Omdia reports that as the automotive industry accelerates into the software-defined era, its rapid evolution is providing a crucial reference for manufacturing and robotics sectors still in early transformation stages. The changes underway in automotive architecture selection, operating models, organizational culture, and ecosystem development are precisely the key elements manufacturing enterprises need for achieving Software-Defined Manufacturing (SDM) and modern digital manufacturing upgrades. A cross-industry "Software-Defined Everything (SDX)" trend is now emerging, with different sectors converging on a shared underlying framework.

Key facts: SDV maturity is progressive and gradual

Omdia categorizes Software-Defined Vehicle (SDV) maturity into five levels: SDV 0-1 (foundational stage) where vehicles have software features or connectivity but fixed functionality, with OTA limited mainly to firmware and security patches. SDV 2 (semi-SDV) enables OTA to add new features but mainly restricted to infotainment systems. SDV 3 (true SDV) allows continuous vehicle upgrades with software evolving across multiple functional domains. SDV 4 achieves a unified software platform with complete hardware-software decoupling and cross-generational portability. SDV 5 introduces Agentic Orchestrator enabling autonomous AI workflow coordination within vehicles and across ecosystems.

Omdia forecasts that by 2026, 90% of new vehicles will still be non-software-defined (SDV 0-2), with SDV 2 peaking in 2028 and dominating the market through 2031. For traditional automakers, this represents a costly and complex transition phase. Omdia expects 2032 to be the watershed year for a SDV-dominated market, with SDV-related revenue reaching $2.6 trillion. By 2037, SDV (levels 3-5) will comprise 81% of new vehicles, while non-SDV drops to 6%. The key takeaway is clear: maturity comes through a 15-20 year transition, not an overnight leap.

Why automotive serves as the benchmark for software-defined transformation

While automotive is not the first industry pursuing software-defined transformation, it is uniquely positioned as a benchmark. Electrification has lowered entry barriers by reducing powertrain complexity, enabling companies without historical baggage to enter the market. Legacy is the hardest obstacle in any software-defined transformation; disruptors unburdened by it, such as Tesla and Xiaomi EV, are not only deploying SDV today but rapidly advancing the concept's evolution. These companies demonstrate what becomes possible when enterprises design around software-first value creation from the start, providing a clear reference path for other industries.

In contrast, traditional OEMs must dismantle decades-old architectures, supplier contracts, and operating models. Their responses, including platform consolidation, next-generation electrical/electronic (E/E) architectures, centralized software organizations, and massive investments in OTA and cybersecurity, offer practical lessons for other industries. Cases of major automakers resetting software platforms and delaying projects also illustrate that while this transformation is difficult, it remains necessary, with other industries able to learn from both successes and setbacks. Critically, the automotive industry has undergone more and faster transformation waves compared to industries that started earlier. Unlike telecommunications, automotive has compressed multiple waves of change within just a few product cycles: electrification, new E/E architectures, platform-based software, and new business models. Finally, SDV integrates safety-critical control, consumer-grade experiences, and cloud-scale data and AI capabilities, making automotive an ideal reference for operations and digital modernization across manufacturing, robotics, and industrial automation involving physical systems.

Why the automotive SDV blueprint transfers to manufacturing

Software-Defined Data Centers (SDDC) and Software-Defined Networking (SDN) primarily address virtualization of digital assets and information flows. However, Software-Defined Automation (SDA) and SDV represent a paradigm shift toward "software-defined physics," where code directly controls dynamic motion and mechanical safety. Both verticals require complex decoupling of hardware from control logic to achieve lifecycle agility, yet they remain fundamentally constrained by deterministic requirements of real-world cyber-physical interactions. Their evolution is therefore transformational, with software needing to master functional safety and strict physical environment requirements.

SDV has driven convergence of modern technology stacks: integrated computing, high-speed Ethernet networking, service-oriented software, and CI/CT/CD with telemetry-driven feedback loops. These same elements can now orchestrate industrial robots, machinery, and production lines. In fact, technology stacks achieving levels 3-4 capabilities are increasingly appearing in advanced factories. Three migration patterns deserve the most attention: architectural convergence where central computing and zonal networks in vehicles map to edge node orchestration, distributed and executed containerized workloads with time-sensitive networking on factory floors; digital twins and simulation where advanced digital twin toolchains used for validating SDV functions are now being adopted to accelerate debugging and process optimization in digital manufacturing; and platform operations where beyond common OTA/remote updates, SDV-pioneered concepts such as software bills of materials (SBOM), signing, and phased rollouts represent logical evolution for industrial operations. While not yet widely applied, these frameworks are expected to be adopted in customized forms as manufacturers seek greater security and lifecycle stability.

From SDV to SDX: common patterns recurring across industries

SDV's extensibility to SDX stems from software-defined transformations across different industries relying on the same core capabilities. The reality is that Software-Defined Automation (SDA) remains in its early stages, with current manufacturing development closely resembling early automotive SDV phases. Software-defined control is not new to manufacturers; in process industries, 54% of manufacturers already use PC-based DCS. Omdia data also reveals strong network convergence: over 50% of manufacturers have highly connected and converged networks. However, this investment has not been fully capitalized at the application layer: over 20% of manufacturing enterprises remain at basic local OT-IT system integration with one-way downward data flows, while another 20% still use isolated local software systems. Despite increasing virtualization adoption, nearly 40% of manufacturers still operate non-virtualized monolithic systems. For digital project implementation, personnel and organizational barriers currently exceed technical constraints. Insufficient IT/OT collaboration consistently ranks high among all priorities: over 60% of digital transformation projects are IT-led, while only 20% report equal IT-OT participation.

Manufacturing digitalization maturity varies considerably. Although market activity is real, the overall landscape remains fragmented. Digital implementation typically begins with customized proof-of-concept (PoC) tied to proprietary ecosystems. The core lesson: avoid lock-in effects that hinder scaling while establishing unified operating models covering software, security, and production.

Five lessons manufacturing should learn from SDV

First, expect a long evolution path rather than a leap: plan transition phases where virtualized controls, containerized applications, and edge orchestration coexist with traditional PLC/fieldbus systems and emerging Ethernet/TSN networks. Second, the main bottleneck is organizational, not technical: SDV leaders' early investment should focus on operating model transformation, including platform product management, security and cybersecurity governance, and unified cadence between hardware and software teams. Third, establish joint IT-OT platform organizations before scaling pilots. The "semi" phase is painful but essential: in automotive, the semi-SDV stage matured CI/CT/CD, OTA policies, and safety case tooling. On the factory side, leverage the semi-SDA phase to bridge brownfield gaps: standardize software sourcing and workload partitioning to close the gap between traditional reliability and software agility. Fourth, target data-driven low-hanging fruit: projects initially prioritize connected services and cabin experiences with clear ROI. In smart manufacturing, begin with predictive maintenance, quality applications, and energy optimization using data availability provided by SDA. Fifth, don't wait for perfect definitions before starting: leaders create advantages through delivery, measurement, and improvement. In software-defined manufacturing, encode learning outcomes as reusable building blocks: standard equipment APIs, common data models, and repeatable deployment patterns.

A phased roadmap for manufacturing enterprises

Connectivity: establish converged network backbones and unified data semantics. Focus on asset identity and edge observability. This represents the SDV level 0-1 foundation and the factory's platform basis. Enhancement: decouple non-critical functions and applications at the edge for supervision, analytics, and management. Introduce lifecycle operations along with remote updates and patches. Adaptation: expand virtualization and container orchestration. Virtualize real-time execution through production line-level orchestration. Transition to virtual commissioning and phased software rollouts. Integrate digital twins and enable cross-system optimization. Intelligence: move toward autonomous closed-loop optimization under clear policy guardrails. Integrate factories into vertical ecosystems such as supply chains or smart grids. This represents the SDV level 5 analog on the factory floor.

Applying SDV approaches to manage risk

Safety and real-time requirements: separate hard real-time controls from soft real-time optimization; certify what must be deterministic while iterating where possible. Cybersecurity spans entire lifecycle management: enforce signed artifacts, SBOMs, and continuous vulnerability management at the industrial edge. Economics shift from capital expenditure to total cost of ownership: plan computing, networking, and tooling for multi-year software evolution rather than one-time purchases. Ecosystem strategy: prioritize open interfaces and avoid single-vendor control planes to maintain flexibility and scale. Data governance: align architectures with data sovereignty and retention requirements common in industrial environments.

Summary

Manufacturing is evolving along the same "software-defined" path the automotive industry has traversed. Facing similar physical constraints, both will ultimately converge on analogous technical architectures and operating models. SDV is not a fully mature, ready-to-copy solution, but it provides clear direction: software value does not come from one-time deployment but accumulates through continuous evolution of platform architecture and organizational capabilities. SDM and smart manufacturing will follow the same logic. Consolidate platform foundations, prioritize use cases by return, industrialize governance, and maintain deliberate iteration. The sooner factories understand the SDV blueprint while acknowledging it as a gradual, long-term path, the greater their opportunity to move quickly from pilots to scaled advantages.