Each year, struck-by and caught-in/between incidents are a major problem for the logistics industry, creating disruptions to safety, operations, and the bottom line. “Struck by object or equipment” remains a top injury driver, costing U.S. employers $11.6 billion annually (Source: Liberty Mutual Workplace Safety Index).
The good news: most of these occurrences are avoidable. By blending proven safety protocols with modern video intelligence, logistics leaders can move from reacting after the fact to tackling problems before they escalate. This guide breaks down the true costs, root causes, and offers 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 pain points in logistics. Here’s why:
Financial Impact: The direct costs of these events are substantial, running into billions of dollars annually for the logistics sector. 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 substantial, with fines for these violations sometimes exceeding $100,000 (Source: OSHA).
Ultimately, struck-by and caught-in/between incidents are a daily operational risk that demands a smarter, more forward-thinking approach.
The top 5 reasons for struck-by and caught-in/between incidents in logistics—and how to mitigate them
Understanding why these injuries happen is the first step to mitigating them. Here are the five leading causes in logistics, with practical mitigation strategies and how video AI can make your efforts go further.
1. Improper Vehicle Operation
The Hazard:
In a busy loading dock, a powered industrial truck reverses without a spotter, creating a high-risk situation near pedestrians. Incidents involving reversing vehicles or poor communication between operators and floor staff are a leading cause of injuries in logistics.
Traditional Mitigation:
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 Mitigation:
Video AI analytics detect when forklifts or other vehicles enter no-go zones, flagging potentially unsafe interactions. Real-time alerts allow supervisors to intervene so they can manage unsafe behavior—and historic footage makes root cause analysis faster and more objective.
2. Obstructed Visibility and Poor Lighting
The Hazard:
In a dimly lit warehouse aisle, a forklift operates with an obstructed view from a frosted windshield. Poor lighting and compromised sightlines significantly increase the risk of collisions between equipment and pedestrians, a common scenario in many facilities.
Traditional Mitigation:
Scheduled lighting inspections and upgrades.
Mandatory headlamps and reflective vests.
Routine clearing of stacked materials from sightlines.
How AI Amplifies Mitigation:
Video AI analytics monitor for vehicles and people in restricted zones, automatically flagging “person enters no-go zone” or “forklift enters no-go zone” events. This aids safety teams in targeting problem areas for lighting upgrades and adjusting 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 Mitigation:
Load securement training and checklists.
Visual inspections and use of chocks/locks.
Regular audits of stacking and racking systems.
How AI Amplifies Mitigation:
Ongoing video monitoring detects when workers are present in high-risk loading zones and flags “vehicle enters no-go zone” 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 injuries.
Traditional Mitigation:
Strict lockout/tagout (LOTO) procedures during maintenance.
Regular machine guarding inspections and upgrades.
Mandatory PPE (gloves, steel-toed boots, etc.).
How AI Amplifies Mitigation:
Video AI 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—allowing them to correct risky behaviors.
5. Time Pressure
The Hazard:
During peak season, a worker cuts through a busy cross-dock area on foot instead of using designated walkways, a common shortcut taken under pressure. This behavior puts them in the direct path of moving vehicles like yard trucks, increasing risk—an all-too-familiar scenario when deadlines push workers to take risks.
Traditional Mitigation:
Pre-shift safety briefings and “stop work” authority.
Reinforcement of traffic management plans.
Reward programs for safe behaviors.
How AI Amplifies Mitigation:
Analytics flag “running,” “crowding,” and “loitering” in unsafe areas—proxies for rushed or distracted behavior. Prompt notifications enable supervisors to manage risky shortcuts as they happen, while trends in the data highlight when and where to focus additional training.
Integrating a video AI camera system: from NVR to operational insights in logistics
Upgrading your safety tech doesn’t mean starting from scratch. Today's video AI camera systems are designed to work with your existing IP 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 any IP camera |
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 | Video AI-powered search & incident detection |
Actionable Insights | Passive footage | Real-time alerts for unsafe events, missing PPE, and more |
A cloud-based, video AI-driven platform bridges your IP cameras to a secure dashboard—so you can automate incident detection and speed up investigations. Unlike a standard NVR, the AI analysis layer sits on top of your video feeds, reshaping your video security system into a preemptive 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 video AI templates: Focus on analytics for “forklift enters no-go zone,” “person enters no-go zone,” and “missing PPE” to align with OSHA 29 CFR 1910.176 and 1910.178 requirements.
Support Team Buy-in: Involve safety, ops, and IT leaders early. Show how video AI analytics complement—not replace—human judgment and existing protocols.
Review and Adapt: Use event data to refine workflows, update training, and celebrate improvements as a team win.
Transform logistics safety
Every struck-by or caught-in/between event is a chance to strengthen your safety program. By adding video AI analytics, you can drive down incident rates, review investigations faster, and support safer, more efficient operations. Want to see how Spot AI works in real logistics environments? Request a live demo and experience the platform’s capabilities firsthand.
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.
How can logistics companies implement safety technology without interrupting daily operations?
Video AI platforms connect to your existing IP cameras, overlaying analytics without disruption. Installation is streamlined, and all event review is handled through a secure cloud dashboard.
Are there compliance standards for reducing 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.
What practical steps should logistics companies take before adopting video AI 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 do video AI analytics assist with event investigations?
Video AI-powered platforms flag events like 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 aid in reducing stress for safety professionals?
Video AI-driven analytics automate detection of specific unsafe events and reporting, freeing safety professionals from endless manual monitoring. This lets them focus on strategic planning and supporting their teams.
What features define the best video analytics for workplace safety?
A top-tier system works with your existing IP cameras, eliminating costly replacements. It moves beyond passive recording to provide real-time, actionable intelligence with alerts for specific events like 'person enters no-go zone.' It should also feature AI-powered search to find critical footage in seconds and a unified, cloud-native dashboard to manage unlimited users and locations from a single interface.
About the author
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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