Search results

The study aims to investigate the impact of additive manufacturing (AM) on the performance of a spare parts supply chain with a particular focus on underlying spare part demand patterns.

This work evaluates various AM-enabled supply chain configurations through Monte Carlo simulation. Historical demand simulation and intermittent demand forecasting are used in conjunction with a mixed integer linear program to determine optimal network nodal inventory policies. By varying demand characteristics and AM capacity this work assesses how to best employ AM capability within the network.

This research assesses the preferred AM-enabled supply chain configuration for varying levels of intermittent demand patterns and AM production capacity. The research shows that variation in demand patterns alone directly affects the preferred network configuration. The relationship between the demand volume and relative AM production capacity affects the regions of superior network configuration performance.

This research makes several simplifying assumptions regarding AM technical capabilities. AM production time is assumed to be deterministic and does not consider build failure probability, build chamber capacity, part size, part complexity and post-processing requirements.

This research is the first study to link realistic spare part demand characterization to AM supply chain design using quantitative modeling.

Introducing additive manufacturing (AM) in a multinational corporation with a global spare parts operation requires tools for a dynamic supplier selection, considering both cost and delivery performance. In the switchover to AM from conventional manufacturing, the objective of this study is to find situations and ways to improve the spare parts service to end customers.

In this explorative study, the authors develop a procedure – in collaboration with the spare parts operations managers of a case company – for dynamic operational decision-making for the selection of spare parts supply from multiple suppliers. The authors' design proposition is based on a field experiment for the procurement and delivery of 36 problematic spare parts.

The practice intervention verified the intended outcomes of increased cost and delivery performance, yielding improved customer service through a switchover to AM according to situational context. The successful operational integration of dynamic additive and static conventional supply was triggered by the generative mechanisms of highly interactive model-based supplier relationships and insignificant transaction costs.

The dynamic decision-making proposal extends the product-specific make-to-order practice to the general-purpose build-to-model that selects the mode of supply and supplier for individual spare parts at an operational level through model-based interactions with AM suppliers. The successful outcome of the experiment prompted the case company to begin the introduction of AM into the company's spare parts supply chain.

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 review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

The purpose of this paper is to highlight the opportunity for the energy policy in Brazil to tackle the very high cost-effectiveness potencial of solar energy to the power system. Three mechanisms to achieve ambitious reductions in the greenhouse gas emissions from the power sector by 2030 and 2040 are assessed wherein treated as solar targets under ambitious reductions in the greenhouse gas emissions from the power sector. Then, three mechanisms to achieve these selected solar targets are suggested.

This paper reviews current and future incentive mechanisms to promote solar energy. An integrated energy system optimization model shows the most cost-efficient deployment level. Incentive mechanisms can promote renewable sources, aiming to tackle climate change and ensuring energy security, while taking advantage of endogenous energy resources potential. Based on a literature review, as well as on the specific characteristics of the Brazilian power system, under restrictions for the expansion of hydroelectricity and ambitious limitation in the emissions of greenhouse gases from the power sector.

The potential unexploited of solar energy is huge but it needs the appropriate incentive mechanism to be deployed. These mechanisms would be more effective if they have a specific technological and temporal focus. The solar energy deployment in large scale is important to the mitigation of climate change.

The value of the research is twofold: estimations of the cost-effective potential of solar technologies, generated from an integrated optimization energy model, fully calibrated for the Brazilian power system, while tacking the increasing electricity demand, the expected reduction of greenhouse gas emissions and the need to increase the access to clean and affordable energy, up to 2040; proposals of three mechanisms to deploy centralized PV, distributed PV and solar thermal power, taking the best experiences in several countries and the recent Brazilian cases.

The challenges of the growth of the sharing economy are becoming more and more noticeable and urgent, especially concerning labor relations (e.g. uberization). The purpose of this paper is to understand what app-based drivers think of working conditions and labor relations.

The research was carried out in three stages: bibliographical and documental research, and two empirical research, a quantitative one with the application of a questionnaire in a sample of 54 respondents and another qualitative one using an interview script with ten drivers. For data analysis, the abductive method and the content analysis technique were used.

The results reveal they have an exhausting labor routine, by checking that they work more hours per week than those who have a formal job. They are driven mainly by the extra income and flexibility that digital platforms of the sector of shared private transportation can offer, although the costs intrinsic to the activity often affect their revenues significantly.

The number of answers from women was very small, which hinders the analysis of the potential specificities of women app-based drivers. Future studies could focus on this public for a more precise analysis, to bring the discussion on gender to the working context of app-based drivers.

The authors’ intention with the research reports was to make them relevant, leading to effective policies concerning working conditions and labor relations in the sharing economy, and to stimulate other surveys to understand the activity of an app-based driver of shared private transportation.

The authors’ research and this article contribute to the discussion on new work relationships, motivations and (dis)satisfaction with the activity, from the perspective of app-based drivers.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

Inventory accumulation is a major problem for any organization, as it not only occupies the valuable storage space, but it also blocks the company's capital, leaving the owners with less cash to run the company's business. Aggregation of inventory in any organization contributes to inventory carrying cost; it affects labor productivity, increases equipment expenses and creates a loss of opportunity associated with it. Therefore, it is essential for any organization to come up with a solution to deal with the stockpile of inventory.

This research aims to examine the potential causes of inventory aggregation in an organization. First, the potential factors for the build-up of inventory are identified from survey data collection, such as questionnaire approach and discussion with industry experts, and then weights are assigned to attributes to study the effects for these factors. After the identification of probable causes, they are analyzed through a multi-criterion decision-making (MCDM) approach and the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritize the severity of these causes toward the accumulation of inventory and take corrective actions to prevent their disruptive effect on the business.

The top three causes identified from the TOPSIS analysis are sales and forecasting error, defects and quality related issues and communication gap between departments. Firstly, we focus on these major contributors and prioritize them using the TOPSIS analysis. Then, we proceed further toward other factors. The main reasons identified for the accumulation of inventory are (1) forecasting error, (2) bulk purchase, (3) data entry error, (4) communication gaps, (5) quality-related issues, (6) product category not traceable and (7) wrong material being procured.

To carry out the data analysis in this research paper, first survey data collection is done. Then, discussions with managers and executives in the particular domain are carried out, and weights are assigned to the attributes and the criteria to study the effects of the identified factors. After that root cause analysis (RCA) is performed to get to the genesis of the problem and to take necessary corrective action, for carrying out this study, a total of seven potential causes were identified and the contribution of these seven causes on five attributes or criteria, i.e. quantity (in tons), holding and carrying cost, effect on labor productivity, loss of opportunity cost and storage space were studied.

This research paper is the author’s original work, and all the analyses carried out are from the discussion with experts in the field and through the in-depth analysis carried out. This research aims to examine the potential causes of the accumulation of inventory in organizations and their contribution toward factors like inventory carrying cost, labor productivity, and opportunity loss and excessive storage space have been analyzed. This research provides great value to the readers in the respective domain.

This study examines the causal relationship between exports and economic growth in sub-Saharan African (SSA) countries during the period 1980 to 2017. The study also examines whether the causality between these two macroeconomic variables depends on the countries' stage of development as proxied by their per capita income.

The study uses a panel cointegration test and panel Granger-causality model to examine the link between exports and growth. The study also incorporates external debt as an intermittent variable in a bivariate setting between exports and economic growth, thereby creating a dynamic multivariate panel Granger-causality model.

Although the study found the existence of a long-run relationship between exports and economic growth, the study failed to find any export-led growth response in both low-income and middle-income countries. Instead, the study found evidence of a bidirectional causality and a neutrality response in middle-income and low-income countries, respectively. The study, therefore, concludes that the benefits of an export-led growth hypothesis may have been oversold, and that the strategy may not be desirable to some low-income developing countries.

These findings have important policy implications as they indicate that the causality between exports and economic growth in SSA countries varies with the countries' stage of development. Consistent with the contemporary literature, the study cautions low-income SSA countries against over-relying on an export-led growth strategy to achieve a sustained growth path as no causality between exports and economic growth has been found to exist in those countries. Instead, such countries should consider pursuing new growth strategies by building the domestic demand side of their economies alongside their export promotion strategies in order to expand the real sector of their economies. For middle-income countries, the study recommends that both export promotion strategies and pro-growth policies should be intensified as economic growth and exports have been found to reinforce each other in those countries.

Unlike the previous studies, the current study disaggregated the full sample of SSA countries into two subsets – one comprising of low-income countries and the other consisting of middle-income countries. In addition, the study uses a multivariate Granger-causality model in order to address the emission-of-variable bias. To our knowledge, this may be the first study of its kind in recent years to examine in detail the causal relationship between exports and economic growth in SSA countries using an ECM-based multivariate panel Granger-causality model.

本研究旨在探討在1980年至2017年期間撒哈拉以南非洲國家的出口、與其經濟增長之間的因果關係，亦探討這兩個宏觀經濟變量之間的因果關係、會否取決於有關國家所處以人均收入來衡量的發展階段。

本研究雖然發現出口與經濟增長存有一個長期性關係，唯未能於低收入國家或中等收入國家、找到任何出口帶動的增長反應。研究反而找到證據，證實中等收入國家為一雙向性因果關係反應，而低收入國家則為一中立性反應。因此，研究的結論是：出口必能帶動經濟增長這假設被過度吹噓，而且，對部份低收入發展中國家而言，實施以出口帶動經濟增長的策略或許是沒有用的。

本研究的結果在政策方面有其重要意義。這是因為研究結果顯示、於撒哈拉以南非洲國家、出口與經濟增長之間的因果關係，會因有關國家所處的發展階段而有所變更。與當代文獻一樣，本研究提醒低收入的撒哈拉以南非洲國家，不要過度依賴以出口帶動增長的策略來謀求踏上持續增長之路，這是因為在這些國家，出口與經濟增長之間的因果關係仍未確立。他們反而應考慮推行新增長經濟策略，方法是在實施推動出口的策略的同時，也要建立其經濟的國內需求面，以擴大其經濟實業部門。就中等收入國家而言，本研究建議他們應增強推動出口的策略及強化促進增長的政策，這是因為在這些國家裏，經濟增長及出口已被證實會互為增強。

有別於過去的研究，本研究把撒哈拉以南非洲國家的整體樣本分解為兩個子集：一個包括低收入國家，另一個則包括中等收入國家。而且、研究使用了多變量面板格蘭傑因果關係模型、以處理遺漏變數偏差的問題。據我們了解，這大概是近年首個同類研究、以基於歐洲共同市場多變量面板格蘭傑因果關係模型、來詳細探討於撒哈拉以南非洲國家、出口與經濟增長之間的因果關係。