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 variance between the winning bid and the owner's estimated cost (OEC) is one of the construction management risks in the pre-tendering phase. The study aims to enhance the quality of the owner's estimation for predicting precisely the contract cost at the pre-tendering phase and avoiding future issues that arise through the construction phase.

This paper integrated artificial neural networks (ANN), deep neural networks (DNN) and time series (TS) techniques to estimate the ratio of a low bid to the OEC (R) for different size contracts and three types of contracts (building, electric and mechanic) accurately based on 94 contracts from King Saud University. The ANN and DNN models were evaluated using mean absolute percentage error (MAPE), mean sum square error (MSSE) and root mean sums square error (RMSSE).

The main finding is that the ANN provides high accuracy with MAPE, MSSE and RMSSE a 2.94%, 0.0015 and 0.039, respectively. The DNN's precision was high, with an RMSSE of 0.15 on average.

The owner and consultant are expected to use the study's findings to create more accuracy of the owner's estimate and decrease the difference between the owner's estimate and the lowest submitted offer for better decision-making.

This study fills the knowledge gap by developing an ANN model to handle missing TS data and forecasting the difference between a low bid and an OEC at the pre-tendering phase.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.

The purpose of this paper is to explore the internal interaction mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth from a systematic perspective, based on which this work forecasts their future development trends.

In this study, a multivariate grey model is applied to the prediction of marine scientific research and education, industrial structure upgrading and marine economic growth. Considering the impact of the COVID-19 on marine development, this paper introduces the weakening buffer operator into MGM(1,m) and constructs the AWBO-MGM(1,m) model. To verify the validity and accuracy of the new model, this paper uses AWBO-MGM(1,m), MGM(1,m), GM(1,N), GM(1,1), back propagation neural network and linear regression models for simulation and prediction based on the data from 2010 to 2021, respectively.

From the theoretical perspective, the development of marine scientific research and education can accelerate industrial upgrading and promote marine economic growth by providing high-quality talents, promoting marine science and technology progress and reducing transaction costs; while the upgrading of marine industrial structure and marine economic growth can promote the development of marine scientific research and education by guiding social capital, enhancing talent demand and stimulating market vitality. From the empirical analysis, the AWBO-MGM(1,m) model can effectively deal with epidemic shocks and has higher fitting and prediction accuracy than the other five comparative models.

The government should pay attention to the construction of marine scientific research and education, so as to provide high-quality talents and advanced scientific research results for the high-quality development of marine economy. On the basis of using science and technology to firmly build the primary and secondary marine industries, the government should actively guide the labor, capital and other factors of production to the tertiary industry, thereby promoting the optimization and upgrading of marine industrial structure.

On the one hand, the interplay mechanism of marine scientific research and education, industrial structure upgrading and marine economic growth is analyzed from a systematic perspective; on the other hand, the enhanced AWBO-MGM(1,m) possesses higher forecasting performance and is applicable to the systemic multivariate forecasting problem in the presence of outstanding external shocks.

This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

In this paper, an emerging state-of-the-art machine intelligence technique called the Hierarchical Temporal Memory (HTM) is applied to the task of short-term load forecasting (STLF). A HTM Spatial Pooler (HTM-SP) stage is used to continually form sparse distributed representations (SDRs) from a univariate load time series data, a temporal aggregator is used to transform the SDRs into a sequential bivariate representation space and an overlap classifier makes temporal classifications from the bivariate SDRs through time. The comparative performance of HTM on several daily electrical load time series data including the Eunite competition dataset and the Polish power system dataset from 2002 to 2004 are presented. The robustness performance of HTM is also further validated using hourly load data from three more recent electricity markets. The results obtained from experimenting with the Eunite and Polish dataset indicated that HTM will perform better than the existing techniques reported in the literature. In general, the robustness test also shows that the error distribution performance of the proposed HTM technique is positively skewed for most of the years considered and with kurtosis values mostly lower than a base value of 3 indicating a reasonable level of outlier rejections.

While previous studies focused mainly on East Asia to Europe or United States trade routes, in recent years, trade among South-East Asian countries has increased notably. The price of transporting a container is not fixed and can fluctuate heavily over the course of a week. Besides, extant literature only identified seasonality patterns in the container freight market, but did not explore route-varying seasonality patterns. Hence, this study analyses container freight seasonality patterns of the six South-East Asian routes of the South-East Asian Freight Index (SEAFI) and the index itself and forecasts them.

Data of the composite SEAFI and six routes are collected from the Shanghai Shipping Exchange (SSE) including 167 weekly observations from 2016 to 2019. The SEAFI and individual route data reflect spot rates from the Shanghai Port to South-East Asia base ports. The authors analyse seasonality patterns using polar plots. For forecasting, the study utilize two univariate models, autoregressive integrated moving average (ARIMA) and seasonal autoregressive neural network (SNNAR). For both models, the authors compare forecasting results of original level and log-transformed data.

This study finds that the seasonality patterns of the six South-East Asian container trade routes are identical in an overall but exhibits unique characteristics. ARIMA models perform better than SNNAR models for one-week ahead test-sample forecasting. The SNNAR models offer better performance for 4-week ahead forecasting for two selected routes only.

Major industry players such as shipping lines, shippers, ship-owners and others should take into account the route-level seasonality patterns in their decision-making. Forecast analysts can consider using the original level data without log transformation in their analysis. The authors suggest using ARIMA models in one-step and four-step ahead forecasting for majority of the routes. The SNNAR models are recommended for multi-step forecasting for Shanghai to Vietnam and Shanghai to Thailand routes only.

This study analyses a new shipping index, that is, the SEAFI and its underlying six routes. The authors analyze the seasonality pattern of container freight rate data using polar plot and perform forecasting using ARIMA and SNNAR models. Moreover, the authors experiment forecasting performance of log-transformed and non-transformed series.

This study aims to investigate the possible use of a deep neural network (DNN) as an inverse solver.

Different models based on DNNs are designed and proposed for the resolution of inverse electromagnetic problems either as fast solvers for the direct problem or as straightforward inverse problem solvers, with reference to the TEAM 25 benchmark problem for the sake of exemplification.

Using DNNs as straightforward inverse problem solvers has relevant advantages in terms of promptness but requires a careful treatment of the underlying problem ill-posedness.

This work is one of the first attempts to exploit DNNs for inverse problem resolution in low-frequency electromagnetism. Results on the TEAM 25 test problem show the potential effectiveness of the approach but also highlight the need for a careful choice of the training data set.