THE DREAM TEAM

Inspiration

Our inspiration is to tackle the Moisson Montreal food waste issue. Surprisingly, there is more food wasted than we would think. As a well-developed country, we find it unacceptable that not everyone has access to food and we aim to bridge AI with reducing food waste.

What it does

Our solution attempts to optimize supply chain by leveraging AI to predict inbound food quantities, categorized by perishability. Our vision is align our solution to both meed supply and demand and ultimately reduce unnecessary food waste.

How I built it

The AI takes care of predicting incoming food quantity per category in the next few weeks in order to optimally address the demand across the organizations Moisson Montreal partners with to match supply with demand and decrease unnecessary food waste.

Challenges I ran into

We were provided a lot of data but only a small amount of the data was useful. Additionally, the data had to be cleaned and post-processed before feeding it to our predicting model.

Another challenge we faced was testing the best model for our problem - ultimately, we chose the Prophet Facebook predictive model which helped us predict the food quantity for the next 6 months.

Accomplishments that I'm proud of

The team was able to quickly take a look at the data, analyze it, prioritize it and post-process it in order to have quality data inputted in the model. We also efficiently tried different models and selected the one that performed the best.

What I learned

We learned how to create predictive models using a Time-Series approach which we were not familiar with before the start of the competition.

What's next for THE DREAM TEAM

Our next challenge will be to take the predictive food quantity and use that information to optimally distribute it across the different organisations Moisson Montreal is working with.