Driving sustainable economic development through decision models
A major US city was looking to optimize its use of funding towards sustainable economic development projects while maximizing measurable impact. The city had 7 different sustainability programs running in parallel, each with its own measurement scheme, and each with activities siloed from the rest.
We knew that finding the optimal distribution of budgets across the programs would not only increase impact and eliminate inefficiencies, but also enable the city to vie for increased state and federal program funding.
A data based approach not only revealed inefficiencies, but allowed us to identify the optimal allocation of budgets over time and across the running programs.
We created a quantifiable standard of impact measurement for all sustainability programs for the city through a combination of top-down and bottoms-up analysis.
We created multiple variable models using the new attribution standards in Monte Carlo analysis of potential future scenarios
We determined the optimal allocation of current funds, identifying both potential for increased impact as well as elimination of inefficiencies between the 7 different programs.
We then ran multiple predictive simulation to understand impact shifts based on variability of program budgeting in the future, at various budget levels, and across different time horizons
This allowed us to not only understand the optimal distribution of funds across the programs, but better understand the potential impact of future activities based on the ideal combination of budgets and timing across the programs
Finally, our projective models allowed us to support the Mayor’s team case and request for a doubling of both federal and state funds for the program
We were able to immediately increase measurable impact by 17%
over the next 8 years, the city will be able to drive an incremental 90M worth of economic value from an optimized program structure
We were able to do all this without compromising scope, and while still reaching the 80,000 households as the program set out to reach originally.
We were able to ask for a doubling of federal and state funds