Technical Industrial Placement
• Identified an opportunity to reduce breakdowns by considering when materials had been used in the past. I then started a project to use material data extracted from SAP to identity the trends between book outs of different spare parts across the Stalybridge site. Using Python and Pandas, created tools and processes to highlight parts that may require changes to planned maintenance or investigation into why part life was reduced. • Presented findings to members of the maintenance department and factory management, which led to implementing several early belt changes across the factory. This also showed the benefits of improving elements of the site’s SAP process. This encouraged a move to ensure that all parts were being booked correctly out of the system with a short description detailing the cause of the failure. • Identified opportunities to save over 25 hours of breakdown per year for an additional part cost of £1100. (total ROI of up to 7,000% dependent on cost metric) • Wrote a program to determine annual part usage for department budgets, which was used at 3 sites • Identified that the site was holding too many spare parts due to a lack of knowledge of which machines required certain parts. From this, implemented a project to identify as many parts as possible, and the quantities needed to be held on site. Wrote a Python programme using Pytesseract OCR to read scans of machine PDF drawings. This would then create a bill of materials for a machine. • MPNs could then be compared to information stored in SAP to identify material numbers, where the parts where used, which sites had them, and how many were on the machine• Identified over 4000 parts for 12 different machines from 6 manufacturers, using drawings from Stalybridge and other sites across the UK• Using an existing SQL query, made Power BI dashboards to report daily production figures, compare overall production data, and show performance for individual products, shifts, grades etc
