Electronic manufacturers are challenged by inventory mismanagement that can cause delays in manufacturing or costly errors in the production process. This project aims to create a system for inventory management that can help automate and simplify the auditing and ordering process while eliminating human error. It also provides a solution to the gap between high end part management systems that can be costly, and traditional methods used to store inventory. Our proposed solution will help reduce waste, save time and money in the production process. This will be done by having a lighting array that will assist the employee in locating parts, to reduce time spent identifying components, and prevent them from grabbing incorrect parts. The system will also allow users to audit the part quantities using a high accuracy scale by simply placing a container onto the scale, which will be automatically recognized by a QR code, to update inventory. The inventory will be tracked in a database that will store information such as item numbers, quantity, and minimum quantities, and generate reorder reports for low inventory levels.
To show the feasibility of this system, we have conducted testing on the main hardware included in our system. We ensured that the raspberry pi can take data input from a scale, and read QR codes through a camera, and control individually addressable LEDs. This was accomplished by writing python scripts that can take and interpret the data from the peripherals and save it into a CSV file. Testing was also done to check the scale would be accurate enough to calculate quantities of electrical components. The results showed that the scale would give a very low error of less than 2% for components weighing more than 0.5 grams, but higher error for very small electrical components. We were also able to use the camera to successfully recognize QR codes identifying details for components.
These results suggest that this project will be feasible to have completed by May. We have successfully demonstrated every significant component that will be needed to complete the major deliverables of this project. Throughout the Spring 2022 Semester, we were able to build the software and hardware architecture required to run all components within a single central system.
