Search results

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 research is to gather information about user experiences with overhead scanners, in order to better inform purchasing decisions.

The method of obtaining information was a survey, publicized via several listservs frequented by higher education digitization personnel, and available for four months in 2010. Participants were asked to identify their scanner, rate the extent to which they would recommend it, and describe usage patterns, speed, problems they encountered with hardware and software, and also what they like about their scanner.

In total, 52 respondents described 71 scanners covering 19 different brands (30 models, two unspecified). The number of complaint entries for hardware and software were even, with 39 responses for each (55 per cent). When asked to what extent the respondent would recommend the product, 30 per cent (21 respondents) would highly recommend their scanner (“Yes, it's great!”), 39 per cent (28 respondents) said “Yes, it's pretty good”, 25 per cent (18 respondents) would only possibly recommend it, with caveats, and 6 per cent of respondents (four) would not recommend their scanner at all.

Only 52 respondents participated in the study, reporting on a wide variety of models and brands. Five brands accounted for 69 per cent of the responses, so the quantity of responses per brand or model was far from equivalent, which is a severe weakness in terms of generalization of findings. The survey was heavily designed for open‐ended responses, which are not easily quantifiable. Follow‐up surveys should be designed to capture more easily quantifiable results, and, if possible, a larger and roughly equivalent number of responses for each model included.

Results of the survey will provide potential purchasers of overhead scanners with information that will assist them in making informed decisions and avoid expensive mistakes.

This paper gathers reports about the performance of a wide variety of overhead scanners in the field, which will help inform purchasing decisions in the immediate future.

This paper gathers reports about the performance of a wide variety of overhead scanners in the field, which will help inform purchasing decisions in the immediate future.

This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service.

This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments.

Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance.

This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination.

The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.

Commercial pressure has forced improvements in the reduction of press down time. One restraining factor for the flexographic printing process has been the lack of predictability and consistency. The results of a factorial designed experiment, are reported in this paper. Investigations into the complex interactions of many variable factors that take place during the printing process were carried out. Various statistical methods were employed for the design of the experiment and for the interpretation of experimental data generated. The results of the investigation have been used to optimise the flexographic printing techniques to significantly manipulate the properties of various production components for the end‐use application to enhance the plates printing performance and consistency.

Inconsistency in flexographic platemaking techniques was perceived to influence the print quality and consistency. The results of a factorial designed experiment created to establish which platemaking factors had a significant influence on the flexographic printing quality are reported in this paper. The objective of the experiment was not only to define the optimum platemaking technique, but also to improve the understanding of the characteristics and properties of the photopolymer printing plate and to identify the key interacting factors which have the most significant effect on print quality and consistency. Various statistical methods were employed for the design of the experiment and for the interpretation of experimental data generated. The results of the investigation have been used to fine‐tune the flexographic platemaking techniques to significantly manipulate the properties for the end‐use application to enhance printing performance and consistency of the plates.

The purpose of this study is to develop nonlinear and linearized models of DW printing dynamics that capture the complexity of DW while remaining integrable into control schemes. Control of material metering in extrusion-based additive manufacturing modalities, such as positive displacement direct-write (DW), is critical for manufacturing accuracy. However, in DW, transient flows are poorly controlled due to capacitive pressure dynamics – pressure is stored and slowly released over time from the build material and other compliant system elements, adversely impacting flow rate start-ups and stops. Thus far, modeling of these dynamics has ranged from simplistic, potentially omitting key contributors to the observed phenomena, to highly complex, making usage in control schemes difficult.

The authors present nonlinear and linearized models that seek to both capture the capacitive and nonlinear resistive fluid elements of DW systems and to pose them as ordinary differential equations for integration into control schemes. The authors validate the theoretical study with experimental flow rate and material measurements across a range of extrusion nozzle sizes and materials. The authors explore the contribution of the system and build material bulk modulus to these dynamics.

The authors show that all tested models accurately describe the measured dynamics, facilitating ease of integration into future control systems. Additionally, the authors show that system bulk modulus may be substantially reduced through appropriate system design. However, the remaining build material bulk modulus is sufficient to require feedback control for accurate material delivery.

This study presents new nonlinear and linear models for DW printing dynamics. The authors show that linear models are sufficient to describe the dynamics, with small errors between nonlinear and linear models. The authors demonstrate control is necessary for accurate material delivery in DW.

Histopathology biopsy imaging is currently the gold standard for the diagnosis of breast cancer in clinical practice. Pathologists examine the images at various magnifications to identify the type of tumor because if only one magnification is taken into account, the decision may not be accurate. This study explores the performance of transfer learning and late fusion to construct multi-scale ensembles that fuse different magnification-specific deep learning models for the binary classification of breast tumor slides.

Three pretrained deep learning techniques (DenseNet 201, MobileNet v2 and Inception v3) were used to classify breast tumor images over the four magnification factors of the Breast Cancer Histopathological Image Classification dataset (40×, 100×, 200× and 400×). To fuse the predictions of the models trained on different magnification factors, different aggregators were used, including weighted voting and seven meta-classifiers trained on slide predictions using class labels and the probabilities assigned to each class. The best cluster of the outperforming models was chosen using the Scott–Knott statistical test, and the top models were ranked using the Borda count voting system.

This study recommends the use of transfer learning and late fusion for histopathological breast cancer image classification by constructing multi-magnification ensembles because they perform better than models trained on each magnification separately.

The best multi-scale ensembles outperformed state-of-the-art integrated models and achieved an accuracy mean value of 98.82 per cent, precision of 98.46 per cent, recall of 100 per cent and F1-score of 99.20 per cent.