Low-code and no-code platforms are quietly transforming decision-making in industrial environments. These tools are transforming frontline workers into system designers and simulation engineers without requiring them to write a single line of code.
The industrial shop floor has always been the domain of pragmatism. Machines must work, systems must align, and processes must produce tangible results. For decades, the software that supports these activities has remained stubbornly out of reach for the people closest to the action. Whether configuring layout changes, planning throughput capacity, or assessing the impact of process adjustments, the default path has involved sending requirements to IT, waiting for development, and often receiving tools that do not quite meet the needs.
In an environment where margins are thin and responsiveness is crucial, this model is no longer viable. Low-code and no-code platforms are shifting the balance of power. By making simulation and system configuration visual, modular, and accessible, they allow domain experts to pose their own questions, test their own ideas, and iterate directly without a gatekeeper.
Chris Brett, Chief Technology Officer at Kallikor, describes a future in which operations teams can construct their own digital twins from functional building blocks. Rather than specifying the fine-grained workings of a component, users represent systems such as pallet decanting, robotic storage, or human picking as discrete units. These blocks are connected in a visual flow to simulate the movement of materials and the orchestration of tasks. The result is a system capable of modelling complex logistics without requiring code, scripting, or deep software knowledge.
“The key is to abstract the complexity,” Brett explains. “We are operating at a higher level than traditional programming. One block might represent an entire automated storage system, not an individual sensor. That makes it usable by people who understand the flow of operations but are not software engineers.”
This abstraction is not a compromise.
It is a strategic alignment with how operators think. Engineers and supervisors are already accustomed to interpreting workflows visually – whether through value stream maps, Gantt charts or production line layouts. By mirroring this logic in a simulation tool, no-code systems allow them to express operational ideas directly, without needing translation into software terms.
Importantly, the platform is designed not for narrow tactical exercises but for strategic what-if analysis. Users can model the impact of shifting delivery patterns, test warehouse layouts, or simulate labour allocations under peak loads. The AI component further lowers the technical barrier by assisting with block selection, layout validation and scenario guidance.
“People do not need to know the fine detail of how each warehouse component works,” Brett continues. “If you know what a decanting area does or how a picking station fits into your process, that is enough. The platform handles the logic behind the scenes, and the AI helps guide the user toward effective configurations.”
That blend of domain expertise and machine support is particularly valuable in environments with patchy or incomplete data. Unlike traditional simulation tools that require high-fidelity inputs, the Kallikor platform can operate effectively with rough heuristics or scaled sample data. Users can upload historical order profiles, extrapolate future volumes, or simulate abnormal conditions without full system integration.
This enables simulations to commence earlier, even before the full digital transformation is complete. Where many systems require a perfectly curated data lake, Brett’s approach accepts the operational reality of spreadsheets, PDFs, and semi-structured exports. It meets the factory where it is, rather than expecting it to conform to a software vendor’s roadmap.
“It is not just about what data you have, but how quickly you can explore scenarios that matter,” Brett says. “If all you have is last month’s orders in a CSV, you can still learn something. You can still test resilience, capacity, and efficiency. That agility is critical.”
Shortening the development cycle
What emerges is not just accessibility, but a new development philosophy. Traditional IT-led solutions are governed by long development cycles and centralised control. By contrast, no code systems offer a distributed, exploratory model. The risk of failure is lower, the cost of iteration is reduced, and the control remains with the people who live and breathe the problem every day.
There are risks, of course. Shadow IT has long been a concern in manufacturing, especially where unofficial tools interact with production databases. Brett has seen this first-hand, with operators wiring Excel directly into backend systems to extract or overwrite values. The result is often brittle, undocumented code that no one wants to own. This is where the role of IT does not disappear but evolves. “IT should not be bypassed, it should become the enabler,” Brett says. “Its job is to expose interfaces, maintain data consistency, and help define what is safe and appropriate to do in no code. That way, operations can innovate confidently, without compromising core systems.”
The platform’s modularity facilitates easier governance. Each simulation remains separate from live systems unless it is explicitly integrated, and data exchange can occur either manually or through predefined APIs. As a result, the user can prototype ideas independently and then transition to a more structured deployment only when the value is proven.
Crucially, that value often emerges early. Because users can test real decisions, such as layout options for a new facility or staffing profiles for a peak season, the return on investment is both measurable and immediate. The visual interface helps build cross-functional buy-in, enabling planners, engineers and finance to collaborate on a shared digital representation of the operation.
“Starting small is not a limitation, it is the preferred route,” Brett explains. “Once the first model delivers insights, users gain confidence. They begin exploring other flows, sites, and configurations. Eventually, simulation becomes embedded into daily decision-making, not just for large projects but for continuous optimisation.”
Breaking down boundaries
As AI becomes more deeply embedded, the boundary between simulation and co-pilot begins to blur. In future releases, the platform will proactively suggest improvements, identify anomalous flows, and propose alternative approaches based on accumulated knowledge and experience. These features are not about automating judgment, but about augmenting human insight with scalable pattern recognition.
The ultimate promise is not just faster modelling or cheaper development. It is a structural shift in who owns the logic of industrial systems. In a world where agility is paramount, factories must not wait for permission to improve. They must be equipped with tools that enable them to ask better questions, explore possibilities, and execute on insights, without being code experts.
That transformation is already underway. And it is not being driven by hobbyists or hackers, but by engineers and operators taking control of their own futures. This empowerment does not eliminate the need for structure, oversight, or governance. It makes their role more strategic. When IT is repositioned as a partner rather than a gatekeeper, the cultural shift is significant. Instead of serving as an obstacle between concept and implementation, IT becomes the steward of quality and integration, guiding teams on how to interact with data responsibly and how to transition from exploratory tools to production-grade systems.
This also reframes the role of engineering teams who, traditionally, have been overburdened with requests for reports, process changes or one-off tools that distract from core system development. When operational staff can build, test, and even deploy lightweight tools themselves, engineers are freed to focus on foundational improvements, long-term architecture and systemic stability.
“What we are seeing,” Brett notes, “is not a dilution of engineering but a redistribution. Engineers are still essential. But their energy is no longer consumed by writing bespoke reports or reacting to isolated change requests. That demand is absorbed by frontline teams who now have tools to solve their own problems.”
Embedding AI in training
The education curve, often a point of resistance in digital deployments, is also handled with nuance. Rather than a rigid training programme, Kallikor employs a phased approach to adoption. In the early stages, the tool is operated on behalf of the user, building confidence through observation and collaboration. Gradually, tasks are transferred from interpreting outputs to running simulations, and ultimately, to constructing entirely new scenarios.
This handover strategy is supplemented by embedded AI guidance. If a user misconnects two blocks or omits a key process step, the AI assistant can offer corrective suggestions or raise prompts to help them correct the issue. These nudges serve not only as instructional aids but also as quality controls, ensuring that models remain valid even as complexity grows.
Brett acknowledges that the current AI assistant is reactive, responding to user queries rather than acting autonomously. However, a roadmap for proactive support is well underway. Soon, the AI will continuously scan flows in the background, identifying anomalies, suggesting optimisations, or alerting users to inconsistencies. In essence, the system will move from tutor to collaborator.
“We envision an assistant that is constantly watching, learning from your patterns and pointing out opportunities,” Brett says. “If you are exploring a particular automation flow, it might suggest a comparison or highlight a constraint you missed. It is about building a relationship between the user and the platform.”
Delivering a unified platform
This proactive capability is particularly valuable in large-scale or multi-site operations, where local teams may be making independent adjustments. A unified platform that flags discrepancies or propagates best practices across the network can help maintain consistency while still encouraging decentralised innovation.
The value of this model also becomes clear when dealing with variable or uncertain conditions. Traditional planning tools often assume stable inputs – predictable demand, fixed labour availability, and uniform process times. Real-world operations rarely afford such luxuries. No code simulation platforms allow teams to build in randomness, stress-test processes, and visualise the cascading effects of minor disruptions.
From an executive perspective, this enables better scenario planning and risk management. Instead of treating simulations as specialist exercises conducted by analysts, they become tools for collaborative discussion and analysis. Cross-functional teams can walk through the same model, test decisions before implementation, and quantify trade-offs in a shared environment.
“It creates a single source of operational truth,” Brett adds. “Everyone is looking at the same flow, testing the same assumptions. That alignment makes decision-making faster, more transparent, and easier to justify.” This is also a marked departure from the opacity of traditional simulation software, which often requires specialist interpretation. By simplifying the interface and embedding domain logic directly into the blocks, Kallikor’s platform ensures that the simulation accurately reflects operational reality, rather than just mathematical abstraction.
Bridging the gap between strategy and execution
The broader implication is that the distance between strategy and execution narrows. Shop floor innovation becomes a visible part of the enterprise’s digital strategy. Insights generated locally feed into central planning. Tools developed for one site can be templated and deployed across the network. What begins as a tactical tool quickly becomes a driver of operational excellence.
It also aligns with broader industrial trends. The push toward Industry 4.0, digital twins, and smart manufacturing all rely on real-time data, responsive planning, and embedded intelligence. No code platforms provide a practical pathway to these goals without the cost, delay, or disruption of full-scale replatforming.
“Not every organisation is ready to implement a comprehensive MES overhaul,” Brett says. “But most are ready to model a warehouse process. That is where no code fits, it is a bridge from where you are to where you want to be.” As manufacturing leaders face pressure to do more with less – reducing downtime, increasing throughput, and responding to supply chain shocks – the ability to prototype, iterate, and simulate before committing capital becomes an operational imperative.
And this is not hypothetical. It is already happening on the ground. Across industries, operators are using these platforms to optimise warehouse designs, forecast resource needs, evaluate automation investments and troubleshoot bottlenecks. They are not waiting for a system integrator or a six-month roadmap. They are dragging blocks, pressing simulate, and making better decisions in days.
This may not be the future many envisioned when the term “digital factory” was coined. It is not driven by robotics, a central brain, or omniscient algorithms. It is driven by people, the same individuals who walk the floor, stack the pallets, reroute the flows, and correct the errors. They are no longer just using software. They are building it. And that, perhaps, is the most meaningful shift of all.
