Supply Chain Disruptions and Their Effect on Volatility of Rice Prices in Saudi Arabia
Bibliographic record
Abstract
This research has examined the issue of supply chain disruptions and how they affect price volatility in the commodities marketplace. Specifically, this point was discussed and examined in relation to the rice imports undertaken by Saudi Arabia with respect to how supply chain disruptions in the rice supply channel contributes to price volatility. The supply chain was first identified to consist of various nodes along which market participants work to move the commodity from one point to another. The observation was confirmed that any disruptions up the supply chain tended to manifest themselves in downstream effects such as the bullwhip effect in which increased inventory levels or decreasing inventory levels are felt successively further down the supply chain. These and factors relating to supply as well as demand in other markets also were identified to contribute to price volatility for Saudi Arabia and its rice imports. The analysis demonstrated that every major global economic disturbance over the past 50 years corresponded to fluctuations in the price of food commodities. Saudi Arabia was shown to receive the vast majority of rice supplies from a single market which is India. India supplies Saudi Arabia with some 72% of its rice imports which ensures that any transportation or customs issue encountered by any supply channel participant prior to the Kingdom’s receipt of its rice will alter the price profile of these rice commodities. Saudi Arabia was shown to already have experienced substantial price volatility of its rice imports with much of this volatility originating in India due to suppliers in India responding to competing demand for its Basmati varieties of rice. This volatility was manifested during 2012 and 2013 when rice prices per metric ton increased some 40% overall. Finally, this report also undertook regression analysis of the rice import data that found positive correlations between variables such as time between harvest and distribution, milling facility ownership and road/shipping lane conditions and the price structure of rice. The conclusion is that supply chain disruptions can and periodically do result in price volatility for rice in Saudi Arabia. Hence, this report finds that certain factors such as information access, the establishment of long-term contracts as well as trade group membership can be effective at reducing the transaction costs in the Kingdom’s rice market. Essentially, these factors can work to place downward pressure on rice prices by the metric ton which would flatten out some of the price volatility in Saudi Arabia’s rice imports.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".