The relationships between tool performance, cycle time, and throughput have been described to achieve the highest productivity. Though tool utilization is low for manually operated tools, long waits prior to product processing lead to a productivity loss in our production line. We focus on operator efficiency in the CMP (chemical mechanical polishing) operation, which, as a typical manually operated tool, influences cycle time. This paper introduces an online/offline unit hour analysis method, which has been designed to optimize operator allocation and headcount in our production line, using -factor theory in order to achieve the shortest cycle time. By using online/offline unit hour analysis with -factor theory, the root cause of waiting time can be identified and the relationship between operator headcount and cycle time can be described. The waiting time can be reduced by 35% and the cycle time can be reduced by 25%, according to a simulator with optimized operator allocation and headcount based on online/offline unit hour analysis.

The population of most developed countries is rapidly aging, which has created a growing demand for home care. A key issue in medicine is supporting the increasing number of elderly patients, both physically and mentally. In this study, we developed a wearable computer that contained modules for measuring electrocardiograms (ECGs) and femoral artery pulse waves using an accelerometer. This system has several benefits: (a) it can provide a database server in each patient's home; (b) its high extendibility and flexibility facilitate adaptation to a patient's needs; and (c) it allows patients to keep their own data, thus protecting the privacy of personal information. To clarify the capabilities and reliability of the system, we applied it to 8 healthy young volunteers during states of physical and mental work. This system successfully detected clear ECGs and femoral artery pulse waves to calculate important bioinformation, including heart rate, pulse wave velocity, and the power spectral density of spontaneous beat-to-beat oscillations in the R-R interval. In this study, we proposed the way to provide an assessment of the physical and mental condition of the subject using analysis of the bio-information with respect to the physical and mental workloads. The present study provides useful knowledge for the development of a wearable computer designed to monitor the physical and mental conditions of older persons and patients.

An effective way for preventing injuries and diseases among the elderly is to monitor their daily lives. In this regard, we propose the use of a "Hyper Hospital Network", which is an information support system for elderly people and patients. In the current study, we developed a wearable system for monitoring electromyography (EMG) and acceleration using the Hyper Hospital Network plan. The current system is an upgraded version of our previous system for gait analysis (Yoshida et al. [13], Telemedicine and e-Health 13 703-714), and lets us monitor decreases in exercise and the presence of a hemiplegic gait more accurately. To clarify the capabilities and reliability of the system, we performed three experimental evaluations: one to verify the performance of the wearable system, a second to detect a hemiplegic gait, and a third to monitor EMG and accelerations simultaneously. Our system successfully detected a lack of exercise by monitoring the iEMG in healthy volunteers. Moreover, by using EMG and acceleration signals simultaneously, the reliability of the Hampering Index (HI) for detecting hemiplegia walking was improved significantly. The present study provides useful knowledge for the development of a wearable computer designed to monitor the physical conditions of older persons and patients.

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