Selecting the Best Using Data Envelopment Analysis

One of the most important strengths of Data Envelopment Analysis, (DEA), is that it allows almost complete freedom in the way that each decision making unit, (DMU), evaluates itself relative to its peers. This tends to result in many DMUs receiving a high efficiency score. Particularly when DEA is applied in a decision making context, it may be desirable to select a single option rather than determining the set of efficient alternatives in ranking efficient DMU or to Assist selecting a best DMU. Several extensions to DEA have been proposed and used. This paper examines, compares, and integrates a variety of these methods. A less complicated application area is used to investigate the subtleties of DEA cross-efficiency

Environmental Health Funding Challenges During Difficult Budget Times

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Technology Trajectory Mapping Using Data Envelopment Analysis: The Ex-ante use of Disruptive Innovation Theory on Flat Panel Technologies

In this paper, we propose a technology trajectory mapping approach using Data Envelopment Analysis (DEA) that scrutinizes technology progress patterns from multidimensional perspectives. Literature reviews on technology trajectory mappings have revealed that it is imperative to identify key performance measures that can represent different value propositions and then apply them to the investigation of technology systems in order to capture indications of the future disruption. The proposed approach provides a flexibility not only to take multiple characteristics of technology systems into account but also to deal with various tradeoffs among technology attributes by imposing weight restrictions in the DEA model. The application of this approach to the flat panel technologies is provided to give a strategic insight for the players involved

Predicting U.S. Jet Fighter Aircraft Introductions from 1944 to 1982: A Dogfight Between Regression and TFDEA

Since its inception in 2001, technology forecasting using data envelopment analysis (TFDEA) has been used with a number of applications. This paper presents a formal comparison of TFDEA to a previously published application from Technological Forecasting and Social Change by Joseph Martino. Using the data and Martino’s multiple regression model, we compare results obtained from TFDEA to those previously published. Both techniques predict the first flights of fighter jets introduced between 1960 and 1982 by using the first flights of aircraft introduced between 1944 and 1960. TFDEA was found to better predict the first flight dates than the forecast using multiple regression. These results indicate that TFDEA may be a powerful new technique for predicting complex technological trends and time to market for new products

Improving Time to Market Forecasts: A Comparison of Two Technology Forecasting Techniques for Predicting U.S. Fighter Jet Introductions From 1944 to 1982

Since its origin in 2001, technology forecasting using data envelopment analysis (TFDEA) has been tested with a number of applications. This paper uses a previously published technology forecast comparison of U.S. fighter jets between the years 1944 and 1982 to compare TFDEA to basic regression. Both techniques use aircraft introduced between 1944 and 1960 to predict the first flights of those fighters introduced between 1960 and 1982. TFDEA was found to better predict the first flight dates than the forecast using regression. These results indicate that TFDEA may be a powerful new technique for predicting complex technological trends and time to market for new product

The Fixed Weighting Nature of a Cross-Evaluation Model

Cross-evaluation has been touted as a powerful extension of Data Envelopment Analysis that provides, not only a unique ordering among the Decision Making Units (DMUs), but also eliminates unrealistic weighting schemes without requiring the elicitation of weight restrictions from application area experts. The goal of this paper is to prove, in the single-input, multiple-output case, cross-evaluation implicitly uses a single fixed set of weights. We demonstrate how this unseen fixed set of weights may still be unrealistic

Assigning Projects to Project Managers in a Multiple-Project Management Environment: A Pilot Study of a Decision Support Model

Project assignment is considered one of the most critical project decisions since it influences the performance of projects, and eventually the performance of the organization. Despite its importance, the literature reveals two major gaps on project assignment criteria and methodology. To close these gaps, this study proposes an additional set of project assignment criteria and a systematic methodology for project assignments (DSM). By using the concepts of these case study research combined with a literature review, the important potential criteria for project assignments are identified. These criteria are used in conjunction with the concepts of the analytic hierarchy process (AHP) and the integer programming (IP) to develop a DSM for one company. The DSM is executed and validated with the company\u27s information. As a past of this research project, this paper illustrates the results of the pilot study to be developed for the feasibility study of the DSM development

Vaginal Microbicides: Detecting Toxicities in Vivo that Paradoxically Increase Pathogen Transmission

BACKGROUND: Microbicides must protect against STD pathogens without causing unacceptable toxic effects. Microbicides based on nonoxynol-9 (N9) and other detergents disrupt sperm, HSV and HIV membranes, and these agents are effective contraceptives. But paradoxically N9 fails to protect women against HIV and other STD pathogens, most likely because it causes toxic effects that increase susceptibility. The mouse HSV-2 vaginal transmission model reported here: (a) Directly tests for toxic effects that increase susceptibility to HSV-2, (b) Determines in vivo whether a microbicide can protect against HSV-2 transmission without causing toxicities that increase susceptibility, and (c) Identifies those toxic effects that best correlate with the increased HSV susceptibility. METHODS: Susceptibility was evaluated in progestin-treated mice by delivering a low-dose viral inoculum (0.1 ID50) at various times after delivering the candidate microbicide to detect whether the candidate increased the fraction of mice infected. Ten agents were tested – five detergents: nonionic (N9), cationic (benzalkonium chloride, BZK), anionic (sodium dodecylsulfate, SDS), the pair of detergents in C31G (C14AO and C16B); one surface active agent (chlorhexidine); two non-detergents (BufferGel®, and sulfonated polystyrene, SPS); and HEC placebo gel (hydroxyethylcellulose). Toxic effects were evaluated by histology, uptake of a 'dead cell' dye, colposcopy, enumeration of vaginal macrophages, and measurement of inflammatory cytokines. RESULTS: A single dose of N9 protected against HSV-2 for a few minutes but then rapidly increased susceptibility, which reached maximum at 12 hours. When applied at the minimal concentration needed for brief partial protection, all five detergents caused a subsequent increase in susceptibility at 12 hours of ~20–30-fold. Surprisingly, colposcopy failed to detect visible sign of the N9 toxic effect that increased susceptibility at 12 hours. Toxic effects that occurred contemporaneously with increased susceptibility were rapid exfoliation and re-growth of epithelial cell layers, entry of macrophages into the vaginal lumen, and release of one or more inflammatory cytokines (Il-1β, KC, MIP 1α, RANTES). The non-detergent microbicides and HEC placebo caused no significant increase in susceptibility or toxic effects. CONCLUSION: This mouse HSV-2 model provides a sensitive method to detect microbicide-induced toxicities that increase susceptibility to infection. In this model, there was no concentration at which detergents provided protection without significantly increasing susceptibility.JHU Woodrow Wilson Fellowship; National Institutes of Health (Program Project A1 45967

Choosing Effective Dates From Multiple Optima in Technology Forecasting Using Data Envelopment Analysis (TFDEA)

Technology Forecasting using Data Envelopment Analysis (TFDEA) provides an effective means to forecast technological capability over time without the burden of fixed a priori weighting schemes. However, there are situations where result reproduction can be a challenge as first pointed out in a previous Technological Forecasting and Social Change article. When using a commonly used extension of TFDEA, there are circumstances where multiple optimal solutions can complicate analysis. This paper addresses this issue through extending the TFDEA model in a manner consistent with common Data Envelopment Analysis (DEA) techniques. The extension is then demonstrated using datasets from previous publications on fighter jet and commercial airplane technology where the issue of multiple optima has been observed. The results indicate that traditional TFDEA may generate varying forecasts depending on the software used, which can be dealt with by introducing a secondary objective function. Therefore, researchers should explicitly state which secondary objective function they are using for the TFDEA applications

Applying Technology Forecasting to New Product Development Target Setting of LCD Panels

This chapter illustrates the Technology Forecasting using Data Envelopment Analysis (TFDEA) process on Liquid Crystal Display (LCD) performance characteristics from 1997 to 2012. The objective of this study is to forecast future state-of-the-arts (SOAs) specifications as well as to diagnose past technological advancement of the LCD industry. Appropriate characteristics were determined from a group of LCD technologists. Data was gathered from public databases and outlying data points were cross-referenced as a validity check. The TFDEA process is defined and its application to the dataset is described in detail. The results not only provide information on how LCD industry has evolved but also provide an insight on future NPD targets