Workforce planning for SMEs under stochastic labour turnover
Bibliographic record
Abstract
In today's rapidly changing manufacturing industry, even minor uncertainties can cause significant disruptions for small and medium-sized enterprises (SMEs). Although previous studies on operational supply chain networks have focused primarily on addressing uncertainties related to demand fluctuations, machine breakdowns, and unpredictable events such as natural disasters and geopolitical disruptions, this paper specifically addresses workforce uncertainty due to stochastic turnover rates. As an extension of the multistage workforce capacity planning problem with turnover proposed by \cite{2007Successive}, this study builds on their workforce planning network, which incorporates decisions around transferring, hiring, and firing, by introducing a proficiency ranking system. This system classifies the workforce into three proficiency levels, each with a specific production rate according to the worker's status. Integrating this proficiency ranking system into the planning network allows a more comprehensive evaluation of workforce capabilities to meet the required demand. Results from numerical experiments demonstrate that the modified model offers an optimized workforce planning solution, balancing cost and time effectively, to help SMEs achieve their demand targets under conditions of uncertain workforce turnover.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".