Journal of Defense Analytics and Logistics

Purpose The rapid advancement of new technologies necessitates the adaptation of existing resources to address the evolving demands of contemporary conflicts among nations. As exemplified by the emergence of Unmanned Aerial Systems (UAS) and Unmanned Aerial Vehicles (UAVs), these technologies have become indispensable components of military drone operations. This study investigates the role of innovation models in driving Portuguese Innovation and Development, focusing specifically on military drones. Design/methodology/approach Through an examination of existing scientific and doctrinal literature, this research establishes a connection between theoretical concepts and the practical deployment of drones within the Portuguese ground forces. Employing a qualitative approach, this study follows a case study, with data collection following the principle of triangulation. Findings The findings of this research reveal several attributes crucial to drone utilization, such as optronic capability and drone range. These attributes are vital for ensuring high-quality, real-time imagery transmission and operational effectiveness in military activities. Originality/value This study contributes to the understanding of the implications of drone attributes for the Command, Control, Communications and Information Systems (C3IS) capabilities of the Portuguese ground forces. It highlights the importance of continued emphasis on drone development and innovation as a key priority for the Portuguese Army.

João Pedro Barros

João Reis

Nuno Melão

Adriane Cavalieri

Key features and applications of military drones: a case study from the Portuguese military ground forces

葡萄牙陆军地面部队军用无人机的主要特点与应用：一项案例研究

We are pleased to present this inaugural issue of the Journal of Defense Analytics and Logistics (JDAL).The JDAL will promote and foster discussion and proliferation of new theory and applications across the logistics, operations research, data analytics and supply chain management space, particularly as that space intersects with the defense community.One might ask why introduce this journal now with the plethora of publishing outlets that are already available.The answer is that the community still lacks an outlet for rigorously reviewed, high quality work focused on the unique analytical and logistical challenges faced by the defense community.To this end, we will bring together key research and practical applications, making the work available not only to the academic community but also to the defense community that this research supports.Defense analytics supports a wide range of theory and applications.The growth of operations research since the Second World War has been quite dramatic.Early works firmly established quantitative approaches based on mathematical programming and probabilistic modeling, as well as modeling and simulation and statistical modeling.Current "buzz words" (such as big data and machine learning) represent the new "toolisms" that will dominate defense analysis and discussion for some time.The JDAL welcomes work in both the old and the new techniques, particularly as those works help evolve the defense establishment toward the beneficial employment of these analytical techniques.Governments across the globe spend a combined US$1.76tn on defense.For instance, with an annual budget of nearly US$600bn, the US Department of Defense (DoD) represents the largest government expenditure in the world, representing 54 per cent of discretionary US spending in 2015.Indeed, the US DoD alone manages a US$309bn supply chain, and several of the largest public companies in the world support the global defense industry.Since the dawn of human conflict, logistics capabilities and supply chain management have been used to enable defense power projection.Modern supply chains incorporate information technology, resiliency and other technological and administrative innovations to meet the challenges of an ever-changing military-industrial complex.As the digitization of the supply chain becomes a reality, there is also a need to incorporate data science and analytical breakthroughs to provide greater supply chain insights and enhanced decisionmaking abilities.A focus on these latest technologies is encouraged.That said, the JDAL will be looking for cutting-edge theories and implementations in any area impacting the defense-industrial logistics complex.We developed the JDAL review process for regular research papers around a doubleblind review process; neither the authors nor the reviewers are made aware of the other's identity during the review process.This approach removes potential biases in the review process, thereby retaining quality and objectivity.At JDAL, we believe in a traditional peerreview process versus the convoluted and inefficient "peer-writing" process that seems to be used by many top journals today.We wish to report research findings based on the

Ben Hazen

Raymond R. Hill

Editorial: Welcome to the Journal of Defense Analytics and Logistics

编者按：欢迎阅读《国防分析与物流杂志》

Purpose The purpose of this paper is to demonstrate an improved method for forecasting the US Army recruiting. Design/methodology/approach Time series methods, regression modeling, principle components and marketing research are included in this paper. Findings This paper found the unique ability of multiple statistical methods applied to a forecasting context to consider the effects of inputs that are controlled to some degree by a decision maker. Research limitations/implications This work will successfully inform the US Army recruiting leadership on how this improved methodology will improve their recruitment process. Practical implications Improved US Army analytical technique for forecasting recruiting goals.. Originality/value This work culls data from open sources, using a zip-code-based classification method to develop more comprehensive forecasting methods with which US Army recruiting leaders can better establish recruiting goals.

Joshua L. McDonald

Edward D. White

Christian Pardo

Forecasting US Army enlistment contract production in complex geographical marketing areas

预测复杂地理营销区域中的美国陆军入伍合同生产

Purpose This paper aims to use a data-driven approach to identify the factors and metrics that provide the best indicators of academic attrition in the Korean language program at the Defense Language Institute Foreign Language Center. Design methodology approach This research develops logistic regression models to aid in the identification of at-risk students in the Defense Language Institute’s Korean language school. Findings The results from this research demonstrates that this methodology can detect significant factors and metrics that identify students at-risk. Additionally, this research shows that school policy changes can be detected using logistic regression models and stepwise regression. Originality value This research represents a real-world application of logistic regression modeling methods applied to the problem of identifying at-risk students for the purpose of academic intervention or other negative outcomes. By using logistic regression, the authors are able to gain a greater understanding of the problem and identify statistically significant predictors of student attrition that they believe can be converted into meaningful policy change.

Adam Christian Haupt

Jonathan Alt

Samuel Buttrey

Identifying students at risk in academics

识别学术上存在风险的学生

The concept of automating the testing of software-intensive systems has been around for decades, but the practice of automating testing is scarce in many industries, especially in the government defense sector.A one-year project initiated by the Office of the Secretary of Defense (OSD), Scientific Test and Analysis Techniques Center of Excellence (STAT COE) and sponsored by Navy OPNAV N94 set out to: study the degree to which the Department of Defense (DoD) has adopted automated software testing (AST); share the best software practices used by industry; and develop and distribute an AST implementation guide intended for program management and novice DoD software test automators.

Darryl Ahner

James Wisnowski

James R. Simpson

Automated software testing in the DoD: current practices and opportunities for improvement

国防部自动化软件测试：当前实践与改进机会

Purpose This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&amp;A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&amp;A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer. Design/methodology/approach The study uses panel data regression models on 40 companies between 1985 and 2021. The company's financial health is assessed using industry-standard financial ratios (i.e. measures of profitability, efficiency, solvency and liquidity) while controlling for economic factors such as national productivity, defense budgets and firm size. Findings The results show a significant relationship between efficiency and M&amp;A spending, indicating that companies with lower efficiency tend to spend more on M&amp;As. However, there was no significant relationship between M&amp;A spending and a company's profitability or solvency. These results were consistent with previous research and the study's hypotheses for profitability and solvency. However, the effect of liquidity was the opposite of the expected result, possibly due to the defense industry's different view on liquidity compared to previous research. Originality/value The paper provides insights into the relationship between a prime contractor's financial health and its M&amp;A spending, a topic with limited research. The findings can inform policymakers and regulators on the industrial base's future M&amp;A activity, ensuring competitive markets that benefit the consumer.

Corey Mack

Clay Koschnick

Michael Brown

Jonathan D. Ritschel

Brandon Lucas

A panel data regression model for defense merger and acquisition activity

防御并购活动的面板数据回归模型

Purpose In light of the recently experienced systemic shocks (the COVID-19 pandemic and the war in Ukraine), we investigate supply chain robustness. We aim to understand the potential consequences of uncertain events or adversary’s action on critical supplies in the Alliance. Design/methodology/approach We leverage a parsimonious supply chain model and investigate the relationship between upstream supplier concentration/diversification and the supply chain’s robustness (survival probability) in the presence of uncertain systemic shocks. In several scenarios of shock events, we simulate alternative input sourcing strategies in the presence of uncertainty. Findings A firm-level cost-focused optimisation may lead all upstream suppliers to concentrate in one location, which – when subsequently hit by a shock – would result in a disruption of the entire supply chain. A chain-level forward-looking optimisation diversifies the upstream supplier location and sourcing decisions. As a result, the supply chain’s survival probability is maximised, and critical supplies will continue even under the most demanding circumstances. Research limitations/implications Our findings encourage political and military decision makers to enhance upstream supply chain robustness in critical and strategic sectors, such as the diversification of nitrocellulose supplies currently sourced almost exclusively from China by European gunpowder manufacturers. Practical implications Our findings have direct recommendations to supply chain downstream decision makers and to the government’s policy choices. Since global supply chain (GSC) disruptions in critical sectors may have catastrophic impacts on social welfare and the probability of shocks such as COVID-19 and Russia’s war may not be known even approximately, robust decision rules seem to be the appropriate tools for policymaking in critical and strategic sectors such as energy supplies, food and water, communication and defence. A robust supply chain is one in which the survival probability is maximised, which we show in a central planner strategy’s simulations. Originality/value The paper shows formally why a market-based global input sourcing strategy may be efficient from an individual firm’s perspective but may be suboptimal from a societal resilience perspective.

d'Artis Kancs

Enhancing resilience: model-based simulations

增强韧性：基于模型的模拟

Purpose Considering the size and intricate nature of defense supply chains (DSC), there exists a need for a conceptual understanding regarding the precise dynamics of collaboration among the various participants engaged in these chains. This paper seeks to address the gap by investigating the practices that enable or inhibit collaborations and the development of new competencies to effectively employ a flexible response to temporary or more sustained surges in demand. Ultimately, the study aims to develop a theoretical framework relevant to the practical implementation and scholarly examination of contemporary military supply chains. Design/methodology/approach Semi-structured interviews were conducted with 51 DSC professionals in 7 embedded cases within an enterprise framework. The resulting transcripts were analyzed using constructs and concepts from a supply chain logistics (SC/L) literature analysis and synthesis relevant to our research purpose. Finally, the results were validated by an industry focus group with 12 participants representing the government, military, industry, and academia. Findings This research produced empirical generalizations that provide in-depth and systematic exploratory insights into collaboration’s meaning and characteristics within the DSC context. This study culminates by introducing a conceptual model and definition of defense supply chain collaboration (DSCC) and concludes by proposing future research directions. Originality/value This study makes a novel and empirical contribution to the SC/L body of knowledge by investigating embedded cases through unique access to informants within an enterprise framework that focuses on the antecedent influencing factors of collaboration within the contextual domain of the DSC and positions a future research agenda.

David Loska

Stefan Genchev

Nicholas Rich

Tegwen Malik

Defense supply chain collaboration: an exploratory study and empirical framework

防御供应链协作：探索性研究与实证框架

Purpose The present paper assesses the decision problem of selecting Unmanned Aerial Vehicle Systems (SARP) by the hybrid MPSI-SPOTIS approach for deployment in border control and transborder illicit combat. Design/methodology/approach By the hybrid MCDA MPSI-SPOTIS approach, and from the database available in Gettinger (2019), models were filtered by Endurance, Range, Maximum Take-Off Weight (MTOW), and Payload, fitting within the classification of Categories EB 0 and 2. Category EB 1 was not considered in this study due to the limited number of models in the data source. Findings The use of the Multi-Criteria Decision Analysis (MCDA) tool MPSI-SPOTIS allowed the determination of weights by stochastic criteria, applied in a ranking method resistant to reverse ordering. The application of the method identified the Raybird-3 (Cat EB 0) and Searcher (Mk3) (Cat EB 2) models as the best alternatives. From a proposed clustering, other selection possibilities with close performance in the evaluation were presented. The cost criterion was not taken into consideration due to the absence of information in the data source employed. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models. Research limitations/implications The cost criterion was not taken into consideration due to the absence of information in the data source used. Future studies are suggested to include criteria related to the life cycle and acquisition cost of the models. Originality/value This paper aims to propose a technology selection method applied to complex defense acquisitions when multiple factors influence the decision makers and it is hard to obtain a major optimum solution in multitask and multi-mission platform.

Pablo Santos Torres

Carlos Francisco Simões Gomes

Marcos dos Santos

Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method

使用MPSI-SPOTIS方法选择用于边境监控的无人机系统

涉及主题	计算机科学工程类数学经济法学政治学业务运筹学统计心理学运营管理营销社会心理学社会学地理创造力独创性定性研究人类学社会科学历史医学管理考古哲学机器学习程序设计语言人工智能物理生物操作系统热力学认识论供应链数学优化量子力学
出版信息	出版商: Emerald Publishing，出版周期: ，期刊类型: journal，开源期刊: 是
基本数据	创刊年份: 2017，原创研究文献占比: 0.00%，自引率:N.A.， Gold OA占比: 0.00%
平均审稿周期	网友分享经验：12 Weeks

Journal of Defense Analytics and Logistics

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享

Key features and applications of military drones: a case study from the Portuguese military ground forces

Editorial: Welcome to the Journal of Defense Analytics and Logistics

Forecasting US Army enlistment contract production in complex geographical marketing areas

Identifying students at risk in academics

Automated software testing in the DoD: current practices and opportunities for improvement

Key features and applications of military drones: a case study from the Portuguese military ground forces

A panel data regression model for defense merger and acquisition activity

Enhancing resilience: model-based simulations

Defense supply chain collaboration: an exploratory study and empirical framework

Selection of unmanned aerial vehicle systems for border monitoring using the MPSI-SPOTIS method

ISSN	2399-6439, 2399-6447
h-index	4