Vision technology in the warehouse has been evolving rapidly in recent years as the underlying applications and hardware have gained new capabilities to read codes and text and recognise and identify more items more quickly and with greater accuracy and consistency. These abilities are useful in themselves in providing new ways to interact with
Vision technology in the warehouse has been evolving rapidly in recent years as the underlying applications and hardware have gained new capabilities to read codes and text and recognise and identify more items more quickly and with greater accuracy and consistency. These abilities are useful in themselves in providing new ways to interact with warehouse management systems (WMS) and other supply chain applications. But deploying them with the latest advances in the related areas of AI, robotics, and automation offers the prospect of some exciting and innovative applications that will also drive efficiency, productivity and customer service to new heights. This is another of the WMS trends that we identified earlier this year.
There is nothing new about using vision technology in the warehouse. Barcode scanners to identify storage and picking locations and inventory items have been around almost as long as WMS applications and are so commonplace that most users will not even think of them as vision systems. So too have been technologies to check items passing along a conveyor system and determine if any are, for example, misaligned or out of place. What makes the latest generation technology different is its apparent ability to “see”, recognise, and interpret real-world objects. This is enabling warehouse systems to work more autonomously and make decisions in real time using a wider range of inputs and parameters in conjunction with AI and new forms of programming. This is somewhat different to the old systems which tended to follow predetermined sequential routines and required special codes, labels, or other identifiers to interact with WMS and other equipment.
One of the first applications of modern vision systems is to enable Enhanced Inventory Management. This is no surprise as this builds on the capabilities of earlier technologies such as barcode scanning and pick-to-light systems. It is also relatively simple to introduce to an existing WMS because it involves similar data transactions and processes.
Automated Cycle Counting: vision systems can remove the need for manual and error-prone counts. With AI they can scan shelves and pallets, automatically identifying and recording inventory levels. This frees up staff, improves accuracy, and allows for real-time stock tracking. In reality, most businesses using WMS no longer rely on physical stock checks but the technology may lead to as yet unforeseen uses.
Damage Detection: reducing – ideally eliminating – sending out damaged stock can have a significant positive impact on quality control and customer service and reduce the burden of managing subsequent returns. AI can analyse camera footage to identify damaged goods upon arrival or during picking and may be particularly useful in systems that require little or no human handling.
The second, and allied, area where vision systems are likely to be used sooner is in Optimised Warehouse Operations. This is often perceived as part of a wider drive for automation to make better use of available infrastructure and resources, remove wasteful processes, and liberate operatives for more productive and value-added tasks. Again, these systems should be relatively easy to integrate with WMS because they are essentially about managing the core data of item identity and location.
Handling, Picking, and Packing Automation: AI can streamline order fulfilment by automating picking and packing processes. For example, vision-guided robots (also known as AGVs) and drones equipped with cameras and sensors can navigate through the warehouse environment freely and safely while performing tasks such as picking orders or transporting goods between locations. AI algorithms allow these robots to adapt their movements based on real-time changes in their surroundings, making them more flexible than traditional fixed automation solutions. This type of automation can reduce picking errors even further from the already impressive levels achieved by conventional WMS applications. It also increases pick rates and throughput volumes, which maximises productivity, and can free operatives for higher-value work.
Space & Process Optimisation: vision systems used with AI and related technologies can analyse product dimensions and weight and warehouse layouts to suggest optimal storage configurations. This minimises wasted space and maximises capacity, potentially avoiding the need for more costly space and equipment. Systems can also identify bottlenecks and workflow inefficiencies to support and enable layout and process optimisation. This can include permanently or dynamically managing picking sequences and paths to maximise overall productivity.
Load Planning & Optimisation: similar techniques can enable more efficient load planning. Pallets and cages can be packed efficiently, for example to minimise the risk of damage to items or to optimise unpacking at the next destination. Optimising the space used on the delivery vehicle – and load in the correct sequence for easy access during deliveries – can help to reduce delivery mileage and drop-off times to reduce costs and maximise the number of deliveries completed during each run.
There is a constant need to Improve Safety and Security and vision systems can assist with these objectives. Some of these areas are less relevant to the WMS specifically although they are all part of the wider warehouse picture. Nevertheless, as WMS applications continue to adapt and evolve, they will increasingly be integrated with these types of technology.
Real-Time Hazard Detection: AI can analyse video feeds to identify safety violations such as improper equipment use or blocked aisles. This allows for immediate intervention by the WMS including the dynamic rerouting of pick paths to avoid the temporary obstruction. Warehouse operators will be able to achieve control, visibility, and improvements in efficiency, safety, and security.
Enhanced Access Control: vision systems can use facial recognition to identify authorised personnel and restrict access to sensitive areas. In the WMS context, for example, this might restrict specific operatives from working in particular zones (e.g. bonded areas), handling specific items, or working with equipment for which they are not trained. This can strengthen security, minimise theft risks, and streamline access management.
Predictive Maintenance: AI can analyse data from vision systems and on-board sensors to predict failures before they occur on warehouse equipment such as conveyor systems, carousels, and lift trucks. This proactive approach enables warehouses to schedule maintenance activities at optimal times without waiting for costly breakdowns. It could also allow, for example, the WMS to “lock out” a lift truck from critical operations until it has been fixed.
Demand Forecasting with Image Recognition: AI can analyse images of incoming and outgoing goods to forecast future demand trends. This helps warehouses optimise stock levels and avoid stockouts or excess inventory. Moreover, there are increasing opportunities for using vision systems combined with AI-powered analytics tools for demand forecasting within WMS as well as incorporating machine learning models that continuously learn from historical sales data patterns so that it becomes easier not just predict what customers will order but how much and when.
Improved Worker Training: AI can analyse worker performance through video footage to identify areas for improvement. This approach would support targeted additional training but also offers the intriguing possibility of streamlined interaction with warehouse staff. For example, the WMS could give more detailed instructions to operatives who it recognises need more help or who are making an above average number of errors. Conversely, the system could give shorter or fewer instructions to high performing team members. This personalised approach can boost overall efficiency and enhance worker skillsets.
While the future looks bright, there are challenges to address. The first is cost. Implementing vision systems and AI requires upfront investment. Careful cost-benefit analysis is crucial. Another key consideration is data security. Robust cybersecurity measures are essential. The third critical factor is the human element. Much of the coverage about AI in general has raised the prospect of replacing human workers. In practice, current thinking is less focused on replacing human workers and more on promoting collaboration and upskilling.
The integration of vision systems and AI with WMS will redefine warehouse management. Increased efficiency, accuracy, safety, and security are just the beginning. As these technologies evolve, we can expect even more innovative applications to emerge, propelling warehouses into a smarter, more automated future that is more efficient and adaptive to changing demands.
(WMS) and other supply chain applications. But deploying them with the latest advances in the related areas of AI, robotics, and automation offers the prospect of some exciting and innovative applications that will also drive efficiency, productivity and customer service to new heights. This is another of the WMS trends that we identified earlier this year.
There is nothing new about using vision technology in the warehouse. Barcode scanners to identify storage and picking locations and inventory items have been around almost as long as WMS applications and are so commonplace that most users will not even think of them as vision systems. So too have been technologies to check items passing along a conveyor system and determine if any are, for example, misaligned or out of place. What makes the latest generation technology different is its apparent ability to “see”, recognise, and interpret real-world objects. This is enabling warehouse systems to work more autonomously and make decisions in real time using a wider range of inputs and parameters in conjunction with AI and new forms of programming. This is somewhat different to the old systems which tended to follow predetermined sequential routines and required special codes, labels, or other identifiers to interact with WMS and other equipment.
One of the first applications of modern vision systems is to enable Enhanced Inventory Management. This is no surprise as this builds on the capabilities of earlier technologies such as barcode scanning and pick-to-light systems. It is also relatively simple to introduce to an existing WMS because it involves similar data transactions and processes.
Automated Cycle Counting: vision systems can remove the need for manual and error-prone counts. With AI they can scan shelves and pallets, automatically identifying and recording inventory levels. This frees up staff, improves accuracy, and allows for real-time stock tracking. In reality, most businesses using WMS no longer rely on physical stock checks but the technology may lead to as yet unforeseen uses.
Damage Detection: reducing – ideally eliminating – sending out damaged stock can have a significant positive impact on quality control and customer service and reduce the burden of managing subsequent returns. AI can analyse camera footage to identify damaged goods upon arrival or during picking and may be particularly useful in systems that require little or no human handling.
The second, and allied, area where vision systems are likely to be used sooner is in Optimised Warehouse Operations. This is often perceived as part of a wider drive for automation to make better use of available infrastructure and resources, remove wasteful processes, and liberate operatives for more productive and value-added tasks. Again, these systems should be relatively easy to integrate with WMS because they are essentially about managing the core data of item identity and location.
Handling, Picking, and Packing Automation: AI can streamline order fulfilment by automating picking and packing processes. For example, vision-guided robots (also known as AGVs) and drones equipped with cameras and sensors can navigate through the warehouse environment freely and safely while performing tasks such as picking orders or transporting goods between locations. AI algorithms allow these robots to adapt their movements based on real-time changes in their surroundings, making them more flexible than traditional fixed automation solutions. This type of automation can reduce picking errors even further from the already impressive levels achieved by conventional WMS applications. It also increases pick rates and throughput volumes, which maximises productivity, and can free operatives for higher-value work.
Space & Process Optimisation: vision systems used with AI and related technologies can analyse product dimensions and weight and warehouse layouts to suggest optimal storage configurations. This minimises wasted space and maximises capacity, potentially avoiding the need for more costly space and equipment. Systems can also identify bottlenecks and workflow inefficiencies to support and enable layout and process optimisation. This can include permanently or dynamically managing picking sequences and paths to maximise overall productivity.
Load Planning & Optimisation: similar techniques can enable more efficient load planning. Pallets and cages can be packed efficiently, for example to minimise the risk of damage to items or to optimise unpacking at the next destination. Optimising the space used on the delivery vehicle – and load in the correct sequence for easy access during deliveries – can help to reduce delivery mileage and drop-off times to reduce costs and maximise the number of deliveries completed during each run.
There is a constant need to Improve Safety and Security and vision systems can assist with these objectives. Some of these areas are less relevant to the WMS specifically although they are all part of the wider warehouse picture. Nevertheless, as WMS applications continue to adapt and evolve, they will increasingly be integrated with these types of technology.
Real-Time Hazard Detection: AI can analyse video feeds to identify safety violations such as improper equipment use or blocked aisles. This allows for immediate intervention by the WMS including the dynamic rerouting of pick paths to avoid the temporary obstruction. Warehouse operators will be able to achieve control, visibility, and improvements in efficiency, safety, and security.
Enhanced Access Control: vision systems can use facial recognition to identify authorised personnel and restrict access to sensitive areas. In the WMS context, for example, this might restrict specific operatives from working in particular zones (e.g. bonded areas), handling specific items, or working with equipment for which they are not trained. This can strengthen security, minimise theft risks, and streamline access management.
Predictive Maintenance: AI can analyse data from vision systems and on-board sensors to predict failures before they occur on warehouse equipment such as conveyor systems, carousels, and lift trucks. This proactive approach enables warehouses to schedule maintenance activities at optimal times without waiting for costly breakdowns. It could also allow, for example, the WMS to “lock out” a lift truck from critical operations until it has been fixed.
Demand Forecasting with Image Recognition: AI can analyse images of incoming and outgoing goods to forecast future demand trends. This helps warehouses optimise stock levels and avoid stockouts or excess inventory. Moreover, there are increasing opportunities for using vision systems combined with AI-powered analytics tools for demand forecasting within WMS as well as incorporating machine learning models that continuously learn from historical sales data patterns so that it becomes easier not just predict what customers will order but how much and when.
Improved Worker Training: AI can analyse worker performance through video footage to identify areas for improvement. This approach would support targeted additional training but also offers the intriguing possibility of streamlined interaction with warehouse staff. For example, the WMS could give more detailed instructions to operatives who it recognises need more help or who are making an above average number of errors. Conversely, the system could give shorter or fewer instructions to high performing team members. This personalised approach can boost overall efficiency and enhance worker skillsets.
While the future looks bright, there are challenges to address. The first is cost. Implementing vision systems and AI requires upfront investment. Careful cost-benefit analysis is crucial. Another key consideration is data security. Robust cybersecurity measures are essential. The third critical factor is the human element. Much of the coverage about AI in general has raised the prospect of replacing human workers. In practice, current thinking is less focused on replacing human workers and more on promoting collaboration and upskilling.
The integration of vision systems and AI with WMS will redefine warehouse management. Increased efficiency, accuracy, safety, and security are just the beginning. As these technologies evolve, we can expect even more innovative applications to emerge, propelling warehouses into a smarter, more automated future that is more efficient and adaptive to changing demands.
