# About Flan

In this manual, we're going to calculate a trade analysis of disaggregated data on worldwide bilateral trade flows as easy as a piece of flan. 🍮 😉

![FLAN \~ FLow ANalysis of disaggregated data on worldwide bilateral trade flows.](https://3538199223-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MQ81L7E5VY2im6KUU-z%2F-MQErREiYLZWyOQhLONm%2F-MQEuP-_WDL5DJCGlmNQ%2Fflan.png?alt=media\&token=07fd613e-0aa4-4c15-a0d5-ab617327472f)

But first, let's understand the why behind it.

## Problem

When entrepreneurs want to enter a sector with a new initiative, they usually make research focused on its location and their subjectivity on the experience of the people and organization that interact with them at some level.

This tends to give entrepreneurs an extremely biased perception of what is a good product or service to offer to the public, one that could be scalable and is being demanded at a regional and/or global level, and as a consequence, we have among other economic, cultural and psychological factors a high rate of business failures.

## Opportunity

The datasets of [global trade of imports and exports](http://www.cepii.fr/CEPII/en/bdd_modele/presentation.asp?id=37) are heavy (original datasets are around 380 MB per year), and require considerable computational power to run analysis on them, but with Golem’s global decentralized supercomputer that anyone can access that's not a barrier, and becomes an outstanding use case opportunity.

So what if with just a minimal set of parameters from entrepreneurs we could provide a customized analysis of millions of worldwide trade value records giving them a bold guideline about what sectors they would need to take more attention to? And all that, as simple, as a piece of flan! 🙌🏻

## Components

### flan-docker

Docker project that contains the datasets, and the calculations to provide custom images as a result of the analysis of these records.

{% hint style="info" %}
Because of the VM restrictions, I reduced the size of the datasets from around 380 MB to 4 MB each one, by grouping the product and services categories from 6 to 4 digit codes. It means the most specific values require more computational power.
{% endhint %}

#### BCG Growth-Share Matrix

The BCG Matrix, also known as the Growth-Share Matrix, was created almost five decades ago by Bruce Henderson, founder of Boston Consulting Group. At the height of its success, the BCG Matrix was used by more than half of all Fortune 500 companies. Today, it is central in business strategic planning, and business school teachings.

Most business models analyse situations and products or services that are profitable currently. The current moneymakers are easy to identify, but what about the future? The BCG Matrix can do just that, and that's what I believe could be one of the first milestones for entrepreneurs to discover the sectors that have the highest success probabilities in the future, instead of just trying.

![Growth-Share Matrix explanantion](https://3538199223-files.gitbook.io/~/files/v0/b/gitbook-legacy-files/o/assets%2F-MQ81L7E5VY2im6KUU-z%2F-MQHvOJkVvDne37HOrvO%2F-MQHxXLE3KRW4qNWwkTj%2Fimage.png?alt=media\&token=a00eea0a-e328-4a09-8d31-f0f7ea300e4b)

{% hint style="info" %}
This is the first analysis made in the project, but as the project continues more and more deep studies could be added to the project to provide a stronger guideline to entrepreneurs in their next initiative.
{% endhint %}

### flan-api

NodeJS Express API that uses YAJSAPI to send a request to the Golem network through the Yagna daemon.

### flan-app

React application that works as the front-end to get the user parameters.


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