A Diagnosis Model For Improving The Competitiveness of Small and Medium Manufacturing Enterprises.(Dept.M)
Bibliographic record
Abstract
The developed diagnosis model has been programmed by Microsoft Visual FoxPro to evaluate the enterprise's functional areas in order to determine the most critical ones that represent constraints to the improvement of the competitiveness. This model has diagnosis tools at three different levels: an entry level tool, a holistic level tool and an in-depth analysis level with tools for specific areas. The entry level has a quick analysis tool, which identifies the enterprise attitude towards change. The importance of this entry level analysis lies in the facts that it is necessary that the enterprise feels the urgency towards the change and also has the commitment to implement the recommendations to be suggested during the process. The holistic level evaluates the enterprise in eight different areas, as follows: strategic planning, production and operations, quality assurance, commercialization, human resources management, finance and accounting, information system and environmental performance. The application of this tool allows identifying the most critical areas that need to be addressed. The results obtained at this diagnosis level provide the necessary information for the development of the technical assistance process, which could consists of a plan for the improvement of competitiveness. However in some other cases, there is a need for a more exhaustive analysis of some of the critical areas detected by the holistic tool or there is an obvious priority problem, which needs to be addressed immediately in this context, in-depth analysis level specific tools are implemented (This tool was not presented at this study). The company for the technological and industrial development of central America has developed a model with the support of the international development research center, Canada. An adaptation process was done by the authors from the best practices related to industrial assistance to entrepreneurs in Dakahlia, Egypt. The model was applied for 34 case studies. It confirms its validity and good ability of identifying critical areas and constraints to the improvement of the company's competitiveness. The best company was selected and a benchmarking was done by comparing the diagnosed company (Case study of this paper) to the best one as shown in Figure (11). There are unlimited number of SMEs problems, so it is not possible to have one model handling both problems diagnosis and solutions. The model presented here is tackling the diagnosis and directing SMEs consultant to identify solutions and can be considered as one of the most useful techniques shown and classified in table ( 1 ). This study covers the diagnosis model components and features, also a case study which have a full analysis for the mentioned eight areas and propose suggestions for every area and expected results when applying the suggestions. Finally, the study presents conclusion for both the case study and the whole study. Figure (12) and Figure(13) represent samples of the software program output.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".