Forsighting and Evaluating Iran’s Automotive Industry Development Applying a Scenario Planning Approach
Bibliographic record
Abstract
In the present world that competition increases every day, creating and maintaining a competitive advantage for the businesses have become a nightmare for senior managers. In this complex and turbulent environment, relying solely on strategic planning cannot guarantee the success of businesses, rather businesses have to prepare themselves to react to a wide range of probable futures. In this paper, a comprehensive and fl exible combination of approaches such as Delphi Approach, Cross-Impact Analysis, and Scenario Planning are employed to study forsighting. Mic Mac software is used to analyze data that showed reliable and trustworthy results. The aim of this research is to recognize the development path of Iran’s Automotive Industry. To do this, 45 effective factors on automotive production process were recognized using a Delphi Approach. After that, forsighting experts measured the infl uence of these factors on each other and this resulted in recognizing two key factors. Then, the scenarios ahead of the automotive industry are expressed and strategies and recommendations for preparation and appropriate reaction to each scenario are explained. The four plausible scenarios discovered from two key factors include ”Fast boats”, ”War ships”, ”Passenger ships”, and ”Lifeboats”. Finally, scenario planning applications and results are described. Results show that pathological changes should be implemented in managerial thinking about development and planning issues in order to have a sustainable development. Key words: Scenario Planning; Forsighting; Iran’s Automotive Industry; Uncertainty; Product Development Resume Dans le monde actuel ou la concurrence s’augmente de chaque jour, creer et maintenir un avantage concurrentiel pour les entreprises sont devenus un cauchemar pour les cadres superieurs. Dans cet environnement complexe et turbulent, se fondant uniquement sur la planification strategique ne peut pas garantir le succes des entreprises, plutot les entreprises doivent se preparer a reagir a un large eventail d’avenirs probables. Dans ce papier, une combinaison complete et souple d’approches telles que l’approche Delphi, Croix-etude d’impact, et de la planification de scenarios sont utilises pour etudier forsighting. Mic Mac du logiciel est utilise pour analyser les donnees qui ont montre des resultats fiables et dignes de confiance. L’objectif de cette recherche est de reconnaitre la voie du developpement de l’industrie automobile de l’Iran. Pour ce faire, 45 facteurs effi caces sur le processus de la production automobile ont ete comptabilises, en utilisant une approche Delphi. Apres cela, les experts forsighting mesure l’influence de ces facteurs sur l’autre et cela s’est traduit par la reconnaissance de deux facteurs principaux. Puis, les scenarios a venir de l’industrie automobile sont exprimes et des strategies et des recommandations pour la preparation et la reaction appropriee a chaque scenario sont expliques. Les quatre scenarios plausibles decouverts a partir de deux facteurs cles comprennent «bateaux rapides», «navires de guerre», «les navires a passagers», et «embarcations de sauvetage». Enfin, les applications de planifi cation de scenarios et les resultats sont decrits. Les resultats montrent que les changements pathologiques devraient etre mis en oeuvre dans la pensee manageriale sur les questions de developpement et de planifi cation afi n d’avoir un developpement durable. Mots-cles: Planification de scenarios; Appercevoir; L’industrie d’automobile de l’Iran; L’incertitude; Le developpement du produit
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 | 0.014 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 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".