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 the following challenges in its vehicle parts identification process:
- Lack of Reliability: The current tool was slow in part identification and occasionally failed to recognize certain parts, compromising reliability and throughput.
- Reduced Efficiency: Inefficiency in existing tools led to inconsistencies, errors, and delays in identifying parts, resulting in potential assembly line disruptions and increased costs.
- Quality Concerns: The company’s ability to efficiently identify misplaced parts leads to quality control issues and customer dissatisfaction.
- Limited Scalability: The current infrastructure had limitations in terms of scalability, preventing the company from efficiently handling increased data volumes, processing power requirements, or extending to other warehouses.
- Data Privacy and Security: The current tool faced data security challenges due to its multi-access nature, where various users had differing levels of access to sensitive information. This complexity made it vulnerable to unauthorized access and potential data breaches.
Our client had a clear set of objectives they wished to accomplish to overcome their part identification challenges. These included:
- Accelerated Part Identification and Accuracy: They wanted to reduce the time taken to read and identify spare parts, and achieve a time limit of 4 seconds or less. Additionally, they wanted to make sure that the same part is not scanned twice and accurately identify the similar-looking automobile parts with minute differences, and different orientations.
- Material Identification Tag (MIT): The client required solutions on MIT that could identify how many parts to scan and which slot to process first. The system should require operators to follow the first-in-first-out (FIFO) principle, and allow users to work with multiple MITs during a single session without constant login and logout. Such a system would enhance overall operational efficiency, streamline processes, and improve productivity and accuracy.
- Material Verification: The client required a material verification system to ensure the quality and correctness of parts. The system should validate materials at specified time intervals, scan material IDs, determine SKU capacity, and maintain strict adherence to cycle times. It should allow queued materials to be scanned only after completing the current scanning process, with a little buffer time. Additionally, the system should scan and manage parts stored in TOTE containers, while capturing station numbers for reference.
- A web-based application: They required a web application that could open on mobile and desktop to display the results of automobile part identification. This application should work only within the premises (intranet). Additionally, it should allow users to log in to only one system or device at a time.
- ML Model Deployment on Edge Server: The client wanted to build the vision API model for deployment on Edge to analyze data locally, reduce latency, enable faster real-time decision-making, and ensure data security. For this, once the model was trained and optimized, it was deployed on the client’s Edge server.
- Label Printing: They needed to make connectivity with downstream applications (only one application) on printers to print the label, streamlining the printing process and ensuring accurate and efficient label generation.Read More....
