MLGW reduces preventable vehicle collisions by 20% and accelerates service restoration with Samsara
The problem
MLGW faced a dramatic surge in storm-induced outages and lacked real-time visibility into crew and vehicle locations, causing service delays and inefficiency. They also needed to proactively coach drivers and exonerate employees from false claims to minimize safety-related costs and keep utility rates low.
Workflow diagram · grounded in source
1
AI Dash Cam safety monitoring
Ai action
AI Dash Cams provide MLGW with real-time visibility to meet world-class safety standards.
▾ source quote
“AI Dash Cams provide MLGW with real-time visibility, enabling them to meet their world-class safety standards”
2
In-Cab Nudges proactive coaching
Feedback loop
Supervisors minimize risky behaviors using Samsara's Proactive Coaching solution, achieving a 100% reduction in safety events with In-Cab Nudges.
▾ source quote
“Supervisors can minimize risky behaviors with Samsara's Proactive Coaching solution, achieving a 100% reduction in safety events using In-Cab Nudges”
3
On-demand video exoneration
Output
On-demand video retrieval helps exonerate employees from not-at-fault incidents, minimizing costly insurance and keeping utility rates low.
▾ source quote
“on-demand video retrieval helps exonerate employees from not-at-fault incidents, minimizing costly insurance and keeping utility rates low”
4
Samsara and ArcGIS integration
Integration
By integrating Samsara and ArcGIS, MLGW gains real-time visibility into vehicle location required to dispatch technicians to service outages.
▾ source quote
“By integrating Samsara and ArcGIS, MLGW now has the real-time visibility required to dispatch available technicians based on vehicle location to service outages”
5
Dispatch technicians to outages
Routing
Available technicians are dispatched based on vehicle location to service outages, enhancing efficiency and improving service delivery.
▾ source quote
“dispatch available technicians based on vehicle location to service outages, enhancing efficiency and improving service delivery”
6
Safety Scores driver incentive
Feedback loop
Every month, Samsara Safety Scores are posted during safety meetings to encourage healthy competition among drivers.
▾ source quote
“motivating and rewarding drivers using Samsara Safety Scores. Every month, scores are posted during safety meetings to encourage healthy competition”
7
GPS asset recovery
Output
MLGW used Samsara's real-time GPS to track and recover missing water meters, saving thousands of dollars in lost assets.
▾ source quote
“When multiple temporary water meters, each valued at up to $1,000, went missing, MLGW used Samsara's real-time GPS to track and recover the missing units—saving them thousands of dollars in lost assets”
Reported outcome
MLGW reduced preventable vehicle collisions by 20%, achieved a 100% reduction in safety events using In-Cab Nudges, and gained real-time crew visibility for faster service restoration, while recovering missing assets worth thousands of dollars.
Reported metrics
Preventable vehicle collisions reduction20%
safety events reduction using In-Cab Nudges100%
Value of each missing water meter recoveredup to $1,000
Lost asset recovery savingsthousands of dollars in lost assets
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MLGW reduced preventable vehicle collisions by 20%, achieved a 100% reduction in safety events using In-Cab Nudges, and gained real-time crew visibility for faster service restoration, while recovering missing assets…
Preventable vehicle collisions reduction: 20%; safety events reduction using In-Cab Nudges: 100%; Value of each missing water meter recovered: up to $1,000; Lost asset recovery savings: thousands of dollars in lost assets (source-reported, not independently verified).
How is this field service AI workflow structured?
AI Dash Cam safety monitoring → In-Cab Nudges proactive coaching → On-demand video exoneration → Samsara and ArcGIS integration → Dispatch technicians to outages → Safety Scores driver incentive → GPS asset recovery.
This case is one data point. Whether its pattern fits you depends on your volumes, your stack, and your exception load — that comparison is the step no case study can do for you.