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B Swaminathan
India’s food manufacturing sector is in the midst of a quiet but decisive shift, moving from isolated pockets of automation towards connected, data-led operations. Smart factories, cloud-based ERP and MES platforms, AI-driven quality control and farm-to-fork traceability are no longer futuristic ideas confined to global conglomerates. They are increasingly shaping how Indian food companies, large and small, think about growth, compliance and resilience.
Kamleshwar Pande, Head of Information Technology Infrastructure at BN Group, has spent over 17 years navigating IT infrastructure, ERP implementation and enterprise system integration across manufacturing environments. In this conversation, he speaks on where Indian food manufacturers truly stand on digital transformation, how CIOs are learning to translate technology into board-level business language, and why traceability, predictive maintenance and talent are becoming the defining themes of the sector’s technology journey.
Globally, the food sector is being reshaped by smart factory adoption, with digital twins and IoT-enabled monitoring gaining ground worldwide. How real is this shift on the ground in India?
The shift is real, but far from uniform. Large, export-oriented food manufacturers are moving quickly, deploying connected machines, real-time dashboards, automated quality checks and condition-based maintenance. Mid-sized companies typically take a more focused route, starting with energy monitoring, production visibility, utilities or a few critical assets rather than digitising everything at once.
In food manufacturing, the business case is unusually strong, because even a small deviation in temperature, pressure, hygiene or packaging can affect product quality and consumer trust. IoT-based monitoring provides early warning, while digital twins help simulate capacity and process changes before they are attempted on the shop floor.
A smart factory should not be judged by the number of sensors installed. It should be judged by whether people can prevent loss, improve consistency and take decisions before a problem turns serious. India is moving from isolated automation to connected operations, with the winning model being practical, phased and tied to measurable outcomes.
Food manufacturers are increasingly moving from legacy systems to cloud-based ERP and MES platforms that unify suppliers, distributors and plant-level data. What is pushing this shift, and what tends to be the hardest part of the transition for companies still running on older systems?
The biggest push is the need for one reliable version of the truth. Food businesses operate across procurement, plants, warehouses, quality teams, distributors and finance. When these functions run on disconnected applications or spreadsheets, organisations spend more time reconciling information than acting on it. Cloud-based ERP and MES platforms connect demand, materials, production and dispatch, giving leadership timely visibility.
The hardest part is rarely the software itself. It is cleaning years of inconsistent master data, redesigning old processes, and helping people move away from familiar workarounds. Legacy machines often lack standard interfaces, so integrations must be planned carefully to avoid disrupting production. I treat this as a business transformation rather than an IT installation, with operations, quality, finance and supply chain jointly owning the design and migrating in manageable phases. When people see how a new system reduces rework, adoption becomes natural.
A CIO in food manufacturing has to navigate multiple regulations, from food safety to data privacy. How tough is it to convince the board to invest in technology implementation?
The CIO’s role has become more demanding, because technology now connects with food safety, cybersecurity, privacy, statutory compliance and business growth. The role is no longer limited to technical support, and boards are right to ask difficult questions when benefits are not immediate or implementation could affect running plants.
The answer is not to present technology as a fashionable upgrade. I translate it into business language: the cost of a production stoppage, the risk of an untraceable batch, the time required to respond to an audit, or the impact of a data breach. Food-safety obligations and India’s evolving digital-personal-data framework make governance-by-design increasingly important, and compliance can no longer be an afterthought.
Trust is built through transparency, a phased roadmap and honest reporting of risks. A small pilot that solves a genuine plant problem often builds more confidence than an ambitious presentation. A CIO must be both a technologist and a translator, linking systems, people, risk and strategy.
The Government of India is pushing technology to strengthen compliance and traceability, tracking ingredients and batches from farm to fork. What is changing in the way the industry approaches traceability, and how far are most manufacturers from true end-to-end visibility?
Traceability is shifting from a compliance record to a core operating capability. Earlier, organisations could trace a batch only after collecting information from paper registers, spreadsheets and different departments. The emerging expectation is near-real-time visibility across raw-material receipt, production lots and dispatch.
Technologies such as QR codes, barcodes, integrated ERP, supplier portals and IoT data make this possible, but technology cannot compensate for missing discipline at the source. Supplier onboarding, standard lot definitions, timely scanning and accurate master data matter just as much.
Most manufacturers have reasonable visibility within their own operations, but end-to-end visibility across farmers and distributors is still developing. The immediate goal should be practical: quickly answer which material came in, where it was used, what quality checks were performed and where the batch went. In a modern edible-oil company, mustard procurement is now being digitally monitored from the farm all the way to the oil mill, creating a traceable farm-to-factory supply chain. This uses AI and IoT sensors to monitor crops and predict production, with the data integrated into SAP for more effective analysis.
Predictive maintenance and AI-driven quality control are being credited with major reductions in downtime and defects across the sector. How mature is this adoption really, and what typically holds companies back?
Adoption is promising, but maturity varies by use case. Vision systems for label, seal, fill-level and packaging inspection are becoming practical. Predictive maintenance is proving valuable for critical assets such as compressors, boilers and packaging equipment, where failure can stop production.
The biggest constraint is not cost alone. Older machines may not generate usable data, maintenance history can be inconsistent, and skilled people are needed to interpret alerts. Many companies also attempt AI before establishing stable processes and good-quality data, so the model may be sophisticated, but the decision it produces is not trusted.
My recommendation is to begin with one painful, measurable problem and compare alerts against real-world observations. AI should strengthen human judgement, not distance people from the process. When operators see the system helping them catch a defect earlier, confidence grows, and scaling becomes a business decision rather than a technology experiment.
Industry surveys suggest most food and beverage companies now see digital transformation as their primary lever for competitiveness. How has the perception of IT shifted, from a support function to a strategic driver?
This touches a real pain point for IT in manufacturing, which was once judged mainly on whether email and applications were working. Those responsibilities still matter, but today almost every strategic discussion has a technology dimension, spanning plant efficiency, distribution visibility, cybersecurity, compliance and analytics.
The real shift happens when IT participates before a business decision is finalised, not after the purchase order is raised. With timely operational data, leadership can connect decisions across functions, from anticipating material needs to reducing changeover losses. Strategic influence must be earned. IT teams need to understand the plant floor, speak the language of throughput and quality, and stay accountable for adoption, not just implementation. At BN Group, the objective is to build a secure, integrated and data-led foundation that helps the business grow with control. When technology removes friction for employees, IT naturally becomes a partner in strategy.
Food manufacturing leadership is increasingly drawing talent from technology, automotive and other non-traditional backgrounds as plants become more connected. What does this shift mean for how IT and operations teams are being built, and where are the biggest skill gaps today?
It is a healthy development, because connected plants need multidisciplinary teams. Technology professionals bring cloud, data and cybersecurity skills, while people from automotive and other mature sectors bring reliability and process discipline. Food-industry professionals contribute the essential understanding of hygiene, product variability and consumer safety. The challenge is to prevent these groups from working in silos. The most valuable professional is often the one who can stand on the plant floor, understand an operator’s concern, and translate it into a workable data or automation solution.
The biggest gaps today lie in operational-technology cybersecurity, industrial networking and ERP integration. Organisations should build mixed project teams, create rotational exposure between IT and operations, and invest in continuous learning. Transformation accelerates when people feel that expertise is being combined, not replaced. At BN Group, several leaders work across multiple departments, and their contributions are significant.
India has a large number of small-scale food manufacturers. What are three pieces of advice for smaller companies deploying technology in their organisations?
My advice to smaller manufacturers is to avoid copying a large company’s roadmap. A smaller organisation can often move faster, because decisions are closer to the shop floor. Three things matter most. First, start with the business problem, not the product. Identify where money, time or quality is being lost, and select one use case with a clear owner and measurable outcome.
Second, build the digital foundation first. Reliable connectivity, clean master data and basic cybersecurity matter more than an impressive dashboard. Choose modular, cloud-ready solutions that can grow with the business. Third, take people along from day one. Involve users in the process, explain their roles in simple language, and train them in the context of their daily work. Keep a manual fallback during the transition, and scale only once the first implementation is stable.
