The impact of point-of-sale data in demand planning in the South African clothing retail industry
Bibliographic record
Abstract
Background: In modern days’ dynamic consumer markets, supply chains need to be value driven and consumer oriented. Demand planning allows supply chain members to focus on the consumer and create optimal value. In demand planning, Point-of-Sale (POS) data are an essential input to the process thereof; however, literature suggests that POS-based demand planning is often overlooked by demand planners in practice.Objective: The main purpose of this study was to determine the extent to which South African clothing retailers use POS data in demand planning.Method: This study followed the grounded theory approach based on the collection of qualitative data. The data collected was analysed following the grounded theory analysis using codes that resulted in various categories which then developed into themes.Findings: Findings suggest that companies within the clothing retail industry make considerable use of POS data and is a fundamental input factor in the demand planning process. However, this study also found that POS data cannot be applied in the planning for all types of clothing products, and that there are variables other than POS data that form a critical part of the demand planning process.Conclusion: POS data plays a fundamental role is the demand planning process and should be accurately collected and used with other qualitative and quantitative factors as an input factor to the demand planning process. The role of POS data in demand planning is expected to grow as customers are becoming increasingly demanding concerning customer service levels.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".