From a Black Hole to a Hole-in-One: How a Performance Evaluation of a Golf Course Can Lead to a Good Scorecard for Both Players and Facility Managers
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Notice bibliographique
Résumé
HOW A PERFORMANCE EVALUATION OF A GOLF COURSE CAN LEAD TO A GOOD SCORECARD FOR BOTH PLAYERS AND FACILITY MANAGERS. EXECUTIVE SUMMARY While interest in golf has been growing over the years, many golf courses have been losing money. This article describes a spreadsheet-based performance evaluation system for a Texas golf course. A profitlinked, productivity measurement model is used on multiperiod data to analyze performance, isolate problem areas, and recommend measures to revitalize the course. Tiger Woods and Michelle Wie, the teenage phenom, are golf superstars. Thanks in part to their fame (and to the fame of some of their predecessors, such as Arnold Palmer and Jack Nicklaus), golf has been growing in popularity over the years. Since the 1950s, public courses have shown the most growth and currently have the largest market share with 46% of the golf market.1 Statistics show, however, that the industry is overbuilt and that half of the golf courses do not make money.2 In fact, cities around the country are reporting financial problems with their golf courses.3 If the municipal golf courses keep on losing money year after year, taxpayers are not going to be happy. The financial viability of golf courses is essential for producing many more Woodses and Wies of the future. That is why I used a productivity measurement model to help identify the problem areas in a typical municipal golf course, collected the financial data, developed a spreadsheet-based performance evaluation system, analyzed the results, identified any problems, and developed possible solutions. PERFORMANCE MEASUREMENT MODELS Profit margins and productivity are the two most important performance indicators for CEOs in their strategic decision making, according to Industry Week's 27th annual survey.4 Performance measurement has gained some importance in recent years because of the balanced scorecard created by Robert Kaplan and David Norton.5 The scorecard does not attempt to link productivity to profitability, and it uses both financial and nonfinancial measures, but, according to a 1998 survey of U.S. and Canadian companies, financial measures are given more importance and used most often.6 The significant problems facing many golf courses now seem to be financial; therefore, I will focus on financial performance. The objective of performance evaluation is to identify the problem areas and their root causes so that management can take corrective action to improve the situation. Profit-linked, totalfactor productivity measurement models are more suitable for organization-level performance measurement. Their advantage lies in the fact that they link productivity to profitability.7 Although Ebony Hills Golf Course, the subject of this study, is not a for-profit organization, it generates revenues. So a model such as the APC model is appropriate for its performance measurement. The APC model was developed in 1980 at the American Productivity and Quality Center (formerly called The American Productivity Center-APC).8 The terms total-factor and multi-factor are sometimes used interchangeably. When all factors of production are not used in the model, becomes a measurement model.9 The APC model is attractive to the business community because it uses readily available accounting data and provides performance results in dollars as opposed to abstract indexes. In the APC model, data from two periods are compared simultaneously. A first-period performance is used as the standard against which the performances of other future periods are measured. To gauge firm performance over time, [w]hat matters...is not the absolute magnitude in any area, but the trend...that the measurements will give...no matter how crude and approximate the individual readings are by themselves.10 This model also can be easily implemented in popular spreadsheet software such as Microsoft Excel and can facilitate easy creation of graphs that are useful for trend analysis. …
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 découle