Each year, struck-by and caught-in/between incidents hit the logistics industry hard—not just as statistics, but as real disruptions to safety, operations, and the bottom line. In 2025, Liberty Mutual’s Workplace Safety Index shows that “struck by object or equipment” remains a top injury driver, costing U.S. employers $11.6 billion annually.
The good news: most of these incidents are preventable. By blending proven safety protocols with modern video intelligence, logistics leaders can move from reacting after the fact to stopping problems before they start. This guide breaks down the true costs, root causes, and provides a clear framework for shifting from reactive to proactive safety using technology that fits right into your current operations.
Why Struck-by and Caught-in/between Incidents Are a Major Risk for Logistics Operations
Struck-by and caught-in/between incidents are among the most disruptive—and expensive—safety challenges in logistics. Here’s why:
Financial Impact: The direct cost of these incidents in transportation and delivery hit $11.2 billion in 2023, while warehousing saw $5.8 billion (Source: BLS, 2023). But that’s just the start. Indirect costs include operational slowdowns, hiring temp replacements, equipment repairs, and possible legal action.
Operational Disruption: When a key team member is injured, workflows stall. Missed shipments, overtime, and process bottlenecks ripple across the supply chain.
Regulatory and Compliance Risk: OSHA penalties are real and rising. The average penalty for a struck-by or caught-in/between incident in 2025 is $8,955, with some incidents exceeding $100,000.
The bottom line: struck-by and caught-in/between incidents in logistics are more than just a line item on a safety report—they’re a daily operational risk that demands a smarter approach.
The Top 5 Reasons for Struck-by and Caught-in/between Incidents in Logistics—and How to Stop Them
Understanding why these injuries happen is the first step to preventing them. Here are the five leading causes in logistics, with practical prevention strategies and how AI-powered video can make your efforts go further.
1. Improper Vehicle Operation
The Hazard:
A warehouse associate is helping unload pallets in the loading dock. A forklift operator, backing up to reposition, doesn’t see the spotter behind him and grazes the worker’s arm. Incidents like this—especially involving reversing vehicles or poor communication—are a leading cause of injuries in logistics.
Traditional Prevention:
Strict driver training and certification programs.
Use of spotters when reversing or maneuvering in tight areas.
Painted lines and signage for pedestrian and vehicle pathways.
How AI Amplifies Prevention:
AI video analytics flag “forklift near-miss” events and detect when forklifts or other vehicles enter no-go zones. Real-time alerts help supervisors intervene before a close call becomes an injury—and historic footage makes root cause analysis faster and more objective.
2. Obstructed Visibility and Poor Lighting
The Hazard:
On an early morning shift, an employee walks through a dimly lit warehouse aisle. A forklift operator, with a partially frosted windshield, doesn’t see the worker and nearly collides—a scenario all too common when lighting or sightlines are poor.
Traditional Prevention:
Scheduled lighting inspections and upgrades.
Mandatory headlamps and reflective vests.
Routine clearing of stacked materials from sightlines.
How AI Amplifies Prevention:
Video analytics monitor for vehicles and people in restricted or poorly lit zones, automatically flagging “person enters no-go zone” or “forklift enters no-go zone” events. This helps safety teams target problem areas for lighting upgrades and adjust traffic flows faster.
3. Poor Materials Handling and Unsecured Loads
The Hazard:
A team member removes a load strap from a flatbed, not realizing the stacked materials have shifted. As the strap releases, boxes tumble down, pinching the worker’s hand and narrowly missing his leg. Unsecured loads and improper handling are a leading source of both struck-by and caught-in/between injuries.
Traditional Prevention:
Load securement training and checklists.
Visual inspections and use of chocks/locks.
Regular audits of stacking and racking systems.
How AI Amplifies Prevention:
Continuous video monitoring detects when workers are present in high-risk loading zones and flags “vehicle enters no-go zone” or “possible fall” events. Reviewing flagged clips pinpoints whether loads were stacked or handled unsafely, supporting training and process improvement.
4. Inadequate Machine Guarding and Maintenance
The Hazard:
A mechanic reaches into a conveyor system that’s still powered, attempting a quick fix between cycles. His glove is caught, resulting in a hand injury. Missing guards and skipped lockout/tagout steps are common contributors to caught-in/between incidents.
Traditional Prevention:
Strict lockout/tagout (LOTO) procedures during maintenance.
Regular machine guarding inspections and upgrades.
Mandatory PPE (gloves, steel-toed boots, etc.).
How AI Amplifies Prevention:
AI video can detect “missing PPE” and monitor for “loitering” in restricted areas, signaling when workers may be bypassing safety protocols or entering active machinery zones. This gives safety teams a heads up—before a shortcut turns into a reportable incident.
5. Time Pressure
The Hazard:
During peak season, a driver rushes to finish a delivery, cutting through a cross-dock area on foot instead of using designated walkways. He’s nearly struck by a yard truck—an all-too-familiar scenario when deadlines push workers to take risks.
Traditional Prevention:
Pre-shift safety briefings and “stop work” authority.
Reinforcement of traffic management plans.
Near-miss reporting and reward programs for safe behaviors.
How AI Amplifies Prevention:
Analytics flag “running,” “crowding,” and “loitering” in unsafe areas—proxies for rushed or distracted behavior. Real-time notifications help supervisors address risky shortcuts as they happen, while trends in the data highlight when and where to focus additional training.
Integrating a Modern AI Camera System: From NVR to AI Insights in Logistics
Upgrading your safety tech doesn’t mean starting from scratch. Modern AI camera systems are designed to work with your existing POE or analog cameras—no full rip-and-replace required. Here’s how it works:
Feature | Traditional NVR System | Modern AI Camera Platform |
|---|---|---|
Camera Compatibility | Requires new/specific models | Works with existing POE/legacy cameras |
Storage | On-premise hardware | Secure, cloud-native, scalable |
Maintenance | Frequent, on-site | Minimal, remote updates |
User Access | Limited seats | Unlimited users, unified dashboard |
Video Review | Manual, slow | AI-powered search & incident detection |
Actionable Insights | Passive footage | Real-time alerts for near-misses, PPE, etc. |
A cloud-based, AI-driven platform bridges your legacy cameras to a secure dashboard—so you can automate incident detection and reduce investigation time by up to 95% (Source: Spot AI Capabilities). Unlike a standard NVR, the AI analysis layer sits on top of your video feeds, transforming your surveillance system into a proactive safety tool.
Implementation Tips for Logistics Teams:
Assess Your Risk Zones: Map out loading docks, cross-docks, and warehouse aisles where struck-by and caught-in/between incidents are most likely.
Check Camera Coverage: Identify gaps in your current system—especially in areas with heavy equipment traffic or poor visibility.
Prioritize AI Templates: Focus on analytics for “forklift near-miss,” “vehicle enters no-go zone,” and “missing PPE” to align with OSHA 29 CFR 1910.176 and 1910.178 requirements (Source: OSHA, 2025).
Support Team Buy-in: Involve safety, ops, and IT leaders early. Show how AI analytics complement—not replace—human judgment and existing protocols.
Review and Adapt: Use incident data to refine workflows, update training, and celebrate improvements as a team win.
Transform Logistics Safety
Every struck-by or caught-in/between incident is a chance to improve—not just react. By layering modern AI video analytics onto your existing safety program, you’ll cut incident rates, speed up investigations, and empower your team to run safer, more productive operations. Ready to see where your safety program stands—or how you can make the next leap? Book a consultation with Spot AI’s experts and get tailored, actionable advice for your facility.
Frequently asked questions
What are the main causes of struck-by and caught-in/between injuries in logistics?
The leading causes include improper vehicle operation, poor visibility, unsecured loads, inadequate machine guarding, and time pressure that leads to shortcuts (Source: OSHA, 2025; Liberty Mutual, 2025).
How can logistics companies implement safety technology without interrupting daily operations?
Modern AI video platforms connect to your existing cameras, overlaying analytics without disruption. Installation is plug-and-play, and all incident review is handled through a secure cloud dashboard.
Are there compliance standards for preventing these incidents in logistics?
Yes. OSHA 29 CFR 1910.176 and 1910.178 require clear aisles, safe material handling, and proper forklift operation. Regular audits and staff training are best practice (Source: OSHA, 2025).
What practical steps should logistics companies take before adopting AI video analytics?
Start with a risk assessment to identify high-traffic or poorly lit areas. Engage your IT and safety teams early, pilot the system in a known problem zone, and expand based on results.
How does AI video analytics help with incident investigations?
AI-powered platforms flag events like near-misses, no-go zone violations, and missing PPE. This makes it easy to find the footage you need, understand root causes, and support faster, more accurate investigations.
How can safety technology help reduce stress for safety managers?
AI-driven analytics automate hazard detection and reporting, freeing safety managers from endless manual monitoring. This lets them focus on proactive planning and supporting their teams.
About the author
Joshua Foster
IT Systems Engineer, Spot AI
Joshua Foster is an IT Systems Engineer at Spot AI, where he focuses on designing and securing scalable enterprise networks, managing cloud-integrated infrastructure, and automating system workflows to enhance operational efficiency. He is passionate about cross-functional collaboration and takes pride in delivering robust technical solutions that empower both the Spot AI team and its customers.
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