In 2011 and 2012, California Governor Jerry Brown issued a pair of executive orders that created both an opportunity and a dilemma for the Office of Fleet and Asset Management (OFAM) in the state’s Department of General Services (DGS). Brown directed OFAM—which oversees the state’s 50,000 vehicles—to reduce its fleet by 7,000 vehicles, increase the proportion of light duty fleet purchases that are Zero Emission Vehicles (ZEV) to 25 percent by 2020, and cut statewide petroleum use by 50 percent by 2030. On the one hand, this meant that OFAM had the chance to make a significant contribution to the mission of reducing greenhouse gas emissions. On the other hand, it raised difficult questions. How would OFAM balance “green goals” and departmental needs (e.g., public safety vehicles with special performance requirements)? In addition, how would the division remain competitive with other suppliers (e.g., rental car agencies)? “If we just have a green fleet and vehicles that people don’t like at a higher cost, they’re not going to go to us,” said DGS Director Daniel Kim. “Our costs escalate and then that’s just a vicious cycle.”
DGS responded to these directives with a multifaceted strategy that blends data and analytics, nudge principles, design thinking, and shifts in human capital to help OFAM maximize the environmental rewards while minimizing financial risk. “It doesn’t take a lot of big ideas,” said Kim of the agency’s approach. “It could be a bunch of small ideas done simultaneously that can make a difference.”
In crucial respects, the foundation for DGS’s response to Brown’s directives began to take shape in 2005 when the agency faced a very different kind of challenge: The Sacramento Bee reported that OFAM could not locate 30,000 of its 70,000 assets. “It was true,” Kim reflected. “It’s not like they were stolen. We didn’t know where they were at any given time.” DGS swung into action by creating a Fleet and Asset Management System (FAMS) that required all state departments to submit data on their vehicles (e.g., vehicle identification numbers, fuel type, mileage, and purchase date). Looking back, Kim suggested that the “crisis was a good thing because it forced us to recognize [that] we need some kind of system.”
In 2011 and 2012, OFAM began leveraging FAMS to gather and analyze data that would help the agency determine how best to achieve the goals identified in Brown’s executive orders. A case in point was that DGS employed this data to evaluate whether each asset in the state’s fleet was “mission critical” and “cost effective.” The analysis yielded valuable information, such as that the California Department of Transportation (CALTRANS) deployed two trucks to respond to every freeway accident. This was not because they needed two vehicles to clear most accidents; instead, one truck typically provided protection so that the other did not get hit. To remedy this inefficiency, CALTRANS created a mobile barrier to protect the truck that was required. This eliminated the need for the second vehicle and dramatically reduced the size of their fleet and the environmental impact of the crews’ response to freeway accidents.
This data and analysis has also informed the application of a series of nudges to help OFAM reduce greenhouse gas emissions. One set of nudges involves creating pre-commitment goals. These include the targets established in Brown’s executive orders as well as a subsequent directive that state agencies submit three-year ZEV purchasing plans to DGS. DGS has also begun altering default settings by making ZEVs the default selection for all light-duty fleet purchases; furthermore, the agency is considering eliminating internal combustion engine sedans as a purchasing option. More broadly, Kim emphasizes that data and analytics are foundational for all of the nudges that the agency uses. “Data really informs nudge,” Kim explained. “We had to do the data collection and analysis to figure out where we wanted to target our various nudge policies.”
To increase the likelihood that other agencies will respond to these nudges, DGS has leveraged both design thinking and shifts in human capital. The most important design change involves creating more charge stations for electronic vehicles. Specifically, the governor’s 2016 ZEV action plan called for the electrification of five percent of all workplace parking spaces at state-owned facilities as well as the construction of 1,500 additional vehicle powering stations over the next five years. At the same time, Kim has tried to prime the workforce to promote and embrace change. This includes disseminating knowledge about electronic vehicles through marketing materials and demonstrations as well as encouraging his team to become more comfortable taking risks to develop best practices and strategies. Kim explained, “This is harder than it seems because you can make the data say what you want it to say…. So, I think we have to create a safe environment for our staff to be intellectually honest and figure out what we can do about this.”
DGS still has a long way to go to achieve the ambitious, long-term goals outlined in Governor Brown’s executive orders. Nonetheless, the agency has already made significant progress. The state has decreased its total sedan purchases from over 800 in 2013 to fewer than 200 in 2016, and the proportion of the acquired vehicles that are ZEV has increased dramatically. What’s more, compared to 2003, total petroleum consumption has decreased by over 19 percent and was projected to decline significantly further by the time the final data from 2016 was calculated. Finally, the fleet’s total greenhouse gas emissions have fallen from well over 700 million pounds of carbon dioxide in 2003 to a little more than 600 million pounds in 2015.
More broadly, Kim and his colleagues can point to a deeper lesson about how to integrate data, nudges, and design thinking to generate impact: it is imperative to be “relentlessly incremental. “Cover all of the angles,” Kim said. “Look at data, come up with goals, set different rates, look at different policies, review the data, and continuously monitor what you’re doing.”
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