Effective partnership management is a crucial strategy for tourism firms in designing the services successfully as well as gaining the advantages dynamically. Literature suggests that the successful partnerships may be initiated at the beginning of selection process; however, there is a dearth of research on how to manage tourism partnerships effectively. This paper suggests the evaluation criteria proposed to assist tourism firms to effectively make a decision on selecting partners to start working with. In developing criteria, the authors first reviewed the available criteria in the other contexts, then conducted qualitative research using buyer and supplier firms in Thailand by in-depth interviewing with ten experts to refine and to verify these criteria proposed. After statistical verification, the results indicated that the proposed criteria consist of five main categories: performances, profiles, risk factors, product's characteristics and compatibilities. This study contributes the useful knowledge on how to select partners and manage partnerships effectively in tourism supply chain.
As the intelligent machine and manufacturing system plays an important role in the near future, the monitoring system in turning process is required to improve the productivity during the cutting process. Hence, the aim of this research is to propose and develop the in-process monitoring system of the tool wear and the cutting states of chip and chatter for the carbon steel in CNC turning process by utilizing the sensor fusion which are the force sensor, the sound sensor, the accelerometer sensor and the acoustic emission sensor. Their signals have been integrated via the neural network with the back propagation and the pattern recognition technique to monitor the tool wear and detect the cutting states which are the continuous chip, the broken chip and the chatter occurred. The experimentally obtained results showed that the in-process monitoring system proposed and developed in this research can be effectively used to estimate the tool wear level and identify the chip breaking and the chatter with the higher accuracy and reliability.
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