Machine Learning to reduce food waste in public meal organizations
Purpose and goal
To develop and examine the market potential of a full scale forecasting tool, matomaticML, that via machine learning helps the end user to reduce food waste.
Expected results and effects
The overall aim with the project for step 2 in the process is to create a zero series of the prototype matomaticML and to scale it in such a way that it can handle a whole public meal organization. This in turn serves the purpose to find out what effect the zero series has on the food waste in a whole organization over time, but also examine how scaling up from a prototype will work business-wise and how data in realtime can interact with the machinelearning algorithm and the users. The project will also look at how to scale the business to reach more customers.
Planned approach and implementation
The project is divided into 6 work packages 1. Project management 2: Preliminary investigation 3: Development. 4: Usability testing with users 5: Market plan for commercialization of matomaticML for public meal organizations 6: Market potential in the hospitalitcy sector The project will start in the end of 2018 and end in late 2020. The first part of the project is to develop the prototype to a zero series product that will be continuously tested by users, the later part of the project has more of a business focus in the shape of market plans and market potential.