This week Tenzo’s chief of staff Maury Ueta breaks down the project with Innovate UK we’ve been working on for the last 2 years.

In this blog post, we will: a) highlight how critical demand forecasting is for efficient restaurant operations, b) share some insights and c) reflect on our learnings from the forecasting project we collaborated on with Innovate UK.


In the UK alone, food waste contributes to £3.2 billion in lost revenue for restaurants, and 4.5 million tonnes of CO2 emitted. That’s a huge impact both on the environment and on a restaurant’s bottom line.

One of our main goals at Tenzo has been reducing that number. We’ve always prioritised accurate forecasting to help restaurants run more efficiently as well as reduce the amount of waste they generate. 

In late 2019 we saw firsthand how we could help when we gave Nando’s Singapore 30% more accurate forecasts. Thanks to those numbers, we were able to help the team there increase their labour productivity by 15%. The logical next step was how we could make our forecasts even better and make them available to many more businesses.

That’s why we set out to partner with Innovate UK, the UK’s innovation agency, to help the hospitality industry save over £100m in food waste by 2025 by creating the most accurate sales forecasting platform for restaurants using artificial intelligence and machine learning.

A lot has happened since we embarked on the project in November 2020, not least that the key to resiliency for restaurants lies in accurate demand forecasting. 

Currently, most restaurants rely on last week’s demand, rigid 4-week demand averages or gut instinct for operational decisions like shift scheduling, food ordering and preparation. These routinely exceed real demand – indicating demand for staff they cannot attract and creating costs for excess food they cannot afford.

Our approach

We kicked off the project by investigating the different variables that can be used as predictors for forecasts. Along with historical sales data, some external factors can be beneficial for the forecasting process: weather, events and footfall, for example.

After interviewing academic and industry forecasting experts, we experimented with new machine learning methods. In this research, we not only tested new algorithms but also explored new tools and new processes.

To make this really successful though, just having high forecast accuracy is not enough. Operators also need to be able to fit this process into their current operations. This is why, in early 2021, we created a select group of customers to investigate how the forecasting processes were performed inside restaurants. We then started drafting the user journey for the new tool and started a pilot.

On the product side, we revisited the entire infrastructure supporting our forecasting engine to ensure we had a reliable and scalable tool for the new methods. 

We’ve now added  ‘write integrations’ which means that we provide the forecasts in our own API that other software businesses can ‘read’, or we write our forecasts to our partner’s API. 

This means that Tenzo forecasts are now available directly in our customers’ labour schedulers (eg Planday, Deputy,, and more) so they don’t have to switch platforms to schedule as efficiently as possible. 

Plus, we’re working on integrating these forecasts into inventory platforms as well so that businesses can order ingredients according to demand and further reduce waste. 

We’ve also created a new whole new internal function for quality assurance (hi Kieran ) that means we can test these more complicated integrations.

The new hybrid working style


We submitted our application just before COVID, so there were obviously aspects within the project that needed to be addressed. 

For example, unstable datasets with periods without sales made us explore new machine learning methods. In this new context, we received the resilience fund grant for this project.

“During the Pandemic, one of the hardest-hit industries was hospitality. Many restaurants struggled to manage the unpredictable demand for freshly prepared food and getting the resources right to meet fluctuating needs, resulting in food waste and lost income.

The Tenzo team found a way to capture and share insights data, so that purchasing decisions and staffing levels can be optimised. Their software has had a really positive impact on its users, the economy and the environment at a time when it’s been needed the most. 

Being able to provide Resilience funding to an innovative project such as this and observing the impact this work has had on a struggling industry gives me huge job satisfaction!” Lisa Gould-Davies – Programme Manager at Innovate-UK.

Due to the pandemic, we switched between fully remote and hybrid working in the UK.

We established more regular status updates among different functions in the project to increase information sharing. The cross-collaboration and visibility were critical to leverage the iteration feedback of the product considering the customer.

Internal Knowledge

The experience in working with innovate-UK went beyond the financial support. Risk monitoring and project management skills developed in the project helped us identify new issues and bottlenecks ahead of time.

During the project, we hired one project manager, a dedicated data scientist, and a quality assurance analyst. The value from these functions can (and will) be exploited by other features at Tenzo.

What’s Next?

Our team is now better equipped to keep improving our current forecasting features while developing new tools and functionalities. In the near future, we want to build more detailed forecasting capabilities so users can get deeper insights by being able to drill down into different parts of the forecast.

This will make ordering and prepping food all the more accurate, reducing the tons of waste currently produced by F&B businesses and will give operators the ability to schedule as efficiently as possible – a huge impact given the current staff shortages. 

Keep an eye out for what’s coming, we promise it’s very exciting!