A sector analysis for RFID technologies: fundamental and technical analysis for financial decision making problems

Automatic identification technologies have been used in a wide range of applications for reducing the amount of time and labor needed to input data and improving data accuracy. As an important automatic identification technology, radio frequency identification (RFID) technologies allow contactless reading and these technologies are particularly successful in manufacturing and other environments where traditional identification technologies such as bar codes can not perform well. By integrating the RFID technology into their business models, companies may save time, lower labor cost, improve products quality and provide better service. RFID is the wireless technology that uses RF communication to identify, track and manage objects and collect and store data. RFID technology enables companies to develop applications that create value by tracking and identifying objects, animals or people. Business applications of RFID technology can be seen in areas such as manufacturing, supply chain management, software integration, security systems, asset tracking and many others. RFID technology was predicted to be one of the “top ten” technologies in 2004 by CNN. Although, the RFID market is less than five years old, it has been applied to many different industries, from retail industry to logistics, or from healthcare to service business industry – and it is still growing. Particularly, RFID has fundamental influences on today's retailing and supply chain management for applications like asset tracking the inventory control and management. RFID technology also finds major application in mobile phones and is widely used in toll collection of highways, for payments in restaurants, vending machines, retail and parking lots. There are a wide range of RFID systems currently being used or being developed. Examples to these systems include but not limited to the following; automatic vehicle and personnel access control for security (Simpson, 2006), airport passenger and baggage tracking (Ferguson, 2006), tracing blood for cutting down errors such as giving patients wrong blood types (Ranger, 2006), payment process systems (Ramachandran, 2006), production control in manufacturing (Liu & Miao, 2006), transfusion medicine (Knels, 2006) real-time inventory control by automated identification of items in warehouses, tracking and management of physical files, tracking of books in the libraries (Shadid, 2005). For some other applications, interested reader is referred to (Finkenzeller, 2003; Smith, 2004). RFID solution providers claim that their technology and solutions bring significant benefits and have valuable advantages in practice. As new RFID solutions being developed and more RFID tags and equipments being used, these solutions will become more cost effective and RFID businesses are expected to grow rapidly. Since RFID is fairly new, it’s difficult to measure resulting sales increases or heightened customer satisfaction quotients. On the other hand, according to IDC estimation (IDC is a subsidiary of International Data Group, a leading technology media, research, and events company and provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets), companies in the retail sector will spend nearly $1.3 billion on RFID in their supply chain operations in 2008, compared to about$91.5 million in 2003 which corresponds to annual growth rate of 70 percent. In a similar look; the Wireless Data Research Group projected that the global market for RFID increased from $1 billion in 2003 to$3 billion in 2007 (Asif & Mandviwalla, 2005). There are two major drivers of this growth. The first one is the adoption of RFID technology by major retailers and government agencies. The second one is the reduction in the price of RFID tags, readers, and IT systems required to deploy RFID. Given the huge potential of RFID technology, there has been a huge emergence of RFID specialty companies and the development of RFID practices within many market-leading companies. Due to huge emergence, it is desirable to make a sector analysis. In this study, we perform a sector analysis for RFID technologies for researchers and analysts. We investigate public RFID companies traded on the stock exchange markets, summarize their financial performance, describe their RF products, services, and applications, and perform fundamental and technical analysis

Assessment of OTT Pluvio2 Rain Intensity Measurements

AbstractThis study investigates the OTT Pluvio2 weighing precipitation gauge’s random and systematic error components as well as stabilization of the measurements on time-varying rainfall intensities (RI) under laboratory conditions. A highly precise programmable peristaltic pump that provided both constant and time-varying RI was utilized in the experiments. Abrupt, gradual step, and cyclic step changes in the RI values were evaluated. RI readings were taken in real time (RT) at different time resolutions (6–60 s) for the RI range of 6–70 mm h−1. Our analysis indicates that the lower threshold for the OTT Pluvio2’s real-time RI measurements should be redefined as 7 mm h−1 at a 1-min resolution. Tolerance intervals containing 95% of the repeated measurements with a probability of 0.95 are given. It is shown that the measurement variances are unequal over the range of RI and the measurement repeatability is not uniform. A statistically significant negative bias was observed for the RI values of 7 and 8 mm h−1, while there was not a statistically significant linearity problem. Through the use of statistical control limits, it is shown that means of the RI measurements stabilized on the actual RI value. A detailed investigation on RT bucket weight measurements revealed a time delay in bucket weight measurements, which causes notable errors in reported RI measurements under dynamic rainfall conditions. To demonstrate the potentiality of large errors in Pluvio2’s real-time RI measurements, a set of equations was developed that faithfully reproduced the Pluvio2’s internal (hidden) algorithm, and results from dynamic laboratory and in situ rainfall scenarios were simulated. The results of this investigation show the necessity of modifying the present Pluvio2 RI algorithm to enhance its performance and show the possibility of postprocessing the existing Pluvio2 RI datasets for improved measurement accuracies.</jats:p

Understanding the tendency of software development teams to develop software over the cloud [Yazilim geliştirme takimlarinin bulut üstünden yazilim gelistirme eǧilimlerinin incelenmesi]

Today, Cloud Computing offers attractive and effective solutions for organizations which enable them to decrease IT costs, provide flexibility to ser-vices and make it easier to access IT services -Therefore enable faster market entries. For an organization that decides to make use of Cloud services, there are various factors to evaluate - similar to outsourcing. In this paper, we studied these factors through the literature and then we tried to understand the viewpoints of software developers regarding the existing and possible future usage of Cloud in software development processes. In this context, we prepared a questionnaire based on the findings in the literature and applied it to software development team members working in technoparks in Turkey. We used the dataset which is obtained from this questionnaire to observe the relationship between the tendency of using Cloud in software development processes and the factors effecting them. This research is performed as the first phase of a study with a larger scope, de-signed to forecast the Cloud needs of software developing organizations and it provides important findings. The questionnaire findings also describe the current demographics of software development organizations in Turkish technoparks to-gether with their perception of Cloud services

Data Mining and Knowledge Discovery in Healthcare Organizations

Health care organizations are struggling to find new ways to cut healthcare utilization and costs while improving quality and outcomes. Predictive models that have been developed to predict global utilization for a healthcare organization cannot be used to predict the behavior of individuals. On the other hand, massive amounts of healthcare data are available in databases that can be used for exploring patterns and therefore knowledge discovery. Diversity and complexity of the healthcare data requires attention to the use of statistical methods. By nature, healthcare data are multivariate, making the analysis difficult as well as interesting. In this chapter, our intention is to classify individuals that are future high-utilizers of healthcare. In particular, we answer the question of whether a mathematical model can be generated utilizing a large claims database that will predict which individuals who are not using a service in a yet untested database will be high utilizers of that health service in the future. For this purpose, an integrated dataset from enrollment, medical claims, and pharmacy databases containing more than 150 million medical and pharmacy claim line items and for over four million patients is analyzed for knowledge discovery. A modern data-mining tool, namely decision trees, which may have a broad range of applications in healthcare organizations, was used in our analyses and a discussion of this valuable tool is provided. The results and managerial aspects are discussed. Several approaches are proposed for the use of this technique depending on the health plan. </jats:p

Detection of Abrupt Changes in Count Data Time Series: Cumulative Sum Derivations for INARCH(1) Models

The INARCH(1) model has been proposed in the literature as a simple, but practically relevant, two-parameter model for processes of overdispersed counts with an autoregressive serial dependence structure. In this research, we develop approaches for monitoring INARCH(1) processes for detecting shifts in the process parameters. Several cumulative sum control charts are derived directly from the log-likelihood ratios for various types of shifts in the INARCH(1) model parameters. We define zero-state (worst-state) and steady-state average run length metrics and discuss their computation for the proposed charts. An extensive study indicates that these charts perform well in detecting changes in the process. A real-data example of strike counts is used to illustrate process monitoring

CUSUM Monitoring of First-Order Integer-Valued Autoregressive Processes of Poisson Counts

Attributes control charts for counts generally assume that the process being monitored is independent and identically distributed in its in-control state. However, violation of this assumption in practice may significantly degrade a chart's performance and usefulness if the autocorrelation structure is not taken into account. To describe the autocorrelation structure of counts in an in-control process, integer-valued autoregressive moving average process models can be employed. This paper investigates the cumulative sum (CUSUM) control chart for monitoring autocorrelated processes of counts modeled by a Poisson integer-valued autoregressive model of order 1, namely Poisson INAR(14). The CUSUM chart is designed to detect assignable causes affecting the process mean, but also changes in the autocorrelation structure are considered. Exact numerical results obtained through a bivariate Markov chain approach are provided for sustained shifts in any or both of these process parameters. Some numerical results from a simulation study of the residuals' monitoring are also presented. It is shown that the considered CUSUM chart of observations has good overall performance in detecting assignable causes in autocorrelated count processes