Target Days versus Actual Days of Finished Goods Inventory in Fast Moving Consumer Goods
Bibliographic record
Abstract
In a bid to maximize corporate profits, many multi-national corporations and even small medium enterprises create many products and place them on shelves at hypermarkets or supermarkets. We can observe an abundance of stock keeping units on shelves as well as observe a variety of such finished goods held by various fast moving consumer goods industries in the home appliance, beverage, canned food, clothes, soft-drinks, cordials and confectionery product ranges, just to name a few. From supplier, manufacturer, distributor, wholesaler and retailer, it appears that there is a constant flow of new products and stock keeping units held for these fast moving consumer goods. We can say that we humans are a rather wasteful species because a large proportion of products become obsolete or slow moving over time and organizations push products into the marketplace to gain competitive advantage and optimize profits. Hence, there is need to address this issue in the field of Supply Chain Management because resources on this planet are limited and we humans live in a very fragile planet. Yet, as population grows, we humans have become used to this over-abundance even though the resources within this planet are becoming more and more scarce. Consumption levels have increased with population growth and with capitalist thinking, virtually anybody can develop businesses that will create products to meet human needs. In the field of Supply Chain Management, managers set polices on when to order and how much to order and the average inventory that results from these inventory replenishment policies become targets. This paper attempts to compare target days of inventory with actual days of inventory held in warehouses for a single organization with many warehouses/ stock keeping units, in an attempt to understand further approaches that can be used to improve inventory waste within supply chains.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".