Governments around the world are planning to ban sales of vehicles running on petroleum-based fuels as an effort to reduce greenhouse gas emissions, and electric vehicles surfaced as a solution to decrease pollutants produced by the transportation sector. As a result, wireless power transfer technology has recently gained much attention as a convenient and practical method for charging electric vehicles. In this paper, patent analysis is conducted to identify emerging and vacant technology areas of wireless power transfer. Topics are first extracted from patents by text mining, and the topics with similar semantics are grouped together to form clusters. Then, the process of identifying emerging and vacant technology areas is improved by applying a time series analysis and innovation cycle of technology to the clustering result. Lastly, the results of clustering, time series, and innovation cycle are compared to minimize the possibility of misidentifying emerging and vacant technology areas, thus improving the accuracy of the identification process and the validity of the identified technology areas. The analysis results revealed that one emerging technology area and two vacant technology areas exist in wireless power transfer. The emerging technology area identified is circuitries consisting of transmitter coils and receiver coils for wireless power transfer, and the two vacant technology areas identified are wireless charging methods based on resonant inductive coupling and wireless power transfer condition monitoring methods or devices.

With increasing concern over the environment, shipment consolidation has become one of a main initiative to reduce CO₂ emissions and transportation cost among the logistics service providers. Increased delivery time caused by shipment consolidation may lead to customer’s order cancellation. Thus, order cancellation should be considered as a factor in order uncertainty to determine the optimal shipment consolidation policy. We develop mathematical models for quantity-based and time-based policies and obtain optimality properties for the models. Efficient algorithms using optimal properties are provided to compute the optimal parameters for ordering and shipment decisions. To compare the performances of the quantity-based policy with the time-based policy, extensive numerical experiments are conducted, and the total cost is compared.

The modern semiconductor industry is going through rapid changes as new markets and technologies appear. In this paper, such technology-intensive firms’ relationship between technological capability and financial performance is analyzed with regression analysis. Revenue and market capitalization are used as dependent variables. For the independent variables, the technological intensity, technological diversity, technological asset, and technological efficiency are used. The analysis results revealed different effects of technological capability on financial performance. Also, regression analysis is conducted by dividing firms into high and low groups based on technological asset and technological efficiency, and the analysis result revealed different effects of technological intensity and technological diversity on financial performance. For technological asset, the financial performance in the high group is affected more by technological intensity, and the financial performance in the low group is affected more by technological diversity. For technological efficiency, only the financial performance in the high group is affected by technological intensity. Although both groups’ financial performance is somewhat affected by technological diversity, there was no statistically significant differences between the groups. By separating the effect of technological capability on financial performance, this research can provide more detailed analysis results compared to previous literature and the methods of managing technological capability for semiconductor firms.

This paper aims to analyze the influence of the technological diversification on a firm’s innovation capabilities and investigates the effect of various strategies on the firm’s financial performance in a technology-oriented environment. We employ the entropy measurement to calculate technological diversification with 2095 patents, which are applied from years 2009 to 2011 by 507 firms that have participated in Korean government Information Technology (IT) Research and Development (R&D) supporting programs. In our framework, a firm should not diversify among the related technology fields, but should concentrate on a specific technology to reinforce the competitive advantage. However, in the case of the firms with sufficient resources, increasing technological diversification among the unrelated technology fields plays a key role on the firms’ performance. Furthermore, the degree of the technological diversification should be adjusted dynamically in compliance with the change of a firm’s innovation capabilities. Therefore, these results suggest that a firm should develop differentiated competitiveness through specialization by prioritizing its capabilities, and then exploit unrelated technological diversification to search for new opportunities.

Research and pilot fabrication lines must plan capacity under highly diversified product portfolios, unstable demand, and frequent recipe changes. We present a prescriptive mixed integer linear programming framework that encodes internal mechanisms of cluster tools, including expected clean time after recipe transitions, finite concurrency at load ports and side buffers under a nonmixing policy, and sequential and parallel routing across chambers. The framework supports two planning tasks, minimizing added capacity to relieve bottlenecks and maximizing wafer starts for a given product mix and planning horizon. Using 12 monthly instances from an industrial R&D line, the proposed model improves agreement with the fab reported bottleneck set under substantial mix changes. Relative to a baseline formulation that abstracts cluster tool internals, the bottleneck hit rate increases by 0.0 to 10.5 percentage points across months (average improvement 4.8 percentage points). In addition, the model can screen incremental product releases that do not increase the modeled bottleneck loading, enabling approximately 1.2% additional starts without new equipment.