Using Operations Management to Improve Public Housing
Trinh Nguyen is director of the Mayor’s Office of Jobs and Community Services in Boston, MA. She is the former chief of staff at the Boston Housing Authority and a member of the MIT EMBA class of 2014.
With increasing demand for public services and dwindling federal funds, a big issue is how to efficiently run public programs. In other words: How do you get taxpayers the most bang for the buck when investing in social programs?
One area where we can make a big impact is operating efficiencies. We can streamline our processes to better deliver quality services to our constituents.
Using techniques I learned in my Operations Management (OM) class at MIT, I began a process to address this in my former role at the Boston Housing Authority (BHA). While much of OM was geared toward manufacturing, I found that workflow and assembly line concepts were very applicable to public housing turnover.
When a tenant moves out of public housing, the down time is supposed to be two days, but we found it was 16 days. Additionally, we found that the move in time for new tenants was up to 60 days. Why did it take so long to get a new tenant in a unit? The BHA’s mission is twofold: to house those more in need and obtain rental income. Neither of those things will happen when a unit sits empty.
I wanted to better understand why this happened by designing a workflow to break down sub-processes and identify bottlenecks.
Show me the Numbers
It’s widely known that data analysis is an invaluable tool for making business decisions. However, some may be suprised at the amount of useful data that government agencies have at their disposal to help make better decisions. In the case of the BHA, we have enormous amounts of data related to housing that can be analyzed to improve the efficiency of public housing.
Breaking it Down
After cleaning up the data, we used regression analysis and correlation analysis to determine the main factors causing delays. Immediately, we found high standard deviations suggesting that a standard process for turnovers was needed.
Using the analysis from the data, we were able to make recommendations to decrease turnover time. For example, we recommended standardizing processes so that everyone was coding units in the same way. We also flagged the outliers, meaning the units in need of major repairs. Those needed to be fixed immediately or taken offline so they wouldn’t count as vacancies. Those kinds of changes were low-hanging fruit that didn’t cost the agency a cent. However, they led to some dramatic improvements.
How do you use operations management at your organization? Have you studied your workflow? What low-hanging fruit have you address?