Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

Small and Medium-sized Enterprises (SMEs) are being faced with rapid changes in their business environments due to evolution of technologies and innovation in societal eco-systems. Particularly, dynamic interactions between such environments and enterprise activities have become significant, so technology planning, which is a process of identifying appropriate directions regarding product and technology development, has received much attention to cope with such dynamics proactively. However, SMEs typically have limits in performing independent, strategical and systematical technology planning activities due to the lack of human, material and financial resources. This paper proposes the development of a product roadmapping method so that SMEs carry out efficient technology planning activities with interconnections of external business environments. The present work provides product roadmap templates that directly accommodate the influence of business environments on the product’s system and its associated super/sub-systems with the use of external environment analysis techniques including TRIZ methodology, PEST and 5Forces analysis. These templates are useful to efficiently forecast the directions of product’s development and evolution, which arise from changes in external environments. Consequently, the present work enables SMEs to flexibly cope with the era of the next R&D generation, which pursues value creation through mutual interconnection between business environments and technology development.

The systematic management of technology infrastructure increasingly becomes critical as it represents the driving power for creating competitive advantage of nations and enterprises. In this sense, public facility and equipment, which is one major asset of technology infrastructure, should be managed effectively, systematically and efficiently to increase its overall equipment effectiveness. However, the manual and off-line communication was the unique way to communicate between the suppliers who provide facilities in the public sector and the customers who utilize the facilities in the industrial sector. This communication way caused invisibility of available usage time and increase of idle time, thereby decreasing the equipment effectiveness. This paper presents an web-based scheduling system that enables real-time collaboration and information sharing for increasing the equipment effectiveness. The designed system performs scheduling, facility management, order management and customer relationship management in an on-line platform, and carries out the processing and storing of relevant information in an integrated database. The paper includes a development approach to identify business process models and standardized system models by using object-modeling technique. The deliverable of the present work will contribute to increasing the equipment effectiveness by enhancing visibility, transparency and predictability by means of the integration with a legacy Enterprise Resource Planning.

A field study was conducted to compare growth, feed utilization and nutrient excretions by feeding extruded pellet (EP) and moist pellet (MP). Slow growing flounder were used as the experimental animal to find out a prominent result in terms of growth and feed utilization. Weight gain of fish fed the experimental diets for 8 weeks reached 105.3 g for EP and 107.1 g for MP, while fresh feed intake was 98.7 g for EP and 279.4 g for MP. Feed conversion ratio was 0.94 and 2.61 for EP and MP, respectively which was 0.77 and 0.94 when corrected on dry matter basis. Even though protein efficiency ratio (2.26 vs 2.21), specific growth rate (0.69% vs 0.70%), daily weight gain (2.06 g vs 2.10 g) and mortality (4.0% vs 4.1%) were similar between EP and MP, protein retention efficiency of fish fed EP was superior (53.3%) to that of fish fed MP (39.6%). Protein intake of fish for 8 weeks was similar from 52.3 g for EP to 53.3 g for MP, while protein gain was higher in fish fed EP (27.9 g) than in fish fed MP (21.1 g). Protein excretion by feeding EP and MP reached 232 g and 300 g, respectively per kg gain, suggesting that protein loads could be greatly reduced in practical culture system through the use of EP. Phosphorus (P) intake was much lower in fish fed EP (1.63 g) than in fish fed MP (2.18 g), while retention of P was higher in fish fed EP (0.88 g) than in fish fed MP (0.78 g), showing the retention efficiency of 54.0% and 35.9%, respectively. Fish fed EP excreted 7.1 g of P per kg gain which was much lower than that of fish fed MP (13.1 g). The present results clearly showed that EP feeding for flounder is not only productive but also environmentally friendly.