Waste Minimization in Peanut Snack Production using Lean Manufacturing tools: a Case Study
Bibliographic record
Abstract
This research project addresses the challenges of waste in a Small and Medium-sized Enterprises (SME) dedicated to the production of peanut snacks in Peru.Production waste, a persistent problem in production processes, has negatively impacted the company's operational efficiency and profitability.To overcome this problem, three fundamental engineering tools will be implemented: 5S, Informative Poka Yoke and Total productive maintenance (TPM).The 5S methodology will be used to redefine and optimize work spaces, promoting organization and discipline at each stage of the production process.Informational Poka Yoke systems will be introduced to prevent and correct errors in real time, reducing the generation of production waste and improving consistency in the quality of the final product.Additionally, the TPM methodology focuses on reducing production waste in processes that involve machinery, using a simulator to address failures of said equipment The main objective of this project is to significantly reduce waste in the production of peanut snacks, simultaneously improving the overall efficiency of the processes and guaranteeing high quality standards.The results obtained after the application of the three tools were positive.In the final 5S audit, an increase in the score was achieved, going from 37.6% in the initial stage to 90.4% in the final.The results of the Poka Yoke template showed reduced error rates: 4.25% in selection process, 2.86% in toasting and 2.73% in the semi-toasting.In the TPM simulator the availability of the machine was 98.45%.These achievements demonstrate the effectiveness of the strategies implemented in improving the efficiency and profitability of the production of peanut snacks in the SME company, Likewise, the company's environmental pollution was considerably reduced.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".