An Approach to Obsolescence Forecasting based on Hidden Markov Model and Compound Poisson Process
Bibliographic record
Abstract
The popularity of electronic devices has sparked research to implement components that can achieve better performance and scalability. However, companies face significant challenges when they use systems with a long-life cycle, such as in avionics, which leads to obsolescence problems. Obsolescence can be driven by many factors, primary among which could be the rapid development of technologies that lead to a short life cycle of parts. Moreover, obsolescence problems can prove costly in terms of intermittent stock availability and unmet demand. Therefore, obsolescence forecasting appears to be one of the most efficient solutions. This paper presents a review of gaps in the actual approaches and proposes a method that can better forecast the product life cycle. The proposed approach will help companies to improve obsolescence forecasting and reduce its impact in the supply chain. The method introduces a stochastic approach to estimate the obsolescence life cycle through simulation of demand data using Markov chain and homogeneous compound Poisson process. This approach uses multiple states of the life cycle curve based on the change in demand rate and introduces hidden Markov theory to estimate the model parameters. Numerical results are provided to validate the proposed method. To examine the accuracy of this approach, the standard deviation (STD) of obsolescence time is calculated. The results showed that the life cycle curves of parts can be predicted with high accuracy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".