How digital twins are reshaping energy use in packing plants

Across the Australian fresh-produce sector, packing plants run on tight margins and tighter energy budgets. A facility that grades, washes, cools and pallets bananas, pineapples or melons must keep a dozen different systems humming at once, often in remote corners of Queensland or Western Australia where the grid behaves nothing like it does in Sydney. To make sense of it all, Fresh Del Monte Produce has begun building digital twins of its packing operations — living virtual replicas that mirror the way a real plant breathes, hums and consumes power.

The idea is straightforward but quietly powerful. Instead of waiting for an energy bill or an equipment failure to reveal a problem, the company can now test changes in a virtual copy of the plant first. The approach is helping engineers see which conveyor belts, compressors and cold-room set-ups are pulling the most kilowatt-hours, and what happens to the entire system when a small adjustment is made.

What a digital twin actually is

A digital twin is not a static 3D model or a pretty schematic on a screen. It is a continuously updated software mirror of a physical facility, fed by sensors that measure temperature, current draw, motor speed and refrigerant pressure in real time. Every time a chiller cycles on or a sorting line ramps up, the twin registers it. Over days and weeks, the model learns the rhythms of the plant — the morning banana wash, the afternoon pineapple grading peak, the overnight cold-store stabilisation.

For a packing plant, this matters more than it might in an office block. Energy demand here is lumpy and weather-sensitive. A heatwave in the Atherton Tablelands pushes refrigeration harder. A humid Brisbane summer changes how much drying air the facility needs. A digital twin captures those nuances so engineers can plan around them rather than reacting after the fact. It also bridges the gap between the maintenance crew on the floor and the sustainability team tracking Scope 1 and Scope 2 emissions.

Building the virtual replica of an energy system

Creating a useful twin starts with instrumentation. Sensors are fitted to motors, compressors, lighting circuits and HVAC units so that each load centre reports back to a central platform. That data stream is then stitched together with weather feeds, shift schedules and the specific properties of the produce being handled. A batch of mangoes from the Northern Territory behaves differently to a load of rockmelons from Bundaberg, and the model is built to reflect that.

The model's value grows once it is calibrated against historical bills and known equipment ratings. Once the simulation matches reality within a tight margin, the team can start asking "what if" questions with confidence. What if the variable-speed drives on the conveyors are dialled down by ten per cent? What if the cold-room set-point is lifted half a degree outside peak demand windows? Each question produces a forecast, not a guess, and the plant manager can see how much energy — and therefore dollars — the change is likely to save before a single switch is flipped.

Testing scenarios without halting the line

The real gift of a digital twin is that it lets operators experiment without risk. A line that handles forty tonnes of fruit a day cannot afford trial-and-error; every minute of downtime costs money and risks product quality. In a virtual environment, however, dozens of scenarios can be run overnight, each one a stress test of the plant's energy profile under different operating conditions.

The snapshot below shows the kinds of comparisons the digital twin makes possible across a typical Australian packing facility.

These figures are not promises — they are modelled outcomes that the twin refines as new data arrives. Engineers can compare options side by side, weigh trade-offs and pick the changes that genuinely move the needle rather than the ones that look good in a slide deck.

Aligning with Australia's renewable energy landscape

Australia's energy story is unusual. The National Electricity Market stretches across five states, but Western Australia runs on its own grid, and far north Queensland — where much of the country's tropical fruit is packed — sits on yet another. Solar penetration is among the highest in the world per capita, yet gas and coal still set the marginal price on many afternoons, which is exactly when a packing plant's refrigeration load peaks.

A digital twin helps a facility navigate that complexity. Because it knows how much power the plant draws at 2 pm on a hot Brisbane day in February, it can advise when to pre-cool, when to defer non-essential loads and when to lean on rooftop solar or a battery. For sites connected to large-scale renewables through a corporate power purchase agreement, the twin can also flag the hours when grid emissions intensity is lowest, nudging operations into greener windows. That kind of orchestration is increasingly what Australian retailers and their customers expect to see in any credible sustainability report.

From simulation to verified reductions

The final step is making sure the savings modelled in the virtual world actually show up in the real one. Each change recommended by the twin is rolled out carefully, with metering in place to verify the outcome against the prediction. If reality beats the model, the team investigates why. If it falls short, the twin is recalibrated and the lessons feed back into the next round of scenarios.

Over time, the packing plant stops being a black box that simply consumes power and starts behaving like an intelligent system that responds to price signals, weather, product mix and sustainability targets at once. Engineers can speak about kilowatt-hours the way finance teams speak about margins — with real numbers and real confidence. That verified data then feeds straight into the public reporting on Fresh Del Monte's CSR site, closing the loop between operational decisions and corporate disclosure.

The thing worth holding onto is simple. Energy in a packing plant is no longer something to be guessed at or audited once a year. With a digital twin, every compressor cycle and every conveyor ramp becomes a data point, and every operational decision can be tested before it touches a piece of fruit. As the technology matures across more Australian sites, the focus shifts steadily from reporting past usage to shaping smarter usage tomorrow, one validated scenario at a time.