Improvement Model to Reduce Stock-Outs Using Economic Order Quantity, Material Requirements Planning and ABC Classification tools In a Commercial Company Specializing in Solar Thermals: A Case Study
Bibliographic record
Abstract
The objective of the research work is to reduce the stock of a company in San Martín, Peru, that offers the installation service of solar thermal heaters.First, a brief description is presented, the background of the organization and an evaluation of the company's indicators to find the problem under study.With this we find the technical gap of 31.11%during the year 2022, which was calculated with the difference of 47.78% of stock outage cases of the company studied compared to 16.67% of a trading Mype found within the analysis of 40 articles scientists.Subsequently, the design of the solution is proposed linking the root causes with the ABC Classification, Material Requirements Planning and Economic Order Quantity.The latter were analysed and the positive contribution to the solution of the problem was concluded.Subsequently, the development of the proposal is known in detail, developing the validation methodologies to verify that the proposed tools are effective for the improvement project through the training of workers, the reorganization of the warehouse and its subsequent verification, where results were obtained.such as the increase of 12.69% in the capacity used in the warehouse, the decrease of 68.75% in cases of stockouts, during the first month of implementation, which implies the reduction of costs and, finally, the reduction in the average assembly time of an accessory kit from 72 to 33 minutes.Finally, a financial analysis is carried out that involves the evaluation of economic flows before and after implementation with a budget of $982.35 to examine the financial viability of the company.With the aforementioned budget, economic results were obtained such as a NPV of $6972.95,an IRR of 187% and finally a B/C of 7.86.Furthermore, it is concluded that the amount invested will be recovered in 0.585 years.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".