execution: Rethinking Manufacturing Operations
Key Takeaways Manufacturing breaks where planning and execution operate on different assumptions about capacity, materials, and sequencing. Replanning cycles are a symptom of constraints being surfaced too late, not a lack of planning capability. Manual coordination is the invisible layer holding fragmented systems together—and the first point of failure at scale. Capacity loss is driven more by misalignment in execution than by structural limitations in assets or resources. A connected view of demand, capacity, and supply is what allows decisions to hold beyond the planning stage. When planning stops carrying execution Manufacturing has long operated on the assumption that if a plan is sufficiently detailed, execution will follow with limited deviation. Demand is forecast, production is scheduled, and resources are allocated with the expectation that variability can be absorbed through incremental adjustments. That assumption weakens as production environments become more dynamic, where orders shift, materials arrive out of sequence, and equipment availability changes in ways that cannot be fully anticipated at the planning stage. What begins as a structured plan gradually encounters conditions it was not designed to handle, and while planning continues to define intent, it becomes less capable of sustaining execution without intervention. Where execution begins to rely on intervention On the shop floor, this shift is visible in how schedules behave over time. A production plan may initially reflect demand, capacity, and sequencing assumptions, but as constraints surface—maintenance windows extending, shift availability changing, materials arriving out of alignment—the schedule is revised repeatedly to accommodate what is actually possible. Sequencing decisions are reworked, capacity is reallocated, and downtime reshapes how work is organised. Over time, the schedule reflects not the original plan, but the accumulated adjustments required to keep production moving. Execution becomes dependent on planners and supervisors continuously reconciling gaps between what was planned and what can be executed, creating a model where performance relies on effort rather than system stability. From planning accuracy to execution alignment The shift required here is not toward more detailed planning, but toward reducing the gap between planning and execution so that the system itself can absorb variability. Planning and scheduling can no longer operate as separate layers where one defines intent and the other absorbs disruption. They need to function as part of a continuous process where constraints such as capacity, sequencing, material availability, and downtime are accounted for within the system rather than corrected outside it. Technology becomes relevant at this point, not as a tool for generating better plans, but as a means of ensuring that planning remains aligned with execution as conditions evolve. Platforms such as Oracle Fusion Cloud Planning enable constraint-based scheduling by embedding capacity limits, shift patterns, maintenance windows, and sequencing dependencies directly into how schedules are formed and adjusted, allowing decisions to reflect actual operating conditions rather than assumptions. A connected view of manufacturing decisions As manufacturing moves in this direction, the requirement extends beyond responsiveness into coordination. Demand signals, capacity constraints, material availability, and financial impact are often captured across different systems, but they are rarely understood together when decisions are made. The result is that actions taken in one part of the system create consequences elsewhere, which are only addressed after the fact. A connected view brings these signals into a shared operational context, allowing decisions to be made with an understanding of their downstream impact at the point they are taken. This is the foundation of Connected Intelligence, where data, process context, and decision workflows are aligned so that planning and execution operate from the same set of conditions. In practice, this takes shape through a command environment that brings together signals from demand, supply, production, and financial impact into a single operating view. Supported by Oracle’s planning and integration capabilities, this allows organizations to anticipate disruptions earlier, coordinate decisions across functions, and maintain stability across complex production environments. At that point, performance is no longer defined by how effectively teams respond to breakdowns in the plan, but by how consistently the system can operate without requiring those breakdowns to be corrected.
Bringing Order Visibility and Fulfilment into One Operating Model
About the Client De Beers is a global luxury jewellery retailer operating boutique stores across major cities, known for high-value, individually traceable products and a personalised customer experience. As the business expanded into online channels, order fulfilment needed to operate seamlessly across store, digital, and partner networks, without compromising service quality or product traceability. Business Challenges Inventory was distributed across locations, limiting global availability and visibility No real-time view of order status across channels Order routing decisions were complex and handled manually Product traceability (individual gems and jewellery pieces) was not consistently maintained across systems Order management processes varied across store, e-commerce, and partner channels Manual interventions increased errors, delays, and operational effort Existing ERP systems did not fully represent the complexity of fulfilment flows and business rules What We Did Order management and fulfilment were restructured into a single, orchestrated flow across channels, enabled by Oracle Cloud Order Management. Established a unified order orchestration layer to manage orders across store, e-commerce, and partner channels Enabled real-time inventory visibility across locations, unlocking stock for global fulfilment Designed intelligent order routing to direct orders to the appropriate fulfilment source based on availability and rules Built traceability into the order flow to track individual items across the supply chain Integrated downstream systems including Salesforce Commerce Cloud, POS, SAP, and service platforms using Oracle Integration Cloud Configured business rules to support complex scenarios such as consigned inventory, down-payments, and partner fulfilment Enabled in-store stock visibility and click-and-collect capabilities using Oracle APEX Value Delivered 20% reduction in unplanned downtime, improving production continuity 10% reduction in maintenance costs through better planning and reduced rework More consistent maintenance execution across offshore operations Reduced delays caused by misalignment between work orders, materials, and workforce Improved workforce productivity with less manual coordination in remote environments Stronger audit readiness with complete, traceable maintenance records Increased visibility into asset health, enabling earlier intervention and risk reduction
InspireXT at Hannover Messe
When Shopfloor Intelligence Meets business Execution Manufacturers generate enormous volumes of operational data across machines, sensors, and production systems. Yet in many organisations, this data remains trapped within disconnected systems and rarely reaches the platforms where strategic decisions are made. The result is limited operational visibility, slower response times, and missed opportunities to optimise production performance. At Hannover Messe, InspireXT will demonstrate how manufacturers can bridge this gap by connecting shopfloor intelligence with business execution. Together with Litmus and Oracle, we help organisations transform industrial data into real-time insights that improve manufacturing performance, strengthen operational visibility, and support faster, data-driven decision-making across the business. Better Together: Litmus + Oracle + InspireXT Manufacturers generate vast amounts of operational data across machines, sensors, and production systems. The real challenge is not collecting this data — it is turning it into decisions that improve performance across the business. This is where Litmus, Oracle, and InspireXT work better together. Litmus captures and contextualises real-time machine and shopfloor data, creating a strong foundation for industrial intelligence. Oracle brings that intelligence into business workflows across manufacturing, maintenance, quality, and supply chain operations, enabling organisations to move from visibility to execution. InspireXT connects these capabilities through its Connected Shopfloor approach, ensuring shopfloor data flows seamlessly into business systems and translates into measurable operational outcomes. Together, Litmus, Oracle, and InspireXT help manufacturers move beyond disconnected systems to a connected manufacturing ecosystem where data, operations, and decision-making work as one. The outcome is greater visibility, improved production performance, and faster, data-driven decisions across manufacturing and supply chain operations. Why Meet Us at Hannover Messe Manufacturing leaders today are exploring new ways to connect industrial data, optimise production operations, and build more resilient supply chains. A short conversation with the InspireXT team can help you explore practical approaches to achieving this. During our discussion, we can explore: How to connect machines, sensors, and production systems with business platforms Ways to improve real-time visibility across manufacturing operations Strategies to reduce downtime and improve production performance How connected manufacturing ecosystems support smarter decision-making Meet the Team Our manufacturing and supply chain experts will be available during Hannover Messe to discuss connected shopfloor strategies and operational transformation. Kuldeep Thakur Managing Director Geoff Lloyd Regional Delivery Leader – Europe Justyna Kowalczyk Global Alliances Director Shridhar Khandekar Associate Director Conner Pearce Business Relationship Manager Saurabh Gupta Solutions Manager – EMEA Register Now
Oracle EPM Clinic – Singapore
Oracle EPM Clinics are an opportunity for users to discuss candidly about their experience of adopting the EPM platform. The session is for customers to ask questions, explore the art of possible and discuss any current issues they are facing with the platform. This forum has been organised jointly by InspireXT and Oracle. InspireXT are a global partner for Oracle with extensive experience of EPM and Supply Chain deployments. EPM Online Clinic Schedule: The Oracle EPM Clinic will be conducted as four separate online sessions on 26th March30th April21st MaySession timings: 2.00 pm to 3.00 pm SGT
InspireXT Partners With Databricks to Accelerate Digital Core + AI in Supply Chains
Introduction London, UK – 8th October, 2025 – InspireXT, the supply chain experts with a mission to build the most trusted supply chains by connecting commerce to operations, today announced a partnership with Databricks, the Data and AI company whose Data Intelligence Platform helps more than 20,000 organisations worldwide to unify their data, analytics and AI at scale, to accelerate the shift to Digital Core + AI. The next wave of supply chain transformation will be defined by Digital Core + AI – a connected, cloud-native foundation that combines the data and process integrity of ERPs and other business systems with applied intelligence at scale, enabling supply chains that deliver agility, resilience, and continuous innovation. The Databricks Data Intelligence Platform will provide the technology anchor for InspireXT’s Connected Intelligence solutions – extending the Digital Core by combining data, process and AI to deliver governed, validated, industry-aligned solutions that embed insight and automation across supply chains. Through the partnership with Databricks, InspireXT will focus on transformation within Pharmaceuticals and Life Sciences, Manufacturing, and Retail, where InspireXT brings its supply chain expertise, GxP and CSV-ready accelerators, and Connected Process Model together with Databricks’ unified data foundation, governance and AI capabilities to deliver industry-specific outcomes such as accelerated product development cycles, streamlined compliance, end-to-end supply chain visibility, improved inventory efficiency, and stronger sustainability performance. InspireXT will continue to scale the global delivery of its Data and AI services, spanning strategy and platform modernisation, data engineering and operations, business AI agent, and managed services – underpinned by its Connected Delivery Model to provide customers with assured and repeatable outcomes that unlock value from process and data. The partnership will enable integration of the Databricks Data Intelligence Platform with NaturalAITM – InspireXT’s proprietary platform for business process modelling, process and AI orchestration and AI-enabled solution delivery – creating additional value for customers and accelerating time-to-value for supply chain transformation initiatives. Kuldeep Thakur, Founder & CEO of InspireXT, said“Our mission is to build the most trusted supply chains by connecting commerce to operations. With Databricks as a strategic partner, we extend Digital Core + AI through Connected Intelligence – creating supply chains that are resilient, responsive and cost-efficient, while helping people and organisations to do more and be more. The Databricks Data Intelligence Platform is crucial to this mission and complements our existing partnerships. Customers don’t want another platform in isolation – they expect us to have already designed, blueprinted and connected the pieces, so they can deliver on their promises with confidence. With NaturalAI™ and our Connected Delivery Model, we provide validated, solution-driven outcomes that unlock value and ensure sustainable transformation.” About InspireXT InspireXT specialises in customer-centric supply chain transformation, connecting commerce to operations and delivering innovative digital solutions powered by leading platforms including Databricks, Oracle and Salesforce. Headquartered in London, InspireXT serves a global client base across Pharmaceuticals & Life Sciences, Manufacturing and Retail industries. InspireXT has operations in the UK, Americas, Middle East, India and Singapore. InspireXT’s Value and Operating System is anchored in our mission to lead customers in building the most trusted supply chains by connecting commerce to operations and driving measurable outcomes. Guided by our vision to become a leading brand in supply chain transformation through Connected Intelligence, business Apps, and Sustainable Services, we are driven by a deeper purpose: to empower people and organisations to do more, and realise their potential by connecting technology, operations, and intelligence for sustainable, values-led growth.