Classifying the Stability Scores of the Big-Three American Automotive Companies Using DEA Window Analysis
Notice bibliographique
Résumé
ABSTRACT This paper evaluates the stability of the efficiency scores of the three largest American automotive companies over three 4-year windows (1986-1989, 1990-1993, and 1994-1997). The study employs a nonparametric technique called Data Envelopment Analysis (DEA), in particular DEA window analysis, to evaluate the stability of the companies' efficiency. The automotive firms are classified based on their stability scores, using cluster analysis into four groups: efficient stable, efficient unstable, inefficient unstable, and inefficient stable. The results of two DEA models (CCR and BCC) are consistent to a great extent in classifying the firms over the three 4-year windows. Both models indicate that, for the studied period, Ford Motor Company is classified by both DEA models 92% (22/24) of the time as either efficient stable or efficient unstable. Chrysler Corporation is classified 71% (17/24) of the time as efficient stable or efficient unstable. On the other hand, General Motors Company is classified 67% (16/24) as efficient stable or efficient unstable. The empirical results reveal that each company could have reduced advertising spending, total assets and number of employees while maintaining sales volume and market share during the period. BACKGROUND The efficiency in the auto manufacturing industry has been, over the last decade, analyzed in two major studies (Womack, Jones, and Roos, 1990; Fuss and Waverman, 1993). However, there are not many articles that have addressed the efficiency performance of the U.S. big-three auto companies using Data Envelopment Analysis (DEA). To fill this void, this paper is intended to evaluate and analyze the stability of the efficiency of U.S. automotive companies during the period 1986-1997. DEA is used to measure efficiency when there are multiple inputs and outputs and there are not generally accepted weight for aggregating inputs and aggregating outputs. The present study has a significant marketing orientation in the sense that most of the considered inputs and outputs are basically measures of marketing phenomena. In the marketing literature, a number of scholars applied DEA in order to gauge and analyze efficiency. Notable examples include the study of Charnes et al. (1985), who have first discussed potential applications of DEA in retailing and sales research. Metzger (1993) presented DEA methods in measuring the effects of appraisal and prevention costs on productivity. Chebat et al. (1994) used DEA to assess the degree to which allocation of marketing resources affects the corporate profits of Canadian firms. Boles, Donthu, and Ritu (1995) proposed DEA to evaluate salesperson performance. Horsky and Nelson (1996) evaluated and benchmarked the salesforce size and productivity by using DEA. Donthu and Yoo (1998) utilized DEA to assess the productivity of 200 retail stores. Thomas et al. (1998) evaluated the efficiency of 552 individual stores for a multi-store, multi-market retailer using DEA. Pilling, Donthu, and Henson (1999) employed DEA to adjust sales performance by territory characteristic, derived from the Census of Retail Trade. The above review reveals the absence of advertising applications from the literature. Although a number of authors (e.g., Asker and Carman 1982; Tull et al. 1986) have examined the issue of advertising efficiency within the context of a single firm, there has not been work involving studying the relative efficiency of advertising spending of competing firms in the same industry. DEA can be instrumental in achieving such a goal. DEA is intended to measure the relative efficiencies among DMUs (Decision Making Units) and enables direct measurements of efficiency. DEA formulates a series of linear programming models, one for each DMU. These models can identify the relatively efficient DMUs and accord them a rating of value one, or 100% efficiency. The major advantages of DEA over traditional ratio and regression approaches include (1) DEA doesn't require a knowledge of a production function linking input variables with output variables; (2) In DEA, there is no need to assign rigid weights to inputs or outputs; and (3) DEA offers management a variety of useful insights including relative productivity scales and efficiency gaps within different inputs and outputs that help in indicating causes for inefficiency (Charnes et al. …
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».