Evaluating Smartphones for Infrastructure Work Order Management

Infrastructure managers require timely and accurate state information to diagnose, prioritize, and repair the substantial infrastructure assets supporting modern society. Challenges in obtaining sufficient information can often be attributed to inadequate data collection procedures (phone calls, paper reports, etc.) or a general lack of knowledge or ability on the part of the reporting individual to accurately convey what is actually wrong with the facility. Fortunately, modern smart-phone technology offers the potential to improve maintenance work requests by providing better geolocation and problem description accuracy. An experiment simulating real-world maintenance requests was conducted comparing smart-phones with traditional verbal work order request systems. Usefulness and description accuracy ratios revealed smartphone systems generated more useful information regardless of submitter background or experience. However, interestingly the smart-phone applications did not improve asset geolocation and actually negatively impacted the ability of maintenance personnel to accurately relocate the asset needing service. Given the ubiquitous nature of smartphone technology, the potential exists to turn any citizen into an infrastructure sensor. This study takes a step toward understanding the benefits, as well as the limitations, of the smart-phone based work order submission systems

Establishing the Foundations to Measure Organizational Agility for Military Organizations

There is an ongoing demand for organizations to become more agile in order to prosper amongst their competitors. Many military organizations have declared a renewed focus towards organizational agility. The goal of this research is to isolate the variables needed to measure organizational agility (OA) in military organizations, allowing for the future development of a suitable method to measure OA without the need to interact with outside organizations. This article begins by providing a suitable and formal definition of organizational agility by exploring and analyzing relevant scholarly literature on the subject. Related terms, such as organizational resiliency, flexibility, robustness, versatility, and adaptability are also explored to examine their definition boundaries and any overlapping areas. Existing methods to measure organizational agility are examined and summarized, and the current limitations to their application are highlighted. Previous studies to find characteristics associated with organizational agility were also examined, and an initial set of 88 organizational agility characteristics was built. Since these included possible redundant or overlapping characteristics, the Q-sort method was employed to discover, analyze, and eliminate redundant items from the dataset, ultimately resulting in 64 unique characteristics. The result is a suitable definition for organization agility applicable to military organizations and a list of potential associated characteristics that summarizes related research to date. This groundwork establishes the foundation to conduct a multi-organization study to further refine the characteristic list and ultimately develop a method to measure organizational agility

The Impact of Learning Curve Model Selection and Criteria for Cost Estimation Accuracy in the DoD

The first part of this manuscript examines the impact of configuration changes to the learning curve when implemented during production. This research is a study on the impact to the learning curve slope when production is continuous but a configuration change occurs. Analysis discovered the learning curve slope after a configuration change is different from the stable learning curve slope pre-configuration change. The newly configured units were statistically different from previous units. This supports that the new configuration should be estimated with a new learning curve equation. The research also discovered the post-configuration slope is always steeper than the stable learning slope. Secondly, this research investigates flattening effects at tail of production. Analysis compares the conventional and contemporary learning curve models in order to determine if there is a more accurate learning model. Results in this are inconclusive. Examining models that incorporate automation was important, as technology and machinery play a larger role in production. Conventional models appear to be most accurate, although a trend for all models appeared. The trend supports that the conventional curve was accurate early in production and the contemporary models were more accurate later in production

Acquisition Challenge: The Importance of Incompressibility in Comparing Learning Curve Models

The Department of Defense (DoD) cost estimating methodology currently employs T. P. Wrights 75-plus-year-old learning curve formula. The goal of this research was to examine alternative learning curve models and determine if a more reliable and valid cost estimation method exists, which could be incorporated within the DoD acquisition environment. This study tested three alternative learning models (the Stanford-B model, DeJong\u27s learning formula, and the S-Curve model) to compare predicted against actual costs for the F-15 A-E jet fighter platform. The results indicate that the S-Curve and DeJong models offer improvement over current estimation techniques, but more importantly and unexpectedly highlight the importance of incompressibility (the amount of a process that is automated) in learning curve estimating

Sum-Based Scoring for Dichotomous and Likert-scale Questions

In this article we investigate how to score a dichotomous scored question when co-mingled with a typically scored set of Likert scale questions. The goal is to find the upper value of the dichotomous response such that no single question is overly weighted when analyzing the summed values of the entire set of questions. Results demonstrate that setting the upper value of the dichotomous value to the max value of the Likert scale question scale is inappropriate. We provide a more appropriate value to use when considering Likert scale questions up to the max value of 10.Comment: 7 pages, 1 Tabl

A Learning Curve Model Accounting for the Flattening Effect in Production Cycles

We investigate production cost estimates to identify and model modifications to a prescribed learning curve. Our new model examines the learning rate as a decreasing function over time as opposed to a constant rate that is frequently used. The purpose of this research is to determine whether a new learning curve model could be implemented to reduce the error in cost estimates for production processes. A new model was created that mathematically allows for a “flattening effect,” which typically occurs later in the production process. This model was then compared to Wright’s learning curve, which is a popular method used by many organizations today. The results showed a statistically significant reduction in error through the measurement of the two error terms, Sum of Squared Errors and Mean Absolute Percentage Error

Cost Estimating Using a New Learning Curve Theory for Non-Constant Production Rates

Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced. However, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, namely Boone’s learning curve, was recently developed to model this phenomenon. This research confirms that Boone’s learning curve systematically reduced error in modeling observed learning curves using production data from 169 Department of Defense end-items. However, high amounts of variability in error reduction precluded concluding the degree to which Boone’s learning curve reduced error on average. This research further justifies the necessity of a diminishing learning rate forecasting model and assesses a potential solution to model diminishing learning rates

Trust in a virtual workplace: A multi-level model examining implications of virtualness and the link to performance and commitment

This dissertation introduces a theory and presents a model that examines virtualness as an antecedent to key individual and organizational outcomes. In the model, virtualness is examined as a predictor of both trust and self-efficacy with performance and affective commitment as key outcomes. I examine a multi-dimensional view of trust and whether it can predict different cognitive and affective outcomes. Additionally, I introduce a multi-level model which incorporates justice climate level as a group-level (level 2) moderator on the individual-level relationships. This dissertation attempts to resolve gaps and clear up inconsistencies in the virtualness literature as well as enhance our understanding of trust and self-efficacy by examining organization-level effects on individual-level relationships. The multi-level model of virtualness, self-efficacy, and trust is examined in the context of the United States Air Force, where individuals, groups, and squadrons experience a large variance in virtualness, and where trust and self-efficacy are critical to mission accomplishment

Operator Suspicion and Human-Machine Team Performance under Mission Scenarios of Unmanned Ground Vehicle Operation

Emergent cyber-attack threats against cyber-physical systems can create potentially catastrophic impacts. The operators must intervene at the right moment when suspected attacks occur, without over-reliance on systems to detect the cyber-attacks. However, military operators are normally trained to trust, rather than suspect systems. We applied suspicion theory to explore how operators detect and respond to cyber-attacks against an unmanned ground vehicle (UGV) system in the operational context of a human-machine team (HMT). We investigated the relationships between the operator suspicion and HMT performance by conducting human-in-the-loop experiments on eight mission scenarios with 32 air-force officers. The experiment yielded a significant, negative relationship between operator suspicion and HMT performance (quantified both in terms of the desirability of decision response and the time to respond). Notably, operator suspicion increased with the combined effects of cyber-attacks and a sentinel alert but not with the alert alone. This finding was particularly meaningful for “false-negative” scenarios, in which no sentinel alert was sent despite cyber-attacks having occurred. Although the operators did not receive an alert, the operators grew more suspicious, seeking more information; it took longer for the operators to respond, and their decision responses were highly divergent (17.2% came with less-desirable responses, and 21.9% were considered instances of over-reliance). In contrast, in “false-positive” scenarios, 95.3% of the operator responses were highly desirable. This experiment has implications for the role of a sentinel alert in engineering trustworthy HMT systems so that the operators can quickly transition through state-suspicion to the most desirable decision

An Inexpensive Workplace Initiative to Motivate High-Risk Individual Health Improvement

Unhealthy lifestyles cost businesses, governmental organizations, and the U.S. military billions of dollars every year, not to mention intangible costs associated with increased mortality. This study implemented a low-cost cognitive-behavioral motivational intervention to effect behavioral change in high-risk civilian employees working for a U.S. military organization, with accompanying improvement in certain health indicators after 120 days compared with a control group. Our analysis of these results led to two conclusions: first, low-cost cognitive-behavioral motivational treatments can improve both behavior and health, and second, tentative results indicate a fully mediated relationship may exist among the cognitive variables of locus of control and self-efficacy, vice the predicted parallel relationship. Overall, we assert that effective implementation of an intervention like the one used in this study might lower the U.S. Air Force\u27s health care bill by as much as $40 million, improve employee efficiency and mission capability, enable healthier lives, and prevent premature death