Supply Chain Disruptions and Their Effect on Volatility of Rice Prices in Saudi Arabia
Notice bibliographique
Résumé
<p>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.</p>
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».