3.4.1 Technology and Obsolescence Sustainment for Integrated Systems
Bibliographic record
Abstract
Abstract Large, network‐centric systems utilizing legacy elements, integrating newest commercial technologies and involving highly diverse joint‐forces and international stakeholders have increased the magnitude and complexity of systems development and, subsequently, the system sustainment. This forces the need for additional bottoms‐up tangible change management decision and optimization support for the systems and supportability engineers. This paper addresses two critical aspects in the context of system‐level obsolescence management. The first is System Obsolescence Life Cycle Forecasting and the second is Product and Technology Obsolescence Surveillance and Health Assessment. Proactive forecasting and assessment condense into a sustainment plan that encompasses the complete product operational life cycle. Product surveillance is the real‐time data monitoring mechanism that calibrates the accuracy of the forecast and the implementation plan. This paper provides recommendations specifically for the supportability engineer and the systems engineer in order to optimize the design for system affordability and to monitor change through market surveillance of the system baseline elements toward continued support of program decisions. This paper is broken into five parts in order to highlight the needs and solutions which apply to each life cycle phase. This provides a complete technology management strategy for an integrated system that is comprised of varying commercial technologies, readily available Commercial‐off‐the‐Shelf (COTS) elements, and required legacy systems. Part 1: Overview of Technology Management (TM) Requirements and Approach. Includes a discussion of the “life cycle mismatch” facing any operational system, wherein the desired system operation period is much longer than the life cycle of the constituent parts. This obsolescence mismatch must be managed at the system level to ensure program affordability. Parts 2‐4: Development, Production and Sustainment phase TM needs and solutions. Part 5: Conclusions and Recommendations. Two distinct TM variables are covered; the life cycle phases and the program complexity. Understanding these variables guides the systems and logistics engineers to an optimal operational solution.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".