Foreword by Guest Editors COL Paul F. Evangelista & LTC James H. Schreiner

This special issue of the Industrial and Systems Engineering Review once again showcases the top papers from the annual General Donald R. Keith memorial capstone conference at the United States Military Academy in West Point, NY. Despite continued COVID restrictions, the truly innovative conference included a mix of in-person presentations with over 50 live and remote judges from across academia and industry to create a high-quality event highlighting the undergraduate student team research. After consideration of over 50 academic papers, the eight listed in this issue were selected for publication in this special issue of the journal. The topics discussed are broad and diverse, however decision support within an uncertain and complex environment emerges as a theme. Much of the work completed by industrial and systems engineers focuses on getting decisions right by means of the tools of our trade. The suite of tools surveyed within these papers represents several state-of-the-art methods as well as time-proven techniques within a unique application domain. Military applications dominated several of the papers. Downey et al. studied massive datasets that represent military operational behaviors in training, seeking to better understand military operational capabilities. Ungrady and Dabkowski tackled the complexities of US Army recruiting through the application of fuzzy cognitive maps, searching for causation. Middlebrooks et al. studied military acquisition system decisions, applying system dynamics modeling. Process improvement represented another sub-theme, with continued focus on decision support. Enos et al. applied lean six sigma techniques to manufacturing processes. Katz et al. explored biomedical machine maintenance scheduling, seeking optimal solutions to a complex scheduling task. Kaloudelis et al. developed a pandemic decision support process for universities. Analytics and machine learning techniques applied to the information domain dominated the third sub-theme. Krueger and Enos developed analytics to support ice hockey strategies. Manzonelli et al. applied natural language processing against information operations, seeking to automate the examination of incredible amounts of narrative data that seek to shape beliefs and attitudes. Please join me in congratulating our authors, especially the young undergraduate scholars that provided the primary intellectual efforts that created the contents of this issue. COL Paul F. Evangelista Chief Data Officer United States Military Academy Taylor Hall, 5th Floor West Point, NY 10996 Email: [email protected] James H. Schreiner, PhD, PMP, CPEM, F.ASEM LTC(P), U.S. Army Associate Professor USMA Academy Professor Director, Engineering Management (EM) Program Department of Systems Engineering Head Officer Representative, Army Softball United States Military Academy Room 420 Mahan Hall West Point, NY 10996 Email: [email protected]

Technology and Policy: System Acquisition in a Complex Operational Environment

The United States’ (US) ability to maintain a technological edge in the current operational environment is challenged by the increased ability of near-peer nations to produce military technology. In response to this problem, the US Army Engineer Research and Development Center (ERDC) seeks to model the three key elements of military system acquisition—context, product, and process—to develop a more comprehensive understanding regarding how and why nations acquire technical solutions. Through the application of the System Dynamics Modeling Process (SDMP), this research examines the interactions between the strategic context of Germany, the military products it acquires to address its operational needs, and the processes it employs to acquire military technology. The results of this research indicate that numerous dynamic variables of context impact the acquisitions process for Germany, particularly political support and subsequent monetary allocations to research and development.

Data Analytics Development from Military Operational Data

Each year, the National Training Center (NTC) located at Fort Irwin, California, hosts multiple Brigade-level rotational units to conduct training exercises. NTC’s Instrumentation Systems (NTC-IS) digitally capture and store characteristics of movement and maneuver, use of fires, and other tactical operations in a vast database. The Army’s Engineer Research and Development Center (ERDC) recently partnered with Training and Doctrine Command (TRADOC) to make some of the data available for introductory analysis within a relational database. While this data has the potential to expose capability gaps, uncover the truth behind doctrinal assumptions, and create a sophisticated feedback platform for Army leaders at all levels, it is largely unexplored and underutilized. The purpose of this project is to demonstrate the value of this data by developing a prototype information system that supports post-rotation analytics, playback capabilities, and repeatable workflows that measure and expose ground-truth operational and logistical behavior and performance during a rotation. The Army modeling and analysis community will use these products to systematically curate and archive the database and enable future analysis of the NTC-IS data.

Fenwick and Corsi in Army Hockey, Incorporating “Shot-Zones” and Rebounds into Weighted Shots

Competitive mindsets and incentives for winning within our society generate an environment that forces athletes and organizations to continuously seek new opportunities to gain a competitive advantage. The Moneyball strategy is a great example of team’s thinking outside the box to improve their chances of winning. This strategy is when teams utilize analytics to determine which players hold the most value. In other words, which player will perform the best while also being minimally expensive to acquire. Building upon the Moneyball strategy, teams across sports today are using statistical data and metrics to analyze all facets of their respective sports in hopes of acquiring this edge. First implemented in baseball, other sports such as basketball and hockey are following in their footsteps while attempting to overcome the obstacle of having less data. The growth of analytics in hockey shows that these strategies are finding their way into the hockey environment.

Biomedical Machine Maintenance Scheduling

Tissue banks procure approximately 45,000 tissue donations per year, providing nearly 9,000,000 individuals (about half the population of New York) with life-enhancing and life-saving medical procedures. Proper biobank machine maintenance is imperative to this process. Mandatory forms of maintenance are critical to avoid unexpected malfunctions, which can halt operations and render samples unusable. Each machine has a unique reliability rate within the system; although some can quickly be repaired or replaced, many processes rely on limited machinery where even planned downtime can significantly influence the tissue processing. AlloSource, one of the largest tissue manufacturers in the United States, too often schedules these preventive events unnecessarily or inconveniently, resulting in machines breaking down at inopportune times. In response to these inefficiencies we ask, “What is the best consolidated and standardized equipment maintenance schedule that maximizes monthly maintenance events to ensure increased equipment availability while meeting the demand of the biomedical manufacturing network?” We use an optimization model to consider equipment reliability, downtime, availability, and demand to develop a preventive maintenance schedule. Our model focuses on scheduling the maximum number of events the maintenance crew can conduct each month to ensure vital equipment to the allograft process is available, which provides more opportunities for tissue therapies. In doing so, the maintenance crew is also able to complete more events, driving up annual throughput while driving down equipment downtime.

Exploring the Causal Relationship between Factors Affecting US Army Recruitment

Every year, United States Army Recruiting Command (USAREC) dedicates considerable resources to recruiting and accessing soldiers. As the largest branch of the United States Armed Forces, the Army must meet a high recruiting quota while competing in the free-labor market for quality recruits. Over the past two decades, the Army’s success in recruiting ebbed and flowed within the broader context of society and global events. While numerous studies have examined the statistical relationship between factors associated with recruitment, these studies are observational and definitively ascribing causality in retrospect is difficult. With this in mind, we apply fuzzy cognitive mapping (FCM), a graphical method of representing uncertainty in a dynamic system, to model and explore the complex causal relationships between factors. We conclude our paper with implications for USAREC’s efforts, as well as our model’s limitations and opportunities for future work.

Design of a University Pandemic Response Decision Support System

The global effort to combat the COVID-19 pandemic has changed how people conduct their daily lives. Institutions of higher education have been greatly impacted by these changes and must find ways to adapt to this new environment. Universities are a unique case because they must control disease spread, while maintaining the same or similar quality of education. The University Pandemic Response Decision Support System (UPRDSS) is a system designed to help universities pick the most suitable method for instruction delivery when faced with any pandemic. Using George Mason University as a case study, the goal was to design a system that allows university administrations to make an educated operations decision. The UPRDSS achieves this by simulating the spread of disease, analyzing learning outcome data, and using a multi-attribute utility function to determine the most appropriate method of instruction that enables positive learning and health outcomes.

Bending Metal: Improving Sheet Metal Repair at Tobyhanna Army Depot through Lean Six Sigma

The Army's Lean Six Sigma methodology includes five phases: Define, Measure, Analyze, Improve, and Control (DMAIC); each of these phases includes interaction between the stakeholder and process team. This paper focuses on the application of Lean Six Sigma methodology at Tobyhanna Army Depot to help reduce overruns and repair cycle time within the sheet metal cost center. At the initiation of the project, the process incurred over 4,000 hours of overruns, a situation in which it takes longer to repair an asset than the standard hours allocated for the repair. Additionally, the average repair cycle time, amount of time required to repair an individual asset, exceeded customer expectations by almost four days. The paper describes recommended solutions to address both problems.

Leveraging Publicly Available Information to Analyze Information Operations

Traditionally, a significant part of assessing information operations (IO) relies on subject matter experts’ time- intensive study of publicly available information (PAI). Now, with massive amounts PAI made available via the Internet, analysts are faced with the challenge of effectively leveraging massive quantities of PAI to draw meaningful conclusions. This paper presents an automated method for collecting and analyzing large amounts of PAI from China that could better inform assessments of IO campaigns. We implement a multi-model system that involves data acquisition via web scraping and analysis using natural language processing (NLP) techniques with a focus on topic modeling and sentiment analysis. After conducting a case study on China’s current relationship with Taiwan and comparing the results to validated research by a subject matter expert, it is clear that our methodology is valuable for drawing general conclusions and pinpointing important dialogue over a massive amount of PAI.

Analyzing the Manufacturing Supply Chain Performance for Urgent Item During COVID-19 Outbreak

As COVID-19 pandemic spreads in different regions with varying intensity, supply chains (SC) need to utilize an effective mechanism to adjust spike in both supply and demand of resources, and need techniques to detect unexpected behavior in SC at an early stage. During COVID-19 pandemic, the demand of medical supplies and essential products increases unexpectedly while the availability of recourses and raw materials decreases significantly. As such, the questions of SC and society survivability were raised. Responding to this urgent demand quickly and predicting how it will vary as the pandemic progresses is a key modeling question. In this research, we take the initiative in addressing the impact of COVID-19 disruption on manufacturing SC performance overwhelmed by the unprecedented demands of urgent items by developing a digital twin model for the manufacturing SC. In this model, we combine system dynamic simulation and artificial intelligence to dynamically monitor SC performance and predict SC reaction patterns. The simulation modeling is used to study the disruption propagation in the manufacturing SC and the efficiency of the recovery policy. Then based on this model, we develop artificial neural network models to learn from disruptions and make an online prediction of potential risks. The developed digital twin model is aimed to operate in real-time for early identification of disruptions and the respective SC reaction patterns to increase SC visibility and resilience.