The City of Long Beach partnered with Pitstop to optimize the maintenance and operation of its diverse municipal fleet over a 3-month long analysis. The city’s fleet supports various departments, such as public safety, sanitation, public works, and transportation, with a mix of vehicle types, including gas, CNG, and diesel.
In this case study, learn how Pitstop solved the primary objectives for the City of Long Beach fleet, including minimizing downtime, maximizing safety, efficiently allocating maintenance resources, and confidently projecting annual costs.
The predictive and real-time alerts provided by Pitstop could turn unplanned service events into scheduled ones, avoiding the cost of 147 Tow or Road Call events, saving an estimated $61,000, excluding repair expenses. Based on all these and additional savings you’ll learn in the case study, the City of Long Beach could save $809,500 annually with all of Pitstop’s predictive maintenance solutions.
Download the case study to learn the transformative potential of AI and data analytics in fleet management, from strategic decision-making to operational effectiveness. And how adopting a predictive, data-driven approach with Pitstop led to significant efficiency gains, safety improvements, and substantial annual cost savings for the City of Long Beach, showcasing a successful integration of technology in improving service delivery and cost management.
To read the full case study by Pitstop, click here.
