Exploratory Analysis of CCR-DEA application for Logistics performance management

The use of Data Envelopment Analysis (DEA) to evaluate efficiency is widely discussed in production and logistics environments. The literature uses classical DEA models mainly to carry out benchmarking. This work differs from others in performing an exploratory analysis of the Charnes, Cooper, and R...

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Bibliographic Details
Main Authors: Staudt, F. H., Piran, F. A. S., Alpan, G.
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Benchmark testing
Companies
Composite indicator
Data envelopment analysis
Integrated indicator
KPI
Logistics
Production
Sensitivity
Stochastic processes
Time measurement
Warehouse
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Summary:The use of Data Envelopment Analysis (DEA) to evaluate efficiency is widely discussed in production and logistics environments. The literature uses classical DEA models mainly to carry out benchmarking. This work differs from others in performing an exploratory analysis of the Charnes, Cooper, and Rhodes model (CCR) for internal benchmarking, which means evaluating a company over time. To verify the CCR model utilization, an algorithm in Python is developed to analyze: (i) the periodic applicability of the model, (ii) the results' sensitivity to changes in the scale of indicators, and (iii) variable orientation shifts. The study reveals that CCR used for periodic performance management cannot offer an intuitive interpretation in analyzing efficiency over time since its results are restricted to a variation between 0 and 1 (0 to 100%). In addition, the model is not sensitive to changes in the scale of indicators. However, the model is very sensitive to changes in the variable orientation (number of inputs and outputs of the model). Finally, the definition of the indicators as inputs and outputs following the company's strategy generates good efficiency results.
ISSN:2576-3555
DOI:10.1109/CoDIT62066.2024.10708460