Analytics Lab
The MIT Analytics Lab (A-Lab) is the IDE's flagship educational offering. In this graduate seminar, student teams engage with project host companies to deliver a project using analytics, machine learning, and other methods of analysis to develop results that diagnose, enable, or uncover solutions to real business issues and opportunities.
A-Lab is a graduate level seminar course, spearheaded by the IDE and is part of MIT Sloan School of Management’s suite of Action Learning offerings. The course is led by IDE faculty Sinan Aral.
During its first eight years, A-Lab has attracted a total of 700 students from a dozen MIT programs to work on 180 projects spanning IoT, digital technology, platforms, finance, marketing, e-commerce, retail, manufacturing, medical supply chains, workplace safety, and global health.
Some projects are tightly focused on dilemmas organizations currently face, which requires students to quickly understand particular business circumstances and domains before performing their descriptive, predictive, or causal analysis. Other projects are more open-ended, and students must think entrepreneurially about how to bring new value to existing data and suggest frontiers for future business opportunity.
For Project Host Companies
Propose Projects for A-Lab
If you are interested in hosting a project and submitting a proposal for the 2023-2024 academic year, please reach out to us at a-lab@mit.edu.
Submit an A-Lab Proposal
Organizations are invited to propose projects and become project hosts.
Submit a ProposalFor Students
Apply to A-Lab
The course includes an extended project pitch session and a final presentation event, in addition to normally scheduled class meetings. The course is not open to listeners and attendance at all sessions is mandatory.
Past Projects
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- Challenge: Help Amazon quantify the impact of supply chain forecasting errors to better prioritize forecast improvements in the future
- Data: 75 million rows containing daily demand and forecast data for 206 thousand products over two weeks
- Analysis: Defined different kinds of costs associated with forecasting errors and their magnitudes. Used statistical methods in R running on a cloud computing system to quantify lost profit due to forecast error
- Recommendation: Incorporate indirect costs into the evaluation of forecasting errors. Look for variation across product categories
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