Risk mitigation strategy to eliminate risks associated with claim management in operational procurement through automation
Bibliographic record
Abstract
Proper claim management has always been a challenging task for any organisation. Digital technologies and automation are influencing every sector of business. The digital revolution is having a great impact on the supply chain and procurement industry and is making it more complex and prone to risks. The terms ‘Procurement 4.0’ and ‘Industry 4.0’ are top priorities for companies today. The purpose of this research paper is to describe the operational procurement process in detail and identify the risks associated with manual claim management processes. The focus is further narrowed down to show how these risks of claim management can be mitigated and the benefits this could bring to any organisation in terms of cost savings, supplier management, spend transparency and supplier performance improvements. Emerging literature, practical experiences, interviews, case studies, blogs, expert opinions and citations are used to fulfil this task. This research opens up further avenues of research for improvements of supplier management processes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".