Investigating stock-outs in Johannesburg’s warehouse retail liquor sector
Bibliographic record
Abstract
Purpose: The aim of this exploratory study was to investigate how management practices and processes contribute to stock-outs in the warehouse retail liquor sector in Johannesburg.Research design: A pragmatic research philosophy was used in a multi-case approach to determine the association between business management practices and stock-outs. The unit of analysis of the study is the highest turnover outlets.Findings: The results revealed that retailers base demand forecasting on judgement and naïve or simple moving average forecast methods. There is little consideration of variability, lead time or targeted customer service levels when determining optimal inventory levels. Product breakages are common, implying that data inaccuracies and stock-outs are highly probable. Information sharing between the retailers and suppliers is limited and formal collaboration programmes do not exist. The respondents revealed that frequent stock-outs resulted in poor business performance characterised by declining sales, customer retention and competitive advantage.Limitations: Although the sample selected represented a significant portion of the top 50% contributors to the sector’s sales, the sample is small and the focus of the study is limited, which decreases the generalisability of the results.Practical implications: Retailers can reduce stock-outs by investing in improvements in demand forecasting, synchronisation through collaboration and training of employees.Originality or value: This study provides empirical evidence of the linkages between poor management practices and processes, stock-outs, customer service and business performance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".