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Artificial Intelligence Enabling Product Support

ALCL 191


Alternate Definition

The term artificial intelligence (AI) describes a wide range of technologies that don’t have scientific or industry standard definitions.  The following three AI definitions are mandated for DoD by policy or statute. 

#1: DoD AI Strategy (2018): “The ability of machines to perform tasks that normally require human intelligence. For example, recognizing patterns, learning from experience, drawing conclusions, making predictions, and taking actions.”

#2: John S. McCain National Defense Authorization Act for FY2019 – Sub B/Sect 238g: “The term artificial intelligence includes the following: (1) Any artificial system that performs tasks under varying and unpredictable circumstances without significant human oversight, or that can learn from experience and improve performance when exposed to data sets. (2) An artificial system developed in computer software, physical hardware, or other context that solves tasks requiring human-like perception, cognition, planning, learning, communication, or physical action. (3) An artificial system designed to think or act like a human, including cognitive architectures and neural networks. (4) A set of techniques, including machine learning, that is designed to approximate a cognitive task. (5) An artificial system designed to act rationally, including an intelligent software agent or embodied robot that achieves goals using perception, planning, reasoning, learning, communicating, decision making, and acting.”

#3: National AI Initiative Act of 2020 (FY2021 NDAA): “A machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations or decisions influencing real or virtual environments. Artificial intelligence systems use machine and human-based inputs to—(A) perceive real and virtual environments; (B) abstract such perceptions into models through analysis in an automated manner; and (C) use model inference to formulate options for information or action.”

Alternate Definition Source

#1: DoD AI Strategy (2018)

#2: John S. McCain National Defense Authorization Act for FY2019 – Sub B/Sect 238g

#3: National AI Initiative Act of 2020 (FY2021 NDAA)

General Information


Artificial Intelligence can be applied to any data set including a weapon system’s Product Support Strategy (PSS).  The twelve Integrated Product Support (IPS) Elements which comprise the PSS, can each benefit from utilizing Artificial Intelligence.  This article examines how Artificial Intelligence could enable each of the IPS elements. 

1.Product Support Management 

Artificial Intelligence can enable planning and managing cost and performance across the product support value chain.  AI algorithms can analyze vast amounts of data to predict equipment failures and sustainment needs accurately. By employing machine learning models, organizations can forecast potential failures, optimize Product Support Strategies, and ensure maximum uptime for weapon systems.

As quality data continues to be captured and federated, AI-driven assessments are sure to add value from gathering market intelligence, harvesting market research, conducting cost estimates to recommending resource allocation.  Additionally, AI could assist in continuous process improvement efforts identifying how to improve logistics warfighting workflows, such as a repair or replenishment spares pipeline, while minimizing disruptions to assist the Product Support Manager in decision-making. 


2. Design Interface

Logisticians can also incorporate AI to identify and eliminate risks in product support performance and supportability utilizing predictive analytics to create a common operating picture.  Generative AI affords a Human-Machine Teaming capability where logisticians can work together with AI to solve difficult, complex, evolving, or hazardous tasks while checking the AI’s sources for validation.  This includes a seamless handoff both ways between human and AI team members. Areas of effort include developing effective policies for controlling human and machine initiatives, computing methods that ideally complement people, methods that optimize goals of teamwork, and designs that enhance human-AI interaction. Generative design algorithms can explore numerous design iterations, considering factors such as manufacturability, serviceability, and lifecycle costs.

As with any human computer interaction, human systems integration is an important area to consider as part of a wider systems engineering process to ensure that human performance is optimized.  Responsible and ethical use of AI must be a consideration of all product support uses of AI. 


3. Sustaining Engineering

Logisticians must partner with systems engineering early in the life cycle to design the program’s data analytics strategy, and tools such as machine learning and artificial intelligence can directly support the analytics strategy to include critical product support analyses such as the maintenance task analysis (MTA); Failure Reporting, Analysis, and Corrective Action System (FRACAS); Failure Mode, Effects, and Criticality Analysis (FMECA); and others. This enables proactive measures to improve product reliability, maintainability, and quality throughout the product lifecycle. AI/ML could also be combined with sustaining engineering activities such as tele-maintenance and engineering assistance by evaluating numerous solutions (such as for non-standard repairs) and identifying and communicating the highest confidence solution.


4. Supply Support

To determine effective supply support strategies, AI can assist in the data analysis to include predictive analytics, demand forecasting, and production scheduling for depot level repairables (DLRs).  Using AI to analyze shelf-life, warranties and buffer stock could optimize warehouse management. Additionally, in this era of data, supply chain assurance of critical concern and AI tools can assist with prevention of counterfeits, malicious hardware or software and unauthorized technical transfer. AI-enabled analytics could also add pre-positioning and other elements of war readiness to optimize the warfighter’s ability to operate in a Contested Logistics Environment.  This ensures that the right parts are available at the right time, reducing lead times and improving overall supply chain efficiency.


5. Maintenance Planning and Management

AI-powered predictive Analytics utilizing sensor-based algorithms and maintenance data trends (suck as Natural Language Processor AI/ML of maintenance actions taken) could be used to determine patterns and predict future outcomes and trends to optimize system maintenance schedules based on real-time data and equipment condition. This optimizes fleet maintenance, predicting and preventing weapon system failures as well as analyzing depot workload allocation, planning, activation, and execution reducing downtime and minimizing operational disruptions.

Other applications could include: 

  • Intelligent Sensing to conduct advanced signal processing techniques, data fusion techniques, intelligent algorithms, and AI concepts to better understand sensor data to improve integration of sensors and better feature extraction for condition-based maintenance plus. 
  • Analysis of Logistics Product Data to influence the maintenance task analysis (MTA); Failure Reporting, Analysis, and Corrective Action System (FRACAS); Failure Mode, Effects, and Criticality Analysis (FMECA); and other product support analyses.
  • Robotic Process Automation (RPA), defined as software to help in the automation of tasks, could be utilized to assist robots to conduct maintenance especially in tasks that are tedious, repetitive and/or hazardous to humans. 


6. Packaging, Handling, Storage and Transportation (PHS&T)

AI algorithms can optimize packaging designs to minimize transportation costs and ensure product safety during transit. Using AI powered logistics systems to automate and create adaptive route planning as well as for logistics workflow processes could help navigate around traffic or battlefield delays as well as reduce handling and storage costs. 


7. Technical Data

AI can be used to expand data collection automating the organization and retrieval of technical data, manuals, and documentation.  Natural Language Processing (NLP) algorithms can extract relevant information from unstructured data sources, making technical information readily accessible.  In this application, AI could fill in missing legacy technical data for weapon systems that were not born digital.  AI could also be utilized to create and/or hone work statements and Contract Data Requirements Listings (CDRLs) speeding the time for contract development and government review of delivered data for anomalies and inconsistencies.   Most importantly, AI can automate data validation and cleansing processes, ensuring the accuracy and consistency of product data across various systems and databases. This improves data integrity and facilitates efficient information exchange between different stakeholders.


8. Support Equipment

AI-driven predictive analytics can optimize the management of support equipment by predicting maintenance needs and usage patterns. This ensures that support equipment is available when needed, minimizing downtime and maximizing operational efficiency.  Complex support equipment that are themselves end items can also benefit from the other 11 IPS Elements’ use of AI.


9. Training & Training Support

AI-enabled virtual training platforms can provide immersive and interactive training experiences for personnel. Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) technologies can simulate real-world scenarios, allowing technicians to practice procedures and troubleshoot issues in a risk-free or even a tele-maintenance contingency environment. 


10. Manpower & Personnel

AI-driven assessments can assist in identifying optimal manpower mix and allocation as well as the personnel knowledge, skills, and abilities to perform new or evolving tasks. 


11. Facilities & Infrastructure

AI-enabled platforms could analyze base maps and plans to determine optimal facility maintenance and placement for new mission facilities thereby reducing site activation costs.   


12. Information Technology (IT) Systems Continuous Support

AI-enabled tools can assist in the identification and planning for hardware and software support.  Additionally, AI can assist in software sustainment and AI Chatbots can serve as help desks to minimize customer wait times.  Most importantly, logisticians will need to consider the sustainment of AI-tools, both hardware and software, as part of this product support element.



In conclusion, Artificial Intelligence holds immense potential to enhance the 12 Product Support Elements by leveraging advanced analytics, automation, and predictive capabilities. By harnessing the power of AI, organizations can optimize cost, schedule, and performance across their weapon system product support strategy.