Software-defined manufacturing: Rise of the factory operating system model

The approach uses digital platforms, real-time data and simulation tools to improve production planning, reduce downtime and enhance factory flexibility.

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As automakers juggle between internal combustion engine (ICE), hybrid and electric vehicle (EV) programmes simultaneously, manufacturing floors are facing a structural reset.

The traditional notion of flexible manufacturing, once driven by hardware-heavy automation, is giving way to something more foundational – software-defined manufacturing. “Software-defined manufacturing is an evolution beyond traditional flexible manufacturing,” says Himanshu Agrawal, Global Head Manufacturing, Tata Elxsi.
“Historically, flexibility was achieved through hardware-driven automation at the operational technology layer, such as programmable logic controllers, supervisory control and data acquisition systems, and manufacturing execution systems. These were plant- and line-specific with flexibility embedded in equipment and ladder logic,” he elaborates.

In contrast, software-defined manufacturing abstracts decision-making to a unified software platform, while the operational technology layer becomes an execution layer.

Software abstraction enables manufacturers to simulate deltas rather than rebuild capacityHimanshu Agrawal, Global Head Manufacturing, Tata Elxsi.

From hardware-centric to platform-led

In the traditional model, flexibility meant retooling equipment, rewriting PLC logic, and physically adjusting lines. In contrast, a software-defined architecture orchestrates planning, scheduling, supply chain integration, and simulation at a centralised platform level.Agrawal explains that this shift integrates product lifecycle management, engineering bill of materials, manufacturing bill of materials and supplier data to enable predictive simulations and what-if analysis before physical execution. “The difference lies in abstraction, simulation-led decisions and prescriptive control rather than hardware-driven flexibility,” he says.With ICE, hybrid, and EV programmes running in parallel, capital allocation has become a strategic balancing act. Early EV transitions triggered discussions around separate plants. But manufacturers soon realised that nearly 80 per cent of operations — press shops, body-in-white, weld shops, and paint shops— remain common across powertrains.“The primary differences lie in powertrain assembly and testing processes,” says Agrawal. “Software abstraction enables manufacturers to simulate deltas rather than rebuild capacity.”Instead of committing to full-scale greenfield investments, OEMs are increasingly relying on virtual planning to determine where incremental upgrades, often limited to 10–20 per cent of capital expenditure can suffice. “The risk mitigation lies in simulation-led planning and recipe-driven execution,” he adds.

Towards the powertrain-agnostic factory

Is the industry heading towards fully powertrain-agnostic factories?“It is progressing towards partial powertrain agnosticism,” Agrawal says. “There are common vehicle processes that remain largely the same across ICE, hybrid, and EV lines. But specialisation remains unavoidable in powertrain and transmission assembly, and in the final testing stages.”

Software’s role, he emphasises, is to clearly define these boundaries. Manufacturers separate shared setups from unique powertrain tweaks: this lets them build modular plants smartly.

Rather than duplicating full facilities, they can isolate powertrain-specific processes while maintaining shared upstream and downstream operations. “Future factories won’t fully ignore powertrains: software will shape them to boost shared work and cut waste.”

Solving bottlenecks through code

One of the biggest challenges in mixed-powertrain environments is demand planning and scheduling complexity.

“When multiple powertrains coexist, deciding what to produce in what volume and at which plant becomes significantly more complex,” Agrawal explains. Software abstraction enables integrated planning across geographies, domestic versus export demand, and multi-plant coordination.

Supply chain synchronisation is the second major bottleneck. “Stock-outs, lead times, takt time variability, and promise-to-delivery mismatches impact financial key performance indicators,” he says. Through dynamic scheduling and integrated logistics orchestration, software can ensure right-part, right-time availability.

Another challenge lies in programmable logic controller (PLC) logic rigidity. Traditional ladder programming restricts rapid switching between configurations. “With abstraction layers similar to hardware abstraction models in embedded systems, logic can be dynamically deployed based on production schedules,” Agrawal adds.

Hardware changes remain necessary for powertrain-specific tooling but planning, scheduling, logic deployment and material flow optimisation are realistically solvable through software. “The greatest gains occur upstream in integrated planning and downstream in conjunction with digital work instruction-driven execution,” he says.

Digital twins and virtual commissioning

Before physical changes are made, digital twins and virtual commissioning are emerging as essential tools.

“A digital twin enables a simulation-led what-if analysis before physical modifications occur,” Agrawal says. In multi-powertrain scenarios, it allows manufacturers to validate throughput, model mix feasibility, and layout optimisation prior to committing capital.

Virtual commissioning comes closer to deployment. “It allows validation of installation, control logic, and system readiness in simulated environments before full-scale production starts. This reduces dependence on physical trial runs and shortens commissioning cycles,” he explains. Together, they reduce rework, shorten commissioning cycles, and de-risk capital deployment.

Cutting changeover penalties

Mixed-model lines have traditionally suffered from switch-over time, validation cycles and manual adjustments, resulting in takt time loss and potential errors. These delays often arise from hardware-bound logic and manual sequencing. On the other hand, in a software-defined environment, changeovers are driven by recipe-based logic and pre-validated parameters embedded within the platform.

“Instead of physical reconfiguration or manual reprogramming, transitions between models or powertrains are executed through software-defined sequences. Since these logic flows are simulated and validated beforehand using digital twins, execution becomes faster and more predictable,” says Agrawal.

The gains are visible in reduced downtime, fewer qualification runs, and reduced assembly errors and rework risks. According to him, software-defined bill of materials and digital work instructions along with other gains together improvements enhance efficiency, reduce scrap and improve first-time-right production outcomes.

Capital efficiency and launch compression

As OEMs seek to avoid stranded assets, the shift from fixed hardware investment to reusable digital capabilities is proving decisive.

“Traditional expansion relied on heavy capex for new lines or plants,” Agrawal notes. “Software-defined approaches enable incremental upgrades and modular expansion. Software layer becomes a capability asset that drives long-term adaptability without requiring repeated hardware investments.”

He cites industry shifts such as Tesla’s software-centric manufacturing practices and BMW’s digital plant twins as examples of how digital infrastructure is enhancing utilisation, adaptability and profitability.

Launch timelines are also benefiting. “When implemented holistically, timeline reductions of approximately 25 to 35 per cent are achievable,” Agrawal says. However, these gains depend on disciplined adoption, upfront platform investments, and organisational alignment. He cautions that partial or fragmented implementations deliver limited benefit.

Real-time orchestration and continuous updates

Many plants already collect near real-time data but the shift now is towards closed-loop orchestration.

“Data from machines, robots, automated guided vehicles, warehouses and quality systems is integrated and acted upon automatically,” Agrawal explains. If a line-side material shortage occurs, real-time orchestration can reroute automated guided vehicles to alternative storage locations without manual intervention.

This integrated approach ensures that scheduling, routing and material flow adjustments occur while the line is running, rather than requiring stoppages. “Over time, such orchestration reduces reliance on manual decision-making and enables dynamic response to disruptions.”

OEMs are also inching towards continuous update models. “Today, approximately 60 to 70 per cent of updates can be handled through continuous improvement approaches, with the remaining 30 to 40 per cent still dependent on physical interventions,” he says. The analogy to software-defined vehicles is clear — over-the-air logic updates are mirrored in incremental factory upgrades.

The factory operating system

The trajectory points towards what Agrawal describes as a factory operating system model.

“Just as automotive operating systems centralise vehicle functions and enable over-the-air updates, factory operating systems will centralise planning, execution, monitoring and optimisation across manufacturing environments,” he says. These platforms abstract hardware dependencies and unify simulation, scheduling, and analytics.

This transition is already visible in emerging discussions around plant operating systems and unified manufacturing platforms. These platforms abstract hardware dependencies and provide a common layer for simulation, scheduling, execution and analytics. The result is a software-centric manufacturing architecture capable of continuous improvement and rapid reconfiguration.

Drawing parallels with software-defined vehicles, the factory operating system will become the core enabler of flexibility and scalability. It will allow manufacturers to deploy new logic, introduce new models, and reconfigure production with minimal disruption.

Over time, manufacturing will operate as a networked capability rather than isolated facilities. “Multiple plants will operate as coordinated nodes within a single digital ecosystem,” he adds.

The human shift

As factories become software-defined, workforce skills are also evolving.

“Shop-floor technicians will continue to perform physical tasks but they will increasingly work with real-time data, digital work instructions and software-driven decision support,” Agrawal says. Virtual reality-based simulations and digital training modules are being deployed to prepare workers before they enter production environments.

Importantly, software is not eliminating tribal knowledge — it is codifying it. “Software-driven flexibility is both reducing dependence on tribal knowledge and systematically digitising it,” he explains. This reduces reliance on individual expertise while preserving institutional knowledge in digital form.

However, the transition is not purely technical. As Agrawal says, “Cultural and organisational factors must be managed carefully. Increased monitoring through computer vision or analytics can create workforce concerns around surveillance and privacy, which must be addressed transparently.”

When implemented thoughtfully, software-driven flexibility converts tribal knowledge into scalable digital assets, ensuring consistency, quality, and faster on-boarding while retaining the value of human expertise.