Foodways wants to develop a tool dealing with the topics of good food, sustainability, value chain transparency, future innovation… using machine learning and big data analysis methods. The tool should tap on massive amounts of unstructured data, which humans typically aren’t able to handle. By transforming and interpreting the data, the tool should be able to structure the information and generate relevant insights on selected questions.

 

Background

the food industry's challenges

  • How can sustainability professionals in the food industry keep up with an accelerating world? 
  • There’s too much information available on- and offline, and it becomes hard to decide what’s relevant. It’s often hard to take decisions, especially under pressure, without enough time, not knowing which data is relevant and/or should be taken into account.
  • Companies, as their consumers, need more clear and relevant insights to take decisions.

the food industry's questions

very often the food industry poses itself questions for which she needs quick and relevant insights to make a decision. The following examples give you an idea of such questions:

  1. The sustainability leader of a corporation wants to understand the main sustainability challenges and risks the company will or may experience within the next years, based on the developments of the last months.
  2. The marketing department of a food SME is taking investment decisions for the newest product development. The department wants to understand the latest trends on the consumer food products sector and get an idea which mega-trends are gaining force.
  3. An entrepreneur wants to decide whether a vegan restaurant in the city centre will make sense and if the time is right in a specific location for a 100% vegan food offer. Also, he would like to know if a 100% value chain transparency is possible for his market, and if the consumers are demanding such solutions.
  4. A food corporation wants to know their role in the sustainability developments of the healthy and organic food developments, assessing the public's perception of its role, its competitors' role, as well as available technologies triggering more sustainable options. Furthermore, they specifically ask to search for other relevant unknown variables.
  5. A company willing to export its food products to a different market wants to have access to all needed informations to do so: food market regulations, main actors, consumer trends, among many others.
  6. A government official wants to have an overview on all facts and figures about food waste in his country and around the world: relevant data, most important reports, best practices, among other - all within 24h.
  7. A school wants to know what would be the best concept on sustainable and healthy food for its school, mainly based on best practices from schools with similar challenges and cultural realities as its own.
 

Objectives

the tool's tasks

A technology based on the human-machine cooperation, where technology meets food experts.

  1. Information sources: After curating the information sources (human task), selecting them by quality and relevance, the tool should be able to work with the data crawled or accessed (machine).
  2. Possible IT tasks: The tool should use big-data analysis, machine learning and yield quick smart-data results (insights).
  3. Results: The tool will then deliver the relevant content to the food industry (private, public and not for profit organisations), in different formats. This is where the human role kicks in again, compiling this information into a report or just by sending the raw insights to the decision makers.

 

    1. Information Sources

      News articles / Blogs / RSS:

      Newsletters:

      General Databases:

      agri-Food specific data

      Social Media:

      • Facebook, LinkedIn, Youtube or Pinterest.

      Websites:

      PDFs:

      Research Journals:

      Other sources:

      • Google Maps, Amazon Directory, Conferences notes...

       

      2. Possible IT tasks:

      • Crawling raw data
      • Interpretation of possibly relevant data sets (e.g. finding correlations)
      • Delivering insights and/or Visualisation of data sets

      Via these possible methodologies & technologies:

      • Storage and Index
      • APIs
      • Web crawlers (e.g. Diffbot or open source web crawlers)
      • Automatic Graphs
      • Data analysis and statistics (e.g. correlations)
      • Machine learning, artificial intelligence
      • Everything is open and there's no limitation on the language or technology to be used

       

      3. Results

      • Reports (PDF)
      • Powerpoint Presentations (PDF)
      • Search results (e.g. Google)
      • Infographics (e.g. PDF, web-based)
      • Sprea dsheets (e.g. Excel)