Periodic Reporting for period 2 - Binspector (Cutting-edge food waste solution for restaurants)
Reporting period: 2023-03-01 to 2024-02-29
Worldwide, one-third of all food production is wasted, 15% of which is generated by restaurants, hotels and caterers, adding up to a total of close to 200M tons of edible food being wasted. That’s not only detrimental to the environment, but also to the profitability and thus livelihood of this industry. However, they lack the tools to give them insight in this big waste stream and thus they are incapable of reducing food waste without impeding their guests' experience. Our product, Binspector, combines a smart camera, a smart scale and artificial intelligence image recognition technology to automatically register all food going to waste and supply actionable insights to chefs on how to optimize inefficiencies whilst keeping a high quality service. Integrated with external systems, our product will even pre-fill restaurant ordering systems. We offer our Product-as-a-Service to professional kitchens for a monthly fee with no upfront costs. It will allow an average kitchen to decrease its ecological footprint and food waste level by up to 70% and easily save over €30,000 annually, resulting in a net profit margin increase of up to 3%. The market is estimated to be worth €7,8B in Europe alone.
Within the Binspector, Orbisk heavily invested in the (international) scalability of the Orbi food waste monitoring solution, plus developing additional features to help current and future clients drive down food waste levels in their operations to the lowest conceivable levels.
Firstly, considerable development effort was put into the scalability of our hardware product and remote servicing of our solution. We’ve achieved to design an absolute plug-and-play product that is installed by our clients themselves, in easily under 30 minutes. Even in market circumstances where global material prices have increased by up to 100%, we’ve been able to reduce hardware costs significantly, whereas costs of circumstantial factors such as shipping and servicing have decimated due to these efforts. Furthermore, our health fleet of deployed devices is managed fully remotely through our fleet management tooling, whereas our clients are remotely serviced at scale through our customer success management system, reducing cost and capacity spend across the board.
Secondly, we’ve made a considerable leap in the scalability of the core of our product: our AI computer vision food recognition algorithms. We’ve extensively researched the options for recognizing food items / ingredients at scale across the geographic distribution of our solution, now and in the future. We managed to create the AI model infrastructure to facilitate model quality evaluation, continuous training and improvement and deployment of those models at scale, which now allows us to manage this cutting edge not only at scale, but also within the boundaries of what the business case allows for. Furthermore, we’ve extended the functionality of our AI with features that allow for future applications in parallel food (waste) spaces, leveraged by our IP to its fullest extent.
Finally, we’ve also heavily invested into new features that services our clients in their operations more broadly so as to further outline the origin of food waste in their processes and thus allowing for earlier capture and allowing us to provide more granular advice on what interventions can be done to allow for the lowest possible food waste levels without compromising on quality and service levels towards our clients’ guests.
Firstly, considerable development effort was put into the scalability of our hardware product and remote servicing of our solution. We’ve achieved to design an absolute plug-and-play product that is installed by our clients themselves, in easily under 30 minutes. Even in market circumstances where global material prices have increased by up to 100%, we’ve been able to reduce hardware costs significantly, whereas costs of circumstantial factors such as shipping and servicing have decimated due to these efforts. Furthermore, our health fleet of deployed devices is managed fully remotely through our fleet management tooling, whereas our clients are remotely serviced at scale through our customer success management system, reducing cost and capacity spend across the board.
Secondly, we’ve made a considerable leap in the scalability of the core of our product: our AI computer vision food recognition algorithms. We’ve extensively researched the options for recognizing food items / ingredients at scale across the geographic distribution of our solution, now and in the future. We managed to create the AI model infrastructure to facilitate model quality evaluation, continuous training and improvement and deployment of those models at scale, which now allows us to manage this cutting edge not only at scale, but also within the boundaries of what the business case allows for. Furthermore, we’ve extended the functionality of our AI with features that allow for future applications in parallel food (waste) spaces, leveraged by our IP to its fullest extent.
Finally, we’ve also heavily invested into new features that services our clients in their operations more broadly so as to further outline the origin of food waste in their processes and thus allowing for earlier capture and allowing us to provide more granular advice on what interventions can be done to allow for the lowest possible food waste levels without compromising on quality and service levels towards our clients’ guests.
Our approach is advancing the state of the art by:
- Plug & Play - absolutely effortless food waste registration with no on-site device setup required.
- Accuracy in waste registration - enriched & optimal data granularity to provide personalised and highly relevant insights and advice for maximum waste reduction.
- Demand prediction - accurate consumption forecasting for optimised production and ordering planning.
- Smart Assistant - automated buying (based on demand prediction) through third-party integrations.
- Plug & Play - absolutely effortless food waste registration with no on-site device setup required.
- Accuracy in waste registration - enriched & optimal data granularity to provide personalised and highly relevant insights and advice for maximum waste reduction.
- Demand prediction - accurate consumption forecasting for optimised production and ordering planning.
- Smart Assistant - automated buying (based on demand prediction) through third-party integrations.