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Our client is a rapidly expanding company, specializing in comprehensive Integrated Supply Chain Solutions, Global Forwarding Solutions, and Last Mile Solutions. With a global reach, they cater to customers across more than 50 countries, including India, UK, Europe, America, Asia Pacific, and Oceania.
The client imports vehicle parts from vendors to their warehouse but faces a critical challenge in efficiently identifying these components and promptly detecting any misplaced items.
Although they have an internal subscription-based tool for parts identification, the team recognized the need for an independent tool to customize and unlock significant improvements in their operations and overall quality control efforts.
The company faced following challenges in their vehicle parts identification process:
Our client had a clear set of objectives they wished to accomplish to overcome their part identification challenges. These included:
Pluto7 helped the client create a vision workstation to digitize their warehouse for parts identification. The vision workstation analyzed multi-angle images of vehicle parts captured by cameras to accurately identify them using predefined patterns or characteristics.
The Vision workstation in the warehouse played a crucial role in automating the parts identification process, reducing manual effort, and improving efficiency.
With our Decision Intelligence Platform, we enabled accurate and rapid identification of parts, saving time and improving quality checks:
We gathered a comprehensive dataset of vehicle parts, including images and associated metadata, to train the AI/ML models. We also collected historical data on actual parts for training the system to detect misplaced parts, laying groundwork to enable vision AI.
We created data ingestion pipelines to efficiently and securely transfer data from the customer’s source systems to GCP buckets. Once the data was stored in the GCP buckets, it was further loaded into BigQuery, a fully-managed, serverless data warehouse, allowing efficient querying, analysis, and visualization.
Leveraging AI/ML algorithms, we developed a computer vision model capable of accurately identifying different vehicle parts based on their visual characteristics. The model was trained using deep learning techniques on the collected dataset. The ML solution could
Extensive testing and validation were conducted to ensure accuracy and reliability. The model was then deployed to on-premise edge servers for offline availability.
Pluto7 also developed a web application that would display results of part identification. Everytime an image was uploaded to vision workstation, the backend model would identify the part and show the result of Pass or failure of the image identification on the web application accessible on both desktop and mobile devices.
On successful identification of the Part, command will be sent to the printer for printing the labels. They want to make sure that label can be printed only after the parts have been identified and scanned.
Reduced Parts Identification Time: The time taken for parts identification was reduced to 4 seconds or less after image upload, allowing for faster processing and improved overall productivity.
Accurate Parts Identification: Vision AI solution developed by Pluto7 could identify similar looking automobile parts with minute differences like the mounting hall, and parts with different orientation like left and right orientation.
Increased Misplaced/Missing Part Detection: The system could detect misplaced or missing parts with a high degree of precision, enabling timely interventions and reducing the risk of wrong parts reaching the assembly line.
Minimized Errors: The new system ensures that the same part is not scanned twice, minimizing the chances of misidentified parts and associated errors.
Enhanced data privacy: Part identification model deployment on Edge server enhanced data security by minimizing data exposure to external networks. By keeping sensitive data and computations within the edge server, it reduced the risk of data breaches and unauthorized access.
Scanning progress tracking: The system gives an indication to complete scanning the pending parts of a GRN (Goods Received Note), ensuring that all parts are accounted for, reducing the risk of missing or misplaced items, and helping maintain accurate inventory records.
Web Application for Real-Time Results: Users got access to a web based application that worked both on mobile and desktop to see the results of automobile part identification.
Cost Optimization: Streamlining the parts identification process and minimizing errors, reduced expenses and improved efficiency.
By digitizing their vehicle parts identification process using Pluto7’s Konnect Manufacturing Platform and Vision AI solution, our client transformed their operations, achieving improved efficiency, accurate identification, and proactive detection of misplaced/missing parts. They leveraged the transformative impact of AI/ML technologies in manufacturing to drive operational excellence and deliver superior products to their customers.