SkyOps Mission Planner
Air war operations planning software solution that enables decision-makers to quickly and confidently plan complex mission objectives.
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Customer
Naval Information Warfare Center (Pacific) · 4 Month Contract
Tools
Adobe XD (customer required) · Miro
Role
Design and Project Lead of a 3 person design team
Skills
Rapid Ideation & Concept Generation · Iterative Design · Research/Knowledge Elicitation · User Interviews · Information Architecture · Scenario Development ·
Data Visualization · Wireframing · Rapid Prototyping · Cognitive System Engineering Principles · Project Management · Customer Collaboration & Communication
OVERVIEW
Problem Space
The process of Air Tasking Order (ATO) planning is very complex and time consuming.
How can we help Master Air Attack Planners (MAAP Chiefs) to optimize their part of the planning process and be more efficient with their time and resources?
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Air Tasking Order
ATO
A large document that lists mission plan objectives for a fixed 24-hour period. This document is how combat aircraft get their mission orders.
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Master Air Attack Planner
MAAP Chiefs
Responsible for analyzing, comparing, and adjusting mission plans to create the most successful ATO. The primary user group.
OVERVIEW
Approach and Methodology
The Mile Two design team had a tight three-month deadline to create a proof of concept. To keep up with the pace, we had to devise a plan to rapidly gain knowledge of the mission planning process while making significant progress in concept design.
We adopted a weekly sprint process, building visual concepts based on existing knowledge and making assumptions where information was lacking. Regular meetings with subject matter experts (SMEs) helped us fill those knowledge gaps while simultaneously increasing design fidelity.
This approach led to an effective process of using critiques and validating/correcting assumptions to enhance our subject knowledge. Additionally, our strong collaboration with customers and SMEs fostered a sense of partnership in the design process.
RESEARCH
ATO Planning Process
Our first step was to understand the current ATO planning process by reviewing existing literature, studying models, and conducting interviews with SMEs. At a basic level, we learned that the JFACC (Joint Force Air Component Commander) sets initial plan constraints, which the MAAP Chief optimizes before submitting the plan for approval. Although many external processes contribute to building an ATO, we decided to focus specifically on supporting the MAAP Chief by helping them analyze and compare the top AI-generated plans they receive.
RESEARCH
Process Inefficiencies
As we gained a deeper understanding of the ATO planning process, we identified three key issues that hinder the MAAP Chief's ability to perform their tasks effectively:
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MAAP Chiefs struggle to identify subtle differences between AI-generated plans. They have to switch between files to find key metrics, making direct comparisons very difficult.
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MAAP Chiefs face challenges in visualizing how changes to one metric might positively or negatively impact other metrics.
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The process lacks a clear visual representation of time, making it hard to track how plans evolve over the 24-hour period
Similar representation to the kind of software MAAP Chief's used to support the planning process.
MODELING
Data Modeling
We were provided with numerous data documents to better understand the types of information MAAP Chiefs rely on. Their primary goal is to use this data to build the most optimal "package," which refers to a group of aircraft assigned to carry out a specific mission or task.
To gain deeper insights, we mapped out the hierarchy and relationships within the data. This model illustrates the hierarchical data elements that planners incorporate and consider when creating a package.
MODELING
Functional Decomposition
A functional decomposition is the process of breaking down a system into its constituent parts, identifying functions, and defining how they interact to achieve the system’s objectives. The goal of functional decomposition is to simplify complex systems into smaller, more manageable parts.
In this particular model an additional layer of a process flow is added to understand the larger ideal process of how the SkyOps software should function.
REPRESENTATIONS
High Level Architecture
After analyzing the initial data, our next step was to determine the best way to organize and structure it to minimize cognitive load and highlight meaningful data relationships. To do this, we conducted an information architecture modeling exercise to better understand the MAAP Chief's mental models for grouping data.
We then used these mental models to prompt MAAP Chief SMEs to imagine how they would organize the same information on a screen. This exercise provided a strong starting point for our wireframing process and helped us identify the key elements that the SkyOps software needed:
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ATO Data Visualization Comparison View: A tool for quickly comparing the top imported plans at a macro level, helping the MAAP Chief decide which plan is worth further fine-tuning.
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ATO Timeline: A clear, chronological representation of package data for easier organization and viewing.
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ATO Map Simulation (also called a "Chart"): A spatial visualization to track aircraft movement over time, showing when and where targets will be serviced. Both the timeline and the plan data visualization metrics needed to be adjustable, with any changes reflected in the chart.
Given the complexity of the data required to fully understand a plan, we determined that the SkyOps software should function as a dual-monitor tool. This setup would prevent the need to switch between multiple files and tools, allowing users to have a more holistic view of the plan at all times.
REPRESENTATIONS
Scenarions and Wireframes
Multiple versions of scenarios were created to explore how the mission planner might interact with the SkyOps tool. By augmenting these scenarios with wireframes we were able to more effectively communicate our ideas to both the customer and MAAP Chief SMEs, while confirming the feasibility of our concepts. This process also helped to make sure our concepts fit into the larger mission planning process architecture.
REPRESENTATIONS
Iterative Design Process
Design progression involved moving from low-fidelity wireframes and basic prototypes to higher-fidelity mockups and more refined visual representations. Throughout this iterative process, the design fidelity consistently reflected our growing domain knowledge.
Initially, we emphasized rapid concept iteration, which provided valuable insights. As we gathered knowledge, we narrowed down concepts into more refined designs, allowing us to make efficient use of our limited time.
Low Fidelity Wireframing
High Fidelity Wireframing
Low Fidelity UI Representation
Mid Fidelity UI
TESTING
Testing Round 1
Our initial round of testing showed that our core design aligned well with the MAAP Chief's mental model. The use of timelines to visualize what was happening in the packages over the 24-hour period, along with our map simulation (chart) of aircraft movements and targets, proved to be the most effective way to represent macro-level plan details. However, we identified three key issues that needed to be addressed:
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Data Overload: We presented too much long-term tactical data, which consumed valuable space needed for more critical daily operational data. This operational data was more essential for supporting the MAAP Chief's decision-making process.
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Improved High-Level Data Visualization: MAAP Chiefs required a more visually clear way to compare plans at a macro level, allowing them to quickly determine which plans were worth delving into for making micro-level adjustments.
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Inaccurate Timeline Representation: Some timeline displays, like heat maps and line charts, didn't accurately reflect the data's capabilities, leading to confusion in interpreting the information.
TESTING NEXT STEPS
Plan Comparison Data Visualization Ideation
REPRESENTATIONS
High Fidelity UI
TESTING
Testing Round 2
In testing both bar and radar chart data visualization concepts, the sectional radar chart proved more effective in visually representing spatial relationships for plan comparison. It allowed users to directly compare metrics within a single plan and across multiple plans more intuitively at a high-level.
Additionally, users found the radar chart more engaging, as it made adjusting metrics feel more interactive. Changes to the metrics reflected in a larger spatial layout also made it easier for to them to understand how adjustments might influence the overall macro data representation.
REFINEMENT
Final Concept
The SkyOps UI consists of five main UI components:
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Comparison View
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Chart
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Plan Timeline
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Package Timeline
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Insights
Adjustments or to a plan can be executed in the comparison view, plan timeline, and package timeline. Any adjustments to the plan within these components will be displayed in all the other components. This enables experimenting and investigating capabilities to improve a plan overall while providing understanding of what constraints drive a plan's scores. The UI provides two main ways to make plan adjustments: Nudging and Proactive AI.
REFINEMENT
Importing Plans
The MAAP Chief imports the top 3 plans from the plan generator into SkyOps. Plan summaries are shown in the comparison view in one monitor.
REFINEMENT
Selecting & Nudging Plans
When selecting a plan, the plan summary expands to show more data and the others minimize. With this view, you can begin to nudge and adjust the plan.
REFINEMENT
Selecting & Nudging Plans
In the second monitor, using the package timeline you can add/remove targets from packages, adjust strike time, show overlays of fuel, escort, SEAD missions, and view insights and resources.
The effects of any of these adjustments will be immediately displayed in the other panels as well. This example demonstrates the adjustment of a target service time by moving a slider. The red highlighting shows constraint limits in the slider. The effects of the slider change moves targets in the timeline and map/chart view.
Dual Monitor View
REFINEMENT
Package Details
The MAAP Chief can also drill down into the package timeline details. Here they an add, remove, and adjust targets and package elements. They can also add or remove aircraft from the package itself by utilizing slack resources or adjust the timing of additional resource elements.
REFINEMENT
Proactive AI
An alternative method of plan improvement involves using the proactive AI function that anticipates user needs or potential issues. It can predict what users might require or encounter based on the historical data, user behavior, and contextual information.
