Automation of Level of Service Measurement for Snow Operations

Public works and winter maintenance organizations require a published plan to direct operations and provide metrics to measure performance to provide world-class service to stakeholders. The plan should provide the key performance indicators (KPI) that can be measured and tracked to determine the level of service by the winter maintenance team. The session will outline the three key processes of the winter maintenance plan that is designed to present KPIs to monitor performance:
1. A detailed winter maintenance plan, including KPIs to score effectiveness based on storm intensity.
2. Snow asset telematics to automate data collection that forms the backbone of the KPI determination. Plow status includes lane miles plowed, location of deicing material application, volume of material applied, timing of material application based on road/air temp/grip, performance per defined snow route, and storm intensity.
3. Provide reporting and analytics to get the level of service calculation based on goals provided in the winter maintenance plan. Attendees will take away proven methods to provide near real-time measurement of the level of service, enabling management to lead organizations with a culture of continuous improvement. Real case studies of organizations committed to this process will be presented.
The implementation of an automated level of service process will reap the benefits of:
• Increased safety for your staff and the stakeholders that use your transportation network
• Improved efficiency of labor and materials as you work
• Real data to provide accountability to your plan
• Sustainability as you work with the least possible environmental strain with deicing materials and reduced carbon emissions.

At the conclusion of this session, participants will be better able to:
• Accurately measure snow event material and labor costs through snow operation telematics automation.
• Identify optimized and prioritized route planning.
• Calculate material usage for future snow events based on accurate event data.