According to the US Department of Labor, workplace injuries cost an estimated $161.5 billion yearly. In Wholesale and Retail Trade (WRT) establishments, lost workday injuries are caused mainly by slips, trips, and falls. A study in the United States in 2020 found that falls accounted for 33% of nonfatal injuries, making it the highest cause of preventable workplace nonfatal injuries. Moreover, falls were the third highest cause of preventable fatal workplace injuries at 21%.
According to The National Institute for Occupational Safety and Health (NIOSH), factors that can lead to workplace injuries include:
- Workplace factors – Slippery surface, loose floor coverings, obstructed vision by boxes or containers, poor lighting, lack of maintenance of walking surfaces.
- Work organization factors – High working pace that may cause workers to rush, tasks involving handling greasy or liquid materials that may make surfaces slippery.
- Individual factors – Age, worker fatigue, and poor eyesight may affect vision and balance, and inappropriate footwear can cause tripping or slipping.
However, most WRT establishments have difficulty ensuring all health and safety protocols are adhered to both by employees and customers. The problem increases in a high-density environment with heavy human traffic. Managers are adopting innovative ways to complement the traditional solutions in the WRT stores.
Artificial Intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) have combined to detect, analyze, alert, and prevent hazards in the workplace. Workplace safety is significantly improved using real-time responses.
Computer vision uses digital inputs from images and videos to derive information meaningful to a computer. The computer then analyzes the information to detect defects.
SeeChange (AI provider) and Keymakr Inc. Inc. (data-annotation service provider) partnered to leverage AI in preventing slips, trips, and falls using existing CCTV cameras in Asda (supermarket chain in the UK) stores. Keymakr’s SaaS platform empowers SeeChange’s SpillDetect tool to detect liquid spills automatically. The system then sends notifications to the staff on the location of the hazard.
According to Michael Abramov, CEO of Keylabs, Keymakr’s Saas platform, “AI can be leveraged to detect accidents as soon as they happen and AI-based smart checkout systems can eliminate the human-error factor. Implementing AI can save buyers and business owners from such dangers.”
Abramov says that AI does not suffer from fatigue and can monitor non-stop. “The position of products on the shelves (and alert of a dangerous positioning) The condition of the floors (and report any incidents (spilled products, products that have fallen off shelves)). That’s not all of it as AI surveillance systems can monitor the entire store, providing insights into customer behaviors and preventing thefts.”
relEYEble solutions offer computer vision services and integrate with existing cameras to detect areas with the highest traffic in the store and monitor access to the premises. This feature helps reduce injuries caused by overcrowding and limited access and exits to a building in case of emergencies.
Fire detection systems traditionally have a response time of 3-5 minutes after detecting a fire. This time may be crucial, especially for large and fast-spreading fires, reducing the firefighting response time. Computer vision can detect fires from about 50m away and give an alert within 10-15 seconds. When connected to a PA system, the system can make an immediate announcement providing the fire’s exact location and the best exit route.
Injuries from manual handling of tasks are reduced through ergonomic training of workers. Optimum movement is sent to the worker to self-correct, paving the way for behavioral change.
One such company offering this solution is Soter Analytics. Soter devices worn on the shoulder, headset, helmet, and/or back monitor the risk of injury in real-time. The gadgets are paired with a mobile application to deliver tailored coaching to a specific worker for a particular task. Studies have shown that hazardous movement is reduced by 30-70%. Managers also have access to the data from the soter devices in real-time. The managers can then use the data to:
- Identify hazards.
- Filter hazard risk by task, department, or individual.
- Identify priority areas requiring more focus.
According to Coca-Cola KO +0.4% Amatil Limited (CCA), they reduced the risk from manual handling by approximately 35% after using Soter’s SoterCoach and Clip&Go solutions for six months. Mr. Shawn Rush from Giant Eagle stated that the risk from the hazardous movement was reduced by nearly 50% for the team members who participated in the process.
Predictive data and analytics
Predictive analytics uses various data obtained from the organization and analyzes that data to forecast potential scenarios. The data collected and used in analytics include root causes and complaints and suggestions.
HGS Digital solutions collects, analyzes, and runs what-if scenarios to determine reasons for injury and provide corrective action to mitigate the problem. After entering the data into the program, the tool will analyze the information without being programmed.
Case management software
i-Sight is a case management software similar to HGS Digital Solution. Unlike HGS, I-Sight only collects, tracks, and provides comprehensive reports, and you have to use this information to prevent workplace injuries. I- sight tracks and reports incidents such as:
- Slips and falls
- Near misses
- Dangerous exposures
Managers can use the i-Sight dashboard to monitor incident reports and possible trends to identify high-risk areas or employees that require urgent attention.
The trolley comes with a pre-collision assist to help customers avoid accidents or reduce the effect of a collision. The sensors on the trolley detect people and objects ahead in its path. The self-braking trolley automatically applies the brakes when it detects a potential collision.
Although the trolley is still a prototype in the Ford shop, its application will make run-away trolleys a thing of the past reducing accidents.
Engineers from West Virginia University are developing robots to safeguard workers from workplace hazards. The robots detect risks found on floor surfaces in WRT establishments. Besides providing situational awareness, the robots would provide walkability maps and continually monitor the risks. Unlike other computer vision systems that use existing CCTV cameras in the establishment, the robots would be equipped with in-built cameras to reduce deception from surface appearance. The robots would also drive on the surface to better assess the slip risk.
The development of the robots focuses on three key factors:
- Identification and evaluation of holistic risks involving the operation of the robots in the working spaces.
- Use of robots in other aspects, such as shopping guides.
- Effect of walkability maps and the robots on employees’ injury risk.
I’m a writer fascinated by the obvious and hidden dynamics between tech, culture and politics and how societies and habits are shaped by this interplay.
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