Application of intelligent data management in resource allocation for effective operation of manufacturing systems

• To provide suggestions on the number of resources needed for manufacturing.
• To determine resource allocation by using DMBS and fuzzy logic.
• To measure resource utilization by adopting RFID technology.
• To refine the fuzzy rules for decision making by referring to the RFID-captured data.

Resource allocation has been a critical issue in manufacturing. This paper presents an intelligent data management induced resource allocation system (RAS) which aims at providing effective and timely decision making for resource allocation. This sophisticated system is comprised of product materials, people, information, control and supporting function for the effectiveness in production. The said system incorporates a Database Management System (DBMS) and fuzzy logic to analyze data for intelligent decision making, and Radio Frequency Identification (RFID) for result verification. Numerical data from diverse sources are managed in the DBMS and used for resource allocation determination by using fuzzy logic. The output, representing the essential resources level for production, is then verified with reference to the resource utilization status captured by RFID. The effectiveness of the developed system is verified with a case study carried out in a Hong Kong-based garment manufacturing company. Results show that data gathering before resource allocation determination is more efficient with the use of developed system where the resource allocation decision parameters in the centralize database are effectively determined by using fuzzy logic. Decision makers such as production managers are allowed to determine resource allocation in a standardized approach in a more efficient way. The system also incorporates RFID with Artificial Intelligence techniques for result verification and knowledge refinement. Therefore, fuzzy logic results of resource allocation can be more responsive and adaptive to the actual production situation by refining the fuzzy rules with reference to the RFID-captured data.