Abstract
This study explored the use of discrete event simulation models as a method to inform the Royal Norwegian Navy acquisition process for making manpower requirements decisions. Work/rest data were collected on a Norwegian Coast Guard vessel (9-day underway, 22 crewmembers). Participants completed questionnaires pre-/post-study, wore Ōura rings, and documented their work/rest patterns in activity logs. The Improved Performance Research Integration Tool (IMPRINT) was used to build two manpower models (Baseline and Augmented) that were compared in terms of workload, sleep, and task completion. Results showed that highly prioritized events were completed sufficiently. However, the addition of more tasks reduced sleep among all sailors but especially for those who were watchstanders. This reduced sleep duration resulted primarily from the increased workload, especially for some crewmembers who were already experiencing restricted sleep. Overall, IMPRINT Pro Forces can be a valuable tool for assessing manpower requirements during ship acquisitions.
Introduction
Background
The Royal Norwegian Navy (RNoN) is undergoing a significant restructuring to enhance its naval capabilities and modernize its fleet. This transformation is necessitated by the need to address evolving operational requirements and implement technological advancements while decreasing costs. A substantial portion of the current RNoN fleet, including mine countermeasure vessels, Coast Guard vessels, and auxiliary ships, is either reaching the end of its service life or requires mid-life updates. By mid-2030, the Skjold class coastal corvettes will also reach their end of service (Forsvarsdepartementet, 2020).
In response to these needs, RNoN is pursuing standardized vessels equipped with modular technology. This approach promises several benefits, including streamlined integration of emerging technologies, cost savings, and enhanced performance. Standardization facilitates the development of vessels that can be easily upgraded with autonomous and unmanned system modules, potentially leading to significant operational advantages and cost efficiencies throughout the vessel’s lifecycle (Forsvarsdepartementet, 2022a).
Despite the focus on technological advancements, the RNoN recognizes that human resources are crucial for maintaining system performance (Forsvarsdepartementet, 2022b). The “Lean Manning Concept” (LMC) currently used aims to minimize manpower costs by having personnel handle multiple roles (Forsvaret, 2004). LMC, though, can lead to overwork, billet vacancies, and diminished system performance with a slight gap between manpower requirements and sailors manning the system. Effective manpower management is essential to mitigate these risks, particularly in the context of new system designs. Manpower refers to the number of personnel needed to support system operations effectively (Department of Defence, 2015).
Adequate manpower is critical for sustaining system performance and avoiding negative impacts on crew workload and overall system efficiency. A thorough manpower analysis during the procurement process is vital to address these challenges. This analysis can provide valuable insights into the manpower requirements necessary to support performance levels, control costs, and minimize rework. One method for analyzing manpower needs is through modeling and simulation. As the RNoN continues to develop and acquire new vessel concepts, incorporating advanced manpower modeling and simulation techniques with rapid testing and evaluating potential, can ensure effective resource utilization, smooth transitions to new designs, and enhanced mission effectiveness. By focusing on comprehensive manpower analysis, the RNoN can better manage the complexities of modern naval operations and ensure the success of its fleet modernization efforts. The Improved Performance Research Integration Tool (IMPRINT) is a discrete event simulation tool that can be used to explore various manning configurations and their impact on workload and performance. IMPRINT has been successfully applied to different naval vessels, including the U.S. Navy Littoral Combat Ship and frigates from Canada and Australia. This study builds on previous research conducted at the Naval Postgraduate School, Monterey, CA on workload, work hours, and sleep, which was geared toward creating simulated manpower models using IMPRINT (Attwood, 2020; Hollins & Leszczynski, 2014; Meredith, 2016; Poirier, 2020; Ryan, 2014; Shattuck & Matsangas, 2015; Shattuck et al., 2017).
Scope and Objectives
The overarching goal of the study was to provide insight into methods for the RNoN acquisition process to have a more robust and comprehensive basis for making manpower requirements decisions. The study had three objectives. The first objective was to assess whether the crew was able to conduct its tasks, events, and incidents during a Baseline model configuration without a degradation in performance or sleep levels, and where any negative effects were presumed to occur. Secondly, the study investigated whether the simulated crew was sufficiently scaled to perform the additional tasks required in an Augmented model. We also assessed the effect that the total workload in the Augmented model had on the simulated crew’s ability to complete activities while also accruing sufficient sleep. The third and final goal was to evaluate the strengths and weaknesses of the IMPRINT discrete event simulation tool in accurately representing the workload and sleep of a simulated crew in the Baseline model, in accordance with the information collected from a nine-day underway sailing period on a Norwegian Coast Guard (NoCG) vessel.
Methods
Study Design
IMPRINT was used to build two manpower models. These models (Baseline and Augmented) were compared in terms of workload, sleep, and task completion. To verify the data that were used in developing the models, a data collection effort was conducted on a Norwegian Coast Guard ship similar in scale to the design of future ship acquisitions. The data collection process followed a case study pattern to assess workload demands and task performance with pre-/post-study questionnaires, activity logs, and sleep-tracking wearable devices (Ōura rings: ŌURA Health Ltd, Oulu, Finland; https://ouraring.com/). Additional task data were provided by sailors during the data collection period. Both self-reported data and objective measurements contributed to the final models to provide organizational, task completion, and sleep data.
Procedures
The IMPRINT Pro Forces module was deployed in a stepwise approach. First, work and rest patterns were collected from 22 crewmembers on a Norwegian Coast Guard vessel during a 9-day underway (August 2022). At the conclusion of the study, the crew was scheduled for the remaining 12 days of sailing before a new rotation and an eventual leave period. Participants were asked to complete questionnaires at the beginning and end of the underway, wear Ōura rings throughout the data collection, and document their work/rest patterns in activity logs.
Second, we developed the IMPRINT models. Input for the models included the data collected from the NoCG vessel, information from subject matter experts, self-reported data, and objective measurements. The process was initiated by developing a list of jobs existing in the unit and allocating roles to that list of jobs, i.e., an Operations Officer (job) able to man roles such as Navigator and Small arms fire director. Next, lists of planned activities and unplanned activities and events were created, followed by determining the parameters for the unplanned activities and events. Finally, job schedules conducted by the crewmembers were established, including all planned activities, prioritizing both planned and unplanned activities and events into an activity trump matrix as shown in Table 1, to be used in the Baseline and Augmented models.
Example of Some of the Planned and Unplanned Activities That Could Occur on the Simulated Ship During the 21-day Underway. Planned Activities Were Only Used in the Baseline Model, and Unplanned Activities Were Only Included in the Augmented Model.
The Baseline model of the ship’s crew simulated crewmembers while they were conducting planned tasks, preventive maintenance, and routine training exercises during a 21-day underway. The Augmented model included all activities of the Baseline model, unplanned events such as major corrective maintenance and operational tasks, and extreme events (e.g., fires, floods, and Search and Rescue [SAR] operations). The Augmented model also included ship-wide exercises, typically planned by a small contingent of crewmembers but occurring randomly for the other members of the crew. Depending on the prioritization of the modeled activities, the model assesses which activities are accomplished and which ones are canceled or delayed.
Analysis
Statistical analysis was conducted with JMP statistical software (JMP Pro 16; SAS Institute; Cary, NC). The activity logs provided the outline for on-duty and off-duty activities used to populate the schedules in the models, validated by objective sleep data measured by the Ōura rings. Questionnaires were used to assess any confounding sleep issues that would affect the registered data, for example, a chronic pattern of poor sleep quality, insomnia, or other recurring sleep problems.
The crewmembers were divided into three occupational groups: Watchstanders, Daymen (who worked during the day and slept at night), and Engineers. These groups were used to identify differences in workload and sleep among the crew that would both enhance the resolution of the models and offer insight into which members of the crew had the heaviest workload and the least sleep. The analysis also looked at task performance by registering which tasks were not completed during the 999 simulated runs of each model. Both the Baseline and Augmented models were analyzed separately to gauge variations between crewmembers for each setting, followed by a comparison between the models—to discover possible manpower discrepancies.
Results
The simulated crew spent more time on duty (11.02 ± 1.35 hr/day) in the Augmented model than in the Baseline model (10.54 ± 1.40 hr/day), an increase of almost 30 min per sailor per day (4.7%), resulting in an accumulated workload of 10.56 hr/day (Augmented 90th quantile = 14.96 hr/day) across the crew. Table 2 shows the differences among occupational groups; watchstanders worked the most hours and were more vulnerable to overtasking. Yet, the influx of activities between the models resulted in only small reductions in sleep duration per day by occupational group.
Mean On-duty Hours per Day, by Occupational Group, and the Mean 90th Quantile.
Watchstanders and engineers saw their sleep decrease by 10.2 min (10th quantile = 22.8 min) between the models (Table 3), while those adhering to a daytime work schedule slept 13.8 min per day less (10th quantile = 22.8 min).
Mean Sleep Hours per Day, by Occupational Group, and the Mean 10th Quantile.
The simulated crew in the Baseline model was able to conduct most activities while maintaining healthy sleep habits and avoiding overwork. The results showed that all crewmembers slept more than 7 hr per day and achieved high completion rates for irregular activities. Following the introduction of several additional irregular activities and unplanned events in the Augmented model, the results showed a reduction in the crew’s ability to complete the full range of tasks. Nevertheless, highly prioritized events were covered sufficiently in nearly all instances. The tasks added in the Augmented model reduced sleep among all sailors, although the reduction targeted the watchstanding occupational group the most, with some seeing their sleep fall below 7 hr per day.
Discussion
Results demonstrated a reduction in the crew’s ability to complete the full range of tasks. Nevertheless, highly prioritized events were completed in nearly all instances. The additional tasks reduced sleep among all sailors, although the reduction affected watchstanders the most. However, the addition of more tasks reduced sleep among all sailors, especially for those who were watchstanders. This reduced sleep duration resulted primarily from the increased workload, especially for some crewmembers who were already experiencing restricted sleep. This reduced sleep duration resulted primarily from the increased workload, especially for some crewmembers who were already experiencing restricted sleep. Watchstanders regularly conduct watches at night; thus, they are more vulnerable to increases in activities that interfere with daytime sleep periods. Recurring sleep disruption or shortened sleep can be an indication of a lack of, or misallocated manpower, with the crew not scaled sufficiently to conduct “normal operations” without interfering with crewmembers’ sleeping patterns. However, the modeling results suggest that the mean sleep periods were appropriate for most iterations of the simulation, and that the crew was adequately scaled to absorb the additional activities and events in the Augmented model. Though the workload and sleep results might be unexpected in the context of navy ships, the data used in the models were based on NoCG which adheres to strict crew rest policies to be ready in case of emergencies. This approach emphasizes rest and sleep periods; unplanned activities with a low probability of occurrence have only a small effect on data aggregated over all 999 iterations of the 21-day models.
Overall, the IMPRINT Pro Forces module can be a valuable tool for assessing manpower requirements during naval ship acquisitions. The findings of this study suggest that a thorough manpower analysis using IMPRINT requires a two-pronged approach to investigate the long-term effects from a full underway, as well as a shorter timeline representing high activity effects on the crew. Focusing exclusively on data aggregated over the entire 21-day simulation run may miss shorter periods of extended high workload/sleep deprivation. IMPRINT has some limitations in replicating a crew’s flexibility to reorganize to meet threats while maintaining a level of resolution to identify manpower weaknesses. Additionally, the quality of the data which is input to the model determines the quality of the output. It was challenging to determine when a manpower simulation tool could exceed the subject matter experts’ ability to assess manpower needs. However, maintaining a model with evidence of why a certain level of manpower was chosen can be valuable to protect crews from operational “requirements creep” during the lifecycle of a vessel. The use of these types of models becomes especially useful given the proposed scalability of standardized, modular-based vessels.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest for the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
Disclaimer
The views expressed in this research are those of the authors and do not necessarily reflect the official policy or position of the U.S. Department of the Navy, the U.S. Department of Defense, the Royal Norwegian Navy, the U.S. Government, or the Norwegian Government.
