Technology forecasting for wireless communication

Abstract

Wireless communications technologies have undergone rapid changes over the last thirty years from analog approaches to digital-based systems. These technologies have improved on many fronts including bandwidth, range, and power requirements.Development of new telecommunications technologies is critical. It requires many years of efforts. In order to be competitive, it is critical to establish a roadmap of future technologies. This paper presents a framework to characterize, assess and forecast the wireless communication technologies. A DEA-based methodology was used for predicting the state of the art in future wireless communications technologies. Literature ReviewThere are many techniques that can be used to develop technology forecasts. Linstone (1999) provides an overview of methods evolving over time. Other researchers including Ayres (1999), Martino (1999) and Porter (1999) also provide comprehensive treatments of many approaches. Technology forecasting can be done both qualitatively as well as quantitatively. Linstone (2003) and du Preez (2003) provide examples of qualitative approaches such as multiple perspectives and threat/opportunity analysis which help to dissect problems so that further analysis can be done with quantitative models. Fildes (2006) provides an excellent review of forecasting research and outlets for publications. De Gooijer et al. (2006) add to this research by focusing on time series forecasting. Meade et al.(2006) provide a similar in-depth analysis for innovation diffusion. The Technology Futures Analysis Methods Working Group (Porter et al., 2004) provides a good review of integrating multiple methods and evolving new methods for technology forecasting. Methods used frequently include scenarios (Sager, 2003; Silberglitt et al.indicates the necessity of combining the forecasting model with the perceived future industry dynamics. He emphasizes that the quantitative forecasting methods such as time series and econometric modeling have become less accurate and cannot be relied upon because the industry no longer has the stable historical relationship that these models rely on. The literature suggests that including forecasts from different statistical methods generally improves accuracy when significant trends are involved. Useful information can be obtained using several sources of forecasts, adjusting for biases. Yoo and Moon (2006) suggest that instead of trying to choose the best single method, one should combine the results from different methods, which would help in reducing errors arising from faulty assumptions, biases, or mistakes in the data.The new product development literature is relevant because the efforts to create these new technologies are relatively similar to product development and it will provide technology platforms upon which other products will be developed. There is an extensive literature on new product development but for the sake of providing a context and linkage to this literature, we will provide a discussion of a few select papers. The importance and u...

Forecasting technology success based on patent data

“…Technology Method(s) Telecommunication technology DEMATEL method and patent citation analysis Database theory and its application Weighted association rules Choi and Jun (2014) Humanoid robot system Bayesian patent clustering Li et al (2014) Green energy Patent analysis and simulation model Chang et al (2014) Dental implant Patent analysis Ranaei et al (2014) Low emission vehicle S-curve Jun and Lee (2012) Nanotechnology Neural networks Jun et al (2012,b) Biotechnology Association rules, time series analysis and k-means clustering Yoon and Kim (2012a) Silicon-based thin film solar cells and umbrellas Property-function analysis, network analysis and TRIZ trend analysis Chiu and Ying (2012) Building-integrated photovoltaic (BIPV) Logistic growth model Lee et al (2012) Display Pareto/NBD (negative binomial distribution) model and gamma-gamma model Chen et al (2011) Hydrogen energy and fuel cell Logistic growth model Trappey et al (2011) Radio frequency identification (RFID) S-curve Jun (2011a) Database theory and application Association rules Jun (2011b) Image and video technology Association rules and self-organizing map Chen et al (2010) Hydrogen energy and fuel cell Bibliometric analysis and growth curve Jun and Uhm (2010) Bio-technology Frequency time series model Nanosized ceramic powders Logistic growth model Karakan and Koc (2008) Isolation technology in white goods sector Pearl curve and technology substitution model Daim et al (2008) Data storage Bibliometric trend analysis, grow curve and technology cycle time Yoon and Park (2007) Thin film transistor-liquid crystal display (TFT-LCD) Morphology analysis and conjoint analysis Daim et al (2006) Fuel cell, food safety and optical storage Bibliometric analysis, grow curves and system dynamics Ernst (1997) Computerized numerical control (CNC) Patent analysis Daim (2009) used the publication data from Science Direct and patent data from World Intellectual Property Organization (WIPO) database to forecast energy storage technologies for future electricity generation. Zhu and Porter (2002) focused on automated extraction and visualization of information for technological intelligence and forecasting.…”

Comparison of Different Performance Measures of Complex Product Systems in Technology Forecasting

“…Sahal (1981) analysed performance improvements for tractors, using average fuel consumption efficiency, average fuel mechanical efficiency and per gallon of fuel used, ratio of drawbar horsepower to belt horsepower, or horsepower to weight ratio. T. R. Anderson, Daim, and Kim (2008) utilized channel bandwidth, number of channels, channel bit rate, transmission power, number of speech channels and data capacity as technical performance parameters for forecasting wireless technologies. Yoon et al (2013) forecasted single-lens reflex camera technology using data envelopment analysis by employing resolution, max FPS, focus point, weight and MSRP data.…”

Key Sub-Disciplines and Methods of Technology Planning in Dynamic Environments

“…Chaos or contingency theories can be also very well used in technology forecasting (Yu, 2007). Individual forecasting methods and forms are also combined and used complementary within technology planning (Anderson et al, 2008). The main reason for this combination is to gain more complex perspectives on a given technology (Martin and Daim, 2012).…”