Appraisal of Logistics Management Issues in the Agro-Food Industry Sector in Ghana
Bibliographic record
Abstract
Logistics management in the Agro-food industries involves coordination of all activities in the value-chain of transporting goods and services to local, regional and international consumers. There are a lot of logistics related challenges facing agro-food industries in developing countries despite the fact that efficiency in logistics management contributes significantly to the competitiveness of agro-industries worldwide. This study investigates the existing logistics management practices in 20 selected agro-food enterprises in Ghana. Using in-depth case studies methodology, the paper addresses logistics related issues such as transportation and fleet management, infrastructure and equipment, quality of customer service and order management as well as level of ICT usage. Challenges facing small scale enterprises understudied include capital investment to replace old equipment with new ones, inadequate cold storage facilities and lack of training in logistics management among others. For the medium-large scale enterprises, logistic related challenges include inadequate financial support, high cost of fuel to support power generation, inadequate cold vans and poor road networks. Lessons learnt from the large scale agro-food enterprises understudied include (i) efficient planning of logistics needs by short, medium and long term requirements, (ii) maintaining good relationship with customers (iii) outsourcing of some logistics needs as much as possible, like raw material supplies, transportation services and other inbound needs but had in place systems for quality assurance and safety management and (iv) pre-processing activities close to the raw material source. Interventions in the agro-food industries were then examined for potential solutions to the challenges raised by the agro-food enterprises.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".