Improvement in production performance can enhance supply chain efficacy. There is a continuous discourse around methodologies like Lean Production, Six Sigma, and Just-in-Time to eliminate waste, reduce defects, minimize inventory, and thus improve production performance. However, technologies like machine vision, image processing, and computer vision, can translate theories into practical implementation across the supply chain.
Advanced applications of computer vision (CV), the latest in visual artificial intelligence (AI) technologies, have increased the visibility, transparency, and resilience of a supply chain.
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What is Computer Vision?
It is important to understand the main difference between machine vision, image processing, and computer vision. Machine vision allows a computer to see things just as humans do, with the help of cameras. Image processing lets a computer see images at the pixel level, in far greater detail than the human eye can discern.
A computer vision system combines machine vision and image processing to see, process, and analyze visual inputs. It also interprets the inputs with more intelligence and accuracy than humans can with the help of data analytics. A subset of AI, machine learning, and its branches, like Deep Learning and Convolutional Neural Networks, empower computer vision with the powers of interpretation. Further, CV solutions take action autonomously or give outputs (insights, recommendations) that find relevance in the industry.
Computer vision is fast gaining popularity across the manufacturing supply chain – on the shop floor and in assembling, testing, and packaging units. It relies on visual data- from camera images and video, increasingly sourced from sensors mounted on machinery, robots, autonomous vehicles, drones, and robotic picking arms. Its main advantage is its ability to work with large amounts of abstract visual data, and it can analyze a large number of variables too.
In this blog, we explore eight use cases for improvement in production performance with computer vision.
Get to learn more about Computer Vision in Manufacturing: Benefits & Top 10 Use Cases
SOP Adherence
Imagine a pair of trained, experienced, and specialized eyes following each step of the manufacturing process, minus the element of “human error.” Following SOPs acts as an enabler to increasing productivity, cost savings, and efficiency.
At Gramener, we have developed one such application that ensures adherence to standard operating procedures (SOPs).
- At the production line, a computer vision application can help to monitor production quality and reduce cycle time continuously.
- Cargo handling SOPs alongside computer vision-powered monitoring can ensure worker safety during loading and unloading, detection of movements to prevent damage to both cargo and carrier vehicles, and inspection to fill unused spaces.
- SOPs for warehouse surveillance and computer vision help protect staff from work hazards and property from vandalism.
An audit approach to logistics and supply chain activities helps to introduce future production performance improvements with computer vision in addition to real-time optimization. It also promotes visibility and transparency across the supply chain.
Material Handling
Repetitive logistics tasks include material handling, which, if not done with the utmost care, can lead to damage and high costs. Computer vision solutions have been developed to pick, sort, depalletize, load and unload, and place material with expert handling. CV helps automated material handling in e-commerce, supply chain operations, and warehouses with specific algorithms for:
- Picking operations in unstructured environments with overlapping and stacked objects from conveyor bulk
- Parcel handling with segmentation (reflective, transparent, color) classification (box, bag, envelope), label reading (this side up), etc.,
- Choice of grasping positions to load and unload boxes, trays, and objects of varying sizes and shapes from pallets
Using computer vision for material handling helps to cut costs, increase speed, and maintain robustness while keeping the supply chain shipshape. This aids in production performance improvement as it brings agility to locating and moving stocks, fulfilling orders, and shipping to customers.
Inventory Management
Inventory management with computer vision is much more than stock keeping. It helps to track the flow of raw materials through the supply chain to the manufacturing plant, the finished goods to the warehouse, and further up to the point of sale. CV not only monitors but also supports speedy and error-free inventory management with data analytics-aided insights that outdo manual checking methods. Computer vision can strengthen the supply chain and intensify production performance improvement efforts in the following manner:
- Locating inventory quickly through object detection in big warehouses & saving time
- Counting stock during inbound receipt & outbound shipping and alerting managers to replenish on time
- Spotting empty spaces in warehouse racks and alerting against suboptimal configurations
- Identifying misplaced stocks, finding them, and replacing them for quick delivery with rapid scanning of barcode
- Ensuring on-shelf availability in terms of product location, quantities required, demand levels, and seasonal volatility
Inspection & Classification of Items
Keeping a closed eye on production processes and materials helps maintain quality. Computer vision solutions have proved to be very effective in inspecting and classifying items with subtle differences, cosmetic variations, and functional problems. CV helps to boost production performance improvement with inspections that prompt “vision-aided decision making” on the shop floor by:
- Identifying ‘bad’ parts of the process, product, or material and alerting humans accordingly
- Continuously comparing quality with specifications and warning against anomalies
- Intelligently analyzing the process to detect inconsistencies and recommend adjustments
Computer vision captures many images from different angles, creates a 3D model of components, and feeds it with AI algorithms that pinpoint even minor deviations from design, be it in electronic circuits, automotive, oil and gas, or energy. This precision is employed even for computer vision-aided rotary and laser die-cutting.
Pest Detection
Businesses are increasingly using computer vision to carry out surveillance of their properties. This includes manufacturing plants, warehouses, and in the case of the food processing industry, farms. Wherever feasible, drones with CV inspection systems fly over large areas to detect rodents and insects and can even predict infestations.
Defect Detection in Manufacturing
Computer vision algorithms have been most effective in defect detection and predictive maintenance. Inferior quality products are a bane to any organization as they invite legal wrangles, customer dissatisfaction, and brand erosion.
Gramener offers a computer vision solution aimed at production performance improvement in the manufacturing phase and has already deployed it in the pharmaceutical industry. For the solution to work:
- Product images are loaded onto the server
- Defects are identified by our AI algorithm
- Deep learning models analyze and classify the defect
- Reports and alerts are sent with actionable insights
- Action rectifies defects, standardizes quality, and ensures compliance
Read more: Computer Vision AI is Unbeatable for Defect Detection in Manufacturing
Defect Detection in Packaging
Defect detection in packaging needs comprehensive automated monitoring and a correctional approach. Gramener has succeeded in providing an “always-on” solution to the pharmaceutical sector, which is plagued by packaging problems such as:
- Breakage of glass that damages injection vials and causes leakage of liquid medications
- Exposure to heat and humidity, which damage medicinal properties
- Lack of adequate and correct drug and dosage information
- New drugs that need innovative packaging
- Lack of foolproof packaging to distinguish fake drugs from the authentic
Depending on the specific defect detected in the packaging, our solution gauges the severity of the problem and makes recommendations. We have been able to ensure quality control in a lean manufacturing environment. In addition to saving costs and time, we helped to increase dealer, distributor, and consumer loyalty.
Defect Detection in Assembly
Checking for defects as products roll off the assembly line is tedious, time-consuming, and error-prone when done manually. The use of CV has enhanced production performance improvement at this critical juncture of the supply chain. The structural complexities and sheer quantities of parts that are produced demand strict assessment criteria. Gramener has developed an AI algorithm that can ensure quality even as an item moves along the production line. It has proved to:
- Shorten the product development cycle time
- Improve the assembly quality
- Reduce the assembly cost
The deep learning model used by our computer vision application constantly learns and turns “smarter” as it leverages more relevant data. Thus, becoming indispensable for workers assisting them in the process and quality control with predictive data analytics.
