An investigation into road freight challenges faced by transport companies in South Africa.
Notice bibliographique
Résumé
The South African economy slipped into recession during the second quarter of 2018. The current economic conditions in the country are characterized by slow economic growth coupled by high unemployment rate. The Reserve Bank has raised interest rates by twenty-five basis points in November 2018, amid inflation concerns. The country’s economy is currently under-performing and sluggish. The government has intervened by appointing a team of five special envoys as well as one economic advisor to the president. The role of the economic advisory team is to sell South Africa to the foreign investors. Logistics remains a catalyst for the country’s economic development. Facilitating trade and transportation are at the core of galvanizing economic development. Efficient logistics services contribute towards a country’s international competitiveness. However, logistics and road freight transport industry in South Africa is marred by several challenges, like port delays, on the road constraints, lack of skills, poor infrastructure, rising costs and other issues. These challenges are hindering freight transportation to deliver on its prime mandate, which is to provide place and time utility for cargo. This research study aimed to understand the various road freight challenges faced by the transport companies in South Africa. The qualitative research methodology was used to conduct the study from road freight transport companies, located in the major cities of Durban, Port Elizabeth, Cape Town and Johannesburg. The research respondents were comprised of General Managers as well as Operations Clerks who are employed by the road freight transport companies. The data was collected through face-to-face interviews with Durban-based respondents, while telephonic interviews were conducted with respondents from other provinces. The collected data was manually analysed by the researcher by identifying themes and grouping findings into clusters. The visual presentation of data was achieved by using Microsoft SmartArt programme. The study highlighted various challenges that confront road freight transport companies. The main challenges were port congestion, poor road conditions, rising costs of doing business, theft and truck hijackings, poor road infrastructure, non-compliant trucks on the South African roads, delays at the border posts, bribery and corruption and lack of skills labour. Due to these challenges, the transport companies have suffered and continue to suffer financial losses. The transport companies are finding it difficult to meet client’s requirements, due to these challenges. Despite these challenges, there is an vii opportunity for the private-public sector partnership to address the road freight challenges. The Department of Transport should engage with all relevant departments to address road freight challenges. Transnet should implement performance-based incentives to improve productivity. The Department of Energy should review diesel prices by considering a reduction on the fuel levy to reduce escalating diesel costs. The law-enforcement agencies should improve policing on the road to protect trucks against criminal elements. In addition, non-compliant trucks should be suspended on the road to improve road safety. On-the-job skills development programs should be developed by the Department of Transport in conjunction with the Department of Labour to ensure that the country’s road freight industry has access to sufficiently skilled labour.
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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 | 0,000 | 0,000 |
| 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 | 0,001 | 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 ».