How Can AI be Used to Help Manage Queues?
When queue management systems are combined with Artificial intelligence (AI), many advantages for the business and the end customer can be gained. AI can be used to help manage queues of people in a number of ways. Here are a few examples:
- Real-time wait time estimation: AI can be used to estimate the wait time for customers in a queue.
- Dynamic queue assignment: AI can be used to dynamically assign customers to queues.
- Queue prioritization: AI can be used to prioritize customers in a queue.
- Queue analytics: AI can be used to collect and analyze data about queues.
- Predictive modeling of queue demand: AI can use historical data to predict how long queues will be at different times so that resources can be correctly allocated.
- Self-service queue management: AI can be used to create self-service queue management systems that allow customers to check in and track their wait times autonomously.
Using AI for Real-time wait time estimation
AI can be used to estimate the wait time for customers in a queue. This information can be displayed to customers in real time, so they can see how long they will have to wait before being served. This can help customers to plan their visits and avoid long lines, possibly deciding to come back later when it is less busy. If this information is made online they can decide to come when the line is at its lowest. This will also help the business by smoothing the demand allowing a better use of resources.
If the establishment is using a virtual queuing system where people receive updates on the wait time straight to their mobile device, location technology can also be used to dynamically prioritize or de-prioritize certain customers depending on their distance from the location. This can be further enhanced to use estimated arrival time based on AI
Machine learning of the wait time estimation can consider the following variables to optimize the results:
- The types of services required by each person in the queue
- Number of staff who are working.
- Individual performance of each working staff member
- Time of day
- Day of the week
- Seasonality
- Public holidays
- Weather conditions
- Current location of person waiting for the queue.
- Etc
Using AI for dynamic queue assignment
AI can be used to dynamically assign customers to queues. This means that customers will be assigned to the queue that is expected to have the shortest wait time or to the staff member that is best suited to dealing with the customers specific needs. This does not have to be categorized manually as AI can calculate it dynamically. This can help to reduce wait times for all customers.
Using AI for queue prioritization
AI can be used to prioritize customers in a queue. This means that customers with urgent needs, such as those who need to make a bank deposit before a certain time, or who are running late for their flight can be served ahead of customers with less urgent needs. This can help to ensure that no one has a catastrophic outcome where they cannot be served. This must be implemented carefully as any deviation from the first come first serve style of queuing can create resentment and the feeling of being treated unfairly.
AI can use customer data to personalize the queue experience. For example, customers with disabilities or who are pregnant can be routed to a shorter queue, or customers who have been waiting a long time can be given priority.
Using AI for queue analytics and optimisation
AI can be used to collect and analyze data about queues. This data can be used to identify trends and patterns, so that banks can improve their queue management strategies.
If a traditional queue management system is hard to implement like in a supermarket where physical queues are more prevalent AI can still be implemented. For example video analytics to track the size of queues and the wait times of customers.
Analytics can be used to improve overall strategies but it can also be used in real time to proactively alert staff when queues are getting too long, or to dynamically open or close lanes to optimize throughput.
Using AI for predictive modeling of queue demand
AI can use historical data to predict how long queues will be at different times of day, on different days of the week, and during different events. This information can be used to staff queues more effectively and make sure enough staff are available for peak times. It can also be used to forewarn customers in order to encourage them to come when the lines are shorter.
Self-service queue management
AI can be used to create self-service queue management systems that allow customers to check in and track their wait times without having to interact with a human agent. This can free up staff to focus on other tasks, and it can also improve the customer experience by giving customers more control over their wait time.
What AI technologies can be used to manage queues and lines?
Here are some specific AI technologies that can be used to help manage queues of people:
- Machine learning: Machine learning can be used to train AI models to predict wait times, assign customers to queues, and prioritize customers.
- Natural language processing: Natural language processing can be used to understand customer requests and to generate text-based reports about queue data. This can be used to improve information provided to customers, possibly avoiding them physically waiting in the queue to begin with.
- Computer vision: Computer vision can be used to track the movement of customers in a queue and to identify customers who are waiting for a long time. This is useful when it is impractical to have them join a queue by taking a ticket or scanning a QR code.
Real life examples of how AI is being used to manage queues of people today
AI helping airports managing queues and lines
AI can help airports manage queues in many ways. For example, AI can predict customer traffic and allocate resources accordingly. It can also offer mobile queue management options, such as virtual queuing and mobile notifications. AI can reduce wait times with complete queue and people density visibility, and dynamic lane balancing. It can provide live wait times for bag check, TSA screening, customs & border control and rideshare queues. AI can match staffing to demand with AI-driven forecasts and lane opening recommendations.
Here are some examples of how airports are using AI to manage queues:
- Schiphol Airport in Amsterdam uses AI to predict passenger traffic and allocate resources accordingly.
- Heathrow Airport in London uses AI to predict passenger traffic and reduce wait times.
- Changi Airport in Singapore uses AI to provide real-time information on wait times for security checkpoints.
AI helping supermarkets manage queues and lines
AI can help supermarkets manage queues in many ways. For example, AI-powered queue management systems can predict future queue volumes and required staffing levels. Video image recognition can measure queue size and wait time and this information can be used to automatically open or close checkouts, directing staff to where they are most needed. This ensures that customers receive the best possible experience while the checkouts are operated as efficiently as possible.
Another way that AI can help supermarkets manage queues is by using token-based queue management platforms. These platforms allow customers to take a token and wait in a virtual queue. This can help to quantify wait times and customer numbers, which can be used to improve the overall queue management process.
An example of a supermarket using AI to manage queues is:
AI helping retail stores manage queues and increasing there revenue
AI can help retail stores manage queues in many ways. For example, computer vision-powered queue management software can use AI and machine learning algorithms to track individual customers until they checkout, identifying their wait time as well as the size of the queue at any given time. It can also identify cart abandonment actions, giving an exact estimate of revenue lost due to long lines.
Virtual queuing has also been used to enable shoppers to keep their place in line but actually keep shopping. This has the added advantage of increasing cart size and increasing customer satisfaction at the same time
Here is an example of how retail stores are using AI to manage queues:
AI helping hospitals manage queues
AI has been used in hospitals to manage queues in a variety of ways. For example, computer vision-powered queue management software can use AI and machine learning algorithms to track individual patients until they are seen by a doctor or nurse, identifying their wait time as well as the size of the queue at any given time. It can also identify patients who have been waiting for too long and alert staff to take action. Another way is by using AI-powered and SaaS-based capacity management, staffing, and patient flow software for health systems.
Here are some examples of how hospitals are using AI to manage queues:
- The University of California San Francisco Medical Center uses AI to predict patient flow and allocate resources accordingly.
- The University of Chicago Medical Center uses AI to predict patient flow and reduce wait times.
- The University of Pittsburgh Medical Center uses AI to provide real-time information on wait times for emergency departments.
Conclusions
In conclusion AI can be used to manage queues in a variety of ways, such as predicting wait times, assigning customers to queues, prioritizing customers, analyzing queue data, and creating self-service queue management systems. AI can help businesses reduce wait times, improve customer satisfaction, and increase operational efficiency.
As AI technology continues to develop, we can expect to see even more innovative ways to use AI to manage queues of people. This will lead to improved customer experiences, increased operational efficiency, and reduced costs for businesses.
