Abstract
In the repair industry, diagnosing the cause of failure is complicated and time consuming. It usually takes experienced repair engineers and trial-and-error processes are often involved causing man power availability and high-cost problems. The study used dynamic statistics, Fuzzy Decision Tree (FDT), Case Based Reasoning (CBR), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to establish an intelligent repair system. One month width of real data on the repairs of cellular phones from three leading brands were collected and analyzed by the intelligent repair system. The results shown that all form tested diagnostic methods are better than the existing. In particular, the FDT and CBR methods show improvements of 21% & 16% in time to locate and fix problems. Contributions of this research include: 1. Proposing four methods of repair diagnosis approach which are all better than the current approach. The FDT method is especially recommended. 2. Establishing an intelligent cellular phone diagnosis system which can be used in the industry.