Supply Chain and the Computer Vision Revolution

A recent report by Zion Market Research states that the integration of AI in supply chain management will increase at a rate of 41.50% CAGR. The expectation is that this will increase the market size to $12798.62 Billion by 2028. This by no means would surprise many for the simple fact that the industry has been racing towards attaining 4.0 industry standards. Key among AI is the supply chain optimisation driven by computer vision technology. In this post, we discuss the key areas where this tech is dramatically altering the traditional approach to provide early adopters massive and reliable advantages.   

Continuous and linear advancements in technology and in process optimization techniques have helped ensure that the global supply chain runs efficiently, turning raw materials into products that make their way to physical stores and e-commerce warehouses. However, recent observations on how external disruptions like the pandemic or a climate change-induced anomaly can fracture the supply chain have highlighted the existing systemic vulnerabilities. These disruptions are not necessarily due to a lack of technology, but more so to a lack of data.   

Manufacturing organizations that have set up actionable data-driven processes backed by computer vision are in a better position to navigate risk and manage demand. Computer vision technology provides diagnostic, proactive and predictive analytics that are essential to maximizing efficiencies. Globally, companies that have made use of these have been able to ensure seamless production, distribution and aftermarket services even in the wake of the recent global catastrophe.    

Quality is another important aspect where computer vision tech plays a major role. Whether you’re talking about components on a high-speed production line or levels in filling machines, every facet of the manufacturing industry focuses on quality detection and quality assurance. Manufacturing companies are increasingly utilising computer vision tech to recognize quality issues in supplier parts, conduct in-line quality checks post-assembly, and avoid quality issues in applications such as robotic path guidance for dispensing.   

Computer vision’s application has been quite diverse, ranging from identifying damages in leather for footwear manufacturing to inspecting component presence and mounting quality on electronic circuit boards. With the help of computer vision tech manufacturers are able to make time-critical and agile business decisions in near-real-time by gathering data at the edge and analysing it at the right speed and velocity.  

Computer vision is the answer to vital supply chain management questions-  

1. How do we upgrade manufacturing personnel and facility well-being?  

Operational processes involve risk and they are mostly invisible to the human eye. Here, computer vision systems can provide surveillance, process images of facility usage as well as identify personnel risk behaviours. The technology can be used to reduce the number of personnel moving freight via autonomous forklifts and prevent incidents in the process. Computer vision tech is been used to track drivers’ eye movement and blinking rate to send them alerts when they are falling asleep and measure their driving behaviour, improving driving risk mitigation.  

For personnel safety, cameras check for personal protective equipment (PPE) usage, such as hard hats and safety glasses, if the parameters are not met, then the system either sends alerts to a manager or a safety officer who can then determine whether training is needed. Geofencing tech constructs a virtual boundary around equipment that that be dangerous to human contact.  

Repetitive movements are another industrial safety issue. Multiple repetitions of the same movement, necessary for assembling a circuit board and other products are major reasons for worker injuries. Computer vision tech can be used to study and optimize workflows that reduce the occurrence of employee injuries, promoting an overall safer workplace.  

The technology is very handy in situations where monitoring objects for which sensors are not practical or to observe processes that are challenging for human beings. An ML trained camera that observes conveyer system bearings for change in temperature, produces a temperature histogram that can precisely indicate a future failure. Thermography is also beneficial in situations with low visibility for perimeter security in poorly lit areas. The tech enables not only data collection but also automated decision-making which is far quicker than the human ability to allow for uninterrupted operations.   

2. How to elevate manufacturing operational efficiencies?  

Operational efficiency in simple terms typically comes down to overall equipment effectiveness. An off-spec product can hamper producing a quality result. Over time the manufacturer will have to either throw it away or perform rework.   

A highly scalable computer vision platform is capable of combining and aggregating the captured data of the overall system and then scrutinising the resulting data set to determine the cause of the off-spec product. Not only that— a computer vision platform can leverage a federated approach, i.e the analysis can be conducted in near real-time without pushing large amounts of data back to a central storage location. This strategy uses AI models at the edge to analyse data, sending only the results back to the central location for training and further refinement of the production process. A far more efficient method of precise and rapid determination of an equipment’s current capabilities- far quicker and more detailed than a physical inspection.   

The future of supply chain management will depend on the enhanced abilities of predictive maintenance backed by computer vision technology, especially for equipment that needs to be in perpetual motion. The introduction of computer vision tech will shorten the time frame for replacements since accurate scheduling can be done ahead of time, which in turn will reduce the overall costs for maintenance. Computer vision with its ability to capture multi-variate attributes from images and video is replacing many sensors and transmitters used at present.   

3. How to reinforce sustainability?  

An important upshot of process optimization is the categorical reduction in consumption of energy, chemical usage, air quality and raw materials. Computer vision tech can study patterns to determine when a motor should run and when it can be idle, which has positive connotations in areas of maintenance efforts and energy usage. The same principle applies to chemical usage. Optimized usage also means less over-and under-dosing. With computer vision, production quality reduces scrap that cannot be reworked improving efficiency and saving wastage.  

Cameras are efficient monitors of stack emissions as part of an effort to reduce sulphur oxide (SOx) and nitrogen oxide (NOx) emissions, and to check the colour of water, such as in a reservoir, to determine the presence of pollutants or algae.  

5. How can we boost revenue?   

Business values ROI, NPV and other similar metrics to justify the financials of a project or to launch a new product line. The above-mentioned benefits of computer vision in manufacturing and the overall supply chain convert into an increase in revenue, reduction in costs and improvement in worker safety.   

Rudimentary forms of computer vision have been in use in manufacturing for years, yet the recent advances in real-time data analytics are completely changing plant owners' and supply chain mgmt professionals’ expectations of the technology. With more plants embracing computer vision for quality assurance, plant visibility, equipment monitoring and worker safety- the global supply chain is building resilience to external disturbance variables and steadfastly continues to operate smoothly.