Life Cycle Costing in Defence Acquisition: The Challenges of Transforming Complex Aspirations into Factual Ground Realities
Bibliographic record
Abstract
Life cycle costing (LCC) is an extremely alluring procurement technique for government contracting professionals in developing countries, given its potential for reducing budgetary outgoes through lowered total cost of ownership during the entire life cycle of procured public assets. However, proper implementation of LCC in a public procurement context inherently requires strict cost visibility, verifiability and contracting discipline during comparative evaluation of proposals as well as during contract administration and implementation, making it an extremely difficult and challenging process, particularly in developing countries with relatively unskilled acquisition workforce and unresponsive legal systems as compared to developed country jurisdictions. Within this background, this short academic note explores certain LCC techniques employed under India’s defence procurement procedures, while also attempting quick comparisons with NATO, US and Canadian guidance on the subject. underlying intent is to use rigorous academic analysis for the purpose of formulating recommendations for suitable reforms in India that could perhaps also be useful for other developing countries interested in implementing LCC-based procurement for obtaining effectiveness and efficiency in their defence acquisition programmes. An initial draft of the paper was published in the journal The Administrator [January 2015, Vol. 56 No. 1, pp. 1-13].
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| 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".