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Case Study (review time 7 minutes) 
by Linda Lane, MSIM
UX Design Principal - Researcher, Project Manager

Photo by Brian McGowan on Unsplash

Project Overview of AI Dashboard Wireframes

These AI iRoadmap user interface interactive chart designs were created to address the engineering challenges of a manned mission to Mars in 2032. They were generated quickly using product user and developer interviews. This product was designed to anticipate and provide alternative supply chains as needed using iRoadmap.


iRoadmap AI dashboard output takes into account the planning of diverse needs such as the education of employees and all the supply chain components, including rare-earth elements, engineering and logistics for space research. Interactive planning would be useful to chart and anticipate the very long-term needs of similar large-scale space exploration projects such as StarShot, which will also benefit from the use of interactive AI in planning and execution.


AI can help companies engaged in aeronautics and astronautics in several ways to develop planning and reduce supply chain issues by anticipating requirements and scoping long-distance voyages:

  1. Supply chain optimization: While planning and building equipment, AI can analyze data from the supply chain, such as supplier performance, delivery times, and inventory levels, to identify potential bottlenecks and inefficiencies. By using machine learning algorithms, AI can also predict demand and optimize inventory levels, reducing the risk of stockouts, and thereby control costs.

  2. Logistics planning: AI can optimize logistics planning for a manned mission to Mars by analyzing transportation data, weather conditions, and traffic patterns to eliminate or reduce delivery delays.

    (However, it's important to note that the logistics planning for a crewed mission to Mars is even more critical than for a typical aerospace mission, due to the limited opportunities for repairs in space. AI can help plan for contingencies and ensure that enough spare parts and supplies are designed, built, and provided to sustain the crew for the duration of the mission.)

    AI can also help prepare for future missions within the current expectations and help identify and calculate currently unknown issues based on discoveries and findings during trips.


  3. Predictive maintenance: AI can be used to predict when a particular component of an aircraft or spacecraft will fail, allowing for proactive maintenance and reducing any downtime. This is especially important for a manned mission to Mars, where the inability to repair or replace a critical component could have severe consequences.

    AI can be used to analyze sensor data, usage patterns, and other data to predict when a component is likely to fail, and schedule maintenance. Such maintenance includes digital monitoring and scheduling physical verification of those components more likely to experience wear. This can help to ensure that critical components are functioning properly and reduce the risk of mission-critical failures.


  4. Quality control: In preparatory phases AI can be used to automatically inspect and classify parts and components, reducing human error and increasing the accuracy of quality control.

  5. Cost reduction: AI can be used to identify cost savings opportunities, such as reducing energy consumption, reducing waste, and improving the efficiency of production processes.

  6. Risk Management: AI can be used to predict and analyze risks, such as safety incidents, and provide recommendations to mitigate the risks, and prevent loss of life.

  7. Design: AI can be used to generate and optimize designs for aircraft and spacecraft, by analyzing data from simulations and testing, and making recommendations for improvements.

Overall, AI can help aerospace companies to improve their planning and optimize their supply chain by analyzing data, identifying patterns, making predictions, and providing recommendations to reduce costs, improve efficiency, enhance safety and risk management, and ultimately increase the chances of success in aeronautics and astronautics projects.

These wireframe charts are designed to facilitate people interfacing with computer simulations, and changing such things as the funding available, and qualified individuals in specialized roles, allowing authorized users to make changes and "play" the iRoadmap system to show how that changes the outcome.

Space Travel iRoadmap AI Planning

Data Visualizations for an artificial intelligence planning application that users can 'play' for events, supply chain management, human resources, to undertake a mission to Mars by 2032, at a Fortune 50 Aerospace firm.

Ideation, Charts, Legends

Dashboard Designer

Sys - system links, title block, legend key

C1 - Phases - pie charts- budget, environmental, efficiency, TBD technology drivers, values

C2 - Euler diagram -dependencies, team sponsors, structure

C2 - Project details - tools, locations

RC3 - Dynamic kendo charts

A - Contributors schedule, staff scheduling, team scheduling
DO - Roadmaps: Time constant, uplift, segments. Funding deadline: software, hardware.


Roadmap Diagram

Warp Engine Planning

Depicts: Example roadmap diagram, Bio Secur system,  Environmental selected, prior projections, year by year requirements, viewable by people, companies, shipping, (supply chain), collapsed cost projections, technologic, server blood, liquid memory, percent change, environmental, political, economy, goals, etc., vision, loss, production, hub, communications, authority, planning, executives, test, rate, legend color, heatmap, dated May 2032, Juniper Jump, 1st Test, Report Services, detailed elements, targets, related data.


Event Relationship

Upstream - Downstream

Depicts: Events relationships, leadership, sponsors, tech, phases, upstream, downstream, tool kit, visualization, time and project label, crossover, prior, future, project project supply chain, monitors effort warning, exceeds available staff, stack cloud project number, faves, requirements.


Encapsulated Activity Report

Projects Progress

Depicts: Encapsulated activity reports, risks, 3D overlapping project scopes, scope, central project, deep resource planning, technical description, project relationships, cumulative project needs, event sphere, critical systems targets, timescale, dates, technology drivers, high level links, alternatives, user base, critical areas, by date.


Burn Down Factors


Depicts: High security Dreamliner, burn down factors, phase based, team sponsor, dependences, budget, environmental, efficiency, numbered tool groups, tools, locations, targets year over year, time constants, people in teams, scheduling, technology drivers, analyst, list of values, software, hardware, uplift, funding deadlines, patterns link, key.


Single Stack Report

Rating, social 

Depicts: A single stack report, filters, linked projects, product images, video, documents, shared / social communication - comments, value, locations, faves, supply chain results, and tooling, such as drones.


Unit App Details

Data Mining / Events

Depicts: Unit application details, events, data agencies, other events, explanatory text, time line, selected dates, related point on scale of time.


3 Node Tree

Data Mining

Depicts: Selected branch, details from data mining wireframe shown above: related activities displayed by 3 node view tree, single node, title source data, funding chart dependencies, branches, architecture data models, shared / social communication - comments, value, locations, faves.


Data Mining Relationships

Data Mining

Data mining, details, people, central event, labels, by project, project descriptions, high level scope view slice, list of scope items, time, nodes.

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