Benchmarking An International Journal · 2011 · 18 citations · 34 references
BusinessDecision SciencesQuality ManagementDecision AnalysisMulti-criteria Decision MakingPerformance MeasurementOperational ExcellencePerformance AppraisalPerformance EvaluationPerformance MeasuresPerformance MetricsPreference ElicitationCompetencyEfficiency ScoresMulti-objective EvaluationObjective Data SynthesisObjective EvaluationQuality AssessmentSubjective BeliefSubjective Performance Evaluation
Purpose This paper aims to propose a new benchmarking framework that uses a series of existing intuitive and analytical methods to systematically capture both objective data and subjective beliefs and preferences from a group of decision makers (DMs). Design/methodology/approach The proposed framework combines the excellence model developed by the European Foundation for Quality Management with the Rembrandt method, the entropy concept, the weighted‐sum approach, and the theory of the displaced ideal. Hard data and personal judgments are synthesized to evaluate a set of business units (BUs) with two overall performance scores plotted in a four quadrant model. Findings The two performance scores are used to benchmark the performance of the BUs in accordance with their Euclidean distance from the “ideal” BU. Quadrants are used to classify the BUs as efficacious, productive ineffectual, proficient unproductive, and inefficacious. The efficacious BUs, referred to as “excellent”, fall in the competency zone and have the shortest Euclidean distance from the ideal BU relative to their peers. Originality/value The benchmarking framework presented in this study has some obvious attractive features. First, the generic nature of the framework allows for the subjective and objective evaluation of a finite number of BUs by a group of DMs. Second, the information requirements of the framework are stratified hierarchically allowing DMs to focus on a small area of the large problem. Third, the framework does not dispel subjectivity; it calibrates the subjective weights with the objective weights determined through the entropy concept.
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