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John Bello, aka the “Lizard King,” has been lionized in the business press as having been the brilliant entrepreneur who started a beverage company from scratch in early 1995 with his partner, Tom Schwalm, and then sold the product line to PepsiCo seven years later for a reported $375 million. What many people do not know is how close Bello came to failing with his SoBe line of innovative drinks.

The emerging literature on computer-mediated communication at the study lacks depth in terms of elucidating the consequences of the effects of incivility on employees. This study aims to compare face-to-face incivility with incivility encountered via e-mail on both task performance and performance evaluation.

In two experimental studies, the authors test whether exposure to incivility via e-mail reduces individual task performance beyond that of face-to-face incivility and weather exposure to that incivility results in lower performance evaluations for third-parties.

The authors show that being exposed to cyber incivility does decrease performance on a subsequent task. The authors also find that exposure to rudeness, both face-to-face and via e-mail, is contagious and results in lower performance evaluation scores for an uninvolved third party.

This research comprises an empirically grounded study of incivility in the context of e-mail at study, highlights distinctions between it and face-to-face rudeness and reveals the potential risks that cyber incivility poses for employees.

Forecasting techniques improve supply chain resilience by ensuring that the correct parts are available when required. In addition, accurate forecasts conserve precious resources and money by avoiding new start contracts to produce unforeseen part requests, reducing labor intensive cannibalization actions and ensuring consistent transportation modality streams where changes incur cost. This study explores the effectiveness of the United States Air Force’s current flying hour-based demand forecast by comparing it with a sortie-based demand forecast to predict future spare part needs.

This study employs a correlation analysis to show that demand for reparable parts on certain aircraft has a stronger correlation to the number of sorties flown than the number of flying hours. The effect of using the number of sorties flown instead of flying hours is analyzed by employing sorties in the United States Air Force (USAF)’s current reparable parts forecasting model. A comparative analysis on D200 forecasting error is conducted across F-16 and B-52 fleets.

This study finds that the USAF could improve its reparable parts forecast, and subsequently part availability, by employing a sortie-based demand rate for particular aircraft such as the F-16. Additionally, our findings indicate that forecasts for reparable parts on aircraft with low sortie count flying profiles, such as the B-52 fleet, perform better modeling demand as a function of flying hours. Thus, evidence is provided that the Air Force should employ multiple forecasting techniques across its possessed, organically supported aircraft fleets. The improvement of the forecast and subsequent decrease in forecast error will be presented in the Results and Discussion section.

This study is limited by the data-collection environment, which is only reported on an annual basis and is limited to 14 years of historical data. Furthermore, some observations were not included because significant data entry errors resulted in unusable observations.

There are few studies addressing the time measure of USAF reparable component failures. To the best of the authors’ knowledge, there are no studies that analyze spare component demand as a function of sortie numbers and compare the results of forecasts made on a sortie-based demand signal to the current flying hour-based approach to spare parts forecasting. The sortie-based forecast is a novel methodology and is shown to outperform the current flying hour-based method for some aircraft fleets.

The purpose of this paper is to investigate US noncombatant evacuation operations (NEO) in South Korea and devise planning and management procedures that improve the efficiency of those missions.

It formulates a time-staged network model of the South Korean noncombatant evacuation system as a mixed integer linear program to determine an optimal flow configuration that minimizes the time required to complete an evacuation. This solution considers the capacity and resource constraints of multiple transportation modes and effectively allocates the limited assets across a time-staged network to create a feasible evacuation plan. That solution is post-processed and a vehicle routing procedure then produces a high resolution schedule for each individual asset throughout the entire duration of the NEO.

This work makes a clear improvement in the decision-making and resource allocation methodology currently used in a NEO on the Korea peninsula. It immediately provides previously unidentifiable information regarding the scope and requirements of a particular evacuation scenario and then produces an executable schedule for assets to facilitate mission accomplishment.

The significance of this work is not relegated only to evacuation operations on the Korean peninsula; there are numerous other NEO and natural disaster related scenarios that can benefit from this approach.