McKinsey's Sustainable Future Labs game: guide
In this guide
Overview
Sustainable Future Labs (SFL) is one of McKinsey Solve's newer modules. You play the role of a decision maker allocating resources across competing priorities — typically balancing sustainability, cost, and throughput under uncertainty. The game presents you with a series of investment or production decisions, each affecting multiple objectives, and asks you to find a configuration that meets an overall target.
The objective
You're given a target outcome (e.g., "reduce emissions by X% while maintaining throughput above Y and keeping cost below Z") and a set of levers — investments, process changes, or technology choices. Each lever moves the dials differently, and you must find a combination that satisfies all constraints simultaneously.
Strategy
- Identify the binding constraint first. Whichever objective is hardest to meet is the one you optimize around; the others become secondary.
- Map each lever's marginal effect on each objective before committing. Many candidates fail by jumping to a "good-looking" lever without checking its downsides.
- Use cheap levers for the binding constraint — the lever that moves the hard objective the most per unit of cost/throughput sacrificed.
- Check feasibility early. Once you have a candidate configuration, verify every constraint is satisfied before refining.
How it's scored
- Constraint satisfaction — did you meet all targets?
- Quality of the solution — how close to optimal on the binding constraint?
- Decision quality — were your intermediate choices defensible?
- Time efficiency.
Practice the underlying logic
SFL doesn't have a dedicated solver, but Ecosystem and Sea Wolf rehearse the same constrained-optimization thinking.