The Next Level of ERP for Manufacturing

In India, in particular, the situation is even more striking. Domestic ERP market growth to 2031 is expected to be almost twice, with SMEs growing at a higher rate than large enterprises in that segment, an inverse of the older dynamics, in which ERP was only meant for large enterprises having the requisite IT budget. Regulatory compliance, GST invoicing requirements, and audit trail requirements will compel thousands of manufacturers to invest in formal solutions, which can no longer be satisfied with spreadsheet-based systems.

However, ERP usage among manufacturing SMEs, who make up the vast majority of manufacturing firms in India, is still under single-digit numbers when it comes to any advanced usage beyond basic computing. The difference between the two is the true story here.

From Passive Database to Active Copilot

‘Manufacturing Copilot’ is no longer marketing hyperbole but refers to an actual architectural paradigm shift currently underway in the market space. In legacy ERP, what had happened was documented. With a copilot layer, interpretation and recommendations of actions based on the situation are provided. In the next phase, currently being piloted by leading manufacturers, systems make decisions and act based on this within certain limitations.

Leading vendors are in a race to create such a copilot layer. Microsoft has created Copilots and agents in Dynamics 365, including features like a Supplier Communications Agent that scans emails and updates purchase orders without any human input. Oracle apparently has built hundreds of AI agents in its Fusion Cloud applications that help perform everything from explaining anomalies to making forecasts. Sage integrated Copilot into operations workflows in manufacturing plants back in early 2026, identifying risks before it turns into downtime instead of only after. Both Siemens and Schneider Electric have been developing such capabilities in manufacturing operations, with Siemens calling it a move from assistants that answer questions to ones that perceive, decide, and act.

This has to do with the mechanics. The copilot reacts to your prompt. Ask it to give you an account of the last night shift, and it gives it to you. On the other hand, the agent has a goal. Ask it to ensure that the efficiency of a certain production line is not below a set level, and it tracks the signals, detects any deviation from the norm, and takes some kind of bounded action like scheduling a new job or increasing the purchase order of a part in shortage.

This is not a smooth transition either. Industrial analysis of agentic pilots within the industry shows that a small fraction of them scale beyond the pilot phase. The companies that succeed in scaling treat the governance aspect, which involves setting boundaries to what an agent can do on its own, as much as they treat the algorithm.

What This Looks Like in a Real Factory

Remove the sales pitch, and here are a few things a Manufacturing Copilot is actually doing:

• Tracking inventory and supplier lead times at the same time, and identifying risks of stockouts days before they become evident in any manual order reports.
• Interpreting quality and downtime statistics from machines in human language, giving the plant manager a written reason why OEE fell, and not a chart with numbers.
• Creating purchase orders and other standard paperwork, while still needing an actual person’s approval instead of the generation of documents from scratch.
• Rescheduling the production plan immediately after detecting a delay, without waiting until tomorrow’s planning session.

None of these things requires a ‘smart’ factory, but rather manufacturing workflow automation, because otherwise, there won’t be anything to work with for a copilot. This is exactly the position where the majority of Indian manufacturing SMEs are at now: they do have production information, but all of it is stored in log books, WhatsApp messages, and Excel spreadsheets.

The Indian SME Reality Check

The systems developed by the global vendors are targeted at companies that have an in-house IT team and large technology budgets in the six to seven figures. This model will not fit well into the situation in India, where a manufacturing SME with margins of five to twelve percent will find the cost of implementing an ERP solution to be in the order of fifteen to fifty lakh rupees before annual licensing fees, an amount equivalent to their total annual profits.

However, the problem lies beyond just cost. Lack of skills among the IT team, lack of consistent connectivity in areas outside the major industrial hubs, and the simple fact that many operations have never been digitized mean that fitting an artificial intelligence copilot on top of a paper-based system from thirty years ago won’t work. There needs to be a system developed that is flexible, affordable, and works within the operational constraints of an Indian SME.

This is precisely what low-code/no-code platform providers aim to achieve by providing the manufacturer with the ability to design their own ERP solution.

Where Dhumi Fits Into This Shift

This is the problem Dhumi was built around. Dhumi is an AI-powered, low-code and no-code platform purpose-built for Indian manufacturing SMEs, already supporting more than 50 clients across five manufacturing verticals. Instead of asking a factory to adopt a generic global template, Dhumi lets manufacturers assemble their own ERP, CRM, and manufacturing workflow automation without writing code or hiring a development team.

In the context of the Manufacturing Copilot shift, that low-code foundation matters more than it might first appear:

• It digitizes the messy, paper-based processes that sit underneath a copilot layer, which is the step most global vendors skip and most Indian SMEs cannot afford to skip.
• Its no-code workflow builder lets a plant manager automate approvals, reorder triggers, and quality checks directly, the same building blocks that an AI agent later needs to act on.
• Its modular pricing means a manufacturer can start with one high-impact workflow, and inventory or production planning are common first moves, and expand only once that module proves its ROI, rather than committing to a multi-lakh rupee rollout upfront.
• Because it is purpose-built for manufacturing rather than adapted from a generic suite, its data structures already match how Indian SMEs track bills of materials, job orders, and shop-floor output, the exact consistency an intelligent layer depends on.

In other words, Dhumi is solving today's foundational problem, getting Indian manufacturing SMEs onto a connected, automated system, while building the kind of clean, structured operational data that tomorrow's copilot and agentic features will need to be useful rather than gimmicky.

The Real Takeaway

The Manufacturing Copilot is not a far-off concept reserved for global giants with Siemens-scale budgets. It is the next visible step on a path that starts with something far more basic: getting a factory's operations onto one connected system in the first place. For the vast majority of Indian manufacturing SMEs, that first step, not the AI layer on top of it, is still the one that decides whether the next decade of manufacturing ERP works for them or happens around them.

Platforms like Dhumi exist precisely to make sure that first step is affordable, fast, and built for how Indian manufacturing actually runs, so that when the copilot era fully arrives, these factories are ready to use it rather than starting from zero.