Developing Strategic Decision Making Process for Product and Service Planning

It is imperative to forecast advanced or emerging technologies to aid in decision making on firm\u27s R&D investments and business plan for commercialization efforts. Even though a company must align R&D planning with overall business planning such as manufacturing, sales and marketing, personnel, and finance, systematic management approaches are limited in it based upon the prediction of technological change and speed. This paper aims to provide a decision support tool to aid in strategic service planning and technology development in a firm. The study is to enhance strategic development of service and product with the consideration of emerging technologies. This model helps decision makers to easily identify emerging technologies and new research fields with systematic decision making process

Exploring Technology Forecasting and its Implications for Strategic Technology Planning

As the importance of R&D has been growing in economic growth, the accountability and effectiveness of R&D programs are highly emphasized. Especially, in times of economic downturn, the evaluation of performance in a firm is needed to justify R&D investment. In response, various attempts have been made to improve success rates of R&D projects, gain competitive advantage, and achieve a firm\u27s growth in profitability. In particular, in industries where technological innovation is significant, strategic technology planning and R&D capabilities may be the lead ones in defining the dynamic capabilities of a firm. In addition, technology forecasting (TF) in technology planning is a crucial step to follow before developing technologies/products/processes in need. In this regard, researchers have an abiding interest in enhancing methods to forecast emerging technology, while practitioners have a considerable interest in selecting appropriate tools to apply in their field for better forecasting results. Nevertheless, so far it is not well documented how appropriately the current research responds to this need. Thus, a thorough review on TF techniques is conducted to help researchers and practitioners capture methodologies in a tangible way and identify the current trends in the TF arena. Moreover, there is still a lack of clear guidance as to where and how particular TF methods are useful in strategic planning based on technology characteristics as well as the nature of industry. The purpose of this study is to enrich the stream of research on TF activities in a firm for practitioners and researchers, a unique context where TF could lead to technological innovation. This research offers a classification of the approaches, and presents technological, industrial, methodological, and organizational aspects of TF methods that are inherent in TF activities. Furthermore, this study provides empirical evidences to support organizational and managerial implications regarding TF activities associated with technology planning in a firm. Research findings in regimes of technological change suggest insights on technological, organizational, and managerial processes within the firm. On the other hand, research on the effects on business performance of best practices of strategic planning, which enable firms to articulate their plans to develop, acquire, and deploy resources for accomplishing firms\u27 financial growth, has so far ignored the roles of strategic technology planning associated with TF. In this regard, this study explores a set of indicators, discusses, and presents the findings from the literature in such a way that they become useful for researchers or managers who are in charge of measuring the R&D performance and business performance from innovation activity. Next, this research tested the hypothetical framework proposed not only to provide a current snapshot of how firms across industries implement best practices in strategic technology planning, but also to improve the effectiveness of strategic planning. The results present the positive linkages between TF, technology planning, and superior business performance. The findings in this research help policy makers, universities, research institutes/national labs, and companies to enhance their decision making process on technology development

Forecasting OLED TV Technology Using Bibliometrics and Fisher-Pry Diffusion Model

The market of flat panel displays is experiencing rapid growth with the advancement of digital technologies in broadcasting service. The next challenge of LCD is OLED in TV market. The study attempts to investigate the trends in advanced or emerging technologies by determining their technology diffusion rates due to the lack of experiential data. With the development of information and communication technology, one of the recent methods to assist in technology forecasting is data mining in bibliometric or textual data from various sources such as patents, journals, and research awards. The information extracted from diverse sources can be employed in technology diffusion models such as Fisher-Pry where emerging technologies substitute older ones. The study uses web of science and compendex for bibliometric analysis to forecast the growth of next-generation OLED technologies based on the analogous growths of LCD technologies

OLED TV Technology Forecasting Using Technology Mining and the Fisher-Pry Diffusion Model

Purpose: Due to rapid technological evolution driven by display manufacturers, the television (TV) market of flat panel displays has been fast growing with the advancement of digital technologies in broadcasting service. Recently, organic light-emitting diode (OLED) successfully penetrated into the large-size TV market, catching up with light-emitting diode (LED)-liquid-crystal display (LCD). This paper aims to investigate the market penetration of OLED technologies by determining their technology adoption rates based on a diffusion model. Design/methodology/approach: Through the rapid evolution of information and communication technology, as well as a flood of data from diverse sources such as research awards, journals, patents, business press, newspaper and Internet social media, data mining, text mining, tech mining and database tomography have become practical techniques for assisting the forecaster to identify early signs of technological change. The information extracted from a variety of sources can be used in a technology diffusion model, such as Fisher-Pry where emerging technologies supplant older ones. This paper uses a comparison-based prediction method to forecast the adoption and diffusion of next-generation OLED technologies by mining journal and patent databases. Findings: In recent years, there has been a drastic reduction of patents related to LCD technologies, which suggests that next-generation OLED technology is penetrating the TV market. A strong industry adoption for OLED has been found. A high level of maturity is expected by 2026. Research limitations/implications: For OLED technologies that are closely tied to industrial applications such as electronic display devices, it may be better to use more industry-oriented data mining, such as patents, market data, trade shows, number of companies or startups, etc. The Fisher-Pry model does not address the level of sales for each technology. Therefore, the comparison between the Bass model and the Fisher-Pry model would be useful to investigate the market trends of OLED TVs further. Another step for forecasting could include using industry experts and a Delphi model for forecasting (and further validation). Originality/value: Fisher-Pry growth curves for journal publications and patents follow the expected sequence. Specially, journal publications and patents growth curves are close for OLED technologies, indicating a strong industry adoption

Forecasting MBT Technologies Using DEA and LR

First, this paper explores Main Battle Tank (MBT) data set with different statistical methods in order to decide the most appropriate variables as reliable yardsticks in applying technology forecasting (TF) using data envelopment analysis (TFDEA) technique. It then applies TF using DEA method to forecast MBT technologies. This article attempts to predict technology development year of MBT commercialised from 1941 to 1994. This article presents the processes of TFDEA in detail and identifies some issues to search for appropriate input and output variables to forecast MBT technologies. The purpose of this study is to address some issues and identify an appropriate data to predict future trends of MBT technologies when using TFDEA and multiple linear regression tools. Finally, the study provides an understanding of the technological advances being sought in MBT technologies and information for use in making decisions regarding development strategy. © 2016 Informa UK Limited, trading as Taylor & Francis Group

Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity

Storage Technology Portfolio Assessment for Integrating Wind Power Generation in the BPA Power Plan

The BPA power plan expects to add 6000 MW of Wind power generation according to its strategic objectives for the year 2010-2016. Power balancing requirements resulting from the intermittency of wind power suggests using energy storage assistance to improve overall generation and load characteristics. Previous research has analyzed possible storage technology options and recommended prioritized and ranked lists of the best storage technologies to support wind energy generation. Based on technology maturity as described in one of the BPA’s report in Energy Storage Landscape, we chose the top four technologies and used Goal Programming (GP) optimization to measure the optimum solutions for helping the BPA generation and load balancing. The results depend on importance and weighting given to the technical, organizational and personal perspectives. The GP result indicated that in both scenarios, using equal weight and weight generated from previous research in the similar field, the portfolios pattern is almost identical given all the constraint and different weight assigned to both scenarios

Energy Technology Adoption: Case of Solar Photovoltaic in the Pacific Northwest USA

Current political, economic, and environmental issues have made energy source selection a delicate decision. These pressures concurrent with the decrease in sustainable energy costs have increased the interest in cleaner alternative energies such as solar and wind in order to mitigate the environmental consequences. Among these energies, solar photovoltaic (PV) energy is one of the most promising sources of power with the potential of providing one fifth of the annual energy in US alone. The purpose of this study is to analyze the associations and correlations of income, education, and solar adoption through surveys and Geographic Information Systems (GIS) respectively. Results from GIS analysis and linear regression show correlations between education, income levels, and the number of solar PV adopters in Oregon census tracts. By identifying adoption hotspots, this study recommends four areas in Oregon State which are ideal for energy companies to promote, educate, and incentivize Solarize programs in order to bolster the adoption and diffusion of solar PVs. The selected areas have high income and education levels but the number of solar adoptions are not significant which makes them good candidates for more analysis and potentially incentives and education programs