Xavier Africa

11/11/2022

Botswana Agricultural Marketing Board

The Botswana Agricultural Marketing Board (BAMB) was established by an Act of Parliament, No. 2 of 1974 mandated to provide a market for locally grown scheduled crops such as cereals, pulses/beans and oilseeds, and ensure that adequate supplies exist for sale to customers at affordable prices.

The Challenge

The Agricultural marketing board’s core business had reached full maturity and was beginning to lead to a decline in profitability. Therefore, they wanted to find ways to leverage their current clientele base to create new revenue opportunities without excessive new investment. 



The Process

The Xavier Africa team held a series of interviews with several BAMB staff, customers and key stakeholders to figure out how the firm could help create a digital solution that could help drive sustained profitability for the client. The key insights Xavier Africa generated were as below;

  1. Farmers in remote areas had minimal access to agronomists and farming supplies throughout their production process and thus their probability of success was significantly low. 
  2. BAMB had 2 inhouse agronomists, and a large network of logistics partners across the country.

The Xavier Africa team went on to brainstorm several solutions that could be built and figured that we could create a solution that worked out for both parties. The solution was a cross platform mobile application that could serve as a farmers’ companion providing real time machine generated agronomy advisory, share tailored best practices for the farmers’ throughout the production period and monitor supplies with reordering done exclusively with BAMB branded products.


The Solution

BAMB Web Application

Web Dashboard Tracking `Key Insights

BAMB Companion Application

Mobile Application Allowing Customers Realtime Tracking

TECHNOLOGIES USED

  • Flutter for cross platform mobile application
  • Firebase – Database & authentication
  • Google Cloud Platform for cloud services
  • Tensorflow for machine learning recommender algorithms.
Flutter
Firebase
Google Cloud
Tensorflow

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