On 15 December 2000, the International Organisation for Standardisation published a new series of ISO 9000 standards. There are significant changes in this third edition standard. Several specific surveys and analysis revealed the key concerned areas of the certified companies and the significant changes in the new standard are on the key system areas. Develops a new model explicatory to assist those certified organisations to link their concerned areas with that of the new standard. In this model, management activity is the key element. The information can be used in the analysis to determine the areas for continual improvement. Reveals that top management are still weak in the adoption of quality management systems, establishment and implementation of the quality policy and objectives. Recommends use of SMART approach. At the same time, certified organisations spend a lot of effort for the compliance with the process approach management. Discusses the benefits of using process approach management. Besides the plan‐do‐check‐act approach in continuous improvement, introduces the Xerox Management Model. Finally, introduces the consideration of improving quality management systems in areas of effectiveness, efficiency, productivity, flexibility and adaptability, rather than product.

No abstract

PurposeWith the increasing concerns about food management, attention is placed on the monitoring of different potential risk factors for food handling. Therefore, the purpose of this paper is to propose a system that helps facilitate and improve the quality of decision making, reduces the level of substandard goods, and facilitates data capturing and manipulation, to help a warehouses improve quality assurance in the inventory‐receiving process with the support of technology.Design/methodology/approachThis system consists of three modules, which integrate the radio frequency identification (RFID) technology, case‐based reasoning (CBR), and fuzzy reasoning (FR) technique to help monitor food quality assurance activities. In the first module, the data collection module, raw warehouse and work station information are collected. In the second module, the data sorting module, the collected data are stored in a database. In this module, data are decoded, and the coding stored in the RFID tags are transformed into meaningful information. The last module is the decision‐making module, through which the operation guidelines and optimal storage conditions are determined.FindingsTo validate the feasibility of the proposed system, a case study was conducted in food manufacturing companies. A pilot run of the system revealed that the performance of the receiving operation assignment and food quality assurance activities improved significantly.Originality/valueIn summary, the major contribution of this paper is to develop an effective infrastructure for managing food‐receiving process and facilitating decision making in quality assurance. Integrating CBR and FR techniques to improve the quality of decision making on food inventories is an emerging idea. The system development roadmap demonstrates the way to future research opportunities for managing food inventories in the receiving operations and implementing artificial intelligent techniques in the logistics industry.

Determination of process conditions for a fluid dispensing process of microchip encapsulation is a highly skilled task, which is usually based on engineers' knowledge and intuitive sense acquired through long-term experience rather than on a theoretical and analytical approach.Facing with the global competition, the current trial-and-error approach is inadequate. Modelling the fluid dispensing process is important because it enables us to understand the process behaviour, as well as determine the optimum operating conditions of the process for a high yield, low cost and robust operation. In this research, modelling and optimization of fluid dispensing processes based on neural fuzzy networks and genetic algorithms are described. First, neural fuzzy networks approach is used to model fluid dispensing process for microchip encapsulation.An N-fold validation tests were conducted. Results of the tests indicate that the mean errors and variances of errors of the modeling based on the neural fuzzy networks approach are all better than those of the other existing approaches, statistical regression, fuzzy regression and neural networks, on modeling the fluid dispensing. It is then followed by the determination of process conditions of the process based on a genetic algorithm approach. Validation tests were 2 conducted. Results of them indicate that process conditions determined based on the proposed approaches can achieve the specified quality requirements.

No abstract