Methodology to create cost estimation models for engineer-to-order operations

This research addresses the problem of cost estimation for product development in engineer-to-order (ETO) operations. An ETO operation starts the product development process with a product specification and ends with delivery of a rather complicated, highly customized product. ETO operations are practiced in various industries such as engineering tooling, factory plants, industrial boilers, pressure vessels, shipbuilding, bridges and buildings. ETO views each product as a delivery item in an industrial project and needs to make an accurate estimation of its development cost at the bidding and/or planning stage before any design or manufacturing activity starts. ^ Many ETO practitioners rely on an ad hoc approach to cost estimation, with use of past projects as reference, adapting them to the new requirements. This process is often carried out on a case-by-case basis and in a non-procedural fashion, thus limiting its applicability to other industry domains and transferability to other estimators. In addition to being time consuming, this approach usually does not lead to an accurate cost estimate, which varies from 30% to 50%. ^ This research proposes a generic cost modeling methodology for application in ETO operations across various industry domains. Using the proposed methodology, a cost estimator will be able to develop a cost estimation model for use in a chosen ETO industry in a more expeditious, systematic and accurate manner. ^ The development of the proposed methodology was carried out by following the meta-methodology as outlined by Thomann. Deploying the methodology, cost estimation models were created in two industry domains (building construction and the steel milling equipment manufacturing). The models are then applied to real cases; the cost estimates are significantly more accurate than the actual estimates, with mean absolute error rate of 17.3%. ^ This research fills an important need of quick and accurate cost estimation across various ETO industries. It differs from existing approaches to the problem in that a methodology is developed for use to quickly customize a cost estimation model for a chosen application domain. In addition to more accurate estimation, the major contributions are in its transferability to other users and applicability to different ETO operations. ^

Factors that differentiate persistence beyond the first session at a for-profit university

An increasing number of students are selecting for-profit universities to pursue their education (Snyder, Tan & Hoffman, 2006). Despite this trend, little empirical research attention has focused on these institutions, and the literature that exists has been classified as rudimentary in nature (Tierney & Hentschke, 2007). The purpose of this study was to investigate the factors that differentiated students who persisted beyond the first session at a for-profit university. A mixed methods research design consisting of three strands was utilized. Utilizing the College Student Inventory, student’s self-reported perceptions of what their college experience would be like was collected during strand 1. The second strand of the study utilized a survey design focusing on the beliefs that guided participants’ decisions to attend college. Discriminant analysis was utilized to determine what factors differentiated students who persisted from those who did not. A purposeful sample and semi-structured interview guide was used during the third strand. Data from this strand were analyzed thematically. Students’ self-reported dropout proneness, predicted academic difficulty, attitudes toward educators, sense of financial security, verbal confidence, gender and number of hours worked while enrolled in school differentiated students who persisted in their studies from those who dropped out. Several themes emerged from the interview data collected. Participants noted that financial concerns, how they would balance the demands of college with the demands of their lives, and a lack of knowledge about how colleges operate were barriers to persistence faced by students. College staff and faculty support were reported to be the most significant supports reported by those interviewed. Implications for future research studies and practice are included in this study.

Pilot Implementation of a Heat Illness Prevention Program in the Southeastern US

Objective: To evaluate the ease of application of a heat illness prevention program (HIPP). Design: A mixed-method research design was used: questionnaire and semi-structured interview. Setting: Eleven South Florida high schools in August (mean ambient temperature=84.0°F, mean relative humidity=69.5%) participated in the HIPP. Participants: Certified Athletic Trainers (AT) (n=11; age=22.2+1.2yr; 63.6% female, 36.4% male; 63.6%) implemented the HIPP with their football athletes which included a pre-screening tool, the Heat Illness Index Score- Risk Assessment. Data Collection and Analysis: Participants completed a 17-item questionnaire, 4 of which provided space for open-ended responses. Additionally, semi-structured interviews were voice recorded, and separately transcribed. Results: Three participants (27.7%) were unable to implement the HIPP with any of their athletes. Of the 7 participants (63.6%) who implemented the HIPP to greater than 50% of their athletes, a majority reported that the HIPP was difficult (54.5%) or exceedingly difficult (18.2%) to implement. Lack of appropriate instrumentation (81.8%, n=9/11), lack of coaching staff/administrative support (54.5%, n=6/11), insufficient support staff (54.5%, n=6/11), too many athletes (45.5%, n=5/11), and financial restrictions (36.4%, n=4/11) deterred complete implementation of the HIPP. Conclusions: Because AT in the high school setting often lack the resources, time, and coaches’ support to identify risk factors, predisposing athletes to exertional heat Illnesses (EHI) researchers should develop and validate a suitable screening tool. Further, ATs charged with the health care of high school athletes should seek out prevention programs and screening tools to identify high-risk athletes and monitor athletes throughout exercise in extreme environments.