A year ago, the industry was focused on adoption. Automation projects were moving from pilots to full rollout, AI was being tested on individual machines, and plants were gaining early wins in energy use, maintenance and uptime. It was a phase defined by new tools entering the factory, each one delivering a measurable benefit on its own.
That approach has reached its limit.
In 2026, the competitive edge comes from connecting systems, not adding more of them. The shift is subtle but profound: food and drink manufacturers are moving from technology projects to integrated operating models where machines, data, software and people work as a single decision environment.
It’s the difference between a smart factory and an intelligent, resilient one.
From Predictive Projects to Prescriptive Operations
Predictive maintenance has transformed asset reliability. By combining sensors, vibration data and AI models, plants now understand when critical equipment is likely to fail — and can plan accordingly. Availability has improved, spares management is cleaner, and shift teams aren’t firefighting quite as often.
But predictive maintenance is still reactive in structure, even when the alert is early.
The next stage isn’t predicting failure. It’s steering the plant so failure never becomes a constraint.
This is where prescriptive operations and digital twins start to change engineering workflows. Instead of alerting a maintenance team that a pump has 72 hours left, prescriptive systems evaluate the context — recipe, line speed, upstream loading, operator availability, deliveries, energy tariffs — and identify the best course of action for the business outcome, not just the machine.
In parallel, digital twins are becoming practical engineering tools. A virtual model of the line or site allows teams to test changes safely: new SKUs, formulation tweaks, line balances, CIP schedules, or heat recovery adjustments. Engineers can run scenarios in hours rather than running live trials that take days, risk waste, or disrupt customers.
This transition marks the industry’s pivot from machine-level optimisation to plant-level decision intelligence.
Sustainability: From Efficiency Gains to Circular Value
Sustainability inside the factory has progressed well. Most modern facilities can demonstrate tangible improvements: heat recovery, better motors, careful use of compressed air, data-led CIP, and a reduction in process losses. These steps have delivered cost benefits as well as carbon reductions.
The next phase looks different.
It puts the spotlight on what leaves the plant, not only what happens inside it.
Circularity turns byproducts into assets. Rather than treating side streams as a disposal challenge, manufacturers are looking at their potential value in the supply chain. Examples range from converting spent grain into functional ingredients for bakery and snacks, to extracting pectin and flavours from fruit waste, to upgrading whey streams into specialised protein products.
Alongside this shift is a rising pressure for transparent data. Retailers and regulators increasingly want verifiable information about product origin, carbon impact and resource use. The result is a move away from static sustainability reports and toward live traceability systems, where data flows continuously from farm inputs to finished goods.
For engineering teams, sustainability now touches controls design, network architecture and data strategy. The question is no longer “How efficient is this pump?” but “How do we prove and optimise the impact of an entire production cycle?”
Closing the Skills Gap from the Inside
No transformation is possible without people who can operate the systems.
The skills gap is still the number-one operational constraint in modern food manufacturing. Plants have experienced mechanical and electrical engineers who understand production deeply — but digital tools, networks and data models are new territory. Equally, IT and data teams rarely understand line balance, hygiene design or shift patterns.
The most effective approach emerging in the industry is developing hybrid talent internally. Rather than trying to hire “digital-native engineers”, companies are investing in their own teams, pairing subject-matter expertise with new technical skills. Practical examples include vendor-led training paths for maintenance teams, short modular digital courses with local colleges, and internal problem-solving sprints focused on real plant challenges such as reducing water usage or improving changeover efficiency.
This approach accelerates adoption because solutions are grounded in the real constraints of the factory, not theoretical models. It also builds loyalty and turns the skills gap into a retention advantage rather than a recruitment headache.
Engineering for Cyber Resilience
As operational technology becomes connected — even indirectly — cybersecurity becomes part of engineering responsibility. The factory floor is now a legitimate target for attacks, because a breach can halt production, corrupt compliance records, or damage brand reputation.
Plants are adopting OT-specific security measures: segmented networks that isolate the production environment from corporate systems, controlled remote access to PLCs and HMIs, permission-based logins, and planned patch cycles that respect continuous production. In parallel, AI tools are beginning to support automated compliance checks, reviewing process data against standards in real time and reducing the administrative load on quality teams.
These measures don’t add visible value to the product, but they protect the ability to produce — and increasingly form part of customer audits and due diligence.
The Plant Operating System: One Environment, Many Technologies
All of these advances — prescriptive operations, digital twins, circularity projects, traceability and hybrid talent — can look like separate trends. In the most advanced factories, they form part of a single approach: a Plant Operating System.
This isn’t one software platform; it’s a connected operating environment. Data flows between equipment, MES, historians, laboratory systems and ERP through a unified backbone. Decisions are made with a view of the full process, not isolated islands of automation. And improvements are validated with consistent metrics that link engineering work to commercial, safety and sustainability outcomes.
This is the evolution from “automated plant” to intelligent manufacturing — a system that can adapt faster to new product demands, regulatory changes, resource volatility or customer sustainability expectations.
Last year was about adopting smart tools.
This year is about engineering how they work together
