LIFT Projects

Unleash Potential, Make an Impact.

What are LIFT Projects?

LIFT Projects offer students from MIT and partner universities an incredible opportunity to transform their knowledge into action. By equipping them with the necessary resources and nurturing their curiosity, we empower students to delve into the micro-retailing industry within their own communities and devise innovative solutions for the challenges faced by nanostore owners in Latin America. Our emphasis on fostering local, positive impact ensures that these projects become catalysts for students to both contribute to their communities and gain invaluable hands-on experience.

Spanning an academic term, LIFT Projects grant students from MIT and partner institutions the chance to witness firsthand the real-world applicability of their studies, while collaborating on a remarkable group endeavor aimed at benefiting their immediate surroundings. To support their efforts, our dedicated LIFT Lab team provides comprehensive training in primary data collection, data visualization, and prescriptive and predictive analysis; tailoring resources to the unique requirements of each project. Together, we believe in nurturing the potential of students and empowering them to make a tangible difference in their local communities through meaningful engagement and impactful action.

Listen to what former participants have to say:

(spanish audio)

Semester Breakdown:

A LIFT Project is split into four phases:

1. Primary data collection

The main purpose of this activity is to collect data in the field from the key stakeholders of the micro retailing sector, i.e. suppliers, MSEs, and consumers. All students will be assigned to a certain area of study in the field and will collect data via surveys and questionnaires that will be available on the Fulcrum App. During the Kick-off session, all students will receive detailed guidelines and instructions of where, when, and how to perform the data collection process in the field.

2. Data Visualization

Using the data gathered in the field, the teams will conduct a graphic display of information (i.e. data visualization) with the objective to provide insights into the micro retailing ecosystem, and potential ideas to improve performance in all stakeholders. This includes observing and analyzing trends, outliers, and patterns in the data, via utilizing visual elements like charts, graphs, and maps. All students will receive workshop(s) to learn the use of some software tools (i.e. Tableau).

3. Data cleansing

In this step, all students will conduct data cleansing by removing missing records, detecting inconsistencies, transforming data, and the treatment of numerical and categorical values to consolidate a database for further analysis.

4. Prescriptive & Predictive Analysis

All students will conduct data analysis using statistics and probabilistic models (e.g. descriptive statistics, regression analysis, concepts and applications of machine learning, etc.). Students are expected to proposed a predictive model that provides managerial insights into the potential recommendations for the three stakeholders for the micro retailing ecosystem (i.e., suppliers, MSEs, and consumers) with the objective to improve performance (e.g. productivity, financial, cost, sales, etc.).

All final projects will be reviewed by the on-site advisors from the partner universities. Selected teams with the best projects will go on to represent their institution in a nationwide and/or international Research Fest.

Research Fest