When UAE confectionery founder Sarah Hamouda created her pistachio-cream, pastry-filled chocolate bar in 2022, few could have predicted it would become the viral Dubai Chocolate phenomenon.
But after a TikTok influencer shared it with her followers – generating more than 125 million views – the product went global almost overnight.
Retailers moved fast. From Aldi to Marks & Spencer, suppliers were asked to recreate the treat at pace; Lidl’s version sold out within hours.
But for manufacturers, the interest lies less in the craze itself and more in what it highlights: how quickly demand can shift, and how difficult it is for factories built for stability to adapt at the same speed.
It’s this challenge – operational agility and competitiveness – that’s driving more producers to explore digital simulation and digital twin technology.
Overcoming rigidity
The operational reality of jumping on trends is complex for manufacturers.
Most factories are engineered for stability rather than rapid change. And production lines rely on fixed assets, short payback cycles, tight schedules, and strict food safety controls with carefully tuned processes.
Dubai chocolate is a good example. A bar combining pistachio cream, tahini and shredded pastry alters rheology, thermal behaviour, allergen management and packing dynamics. Validating these changes usually means stopping equipment, using scarce engineering hours, consuming materials, and running physical trials.
With many UK manufacturers already struggling to recruit skilled technicians and engineers, trial capacity is limited too. Meanwhile, inflation and margin pressure make waste-heavy tests harder to justify.
These issues create a scenario where the commercial window can close before a factory even proves it can make the product.
It’s why many companies are now turning to digital twin technology – to gain a fighting chance of being able to quickly and successfully meet new market opportunities.
Baking in agility with digital simulation
Put simply, a digital twin can be a life-like simulation of a food factory. It lets producers test changes to manufacturing lines virtually before touching the line in the real world.
Digital twins are physics and data-based simulations. They mirror assets, automation logic, and – in more advanced applications – the thermodynamics and mechanical behaviour that define food processing.
In practice, that means teams can model depositor behaviour with different filling viscosities, or whether a nozzle or tool change is enough to maintain accuracy at speed, for example.
Virtual trials cut out much of the hands-on-trial-and-error. They can reduce the engineering time required, protect schedules and minimise wasted materials. For overstretched engineering teams, the ability to validate feasibility in software, rather than stopping a line, is a significant advantage.
Digital twins also reduce operational risk. Even minor alterations can have unintended consequences on yield, allergen control or food safety. Simulating changes first allows operations, technical and quality teams to sign off with far greater confidence.
A more practical path to adopting digital technology
One of the clearest messages from industry leaders is that technology adoption rarely succeeds when it starts with the technology itself.
Several manufacturers at recent forums admitted that chasing ‘the next big digital tool’ led to false starts, stalled projects or solutions looking for a problem. The approaches that worked began with a very clear operational challenge - reducing changeover time, easing pressure on engineering hours, improving energy consistency, or validating a difficult new product.
Seen through that lens, a moment like Dubai chocolate becomes less about the trend itself and more about the underlying capability it tests, which is the ability to adapt quickly without increasing risk. Digital twins fit that requirement because they help manufacturers de-risk the practical questions that slow them down: ‘Can the line run this? What needs to change? What will it cost us in energy, yield or uptime?’
The same approach applies beyond viral products. Manufacturers are already using simulation to explore how they could decarbonise heat processes or reduce energy volatility – all areas where physical trials are expensive, disruptive or impractical.
By starting with a defined challenge and using virtual modelling to test options safely, plants get a much clearer return on investment than they would from a technology-first rollout. It’s an incremental, problem-led way of adopting digital tools, and one that aligns better with the realities of food and drink production.
A strategic tool for growth
For all the excitement generated by viral food trends, the broader question they raise is existential for the sector: how do we build factories that are stable enough for food safety, yet flexible enough for a volatile market?
Digital twins are not a silver bullet, nor should they be introduced simply because they sound innovative. But they do change the equation.
Reactivity stops feeling like a gamble and becomes a controlled, data-supported process. Virtual modelling also helps engineering teams shift their time from firefighting to the interventions that genuinely move the dial. And rather than treating sustainability as an added burden, simulation makes it possible to build efficiency and resource reduction into the process from the outset.
The Dubai chocolate moment is only one example of a trend catching the industry off guard. The next will come just as quickly. Growth in food and drink will increasingly come not from sheer volume, but from the ability to adapt intelligently to new formats, markets and expectations.
Digital twins give manufacturers a way to do that without stepping outside the boundaries of safe, predictable, and efficient production. They turn agility into something quantifiable and operationally manageable. And at a time when competitiveness is under strain from skills shortages, energy volatility and global market pressures, that capability could prove more important than ever.
