Study of Supply Chain of an Indian Shipping Company using Modified SAP-LAP Framework
Bibliographic record
Abstract
Purpose - The purpose of the present work is to study the supply chain of an Indian Shipping Company (SC) which has posted an audited loss of Rs 147.47 crores in the F Y 2013-14 and the three quarter reports of 2014-2015 is also not very encouraging as financial position of case company still indicate southward trend. It is a matter of surprise to everyone that during the same period other private companies have reported profits in their fleet operation. This shows that the cost cutting measures, such as: Reduction in expenses incurring towards ship repairs and dry-docking at least by 50% because the average age of ships has come down to nine years, taken SC management are not sufficient. It has been also noticed that manning agents to provide above personnel engaged by management are not performing their duties with dedication and their services are not of good quality. There is dissatisfaction among permanent floating staff personnel as they are given biased treatment as compared to contract personnel as far as their posting on the vessels. The purpose of this paper is to develop a case of a shipping company and to gain learning insights towards current situation of the company with the help of SAP (Situation-Actor- Process)-LAP (Learning-Action- Performance) framework. The case study points out limitations of the Shipping Company (SC) and suggests modifications and some actions for improving its competitiveness in near future. Approach: With the help of modified SAP - LAP framework, the working of the shipping company in the present scenario have been analyzed. The analysis has been carried out with the help of discussion with the experts of the company having more than ten years of experience. The case study makes some suggestions after analyzing the present situation, so that company can make operational profit in financial year 2016- 17 onwards. Findings - Through development of SAP- LAP framework, past and present situation of the case company has been studied with the focus on direct actors and indirect actors responsible for the growth of the case company. The process of the company has been analyzed from strategic, tactic and operational point of view. Learning is gained for the present and future situation. Actions have been proposed to improve the present situation and for sustainable future growth. Originality - The paper is an original paper which applies SAP-LAP framework with modification. In the development of SAP-LAP framework, the help of experienced experts has been obtained. The questions were prepared after reviewing the related literature and annual reports of the case company and its competitors. The case study suggests simple but effective solutions that may be adopted by the case company for her sustainable growth in future.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 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".