Bibliographic record
Abstract
Inventory shrinkage is a costly issue that confront retailers all over the world as it diminish their profitability.While many researchers have studied this phenomenon at an industrial level in general, however, only a few researchers have documented their findings based on store level.The retail study at store level can help better understand the operational parameters in a detail for making suitable practical suggestions to combat inventory shrink and that is the reason a case study is chosen as a method to examine and analyze inventory shrinkage in depth at a retail outlet level.Almost all researchers work available in literature classified shrink into four types; external theft (Shoplifting), internal theft caused by bad employees, administrative errors and vendor dishonesty.It is reported in most of the findings that external and internal theft constitutes the major part of the total shrink.The rest of the shrink occurs due to administrative errors or/and vendor dishonesty.In the literature it was also reported that merchandise theft was the major contributor to the shrink and that is the reason it is the focus of this study.Factors influencing theft have been examined from operational aspect, which encompasses staff density and productivity metrics, and from human resources aspect, more specifically employee mix and employee tum over rate.The findings of the case study indicated that as staff decreased, theft related numbers increased.On the other hand, variation in staff mix and employee turnover rate was also found to be directly related to the variation of theft figures.Based on the findings of the case study a reasonable set of loss prevention measures have been proposed to prevent shrink.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".